Brain Sciences

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27 pages, 3834 KiB

Open AccessEditor’s ChoiceArticle

Effect of Different Frequencies of Transcutaneous Electrical Acupoint Stimulation (TEAS) on EEG Source Localization in Healthy Volunteers: A Semi-Randomized, Placebo-Controlled, Crossover Study

by Rael Lopes Alves, Maxciel Zortea, David Mayor, Tim Watson and Tony Steffert

Brain Sci. 2025, 15(3), 270; https://doi.org/10.3390/brainsci15030270 - 3 Mar 2025

Viewed by 1185

Abstract

Background/Objectives: Transcutaneous electrical acupoint stimulation (TEAS), also known as transcutaneous electroacupuncture stimulation, delivers electrical pulses to the skin over acupuncture points (“acupoints”) via surface electrodes. Electroencephalography (EEG) is an important tool for assessing the changes in the central nervous system (CNS) that may [...] Read more.

Background/Objectives: Transcutaneous electrical acupoint stimulation (TEAS), also known as transcutaneous electroacupuncture stimulation, delivers electrical pulses to the skin over acupuncture points (“acupoints”) via surface electrodes. Electroencephalography (EEG) is an important tool for assessing the changes in the central nervous system (CNS) that may result from applying different TEAS frequencies peripherally—i.e., acting via the peripheral nervous system (PNS)—and determining how these influence cerebral activity and neural plasticity. Methods: A total of 48 healthy volunteers were allocated in a semi-randomized crossover study to receive four different TEAS frequencies: 2.5 pulses per second (pps); 10 pps; 80 pps; and sham (160 pps at a low, clinically ineffective amplitude). TEAS was applied for 20 min to each hand at the acupuncture point Hegu (LI4). The EEG was recorded during an initial 5 min baseline recording, then during TEAS application, and after stimulation for a further 15 min, separated into three periods of 5 min (initial, intermediate, and final) in order to assess post-stimulation changes. Source localization analysis was conducted for the traditional five EEG frequency bands: delta (0.1–3.9 Hz), theta (4–7.9 Hz), alpha (8–13 Hz), beta (14–30 Hz), and gamma (30.1–45 Hz). Results: Within-group source localization analyses of EEG data showed that during the initial 5 min post-stimulation, theta oscillations in the 2.5 pps TEAS group increased over the parahippocampal gyrus (t = 4.42, p < 0.01). The 10 pps TEAS group exhibited decreased alpha rhythms over the inferior parietal gyrus (t = −4.20, p < 0.05), whereas the sham (160 pps) TEAS group showed decreased delta rhythms over the postcentral gyrus (t = −3.97, p < 0.05). During the intermediate 5 min post-stimulation, the increased theta activity over the left parahippocampal gyrus (BA27) remained in the 2.5 pps TEAS group (t = 3.97, p < 0.05). However, diminished alpha rhythms were observed in the 10 pps TEAS group over the postcentral gyrus (t = −4.20, p < 0.01), as well as in the delta rhythms in the sham (160 pps) TEAS group in the same area (t = −4.35, p < 0.01). In the final 5 min post-stimulation, reduced alpha rhythms were exhibited over the insula in the 10 pps TEAS group (t = −4.07, p < 0.05). Interaction effects of condition by group demonstrate decreased alpha rhythms in the 10 pps TEAS group over the supramarginal gyrus during the initial 5 min post-stimulation (t = −4.31, p < 0.05), and decreased delta rhythms over the insula in the sham TEAS group during the final 5 min post-stimulation (t = −4.42, p < 0.01). Conclusions: This study revealed that low TEAS frequencies of 2.5 pps and 10 pps modulate theta and alpha oscillations over the brain areas related to emotional and attentional processes driven by external stimuli, as well as neural synchronization of delta rhythms in the sham group in brain areas related to stimulus expectation at baseline. It is hoped that these findings will stimulate further research in order to evaluate such TEAS modulation effects in clinical patients. Full article

(This article belongs to the Section Sensory and Motor Neuroscience)

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19 pages, 2306 KiB

Open AccessEditor’s ChoiceArticle

Cognitive Priming During Warmup Enhances Sport and Exercise Performance: A Goldilocks Effect

by Jesús Díaz-García, Ana Rubio-Morales, David Manzano-Rodríguez, Tomás García-Calvo and Christopher Ring

Brain Sci. 2025, 15(3), 235; https://doi.org/10.3390/brainsci15030235 - 23 Feb 2025

Viewed by 1409

Abstract

Background: Mental fatigue can impair sport, exercise and cognitive performance. Warmup activities can improve performance when the individual is rested. However, their effectiveness when the individual is fatigued has yet to be established. The research objectives were to evaluate the effects of physical [...] Read more.

Background: Mental fatigue can impair sport, exercise and cognitive performance. Warmup activities can improve performance when the individual is rested. However, their effectiveness when the individual is fatigued has yet to be established. The research objectives were to evaluate the effects of physical and combined physical plus cognitive warmup activities on subsequent sport, exercise, and cognitive performance when rested and fatigued by sleep restriction in athletes (Study 1) and older adults (Study 2). Methods: In Study 1, 31 padel players completed a padel performance test and Stroop task after physical and combined warmups when rested and fatigued by sleep deprivation. In Study 2, 32 older adults completed sit–stand, arm curl, walking, Stroop, and psychomotor vigilance tests after no warmup, physical warmup, and combined warmup when rested and fatigued by sleep deprivation. In both studies, combined warmups intermixed short-, medium-, or long-duration cognitive tasks between physical warmup activities. Mental fatigue was measured using visual analog scale ratings. Results: In both studies, sleep deprivation increased mental fatigue and impaired performance. In Study 1, relative to a physical warmup, padel and Stroop performance were improved by combined warmups (with short-to-medium cognitive tasks) when rested and fatigued. In Study 2, relative to no warmup, sit–stand, arm curl, walking, Stroop, and reaction time performance were improved by physical and combined warmups (with short-to-medium cognitive tasks) when rested and fatigued. Conclusions: The negative effects of sleep deprivation on sport, exercise, and cognitive performance were best mitigated by combined warmups with short-to-medium cognitive tasks. Combined warmups are effective countermeasures against the deleterious effects of mental fatigue on performance. Full article

(This article belongs to the Section Cognitive, Social and Affective Neuroscience)

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12 pages, 1189 KiB

Open AccessEditor’s ChoiceArticle

PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy

by Alexandru Guranda, Antonia Richter, Johannes Wach, Erdem Güresir and Martin Vychopen

Brain Sci. 2025, 15(1), 58; https://doi.org/10.3390/brainsci15010058 - 10 Jan 2025

Cited by 1 | Viewed by 917

Abstract

Background/Objectives: Traumatic acute subdural hematoma (aSDH) often requires surgical intervention, such as craniotomy, to relieve mass lesions and pressure. The extent of hematoma evacuation significantly impacts patient outcomes. This study utilizes 3D Slicer software to analyse post-craniotomy hematoma volume changes and evaluate their [...] Read more.

Background/Objectives: Traumatic acute subdural hematoma (aSDH) often requires surgical intervention, such as craniotomy, to relieve mass lesions and pressure. The extent of hematoma evacuation significantly impacts patient outcomes. This study utilizes 3D Slicer software to analyse post-craniotomy hematoma volume changes and evaluate their prognostic significance in aSDH patients. Methods: Among 178 adult patients diagnosed with aSDH from January 2015 to December 2022, 64 underwent hematoma evacuation via craniotomy. Initial scans were performed within 24 h of trauma, followed by routine postoperative scans to assess residual hematoma. We conducted radiomic analysis of preoperative and postoperative volumes, surface area, Feret diameter, sphericity, flatness, and elongation. Clinical parameters, including SOFA score, APACHE score, pupillary response, comorbidities, age, anticoagulation status, and preoperative haematocrit and haemoglobin levels, were also evaluated. Results: Changes in Δ surface area significantly correlated with 30-day outcomes (p = 0.03) and showed moderate predictive accuracy (AUC = 0.65). Patients with a Δ surface area > 30,090 mm² experienced poorer outcomes (OR = 6.66, p = 0.02). Significant features included preoperative surface area (p = 0.009), Feret diameter (p = 0.0012). In multivariate analysis, only the Feret diameter remained significant (p = 0.01). Conclusions: Postoperative Δ surface area is, among other variables, a strong predictor of 30-day outcomes, while in multivariate analysis, preoperative Feret diameter remains the only independent predictor. Radiomic analysis with 3D Slicer may enhance prognostic accuracy and inform tailored therapeutic strategies. Full article

(This article belongs to the Section Neurosurgery and Neuroanatomy)

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12 pages, 622 KiB

Open AccessEditor’s ChoiceArticle

Interoceptive Awareness and Female Orgasm Frequency and Satisfaction

by Emily Dixon, Giulia L. Poerio, Gerulf Rieger and Megan Klabunde

Brain Sci. 2024, 14(12), 1236; https://doi.org/10.3390/brainsci14121236 - 9 Dec 2024

Cited by 1 | Viewed by 9985

Abstract

Background: The female orgasm is a highly understudied phenomenon that is linked to both wellbeing and relationship satisfaction in women. Although orgasm has been associated with interoception—the sense of the physiological condition of the body—very few studies have directly examined the influence that [...] Read more.

Background: The female orgasm is a highly understudied phenomenon that is linked to both wellbeing and relationship satisfaction in women. Although orgasm has been associated with interoception—the sense of the physiological condition of the body—very few studies have directly examined the influence that interoception has on orgasm. Objectives: This study investigates how the subjective experience of one’s interoceptive capacities (called interoceptive awareness) is associated with self-reported orgasm frequency and satisfaction in people who identify as women. Methods: In a dataset of 318 women, orgasm frequency and satisfaction were both rated significantly higher for solitary as compared to partnered sexual experiences. Results: Analysis of how dimensions of interoceptive awareness correlated with orgasm frequency and satisfaction showed that (1) ‘Noticing’ predicted orgasm frequency (but not satisfaction) across both solitary and partnered interactions, (2) ‘Attention Regulation’ predicted greater frequency and satisfaction of solitary orgasm (but not partnered interactions), and (3) ‘Body Trusting’ predicted orgasm satisfaction (but not frequency) across both solitary and partnered contexts. Conclusions: Findings underscore the importance of moving beyond orgasmic dysfunction research by investigating how interoception is associated with healthy—and potentially even optimal—orgasmic functioning in women. Full article

(This article belongs to the Special Issue Interoception and Women’s Health)

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31 pages, 8528 KiB

Open AccessEditor’s ChoiceArticle

Neuroplastic Responses to Chiropractic Care: Broad Impacts on Pain, Mood, Sleep, and Quality of Life

by Heidi Haavik, Imran Khan Niazi, Imran Amjad, Nitika Kumari, Usman Ghani, Moeez Ashfaque, Usman Rashid, Muhammad Samran Navid, Ernest Nlandu Kamavuako, Amit N. Pujari and Kelly Holt

Brain Sci. 2024, 14(11), 1124; https://doi.org/10.3390/brainsci14111124 - 7 Nov 2024

Cited by 1 | Viewed by 11744

Abstract

Objectives: This study aimed to elucidate the mechanisms of chiropractic care using resting electroencephalography (EEG), somatosensory evoked potentials (SEPs), clinical health assessments (Fitbit), and Patient-reported Outcomes Measurement Information System (PROMIS-29). Methods: Seventy-six people with chronic low back pain (mean age ± SD: 45 [...] Read more.

Objectives: This study aimed to elucidate the mechanisms of chiropractic care using resting electroencephalography (EEG), somatosensory evoked potentials (SEPs), clinical health assessments (Fitbit), and Patient-reported Outcomes Measurement Information System (PROMIS-29). Methods: Seventy-six people with chronic low back pain (mean age ± SD: 45 ± 11 years, 33 female) were randomised into control (n = 38) and chiropractic (n = 38) groups. EEG and SEPs were collected pre and post the first intervention and post 4 weeks of intervention. PROMIS-29 was measured pre and post 4 weeks. Fitbit data were recorded continuously. Results: Spectral analysis of resting EEG showed a significant increase in Theta, Alpha and Beta, and a significant decrease in Delta power in the chiropractic group post intervention. Source localisation revealed a significant increase in Alpha activity within the Default Mode Network (DMN) post intervention and post 4 weeks. A significant decrease in N30 SEP peak amplitude post intervention and post 4 weeks was found in the chiropractic group. Source localisation demonstrated significant changes in Alpha and Beta power within the DMN post-intervention and post 4 weeks. Significant improvements in light sleep stage were observed in the chiropractic group along with enhanced overall quality of life post 4 weeks, including significant reductions in anxiety, depression, fatigue, and pain. Conclusions: These findings indicate that many health benefits of chiropractic care are due to altered brain activity. Full article

(This article belongs to the Special Issue Altered Musculoskeletal Sensory Input and Neuromechanics)

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12 pages, 1628 KiB

Open AccessEditor’s ChoiceArticle

Emotional and Cognitive “Route” in Decision-Making Process: The Relationship between Executive Functions, Psychophysiological Correlates, Decisional Styles, and Personality

by Davide Crivelli, Carlotta Acconito and Michela Balconi

Brain Sci. 2024, 14(7), 734; https://doi.org/10.3390/brainsci14070734 - 22 Jul 2024

Cited by 3 | Viewed by 9538

Abstract

Studies on decision-making have classically focused exclusively on its cognitive component. Recent research has shown that a further essential component of decisional processes is the emotional one. Indeed, the emotional route in decision-making plays a crucial role, especially in situations characterized by ambiguity, [...] Read more.

Studies on decision-making have classically focused exclusively on its cognitive component. Recent research has shown that a further essential component of decisional processes is the emotional one. Indeed, the emotional route in decision-making plays a crucial role, especially in situations characterized by ambiguity, uncertainty, and risk. Despite that, individual differences concerning such components and their associations with individual traits, decisional styles, and psychophysiological profiles are still understudied. This pilot study aimed at investigating the relationship between individual propensity toward using an emotional or cognitive information-processing route in decision-making, EEG and autonomic correlates of the decisional performance as collected via wearable non-invasive devices, and individual personality and decisional traits. Participants completed a novel task based on realistic decisional scenarios while their physiological activity (EEG and autonomic indices) was monitored. Self-report questionnaires were used to collect data on personality traits, individual differences, and decisional styles. Data analyses highlighted two main findings. Firstly, different personality traits and decisional styles showed significant and specific correlations, with an individual propensity toward either emotional or cognitive information processing for decision-making. Secondly, task-related EEG and autonomic measures presented a specific and distinct correlation pattern with different decisional styles, maximization traits, and personality traits, suggesting different latent profiles. Full article

(This article belongs to the Section Cognitive, Social and Affective Neuroscience)

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10 pages, 241 KiB

Open AccessEditor’s ChoiceArticle

Depression Is Associated with a Higher Risk of Mortality among Breast Cancer Survivors: Results from the National Health and Nutrition Examination Survey–National Death Index Linked Study

by Jagdish Khubchandani, Srikanta Banerjee, Kavita Batra and May A. Beydoun

Brain Sci. 2024, 14(7), 732; https://doi.org/10.3390/brainsci14070732 - 21 Jul 2024

Cited by 3 | Viewed by 4754

Abstract

Breast cancer (BC) and depression are globally prevalent problems. Numerous reviews have indicated the high prevalence of depression among BC survivors. However, the long-term impact of depression on survival among BC survivors has not been well explored. For this investigation, we aimed to [...] Read more.

Breast cancer (BC) and depression are globally prevalent problems. Numerous reviews have indicated the high prevalence of depression among BC survivors. However, the long-term impact of depression on survival among BC survivors has not been well explored. For this investigation, we aimed to explore the relationship between BC, depression, and mortality from a national random sample of adult American women. Data from the U.S. National Health and Nutrition Examination Survey (years 2005–2010) were linked with mortality data from the National Death Index up to December 31st, 2019. A total of 4719 adult women (ages 45 years and older) were included in the study sample with 5.1% having breast cancer and more than a tenth (12.7%) having depression. The adjusted hazard ratio (HR) for all-cause mortality risk among those with BC was 1.50 (95% CI = 1.05–2.13) compared to those without BC. In the adjusted analysis, the risk of all-cause mortality was highest among women with both depression and BC (HR = 3.04; 95% CI = 1.15–8.05) compared to those without BC or depression. The relationship between BC and mortality was moderated by cardiovascular diseases, anemia, smoking, age, PIR, and marital status. Our analysis provides vital information on factors that could be helpful for interventions to reduce mortality risk among those with BC and depression. In addition, given the higher risk of mortality with co-occurring BC and depression, collaborative healthcare practices should help with widespread screening for and treatment of depression among BC survivors. Full article

(This article belongs to the Special Issue Clinical Research on Mood Disorders: Opportunities and Challenges)

21 pages, 2257 KiB

Open AccessEditor’s ChoiceArticle

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

by Eamonn Kennedy, Spencer W. Liebel, Hannah M. Lindsey, Shashank Vadlamani, Pui-Wa Lei, Maheen M. Adamson, Martin Alda, Silvia Alonso-Lana, Tim J. Anderson, Celso Arango, Robert F. Asarnow, Mihai Avram, Rosa Ayesa-Arriola, Talin Babikian, Nerisa Banaj, Laura J. Bird, Stefan Borgwardt, Amy Brodtmann, Katharina Brosch, Karen Caeyenberghs, Vince D. Calhoun, Nancy D. Chiaravalloti, David X. Cifu, Benedicto Crespo-Facorro, John C. Dalrymple-Alford, Kristen Dams-O’Connor, Udo Dannlowski, David Darby, Nicholas Davenport, John DeLuca, Covadonga M. Diaz-Caneja, Seth G. Disner, Ekaterina Dobryakova, Stefan Ehrlich, Carrie Esopenko, Fabio Ferrarelli, Lea E. Frank, Carol E. Franz, Paola Fuentes-Claramonte, Helen Genova, Christopher C. Giza, Janik Goltermann, Dominik Grotegerd, Marius Gruber, Alfonso Gutierrez-Zotes, Minji Ha, Jan Haavik, Charles Hinkin, Kristen R. Hoskinson, Daniela Hubl, Andrei Irimia, Andreas Jansen, Michael Kaess, Xiaojian Kang, Kimbra Kenney, Barbora Keřková, Mohamed Salah Khlif, Minah Kim, Jochen Kindler, Tilo Kircher, Karolina Knížková, Knut K. Kolskår, Denise Krch, William S. Kremen, Taylor Kuhn, Veena Kumari, Junsoo Kwon, Roberto Langella, Sarah Laskowitz, Jungha Lee, Jean Lengenfelder, Victoria Liou-Johnson, Sara M. Lippa, Marianne Løvstad, Astri J. Lundervold, Cassandra Marotta, Craig A. Marquardt, Paulo Mattos, Ahmad Mayeli, Carrie R. McDonald, Susanne Meinert, Tracy R. Melzer, Jessica Merchán-Naranjo, Chantal Michel, Rajendra A. Morey, Benson Mwangi, Daniel J. Myall, Igor Nenadić, Mary R. Newsome, Abraham Nunes, Terence O’Brien, Viola Oertel, John Ollinger, Alexander Olsen, Victor Ortiz García de la Foz, Mustafa Ozmen, Heath Pardoe, Marise Parent, Fabrizio Piras, Federica Piras, Edith Pomarol-Clotet, Jonathan Repple, Geneviève Richard, Jonathan Rodriguez, Mabel Rodriguez, Kelly Rootes-Murdy, Jared Rowland, Nicholas P. Ryan, Raymond Salvador, Anne-Marthe Sanders, Andre Schmidt, Jair C. Soares, Gianfranco Spalleta, Filip Španiel, Scott R. Sponheim, Alena Stasenko, Frederike Stein, Benjamin Straube, April Thames, Florian Thomas-Odenthal, Sophia I. Thomopoulos, Erin B. Tone, Ivan Torres, Maya Troyanskaya, Jessica A. Turner, Kristine M. Ulrichsen, Guillermo Umpierrez, Daniela Vecchio, Elisabet Vilella, Lucy Vivash, William C. Walker, Emilio Werden, Lars T. Westlye, Krista Wild, Adrian Wroblewski, Mon-Ju Wu, Glenn R. Wylie, Lakshmi N. Yatham, Giovana B. Zunta-Soares, Paul M. Thompson, Mary Jo Pugh, David F. Tate, Frank G. Hillary, Elisabeth A. Wilde and Emily L. Dennis Show full author list Hide full author list

Brain Sci. 2024, 14(7), 669; https://doi.org/10.3390/brainsci14070669 - 29 Jun 2024

Cited by 1 | Viewed by 5324

Abstract

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine [...] Read more.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders. Full article

(This article belongs to the Special Issue Cognitive Impairment in Neuropsychiatry)

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13 pages, 1752 KiB

Open AccessEditor’s ChoiceArticle

Selective Activation of the Spinal Cord with Epidural Electrical Stimulation

by Carlos Cuellar, Lauri Lehto, Riaz Islam, Silvia Mangia, Shalom Michaeli and Igor Lavrov

Brain Sci. 2024, 14(7), 650; https://doi.org/10.3390/brainsci14070650 - 27 Jun 2024

Cited by 2 | Viewed by 5239

Abstract

Spinal cord epidural electrical stimulation (EES) has been successfully employed to treat chronic pain and to restore lost functions after spinal cord injury. Yet, the efficacy of this approach is largely challenged by the suboptimal spatial distribution of the electrode contacts across anatomical [...] Read more.

Spinal cord epidural electrical stimulation (EES) has been successfully employed to treat chronic pain and to restore lost functions after spinal cord injury. Yet, the efficacy of this approach is largely challenged by the suboptimal spatial distribution of the electrode contacts across anatomical targets, limiting the spatial selectivity of stimulation. In this study, we exploited different ESS paradigms, designed as either Spatial-Selective Stimulation (SSES) or Orientation-Selective Epidural Stimulation (OSES), and compared them to Conventional Monopolar Epidural Stimulation (CMES). SSES, OSES, and CMES were delivered with a 3- or 4-contact electrode array. Amplitudes and latencies of the Spinally Evoked Motor Potentials (SEMPs) were evaluated with different EES modalities. The results demonstrate that the amplitudes of SEMPs in hindlimb muscles depend on the orientation of the electrical field and vary between stimulation modalities. These findings show that the electric field applied with SSES or OSES provides more selective control of amplitudes of the SEMPs as compared to CMES. We demonstrate that spinal cord epidural stimulation applied with SSES or OSES paradigms in the rodent model could be tailored to the functional spinal cord neuroanatomy and can be tuned to specific target fibers and their orientation, optimizing the effect of neuromodulation. Full article

(This article belongs to the Special Issue New Perspectives in Chronic Pain Research: Focus on Neuroimaging)

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17 pages, 323 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Potential Impact of GLP-1 Receptor Agonists on Substance Use, Compulsive Behavior, and Libido: Insights from Social Media Using a Mixed-Methods Approach

by Davide Arillotta, Giuseppe Floresta, G. Duccio Papanti Pelletier, Amira Guirguis, John Martin Corkery, Giovanni Martinotti and Fabrizio Schifano

Brain Sci. 2024, 14(6), 617; https://doi.org/10.3390/brainsci14060617 - 20 Jun 2024

Cited by 13 | Viewed by 13347

Abstract

Glucagon-like peptide-1 (GLP-1) is involved in a range of central and peripheral pathways related to appetitive behavior. Hence, this study explored the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on substance and behavioral addictions, including alcohol, caffeine, nicotine, cannabis, psychostimulants, compulsive shopping, [...] Read more.

Glucagon-like peptide-1 (GLP-1) is involved in a range of central and peripheral pathways related to appetitive behavior. Hence, this study explored the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on substance and behavioral addictions, including alcohol, caffeine, nicotine, cannabis, psychostimulants, compulsive shopping, and sex drive/libido. Data were collected from various social platforms. Keywords related to GLP-1 RAs and substance/behavioral addiction were used to extract relevant comments. The study employed a mixed-methods approach to analyze online discussions posted from December 2019 to June 2023 and collected using a specialized web application. Reddit entries were the focus here due to limited data from other platforms, such as TikTok and YouTube. A total of 5859 threads and related comments were extracted from six subreddits, which included threads about GLP-1 RAs drugs and associated brand names. To obtain relevant posts, keywords related to potential substance use and compulsive behavior were selected. Further analysis involved two main steps: (1) manually coding posts based on users’ references to the potential impact of GLP-1 RAs on substance use and non-substance habits, excluding irrelevant or unclear comments; (2) performing a thematic analysis on the dataset of keywords, using AI-assisted techniques followed by the manual revision of the generated themes. Second, a thematic analysis was performed on the keyword-related dataset, using AI-assisted techniques followed by the manual revision of the generated themes. In total, 29.75% of alcohol-related; 22.22% of caffeine-related; and 23.08% of nicotine-related comments clearly stated a cessation of the intake of these substances following the start of GLP-1 RAs prescription. Conversely, mixed results were found for cannabis intake, and only limited, anecdotal data were made available for cocaine, entactogens, and dissociative drugs’ misuse. Regarding behavioral addictions, 21.35% of comments reported a compulsive shopping interruption, whilst the sexual drive/libido elements reportedly increased in several users. The current mixed-methods approach appeared to be a useful tool in gaining insight into complex topics such as the effects of GLP-1 RAs on substance and non-substance addiction-related disorders; some GLP-1 RA-related mental health benefits could also be inferred from here. Overall, it appeared that GLP-1 RAs may show the potential to target both substance craving and maladaptive/addictive behaviors, although further empirical research is needed. Full article

(This article belongs to the Special Issue Psychiatry and Addiction: A Multi-Faceted Issue)

25 pages, 4342 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

by Efe Precious Onakpojeruo, Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin and Ilker Ozsahin

Brain Sci. 2024, 14(6), 559; https://doi.org/10.3390/brainsci14060559 - 30 May 2024

Cited by 14 | Viewed by 1744

Abstract

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data [...] Read more.

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis. Full article

(This article belongs to the Section Neuro-oncology)

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17 pages, 2309 KiB

Open AccessEditor’s ChoiceArticle

Low-Cost 3D Models for Cervical Spine Tumor Removal Training for Neurosurgery Residents

by Albert Sufianov, Carlos Salvador Ovalle, Omar Cruz, Javier Contreras, Emir Begagić, Siddarth Kannan, Andreina Rosario Rosario, Gennady Chmutin, Garifullina Nargiza Askatovna, Jesus Lafuente, Jose Soriano Sanchez, Renat Nurmukhametov, Manuel Eduardo Soto García, Nikolay Peev, Mirza Pojskić, Gervith Reyes-Soto, Ismail Bozkurt and Manuel De Jesus Encarnación Ramírez

Brain Sci. 2024, 14(6), 547; https://doi.org/10.3390/brainsci14060547 - 27 May 2024

Cited by 6 | Viewed by 1859

Abstract

Background and Objectives: Spinal surgery, particularly for cervical pathologies such as myelopathy and radiculopathy, requires a blend of theoretical knowledge and practical skill. The complexity of these conditions, often necessitating surgical intervention, underscores the need for intricate understanding and precision in execution. Advancements [...] Read more.

Background and Objectives: Spinal surgery, particularly for cervical pathologies such as myelopathy and radiculopathy, requires a blend of theoretical knowledge and practical skill. The complexity of these conditions, often necessitating surgical intervention, underscores the need for intricate understanding and precision in execution. Advancements in neurosurgical training, especially with the use of low-cost 3D models for simulating cervical spine tumor removal, are revolutionizing this field. These models provide the realistic and hands-on experience crucial for mastering complex neurosurgical techniques, filling gaps left by traditional educational methods. Materials and Methods: This study aimed to assess the effectiveness of 3D-printed cervical vertebrae models in enhancing surgical skills, focusing on tumor removal, and involving 20 young neurosurgery residents. These models, featuring silicone materials to simulate the spinal cord and tumor tissues, provided a realistic training experience. The training protocol included a laminectomy, dural incision, and tumor resection, using a range of microsurgical tools, focusing on steps usually performed by senior surgeons. Results: The training program received high satisfaction rates, with 85% of participants extremely satisfied and 15% satisfied. The 3D models were deemed very realistic by 85% of participants, effectively replicating real-life scenarios. A total of 80% found that the simulated pathologies were varied and accurate, and 90% appreciated the models’ accurate tactile feedback. The training was extremely useful for 85% of the participants in developing surgical skills, with significant post-training confidence boosts and a strong willingness to recommend the program to peers. Conclusions: Continuing laboratory training for residents is crucial. Our model offers essential, accessible training for all hospitals, regardless of their resources, promising improved surgical quality and patient outcomes across various pathologies. Full article

(This article belongs to the Special Issue New Trends and Technologies in Modern Neurosurgery)

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21 pages, 2154 KiB

Open AccessEditor’s ChoiceArticle

Childhood Apraxia of Speech: A Descriptive and Prescriptive Model of Assessment and Diagnosis

by Ahmed Alduais and Hind Alfadda

Brain Sci. 2024, 14(6), 540; https://doi.org/10.3390/brainsci14060540 - 24 May 2024

Viewed by 4929

Abstract

Childhood apraxia of speech (CAS) represents a significant diagnostic and therapeutic challenge within the field of clinical neuropsychology, characterized by its nuanced presentation and multifactorial nature. The aim of this study was to distil and synthesize the broad spectrum of research into a [...] Read more.

Childhood apraxia of speech (CAS) represents a significant diagnostic and therapeutic challenge within the field of clinical neuropsychology, characterized by its nuanced presentation and multifactorial nature. The aim of this study was to distil and synthesize the broad spectrum of research into a coherent model for the assessment and diagnosis of CAS. Through a mixed-method design, the quantitative phase analyzed 290 studies, unveiling 10 clusters: developmental apraxia, tabby talk, intellectual disabilities, underlying speech processes, breakpoint localization, speech characteristics, functional characteristics, clinical practice, and treatment outcome. The qualitative phase conducted a thematic analysis on the most cited and recent literature, identifying 10 categories: neurobiological markers, speech motor control, perceptual speech features, auditory processing, prosody and stress patterns, parent- and self-report measures, intervention response, motor learning and generalization, comorbidity analysis, and cultural and linguistic considerations. Integrating these findings, a descriptive and prescriptive model was developed, encapsulating the complexities of CAS and providing a structured approach for clinicians. This model advances the understanding of CAS and supports the development of targeted interventions. This study concludes with a call for evidence-based personalized treatment plans that account for the diverse neurobiological and cultural backgrounds of children with CAS. Its implications for practice include the integration of cutting-edge assessment tools that embrace the heterogeneity of CAS presentations, ensuring that interventions are as unique as the children they aim to support. Full article

(This article belongs to the Special Issue Language, Communication and the Brain)

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14 pages, 871 KiB

Open AccessEditor’s ChoiceArticle

Repeated Bilateral Transcranial Direct Current Stimulation over Auditory Cortex for Tinnitus Treatment: A Double-Blinded Randomized Controlled Clinical Trial

by Ali Yadollahpour, Samaneh Rashidi, Nader Saki, Pramod Singh Kunwar and Miguel Mayo-Yáñez

Brain Sci. 2024, 14(4), 373; https://doi.org/10.3390/brainsci14040373 - 12 Apr 2024

Cited by 1 | Viewed by 5148

Abstract

Transcranial direct current stimulation (tDCS) is a non-invasive and painless technique of brain neuromodulation that applies a low-intensity galvanic current to the scalp with the aim of stimulating specific areas of the brain. Preliminary investigations have indicated the potential therapeutic efficacy of multisession [...] Read more.

Transcranial direct current stimulation (tDCS) is a non-invasive and painless technique of brain neuromodulation that applies a low-intensity galvanic current to the scalp with the aim of stimulating specific areas of the brain. Preliminary investigations have indicated the potential therapeutic efficacy of multisession tDCS applied to the auditory cortex (AC) in the treatment of chronic tinnitus. The aim of this study was to explore the therapeutic effects of repeated sessions of bilateral tDCS targeting the AC on chronic tinnitus. A double-blinded randomized placebo-controlled trial was conducted on patients (n = 48) with chronic intractable tinnitus (>2 years duration). Participants were randomly allocated to two groups: one receiving tDCS (n = 26), with the anode/cathode placed over the left/right AC, and the other receiving a placebo treatment (n = 22). A 20 min daily session of 2 mA current was administered for five consecutive days per week over two consecutive weeks, employing 35 cm² electrodes. Tinnitus handicap inventory (THI) scores, tinnitus loudness, and tinnitus distress were measured using a visual analogue scale (VAS), and were assessed before intervention, immediately after, and at one-month follow-up. Anodal tDCS significantly reduced THI from 72.93 ± 10.11 score to 46.40 ± 15.36 after the last session and 49.68 ± 14.49 at one-month follow-up in 18 out of 25 participants (p < 0.001). The risk ratio (RR) of presenting an improvement of ≥20 points in the THI after the last session was 10.8 in patients treated with tDCS. Statistically significant reductions were observed in distress VAS and loudness VAS (p < 0.001). No statistically significant differences in the control group were observed. Variables such as age, gender, duration of tinnitus, laterality of tinnitus, baseline THI scores, and baseline distress and loudness VAS scores did not demonstrate significant correlations with treatment response. Repeated sessions of bilateral AC tDCS may potentially serve as a therapeutic modality for chronic tinnitus. Full article

(This article belongs to the Special Issue Computational Methods in Neuroimaging: Advances and Challenges)

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14 pages, 1378 KiB

Open AccessEditor’s ChoiceArticle

Use of Prescribed and Non-Prescribed Treatments for Cluster Headache in a Swedish Cohort

by Gabriella Smedfors, Felicia Jennysdotter Olofsgård, Anna Steinberg, Elisabet Waldenlind, Caroline Ran and Andrea Carmine Belin

Brain Sci. 2024, 14(4), 348; https://doi.org/10.3390/brainsci14040348 - 31 Mar 2024

Cited by 1 | Viewed by 4813

Abstract

Background: Cluster headache (CH) is a debilitating condition, but current therapies leave CH patients in pain. The extent of this problem in Sweden is unknown. Methods: An anonymized questionnaire was sent to 479 Swedish CH patients to investigate patterns and perceived effects of [...] Read more.

Background: Cluster headache (CH) is a debilitating condition, but current therapies leave CH patients in pain. The extent of this problem in Sweden is unknown. Methods: An anonymized questionnaire was sent to 479 Swedish CH patients to investigate patterns and perceived effects of treatments. Results: Three hundred fourteen answers were analyzed. The population was representative regarding age of onset and sex. Less than half (46%) were satisfied with their abortive treatments, 19% terminated functioning abortive treatments due to side effects. Additionally, 17% of chronic CH patients had not tried the first-line preventive drug verapamil. A small subset had tried illicit substances to treat their CH (0–8% depending on substance). Notably, psilocybin was reported effective as an abortive treatment by 100% (n = 8), and with some level of effect as a preventive treatment by 92% (n = 12). For verapamil, some level of preventive effect was reported among 68% (n = 85). Conclusions: Our descriptive data illustrate that many Swedish CH patients are undertreated, lack functional therapies, and experience side effects. Further studies are warranted to search for new treatment strategies as well as a revision of current treatment guidelines with the aim of reducing patient disease burden to the greatest extent possible. Full article

(This article belongs to the Section Sensory and Motor Neuroscience)

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18 pages, 3679 KiB

Open AccessEditor’s ChoiceArticle

Breathing Right… or Left! The Effects of Unilateral Nostril Breathing on Psychological and Cognitive Wellbeing: A Pilot Study

by Maria Elide Vanutelli, Chiara Grigis and Claudio Lucchiari

Brain Sci. 2024, 14(4), 302; https://doi.org/10.3390/brainsci14040302 - 23 Mar 2024

Viewed by 8347

Abstract

The impact of controlled breathing on cognitive and affective processing has been recognized since ancient times, giving rise to multiple practices aimed at achieving different psychophysical states, mostly related to mental clarity and focus, stress reduction, and relaxation. Previous scientific research explored the [...] Read more.

The impact of controlled breathing on cognitive and affective processing has been recognized since ancient times, giving rise to multiple practices aimed at achieving different psychophysical states, mostly related to mental clarity and focus, stress reduction, and relaxation. Previous scientific research explored the effects of forced unilateral nostril breathing (UNB) on brain activity and emotional and cognitive functions. Some evidence concluded that it had a contralateral effect, while other studies presented controversial results, making it difficult to come to an unambiguous interpretation. Also, a few studies specifically addressed wellbeing. In the present study, we invited a pilot sample of 20 participants to take part in an 8-day training program for breathing, and each person was assigned to either a unilateral right nostril (URNB) or left nostril breathing condition (ULNB). Then, each day, we assessed the participants’ wellbeing indices using their moods and mind wandering scales. The results revealed that, after the daily practice, both groups reported improved wellbeing perception. However, the effect was specifically related to the nostril involved. URNB produced more benefits in terms of stress reduction and relaxation, while ULNB significantly and increasingly reduced mind-wandering occurrences over time. Our results suggest that UNB can be effectively used to increase wellbeing in the general population. Additionally, they support the idea that understanding the effects of unilateral breathing on wellbeing and cognition requires a complex interpretive model with multiple brain networks to address bottom-up and top-down processes. Full article

(This article belongs to the Section Neuropsychology)

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12 pages, 1099 KiB

Open AccessEditor’s ChoiceArticle

Individualised Transcranial Magnetic Stimulation Targeting of the Left Dorsolateral Prefrontal Cortex for Enhancing Cognition: A Randomised Controlled Trial

by Donel M. Martin, Yon Su, Ho Fung Chan, Victoria Dielenberg, Esther Chow, Mei Xu, Ashley Wang, Stevan Nikolin, Adriano H. Moffa and Colleen K. Loo

Brain Sci. 2024, 14(4), 299; https://doi.org/10.3390/brainsci14040299 - 22 Mar 2024

Cited by 7 | Viewed by 3385

Abstract

Repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to produce cognitive enhancing effects across different neuropsychiatric disorders; however, so far, these effects have been limited. This trial investigated the efficacy of using a novel individualised approach to target the left dorsolateral prefrontal cortex [...] Read more.

Repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to produce cognitive enhancing effects across different neuropsychiatric disorders; however, so far, these effects have been limited. This trial investigated the efficacy of using a novel individualised approach to target the left dorsolateral prefrontal cortex (L-DLPFC) for enhancing cognitive flexibility based on performance on a cognitive task. First, forty healthy participants had their single target site at the L-DLPFC determined based on each individual’s performance on a random letter generation task. Participants then received, in a cross-over single-blinded experimental design, a single session of intermittent theta burst stimulation (iTBS) to their individualised DLPFC target site, an active control site and sham iTBS. Following each treatment condition, participants completed the Task Switching task and Colour–Word Stroop test. There was no significant main effect of treatment condition on the primary outcome measure of switch reaction times from the Task Switching task [F = 1.16 (2, 21.6), p = 0.33] or for any of the secondary cognitive outcome measures. The current results do not support the use of our novel individualised targeting methodology for enhancing cognitive flexibility in healthy participants. Research into alternative methodological targeting approaches is required to further improve rTMS’s cognitive enhancing effects. Full article

(This article belongs to the Special Issue Brain Stimulation for Psychiatric Disorders: Emerging Evidence and New Perspectives)

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19 pages, 2507 KiB

Open AccessEditor’s ChoiceArticle

Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction

by Jinhao Zhang, Yanrong Hao, Xin Wen, Chenchen Zhang, Haojie Deng, Juanjuan Zhao and Rui Cao

Brain Sci. 2024, 14(3), 271; https://doi.org/10.3390/brainsci14030271 - 12 Mar 2024

Cited by 10 | Viewed by 3571

Abstract

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high [...] Read more.

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies. Full article

(This article belongs to the Special Issue Emerging Trends of Biomedical Signal Processing in Intelligent Emotion Recognition)

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15 pages, 3691 KiB

Open AccessEditor’s ChoiceArticle

Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning

by Xiuzhen Yao, Tianwen Li, Peng Ding, Fan Wang, Lei Zhao, Anmin Gong, Wenya Nan and Yunfa Fu

Brain Sci. 2024, 14(3), 268; https://doi.org/10.3390/brainsci14030268 - 11 Mar 2024

Cited by 15 | Viewed by 5879

Abstract

Objectives: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not [...] Read more.

Objectives: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial–temporal (EEG ST) feature learning to automatic emotion classification. Methods: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG’s spatial and temporal features, which are subsequently classified using Softmax. Results: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal–valence, arousal, and valence dimensions, respectively, for the DEAP dataset. Conclusions: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies. Significance: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition. Full article

(This article belongs to the Special Issue Emerging Trends of Biomedical Signal Processing in Intelligent Emotion Recognition)

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19 pages, 5627 KiB

Open AccessEditor’s ChoiceArticle

Akkermansia muciniphila Is Beneficial to a Mouse Model of Parkinson’s Disease, via Alleviated Neuroinflammation and Promoted Neurogenesis, with Involvement of SCFAs

by Chen-Meng Qiao, Wen-Yan Huang, Yu Zhou, Wei Quan, Gu-Yu Niu, Ting Li, Mei-Xuan Zhang, Jian Wu, Li-Ping Zhao, Wei-Jiang Zhao, Chun Cui and Yan-Qin Shen

Brain Sci. 2024, 14(3), 238; https://doi.org/10.3390/brainsci14030238 - 29 Feb 2024

Cited by 15 | Viewed by 2939

Abstract

Increasing evidence suggests that the gut microbiota may represent potential strategies for Parkinson’s disease (PD) treatment. Our previous research revealed a decreased abundance of Akkermansia muciniphila (Akk) in PD mice; however, whether Akk is beneficial to PD is unknown. To answer this question, [...] Read more.

Increasing evidence suggests that the gut microbiota may represent potential strategies for Parkinson’s disease (PD) treatment. Our previous research revealed a decreased abundance of Akkermansia muciniphila (Akk) in PD mice; however, whether Akk is beneficial to PD is unknown. To answer this question, the mice received MPTP intraperitoneally to construct a subacute model of PD and were then supplemented with Akk orally for 21 consecutive days. Motor function, dopaminergic neurons, neuroinflammation, and neurogenesis were examined. In addition, intestinal inflammation, and serum and fecal short-chain fatty acids (SCFAs) analyses, were assessed. We found that Akk treatment effectively inhibited the reduction of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and partially improved the motor function in PD mice. Additionally, Akk markedly alleviated neuroinflammation in the striatum and hippocampus and promoted hippocampal neurogenesis. It also decreased the level of colon inflammation. Furthermore, these aforementioned changes are mainly accompanied by alterations in serum and fecal isovaleric acid levels, and lower intestinal permeability. Our research strongly suggests that Akk is a potential neuroprotective agent for PD therapy. Full article

(This article belongs to the Special Issue Molecular Mechanism and Pathology of Parkinson's Disease)

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20 pages, 1104 KiB

Open AccessEditor’s ChoiceArticle

The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings

by Benjamin S. Runde, Ajit Alapati and Nicolas G. Bazan

Brain Sci. 2024, 14(3), 211; https://doi.org/10.3390/brainsci14030211 - 25 Feb 2024

Cited by 10 | Viewed by 4379

Abstract

The development of noninvasive and cost-effective methods of detecting Alzheimer’s disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel [...] Read more.

The development of noninvasive and cost-effective methods of detecting Alzheimer’s disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services—locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI—evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system. Full article

(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)

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20 pages, 10515 KiB

Open AccessEditor’s ChoiceArticle

A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

by Vincenzo Ronca, Francois Brambati, Linda Napoletano, Cyril Marx, Sandra Trösterer, Alessia Vozzi, Pietro Aricò, Andrea Giorgi, Rossella Capotorto, Gianluca Borghini, Fabio Babiloni and Gianluca Di Flumeri

Brain Sci. 2024, 14(3), 193; https://doi.org/10.3390/brainsci14030193 - 21 Feb 2024

Cited by 9 | Viewed by 2349

Abstract

The drivers’ distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in [...] Read more.

The drivers’ distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to “detect” eventual events, road traffic, and additional driving tasks that might contribute to the drivers’ distraction. Therefore, the present study aimed at developing an EEG-based “Distraction index” obtained by the combination of the driver’s mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver’s attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers’ distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers’ distraction (all p < 0.01). Full article

(This article belongs to the Section Neurotechnology and Neuroimaging)

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11 pages, 1741 KiB

Open AccessEditor’s ChoiceArticle

Changes in Structural Neural Networks in the Recovery Process of Motor Paralysis after Stroke

by Ikuo Kimura, Atsushi Senoo and Masahiro Abo

Brain Sci. 2024, 14(3), 197; https://doi.org/10.3390/brainsci14030197 - 21 Feb 2024

Cited by 6 | Viewed by 1933

Abstract

In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received [...] Read more.

In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received a combination of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (intensive-OT) as neurorehabilitation. Fugl-Meyer assessment (FMA) for upper extremity (FMA-UE) and Action Research Arm Test (ARAT), both of which reflected upper limb motor function, were conducted before and after rehabilitation therapy. At the same time, diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D T1WI) were performed. After analyzing the structural connectome based on DTI data, measures related to connectivity in neural networks were calculated using graph theory. Rehabilitation therapy prompted a significant increase in connectivity with the isthmus of the cingulate gyrus in the ipsilesional hemisphere (p < 0.05) in patients with left-sided paralysis, as well as a significant decrease in connectivity with the ipsilesional postcentral gyrus (p < 0.05). These results indicate that LF-rTMS combined with intensive-OT may facilitate motor function recovery by enhancing the functional roles of networks in motor-related areas of the ipsilesional cerebral hemisphere. Full article

(This article belongs to the Special Issue Clinical Application of Neuroimaging in Cerebral Vascular Diseases)

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15 pages, 2625 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Acquisition of Social Communication Skills in Children with Autism: Preliminary Findings from Applied Behavior Analysis (ABA), Parent Training, and Video Modeling

by Daniela Bordini, Ana Cláudia Moya, Graccielle Rodrigues da Cunha Asevedo, Cristiane Silvestre Paula, Décio Brunoni, Helena Brentani, Sheila Cavalcante Caetano, Jair de Jesus Mari and Leila Bagaiolo

Brain Sci. 2024, 14(2), 172; https://doi.org/10.3390/brainsci14020172 - 9 Feb 2024

Cited by 4 | Viewed by 6870

Abstract

Social communication skills, especially eye contact and joint attention, are frequently impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied behavior analysis is one of the most common evidence-based treatments for ASD, but it is not accessible to most families in [...] Read more.

Social communication skills, especially eye contact and joint attention, are frequently impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied behavior analysis is one of the most common evidence-based treatments for ASD, but it is not accessible to most families in low- and middle-income countries (LMICs) as it is an expensive and intensive treatment and needs to be delivered by highly specialized professionals. Parental training has emerged as an effective alternative. This is an exploratory study to assess a parental intervention group via video modeling to acquire eye contact and joint attention. Four graded measures of eye contact and joint attention (full physical prompt, partial physical prompt, gestural prompt, and independent) were assessed in 34 children with ASD and intellectual disability (ID). There was a progressive reduction in the level of prompting required over time to acquire eye contact and joint attention, as well as a positive correlation between the time of exposure to the intervention and the acquisition of abilities. This kind of parent training using video modeling to teach eye contact and joint attention skills to children with ASD and ID is a low-cost intervention that can be applied in low-resource settings. Full article

(This article belongs to the Special Issue Autism Spectrum Disorders (ASD) in Low- and Middle-Income Countries (LMICs) and among Immigrants and Minority Groups in High-Income Countries (HICs))

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20 pages, 8080 KiB

Open AccessEditor’s ChoiceArticle

Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study

by Miloš Pušica, Aneta Kartali, Luka Bojović, Ivan Gligorijević, Jelena Jovanović, Maria Chiara Leva and Bogdan Mijović

Brain Sci. 2024, 14(2), 149; https://doi.org/10.3390/brainsci14020149 - 31 Jan 2024

Cited by 7 | Viewed by 5125

Abstract

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual’s effort, mental capacity, or cognitive resources utilized while performing a task. MWL in [...] Read more.

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual’s effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load levels. We designed a convolutional neural network (CNN) to help with two distinct classification tasks. In one setting, the CNN was used to classify EEG segments based on their task load level. In another setting, the same CNN architecture was trained again to detect the presence of individual MATB-II subtasks. Results show that, while the model successfully learns to detect whether a particular subtask is active in a given segment (i.e., to differentiate between different subtasks-related EEG patterns), it struggles to differentiate between the two highest levels of task load (i.e., to distinguish MWL-related EEG patterns). We speculate that the challenge comes from two factors: first, the experiment was designed in a way that these two highest levels differed only in the quantity of work within a given timeframe; and second, the participants’ effective adaptation to increased task demands, as evidenced by low error rates. Consequently, this indicates that under such conditions in multitasking, EEG may not reflect distinct enough patterns to differentiate higher levels of task load. Full article

(This article belongs to the Special Issue Emerging Topics in Brain-Computer Interface)

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15 pages, 294 KiB

Open AccessEditor’s ChoiceArticle

The Interplay of Anxiety, Depression, Sleep Quality, and Socioeconomic Factors in Somali Hemodialysis Patients

by Samet Kose and Nur Adam Mohamed

Brain Sci. 2024, 14(2), 144; https://doi.org/10.3390/brainsci14020144 - 30 Jan 2024

Cited by 7 | Viewed by 2423

Abstract

Objective: This study aimed to assess anxiety, depression, and sleep quality in kidney failure patients receiving hemodialysis (HD) in Somalia and examine the relationship between anxiety, depression, and sleep quality. Methods: We conducted a study with 200 kidney failure patients on HD treatment [...] Read more.

Objective: This study aimed to assess anxiety, depression, and sleep quality in kidney failure patients receiving hemodialysis (HD) in Somalia and examine the relationship between anxiety, depression, and sleep quality. Methods: We conducted a study with 200 kidney failure patients on HD treatment for over 3 months. Participants completed sociodemographic questionnaires, the Patient Health Questionnaire-9 (PHQ-9), the Hospital Anxiety and Depression Scale (HADS), the Insomnia Severity Index (ISI), and the Pittsburgh Sleep Quality Index (PSQI). Results: Among the 200 participants (mean age = 52.3; SD = 14.13), 58.5% were men, 64% had CKD for 1–5 years, and 52.6% received HD for 1–5 years. Depressive symptoms were found in 61.5% (PHQ-9) and 37.5% (HADS depression subscale) of HD patients. Poor sleep quality (PSQI) was observed in 31.5% and significantly correlated with PHQ-9 (r_s = 0.633), HADS anxiety (r_s = 0.491), and HADS depression (r_s = 0.529). The ISI score correlated significantly with PHQ-9 (r_s = 0.611), HADS anxiety (r_s = 0.494), and HADS depression (r_s = 0.586). All PSQI components correlated with depression and anxiety, except sleep medication use. Hierarchical regression analysis revealed that HADS anxiety (β = 0.342) and HADS depression (β = 0.372) predicted ISI scores. HADS anxiety (β = 0.307) and HADS depression (β = 0.419) predicted PSQI scores. Conclusions: Higher anxiety and depression levels negatively correlated with various dimensions of sleep quality in kidney failure patients. Early identification and appropriate management of these psychological disturbances are crucial for enhancing patients’ overall quality of life. Full article

(This article belongs to the Section Sleep and Circadian Neuroscience)

18 pages, 1062 KiB

Open AccessEditor’s ChoiceArticle

Improving Outcomes in People with Spinal Cord Injury: Encouraging Results from a Multidisciplinary Advanced Rehabilitation Pathway

by Maria Grazia Maggio, Mirjam Bonanno, Alfredo Manuli and Rocco Salvatore Calabrò

Brain Sci. 2024, 14(2), 140; https://doi.org/10.3390/brainsci14020140 - 28 Jan 2024

Cited by 8 | Viewed by 3761

Abstract

Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The [...] Read more.

Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The aim of this study is to evaluate the feasibility of a dedicated, multidisciplinary, and integrated intervention path for SCI, encompassing both conventional and technological interventions, while observing their impact on cognitive, motor, and behavioral outcomes and the overall quality of life for individuals with SCI. Forty-two patients with SCI were included in the analysis utilizing electronic recovery system data. The treatment regimen included multidisciplinary rehabilitation approaches, such as traditional physiotherapy sessions, speech therapy, psychological support, robotic devices, advanced cognitive rehabilitation, and other interventions. Pre–post comparisons showed a significant improvement in lower limb function (Fugl Meyer Assessment-FMA < 0.001), global cognitive functioning (Montreal Cognitive Assessment-MoCA p < 0.001), and perceived quality of life at both a physical and mental level (Short Form-12-SF-12 p < 0.001). Furthermore, we found a significant reduction in depressive state (Beck Depression Inventory-BDI p < 0.001). In addition, we assessed patient satisfaction using the Short Form of the Patient Satisfaction Questionnaire (PSQ), offering insights into the subjective evaluation of the intervention. In conclusion, this retrospective study provides positive results in terms of improvements in motor function, cognitive functions, and quality of life, highlighting the importance of exploring multidisciplinary approaches. Full article

(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)

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21 pages, 342 KiB

Open AccessEditor’s ChoiceArticle

Associations between Gross and Fine Motor Skills, Physical Activity, Executive Function, and Academic Achievement: Longitudinal Findings from the UK Millennium Cohort Study

by Yuxi Zhou and Andrew Tolmie

Brain Sci. 2024, 14(2), 121; https://doi.org/10.3390/brainsci14020121 - 24 Jan 2024

Cited by 11 | Viewed by 9408

Abstract

Accumulating evidence from behavioral studies and neuroscience suggests that motor and cognitive development are intrinsically intertwined. To explore the underlying mechanisms of this motor–cognition link, our study examined the longitudinal relationship of early motor skills and physical activity with later cognitive skills. The [...] Read more.

Accumulating evidence from behavioral studies and neuroscience suggests that motor and cognitive development are intrinsically intertwined. To explore the underlying mechanisms of this motor–cognition link, our study examined the longitudinal relationship of early motor skills and physical activity with later cognitive skills. The sample was 3188 children from the United Kingdom Millennium Cohort Study, followed at 9 months and 5, 7, and 11 years. Early motor skills were examined at 9 months. Children’s daily physical activity level was measured using accelerometers at 7 years and a questionnaire was conducted at 11 years. Cognitive skills, including executive function and academic achievement, were measured at age 11. The results suggest that gross motor skills were positively associated with spatial working memory, whereas fine motor skills were predictive of good English and science outcomes. Moderate-to-vigorous activity was found to be negatively associated with English performance, although self-reported activity frequency was positively linked to math. Our results highlight the significant role of both gross and fine motor skills in cognitive development. This study also elucidates the limitations of using activity intensity to assess the impact of motor activity on children’s cognitive development, suggesting that attention to the effects of specific types of physical activity would better elucidate the motor/cognition link. Full article

(This article belongs to the Special Issue Executive Functioning Development—Measurements and Promotion in Naturalistic Setups)

18 pages, 1338 KiB

Open AccessEditor’s ChoiceArticle

Autistic Traits as Predictors of Increased Obsessive–Compulsive Disorder Severity: The Role of Inflexibility and Communication Impairment

by Liliana Dell’Osso, Benedetta Nardi, Chiara Bonelli, Giulia Amatori, Maria Alessandra Pereyra, Enrico Massimetti, Ivan Mirko Cremone, Stefano Pini and Barbara Carpita

Brain Sci. 2024, 14(1), 64; https://doi.org/10.3390/brainsci14010064 - 9 Jan 2024

Cited by 7 | Viewed by 4605

Abstract

Due to similar manifestations, some authors have proposed a potential correlation between autism spectrum disorder (ASD) and obsessive–compulsive disorder (OCD). This link has long been recognized and debated, with some authors arguing that these disorders frequently occur comorbid but distinct while others believe [...] Read more.

Due to similar manifestations, some authors have proposed a potential correlation between autism spectrum disorder (ASD) and obsessive–compulsive disorder (OCD). This link has long been recognized and debated, with some authors arguing that these disorders frequently occur comorbid but distinct while others believe they are part of the same spectrum. The aim of our study was to explore the prevalence and correlates of autistic traits in 55 OCD patients and 55 matched controls and to assess possible autistic dimensions predictive of higher OCD symptoms. All participants were assessed with the Obsessive–Compulsive Spectrum-Short Version (OBS-SV) and the Adult Autism Subthreshold Spectrum (AdAS Spectrum). The OCD group scored significantly higher in both questionnaires. Total OBS-SV scores and domains were significantly correlated with all AdAS Spectrum domains and total score. The AdAS Spectrum total, Verbal Communication and Inflexibility and adherence to routine domain scores were significant positive predictors of higher OBS-SV scores. Lastly, when two clusters of subjects (high and low autism) were determined, Inflexibility and adherence to routine domain presented the greatest influence in forming the clusters. Our findings support the association between OCD and autistic traits in the adult population, supporting the hypothesis of a neurodevelopmental basis for these psychiatric conditions. Full article

(This article belongs to the Section Neuropsychiatry)

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11 pages, 865 KiB

Open AccessEditor’s ChoiceArticle

The Association between Working Memory and Divergent Thinking: The Moderating Role of Formal Musical Background

by Maria Chiara Pino, Marco Giancola, Massimiliano Palmiero and Simonetta D’Amico

Brain Sci. 2024, 14(1), 61; https://doi.org/10.3390/brainsci14010061 - 8 Jan 2024

Viewed by 5113

Abstract

Divergent thinking (DT) is widely considered an essential cognitive dimension of creativity, which involves goal-oriented processes, including working memory (WM), which allows for retrieving and loading of information into the attentional stream and, consequently, enhancing divergence of thinking. Despite the critical role of [...] Read more.

Divergent thinking (DT) is widely considered an essential cognitive dimension of creativity, which involves goal-oriented processes, including working memory (WM), which allows for retrieving and loading of information into the attentional stream and, consequently, enhancing divergence of thinking. Despite the critical role of WM in DT, little work has been done on the mechanism affecting this interplay. The current study addressed the involvement of a formal musical background in the relationship between WM and DT and was conducted with 83 healthy young adults (M = 19.64 years; SD = 0.52 years; 33 females). The participants were requested to indicate if they had a formal background in music in the conservatory (M = 4.78 years; SD = 5.50 years) as well as perform the digit span forward test (DSFT) and the alternative uses task—AUT from the Torrance test of creative thinking (TTCT). The results indicated that years of formal musical background moderated the association between WM and DT. These findings suggest that music enhances the positive effect of high-order cognitive processes, such as WM, on the ability to think divergently. Theoretical and practical implications as well as limitations were discussed. Full article

(This article belongs to the Special Issue The Role of Sounds and Music in Emotion and Cognition)

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17 pages, 541 KiB

Open AccessEditor’s ChoiceArticle

GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach

by Davide Arillotta, Giuseppe Floresta, Amira Guirguis, John Martin Corkery, Valeria Catalani, Giovanni Martinotti, Stefano L. Sensi and Fabrizio Schifano

Brain Sci. 2023, 13(11), 1503; https://doi.org/10.3390/brainsci13111503 - 24 Oct 2023

Cited by 33 | Viewed by 17890

Abstract

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms’ users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has [...] Read more.

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms’ users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has contributed to an ongoing worldwide shortage of the drug associated with levels of non-prescribed semaglutide intake. Furthermore, recent reports emphasized some GLP-1 RA-associated risks of triggering depression and suicidal thoughts. Consistent with the above, we aimed to assess the possible impact of GLP-1 RAs on mental health as being perceived and discussed in popular open platforms with the help of a mixed-methods approach. Reddit posts yielded 12,136 comments, YouTube videos 14,515, and TikTok videos 17,059, respectively. Out of these posts/entries, most represented matches related to sleep-related issues, including insomnia (n = 620 matches); anxiety (n = 353); depression (n = 204); and mental health issues in general (n = 165). After the initiation of GLP-1 RAs, losing weight was associated with either a marked improvement or, in some cases, a deterioration, in mood; increase/decrease in anxiety/insomnia; and better control of a range of addictive behaviors. The challenges of accessing these medications were a hot topic as well. To the best of our knowledge, this is the first study documenting if and how GLP-1 RAs are perceived as affecting mood, mental health, and behaviors. Establishing a clear cause-and-effect link between metabolic diseases, depression and medications is difficult because of their possible reciprocal relationship, shared underlying mechanisms and individual differences. Further research is needed to better understand the safety profile of these molecules and their putative impact on behavioral and non-behavioral addictions. Full article

(This article belongs to the Section Neuropsychology)

22 pages, 3602 KiB

Open AccessEditor’s ChoiceArticle

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

by Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Yazeed Yasin Ghadi, Muhammad Zubair Khan, Muhammad Abbas Khan, Akmalbek Abdusalomov, Fayez Alqahtani and Ahmed M. Shehata

Brain Sci. 2023, 13(9), 1320; https://doi.org/10.3390/brainsci13091320 - 14 Sep 2023

Cited by 51 | Viewed by 5446

Abstract

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need [...] Read more.

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses. Full article

(This article belongs to the Special Issue Advances of AI in Neuroimaging)

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10 pages, 16129 KiB

Open AccessEditor’s ChoiceArticle

Do Individuals with Spinal Cord Injury Benefit from Semi-Immersive Virtual Reality Cognitive Training? Preliminary Results from an Exploratory Study on an Underestimated Problem

by Maria Grazia Maggio, Mirjam Bonanno, Alfredo Manuli, Maria Pia Onesta, Rosaria De Luca, Angelo Quartarone and Rocco Salvatore Calabrò

Brain Sci. 2023, 13(6), 945; https://doi.org/10.3390/brainsci13060945 - 13 Jun 2023

Cited by 11 | Viewed by 2510

Abstract

A spinal cord injury (SCI) is damage to any part of the spinal cord, caused by traumatic or non-traumatic events. Clinically, SCI is associated with partial or complete loss of motor, sensory, and autonomic functions below the site of injury. However, cognitive alterations [...] Read more.

A spinal cord injury (SCI) is damage to any part of the spinal cord, caused by traumatic or non-traumatic events. Clinically, SCI is associated with partial or complete loss of motor, sensory, and autonomic functions below the site of injury. However, cognitive alterations in specific domains can also occur. The aim of this study was to evaluate the effects of semi-immersive virtual reality (VR) cognitive training (using the BTS Nirvana, Italy) in promoting global functional recovery in patients with SCI. Forty-two SCI patients were included in this retrospective case-control study, and the analysis was carried out using an electronic data retrieval system. The enrolled patients were divided into two groups with the same demographic and medical characteristics: the control group (CG: 21 patients) participated in traditional therapy, whereas the experimental group (EG: 21 patients) received training using semi-immersive VR. In both groups, there were patients with A- or B-grade impairments according to the American Spinal Injury Association (ASIA) scale. Both study groups underwent the same amount of cognitive training (but using a different type of training: conventional vs. innovative), consisting of three weekly sessions for eight weeks (24 sessions in total), each session lasting approximately sixty minutes, as well as the same amount of physiotherapy. The effect of the two treatments (EG/CG) was significantly different in global cognitive functioning (MOCA: p = 0.001), mood (BDI: p = 0.006), and overall quality of life (SF12 Total: p < 0.001), especially in physical perception (SF12-Physics: p = 0.004). Our results suggest that SCI patients could benefit from cognitive training using semi-immersive VR. Indeed, the integration of cognitive exercises that require movement and provide increased feedback could allow for better motor and cognitive recovery in people with SCI. Full article

(This article belongs to the Section Neurorehabilitation)

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11 pages, 3580 KiB

Open AccessEditor’s ChoiceArticle

The Use of 3D Printed Models for Surgical Simulation of Cranioplasty in Craniosynostosis as Training and Education

by Jean Francois Uhl, Albert Sufianov, Camillo Ruiz, Yuri Iakimov, Huerta Jose Mogorron, Manuel Encarnacion Ramirez, Guillermo Prat, Barbara Lorea, Matias Baldoncini, Evgeniy Goncharov, Issael Ramirez, José Rafael Cerda Céspedes, Renat Nurmukhametov and Nicola Montemurro

Brain Sci. 2023, 13(6), 894; https://doi.org/10.3390/brainsci13060894 - 1 Jun 2023

Cited by 26 | Viewed by 2053

Abstract

Background: The advance in imaging techniques is useful for 3D models and printing leading to a real revolution in many surgical specialties, in particular, neurosurgery. Methods: We report on a clinical study on the use of 3D printed models to perform cranioplasty in [...] Read more.

Background: The advance in imaging techniques is useful for 3D models and printing leading to a real revolution in many surgical specialties, in particular, neurosurgery. Methods: We report on a clinical study on the use of 3D printed models to perform cranioplasty in patients with craniosynostosis. The participants were recruited from various medical institutions and were divided into two groups: Group A (n = 5) received traditional surgical education (including cadaveric specimens) but without using 3D printed models, while Group B (n = 5) received training using 3D printed models. Results: Group B surgeons had the opportunity to plan different techniques and to simulate the cranioplasty. Group B surgeons reported that models provided a realistic and controlled environment for practicing surgical techniques, allowed for repetitive practice, and helped in visualizing the anatomy and pathology of craniosynostosis. Conclusion: 3D printed models can provide a realistic and controlled environment for neurosurgeons to develop their surgical skills in a safe and efficient manner. The ability to practice on 3D printed models before performing the actual surgery on patients may potentially improve the surgeons’ confidence and competence in performing complex craniosynostosis surgeries. Full article

(This article belongs to the Special Issue Scientific and Clinical Advances in Neurological Surgery)

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17 pages, 1965 KiB

Open AccessEditor’s ChoiceArticle

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

by Aarthi Chelladurai, Dayanand Lal Narayan, Parameshachari Bidare Divakarachari and Umasankar Loganathan

Brain Sci. 2023, 13(6), 893; https://doi.org/10.3390/brainsci13060893 - 31 May 2023

Cited by 43 | Viewed by 4091

Abstract

In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic [...] Read more.

In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models. Full article

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19 pages, 3586 KiB

Open AccessEditor’s ChoiceArticle

Association of Peripheral Inflammatory Biomarkers and Growth Factors Levels with Sex, Therapy and Other Clinical Factors in Schizophrenia and Patient Stratification Based on These Data

by Evgeny A. Ermakov, Mark M. Melamud, Anastasiia S. Boiko, Daria A. Kamaeva, Svetlana A. Ivanova, Georgy A. Nevinsky and Valentina N. Buneva

Brain Sci. 2023, 13(5), 836; https://doi.org/10.3390/brainsci13050836 - 22 May 2023

Cited by 17 | Viewed by 2709

Abstract

Multiple lines of evidence are known to confirm the pro-inflammatory state of some patients with schizophrenia and the involvement of inflammatory mechanisms in the pathogenesis of psychosis. The concentration of peripheral biomarkers is associated with the severity of inflammation and can be used [...] Read more.

Multiple lines of evidence are known to confirm the pro-inflammatory state of some patients with schizophrenia and the involvement of inflammatory mechanisms in the pathogenesis of psychosis. The concentration of peripheral biomarkers is associated with the severity of inflammation and can be used for patient stratification. Here, we analyzed changes in serum concentrations of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-21, APRIL, BAFF, PBEF/Visfatin, IFN-α, and TNF-α) and growth/neurotrophic factors (GM-CSF, NRG1-β1, NGF-β, and GDNF) in patients with schizophrenia in an exacerbation phase. IL-1β, IL-2, IL-4, IL-6, BAFF, IFN-α, GM-CSF, NRG1-β1, and GDNF increased but TNF-α and NGF-β decreased in schizophrenia compared to healthy individuals. Subgroup analysis revealed the effect of sex, prevalent symptoms, and type of antipsychotic therapy on biomarker levels. Females, patients with predominantly negative symptoms, and those taking atypical antipsychotics had a more pro-inflammatory phenotype. Using cluster analysis, we classified participants into “high” and “low inflammation” subgroups. However, no differences were found in the clinical data of patients in these subgroups. Nevertheless, more patients (17% to 25.5%) than healthy donors (8.6% to 14.3%) had evidence of a pro-inflammatory condition depending on the clustering approach used. Such patients may benefit from personalized anti-inflammatory therapy. Full article

(This article belongs to the Section Neuropsychiatry)

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10 pages, 793 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

by Vincenzo Di Stefano, Francesco Prinzi, Marco Luigetti, Massimo Russo, Stefano Tozza, Paolo Alonge, Angela Romano, Maria Ausilia Sciarrone, Francesca Vitali, Anna Mazzeo, Luca Gentile, Giovanni Palumbo, Fiore Manganelli, Salvatore Vitabile and Filippo Brighina

Brain Sci. 2023, 13(5), 805; https://doi.org/10.3390/brainsci13050805 - 16 May 2023

Cited by 15 | Viewed by 3840

Abstract

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis [...] Read more.

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings. Results: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test. Conclusions: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings. Full article

(This article belongs to the Special Issue Attention to Neuromuscular Diseases)

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14 pages, 4838 KiB

Open AccessEditor’s ChoiceArticle

Trimethylamine N-Oxide Exacerbates Neuroinflammation and Motor Dysfunction in an Acute MPTP Mice Model of Parkinson’s Disease

by Wei Quan, Chen-Meng Qiao, Gu-Yu Niu, Jian Wu, Li-Ping Zhao, Chun Cui, Wei-Jiang Zhao and Yan-Qin Shen

Brain Sci. 2023, 13(5), 790; https://doi.org/10.3390/brainsci13050790 - 12 May 2023

Cited by 18 | Viewed by 3203

Abstract

Observational studies have shown abnormal changes in trimethylamine N-oxide (TMAO) levels in the peripheral circulatory system of Parkinson’s disease (PD) patients. TMAO is a gut microbiota metabolite that can cross the blood–brain barrier and is strongly related to neuroinflammation. Neuroinflammation is one of [...] Read more.

Observational studies have shown abnormal changes in trimethylamine N-oxide (TMAO) levels in the peripheral circulatory system of Parkinson’s disease (PD) patients. TMAO is a gut microbiota metabolite that can cross the blood–brain barrier and is strongly related to neuroinflammation. Neuroinflammation is one of the pathological drivers of PD. Herein, we investigated the effect of TMAO on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model mice. TMAO pretreatment was given by adding 1.5% (w/v) TMAO to the drinking water of the mice for 21 days; then, the mice were administered MPTP (20 mg/kg, i.p.) four times a day to construct an acute PD model. Their serum TMAO concentrations, motor function, dopaminergic network integrity, and neuroinflammation were then assayed. The results showed that TMAO partly aggravated the motor dysfunction of the PD mice. Although TMAO had no effect on the dopaminergic neurons, TH protein content, and striatal DA level in the PD mice, it significantly reduced the striatal 5-HT levels and aggravated the metabolism of DA and 5-HT. Meanwhile, TMAO significantly activated glial cells in the striatum and the hippocampi of the PD mice and promoted the release of inflammatory cytokines in the hippocampus. In summary, higher-circulating TMAO had adverse effects on the motor capacity, striatum neurotransmitters, and striatal and hippocampal neuroinflammation in PD mice. Full article

(This article belongs to the Section Neurodegenerative Diseases)

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12 pages, 1087 KiB

Open AccessEditor’s ChoiceArticle

Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis

by Ming-Xiang Xu and Xing-Da Ju

Brain Sci. 2023, 13(5), 779; https://doi.org/10.3390/brainsci13050779 - 10 May 2023

Cited by 12 | Viewed by 5671

Abstract

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain [...] Read more.

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8–12 years old with ASD were matched with 105 children aged 8–12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions. Full article

(This article belongs to the Section Behavioral Neuroscience)

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16 pages, 1467 KiB

Open AccessEditor’s ChoiceArticle

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

by Marija Novičić and Andrej M. Savić

Brain Sci. 2023, 13(5), 766; https://doi.org/10.3390/brainsci13050766 - 5 May 2023

Cited by 11 | Viewed by 2915

Abstract

Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training [...] Read more.

Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user’s forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms. Full article

(This article belongs to the Special Issue Emerging Topics in Brain-Computer Interface)

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20 pages, 4690 KiB

Open AccessEditor’s ChoiceArticle

Differentiated Neurons Are More Vulnerable to Organophosphate and Carbamate Neurotoxicity than Undifferentiated Neurons Due to the Induction of Redox Stress and Accumulate Oxidatively-Damaged Proteins

by Anusha W. Mudyanselage, Buddhika C. Wijamunige, Artur Kocon and Wayne G. Carter

Brain Sci. 2023, 13(5), 728; https://doi.org/10.3390/brainsci13050728 - 26 Apr 2023

Cited by 10 | Viewed by 2174

Abstract

Organophosphate (OP) and carbamate pesticides are toxic to pests through targeted inhibition of acetylcholinesterase (AChE). However, OPs and carbamates may be harmful to non-target species including humans and could induce developmental neurotoxicity if differentiated or differentiating neurons are particularly vulnerable to neurotoxicant exposures. [...] Read more.

Organophosphate (OP) and carbamate pesticides are toxic to pests through targeted inhibition of acetylcholinesterase (AChE). However, OPs and carbamates may be harmful to non-target species including humans and could induce developmental neurotoxicity if differentiated or differentiating neurons are particularly vulnerable to neurotoxicant exposures. Hence, this study compared the neurotoxicity of OPs, chlorpyrifos-oxon (CPO), and azamethiphos (AZO) and the carbamate pesticide, aldicarb, to undifferentiated versus differentiated SH-SY5Y neuroblastoma cells. OP and carbamate concentration-response curves for cell viability were undertaken using 3-(4,5 dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays and cellular bioenergetic capacity assessed via quantitation of cellular ATP levels. Concentration-response curves for inhibition of cellular AChE activity were also generated and the production of reactive oxygen species (ROS) was monitored using a 2′,7′-dichlorofluorescein diacetate (DCFDA) assay. The OPs and aldicarb reduced cell viability, cellular ATP levels, and neurite outgrowth in a concentration-dependent fashion, from a threshold concentration of ≥10 µM. Neurotoxic potency was in the order AZO > CPO > aldicarb for undifferentiated cells but CPO > AZO > aldicarb for differentiated cells and this toxic potency of CPO reflected its more extensive induction of reactive oxygen species (ROS) and generation of carbonylated proteins that were characterized by western blotting. Hence, the relative neurotoxicity of the OPs and aldicarb in part reflects non-cholinergic mechanisms that are likely to contribute to developmental neurotoxicity. Full article

(This article belongs to the Special Issue The Neurotoxicity of Pesticides)

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20 pages, 2172 KiB

Open AccessEditor’s ChoiceArticle

Research and Diagnostic Algorithmic Rules (RADAR) and RADAR Plots for the First Episode of Major Depressive Disorder: Effects of Childhood and Recent Adverse Experiences on Suicidal Behaviors, Neurocognition and Phenome Features

by Michael Maes and Abbas F. Almulla

Brain Sci. 2023, 13(5), 714; https://doi.org/10.3390/brainsci13050714 - 24 Apr 2023

Cited by 14 | Viewed by 2715

Abstract

Recent studies have proposed valid precision models and valid Research and Diagnostic Algorithmic Rules (RADAR) for recurrent major depressive disorder (MDD). The aim of the current study was to construct precision models and RADAR scores in patients experiencing first-episode MDD and to examine [...] Read more.

Recent studies have proposed valid precision models and valid Research and Diagnostic Algorithmic Rules (RADAR) for recurrent major depressive disorder (MDD). The aim of the current study was to construct precision models and RADAR scores in patients experiencing first-episode MDD and to examine whether adverse childhood experiences (ACE) and negative life events (NLE) are associated with suicidal behaviors (SB), cognitive impairment, and phenome RADAR scores. This study recruited 90 patients with major depressive disorder (MDD) in an acute phase, of whom 71 showed a first-episode MDD (FEM), and 40 controls. We constructed RADAR scores for ACE; NLE encountered in the last year; SB; and severity of depression, anxiety, chronic fatigue, and physiosomatic symptoms using the Hamilton Depression and Anxiety Rating Scales and the FibroFatigue scale. The partial least squares analysis showed that in FEM, one latent vector (labeled the phenome of FEM) could be extracted from depressive, anxiety, fatigue, physiosomatic, melancholia, and insomnia symptoms, SB, and cognitive impairments. The latter were conceptualized as a latent vector extracted from the Verbal Fluency Test, the Mini-Mental State Examination, and ratings of memory and judgement, indicating a generalized cognitive decline (G-CoDe). We found that 60.8% of the variance in the FEM phenome was explained by the cumulative effects of NLE and ACE, in particular emotional neglect and, to a lesser extent, physical abuse. In conclusion, the RADAR scores and plots constructed here should be used in research and clinical settings, rather than the binary diagnosis of MDD based on the DSM-5 or ICD. Full article

(This article belongs to the Special Issue Anxious Brain: Stress Influence on the Nervous System)

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17 pages, 2959 KiB

Open AccessEditor’s ChoiceArticle

Emotion Recognition from Spatio-Temporal Representation of EEG Signals via 3D-CNN with Ensemble Learning Techniques

by Rajamanickam Yuvaraj, Arapan Baranwal, A. Amalin Prince, M. Murugappan and Javeed Shaikh Mohammed

Brain Sci. 2023, 13(4), 685; https://doi.org/10.3390/brainsci13040685 - 19 Apr 2023

Cited by 22 | Viewed by 3788

Abstract

The recognition of emotions is one of the most challenging issues in human–computer interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions because of their ease of acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results [...] Read more.

The recognition of emotions is one of the most challenging issues in human–computer interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions because of their ease of acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results in emotion recognition studies. Most studies, however, use other methods to extract handcrafted features, such as Pearson correlation coefficient (PCC), Principal Component Analysis, Higuchi Fractal Dimension (HFD), etc., even though DNN is capable of generating meaningful features. Furthermore, most earlier studies largely ignored spatial information between the different channels, focusing mainly on time domain and frequency domain representations. This study utilizes a pre-trained 3D-CNN MobileNet model with transfer learning on the spatio-temporal representation of EEG signals to extract features for emotion recognition. In addition to fully connected layers, hybrid models were explored using other decision layers such as multilayer perceptron (MLP), k-nearest neighbor (KNN), extreme learning machine (ELM), XGBoost (XGB), random forest (RF), and support vector machine (SVM). Additionally, this study investigates the effects of post-processing or filtering output labels. Extensive experiments were conducted on the SJTU Emotion EEG Dataset (SEED) (three classes) and SEED-IV (four classes) datasets, and the results obtained were comparable to the state-of-the-art. Based on the conventional 3D-CNN with ELM classifier, SEED and SEED-IV datasets showed a maximum accuracy of 89.18% and 81.60%, respectively. Post-filtering improved the emotional classification performance in the hybrid 3D-CNN with ELM model for SEED and SEED-IV datasets to 90.85% and 83.71%, respectively. Accordingly, spatial-temporal features extracted from the EEG, along with ensemble classifiers, were found to be the most effective in recognizing emotions compared to state-of-the-art methods. Full article

(This article belongs to the Special Issue Emerging Trends of Biomedical Signal Processing in Intelligent Emotion Recognition)

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14 pages, 2259 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery

by Kishor Lakshminarayanan, Rakshit Shah, Sohail R. Daulat, Viashen Moodley, Yifei Yao, Puja Sengupta, Vadivelan Ramu and Deepa Madathil

Brain Sci. 2023, 13(4), 656; https://doi.org/10.3390/brainsci13040656 - 13 Apr 2023

Cited by 14 | Viewed by 3084

Abstract

Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities [...] Read more.

Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain–computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices. Full article

(This article belongs to the Topic Human–Machine Interaction)

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17 pages, 4159 KiB

Open AccessEditor’s ChoiceArticle

Intranasal Delivery of Gene-Edited Microglial Exosomes Improves Neurological Outcomes after Intracerebral Hemorrhage by Regulating Neuroinflammation

by Mengtian Guo, Xintong Ge, Conglin Wang, Zhenyu Yin, Zexi Jia, Tianpeng Hu, Meimei Li, Dong Wang, Zhaoli Han, Lu Wang, Xiangyang Xiong, Fanglian Chen and Ping Lei

Brain Sci. 2023, 13(4), 639; https://doi.org/10.3390/brainsci13040639 - 8 Apr 2023

Cited by 20 | Viewed by 3399

Abstract

Neural inflammatory response is a crucial pathological change in intracerebral hemorrhage (ICH) which accelerates the formation of perihematomal edema and aggravates neural cell death. Although surgical and drug treatments for ICH have advanced rapidly in recent years, therapeutic strategies that target and control [...] Read more.

Neural inflammatory response is a crucial pathological change in intracerebral hemorrhage (ICH) which accelerates the formation of perihematomal edema and aggravates neural cell death. Although surgical and drug treatments for ICH have advanced rapidly in recent years, therapeutic strategies that target and control neuroinflammation are still limited. Exosomes are important carriers for information transfer among cells. They have also been regarded as a promising therapeutic tool in translational medicine, with low immunogenicity, high penetration through the blood-brain barrier, and ease of modification. In our previous research, we have found that exogenous administration of miRNA-124-overexpressed microglial exosomes (Exo-124) are effective in improving post-injury cognitive impairment. From this, we evaluated the potential therapeutic effects of miRNA-124-enriched microglial exosomes on the ICH mice in the present study. We found that the gene-edited exosomes could attenuate neuro-deficits and brain edema, improve blood–brain barrier integrity, and reduce neural cell death. Moreover, the protective effect of Exo-124 was abolished in mice depleted of Gr-1⁺ myeloid cells. It suggested that the exosomes exerted their functions by limiting the infiltration of leukocyte into the brain, thus controlling neuroinflammation following the onset of ICH. In conclusion, our findings provided a promising therapeutic strategy for improving neuroinflammation in ICH. It also opens a new avenue for intranasal delivery of exosome therapy using miRNA-edited microglial exosomes. Full article

(This article belongs to the Special Issue Immunomodulation and Immunotherapy in Neurological Disorders)

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22 pages, 7257 KiB

Open AccessEditor’s ChoiceArticle

Molecular Mechanisms of the Anti-Inflammatory Effects of Epigallocatechin 3-Gallate (EGCG) in LPS-Activated BV-2 Microglia Cells

by Ashley Payne, Equar Taka, Getinet M. Adinew and Karam F. A. Soliman

Brain Sci. 2023, 13(4), 632; https://doi.org/10.3390/brainsci13040632 - 7 Apr 2023

Cited by 19 | Viewed by 4564

Abstract

Chronic neuroinflammation is associated with many neurodegenerative diseases, such as Alzheimer’s. Microglia are the brain’s primary immune cells, and when activated, they release various proinflammatory cytokines. Several natural compounds with anti-inflammatory and antioxidant properties, such as epigallocatechin 3-gallate (EGCG), may provide a promising [...] Read more.

Chronic neuroinflammation is associated with many neurodegenerative diseases, such as Alzheimer’s. Microglia are the brain’s primary immune cells, and when activated, they release various proinflammatory cytokines. Several natural compounds with anti-inflammatory and antioxidant properties, such as epigallocatechin 3-gallate (EGCG), may provide a promising strategy for inflammation-related neurodegenerative diseases involving activated microglia cells. The objective of the current study was to examine the molecular targets underlying the anti-inflammatory effects of EGCG in activated microglia cells. BV-2 microglia cells were grown, stimulated, and treated with EGCG. Cytotoxicity and nitric oxide (NO) production were evaluated. Immunoassay, PCR array, and WES™ Technology were utilized to evaluate inflammatory, neuroprotective modulators as well as signaling pathways involved in the mechanistic action of neuroinflammation. Our findings showed that EGCG significantly inhibited proinflammatory mediator NO production in LPS-stimulated BV-2 microglia cells. In addition, ELISA analysis revealed that EGCG significantly decreases the release of proinflammatory cytokine IL-6 while it increases the release of TNF-α. PCR array analysis showed that EGCG downregulated MIF, CCL-2, and CSF2. It also upregulated IL-3, IL-11, and TNFS10. Furthermore, the analysis of inflammatory signaling pathways showed that EGCG significantly downregulated mRNA expression of mTOR, NF-κB2, STAT1, Akt3, CCL5, and SMAD3 while significantly upregulating the expression of mRNA of Ins2, Pld2, A20/TNFAIP3, and GAB1. Additionally, EGCG reduced the relative protein expression of NF-κB2, mTOR, and Akt3. These findings suggest that EGCG may be used for its anti-inflammatory effects to prevent neurodegenerative diseases. Full article

(This article belongs to the Special Issue Advances in Cell Therapy of Neurodegenerative Diseases)

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18 pages, 5565 KiB

Open AccessEditor’s ChoiceArticle

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

by Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan and Heba G. Mohamed

Brain Sci. 2023, 13(4), 602; https://doi.org/10.3390/brainsci13040602 - 1 Apr 2023

Cited by 36 | Viewed by 3566

Abstract

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a [...] Read more.

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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14 pages, 2063 KiB

Open AccessEditor’s ChoiceArticle

The Comorbidity of Depression and Anxiety Symptoms in Tinnitus Sufferers: A Network Analysis

by Xuemin Chen, Lei Ren, Xinmiao Xue, Ning Yu, Peng Liu, Weidong Shen, Hanwen Zhou, Ben Wang, Jingcheng Zhou, Shiming Yang and Qingqing Jiang

Brain Sci. 2023, 13(4), 583; https://doi.org/10.3390/brainsci13040583 - 30 Mar 2023

Cited by 15 | Viewed by 3571

Abstract

Objective: Sufferers of tinnitus, especially of the prolonged type, frequently suffer from comorbid depression and anxiety. From the perspective of the network model, this comorbidity is thought to be an interacting system of these two symptoms. In our study, we conducted a network [...] Read more.

Objective: Sufferers of tinnitus, especially of the prolonged type, frequently suffer from comorbid depression and anxiety. From the perspective of the network model, this comorbidity is thought to be an interacting system of these two symptoms. In our study, we conducted a network analysis of depression and anxiety comorbidity in tinnitus sufferers, aiming to identify the central and bridge symptoms and make informed suggestions for clinical interventions and psychotherapy. Method: A total of 566 tinnitus sufferers were enrolled in our study. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7) were selected to evaluate depression and anxiety symptoms, respectively, followed by network analysis to construct the interacting networks. Results: The findings identified six edges of strongest regularized partial correlations in this network. Of these, three were depression symptoms and three were anxiety symptoms. The anxiety symptoms “Unable to control worry” and “Relaxation difficulty” and the depression symptom “Feeling depressed or hopeless” had the highest expected influence centrality. The analysis results also revealed three bridge symptoms: “Afraid something awful might happen”, “Feeling of worthlessness”, and “Trouble concentrating”. As for “Suicidal ideation”, the direct relations between this symptom and “Afraid something awful might happen” and “Feeling depressed or hopeless” were the strongest. Conclusions: The central and bridge symptoms of the interacting network of depression and anxiety symptoms in tinnitus sufferers can be considered a significant transdiagnostic intervention target for the management of this comorbidity. In particular, clinical prevention and psychotherapy should be implemented, targeting the symptoms that have the strongest associations with suicidal ideation. Full article

(This article belongs to the Section Neuropsychiatry)

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21 pages, 6686 KiB

Open AccessEditor’s ChoiceArticle

Lipopolysaccharide Preconditioning Restricts Microglial Overactivation and Alleviates Inflammation-Induced Depressive-like Behavior in Mice

by Haiping Yu, Junli Kan, Mingming Tang, Yanbing Zhu and Baoyang Hu

Brain Sci. 2023, 13(4), 549; https://doi.org/10.3390/brainsci13040549 - 25 Mar 2023

Cited by 12 | Viewed by 3605

Abstract

Overactive microglia and severe neuroinflammation play crucial roles in the development of major depressive disorder. Preconditioning with lipopolysaccharide (LPS) provides protection against severe neuroinflammation. However, administering high doses of LPS to mice triggers depressive symptoms. Therefore, the optimal dose of LPS preconditioning needs [...] Read more.

Overactive microglia and severe neuroinflammation play crucial roles in the development of major depressive disorder. Preconditioning with lipopolysaccharide (LPS) provides protection against severe neuroinflammation. However, administering high doses of LPS to mice triggers depressive symptoms. Therefore, the optimal dose of LPS preconditioning needs to be determined by further experiments. LPS preconditioning is an effective agent in anti-inflammation and neuroprotection, but the mechanism by which LPS preconditioning acts in depression remain unclear. This study finds that the anti-inflammation mechanism of low-dose LPS preconditioning is mainly dependent on G-protein-coupled receptor 84 (GPR84). We use low-dose LPS for preconditioning and re-challenged mice or BV2 microglia with high-dose LPS. In addition, RNA-seq is used to explore underlying changes with LPS preconditioning. Low-dose LPS preconditioning reduces the expression of pro-inflammatory mediators and inhibits microglial activation, as well as suppresses the depressive-like behavior when the mice are re-challenged with high-dose LPS. Further investigation reveals that the tolerance-like response in microglia is dependent on the GPR84. Here, we show that low-dose LPS preconditioning can exert anti-inflammation effects and alleviates inflammation-induced depressive-like behavior in mice. As a potential therapeutic target for depression, LPS preconditioning needs to be given further attention regarding its effectiveness and safety. Full article

(This article belongs to the Special Issue Advance in Neurodegenerative Diseases: Glial Perspective)

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21 pages, 3794 KiB

Open AccessEditor’s ChoiceArticle

Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory

by Sunusi Bala Abdullahi, Zakariyya Abdullahi Bature, Lubna A. Gabralla and Haruna Chiroma

Brain Sci. 2023, 13(4), 555; https://doi.org/10.3390/brainsci13040555 - 25 Mar 2023

Cited by 13 | Viewed by 2382

Abstract

Recognition of lying is a more complex cognitive process than truth-telling because of the presence of involuntary cognitive cues that are useful to lie recognition. Researchers have proposed different approaches in the literature to solve the problem of lie recognition from either handcrafted [...] Read more.

Recognition of lying is a more complex cognitive process than truth-telling because of the presence of involuntary cognitive cues that are useful to lie recognition. Researchers have proposed different approaches in the literature to solve the problem of lie recognition from either handcrafted and/or automatic lie features during court trials and police interrogations. Unfortunately, due to the cognitive complexity and the lack of involuntary cues related to lying features, the performances of these approaches suffer and their generalization ability is limited. To improve performance, this study proposed state transition patterns based on hands, body motions, and eye blinking features from real-life court trial videos. Each video frame is represented according to a computed threshold value among neighboring pixels to extract spatial–temporal state transition patterns (STSTP) of the hand and face poses as involuntary cues using fully connected convolution neural network layers optimized with the weights of ResNet-152 learning. In addition, this study computed an eye aspect ratio model to obtain eye blinking features. These features were fused together as a single multi-modal STSTP feature model. The model was built using the enhanced calculated weight of bidirectional long short-term memory. The proposed approach was evaluated by comparing its performance with current state-of-the-art methods. It was found that the proposed approach improves the performance of detecting lies. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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