Search Results (37)

Background/Objectives: Red meat is often treated as a single dietary category in nutritional epidemiology, despite substantial heterogeneity in fat content, quality parameters, and preparation methods. This may obscure meaningful associations with brain aging. We tested whether global brain structural associations differed across lean red meat, fatty red meat, pork, processed pork, and organ meat in a large community-based neuroimaging cohort. Methods: Participants were 45,811 UK Biobank adults aged 50 to 80 years with structural MRI, dietary recall, and covariate data. Dietary intake was assessed using up to five administrations of the Oxford WebQ 24 h recall and averaged across available timepoints. Global cortical thickness, total gray matter volume, and total white matter volume were derived from T1-weighted MRI. Continuous predictors were screened for linear quadratic, or spline form prior to grouped penalized variable selection. Final multivariable models incorporated sensitivity analyses stratified by socioeconomic status (SES) and sex. Results: Associations with global brain structure differed by meat type and fat content. Lean red meat showed the most favorable overall pattern, including modest nonlinear favorable association with global cortical thickness and a positive association with white matter volume among higher-SES participants. Fatty red meat showed unfavorable associations with cortical thickness and gray matter volume. Pork showed an unfavorable association with cortical thickness. Organ meat showed an unfavorable association with gray matter volume and with white matter volume among lower-SES participants. Overall, findings suggested that lean red meat tracked with neutral-to-favorable brain structural correlates, whereas fattier red meat and organ meat generally tracked with less favorable structural outcomes. Conclusions: Meat did not relate to global brain structure as a single uniform exposure. Instead, associations differed meaningfully by meat type, fat content, and socioeconomic context. Treating red meat as a single undifferentiated exposure may flatten biologically relevant heterogeneity and contribute to mixed prior findings. These results support more precise dietary phenotyping in brain-health research and suggest that distinctions in meat quality may matter when evaluating long-term brain aging. Findings should not be interpreted to suggest that unlimited meat intake is broadly health-promoting, even if lean, given the established cardiometabolic and vascular risks associated with excess intake of high-fat or processed meats. Full article

Background/Objectives: Mindfulness-based interventions (MBIs) have been linked to psychological and cognitive benefits, yet evidence for their impact on brain structure remains sparse. Neuroimaging suggests MBIs may alter gray matter volume (GMV), density (GMD), and cortical thickness (CT). The purpose of this scoping review was to investigate structural neuroplasticity following MBIs. Methods: Following PRISMA-ScR guidelines, databases were searched for studies published between 2010 and 2023 that used structural MRI to assess structural brain changes in subjects after receiving MBIs. Nine studies met inclusion criteria, including five randomized controlled trials. Results: Mindfulness interventions ranged from 10 h of training to long-term practice spanning decades. Structural changes were most consistently observed in the insula, prefrontal cortex, hippocampus, posterior cingulate, and temporoparietal junction, regions tied to interoception, executive control, and self-referential processing. The greatest structural changes were reported in studies implementing multi-month interventions or long-term meditative practice. Conclusions: MBIs are associated with structural brain changes across a limited and heterogeneous body of literature, but current evidence is insufficient to draw firm conclusions regarding causality or consistency of effect. Larger, diverse, and more methodologically rigorous trials with extended follow-up are needed to clarify the durability and significance of observed changes. Full article

Background: Sleep is a complex physiological process, crucial for cognitive functioning, emotional regulation, and overall health. Recent advances in genomics and neuroimaging have illuminated the intricate relationship between genetics, sleep architecture, and brain changes. This study investigated the association between sleep duration genetics, through a Sleep Duration Polygenic Index (Sleep PGI), and brain changes (total cortical thickness, white matter volume, gray matter volume, white matter hyperintensities volume) in cognitively healthy adults aged 20–80 years old. Methods: Using longitudinal data from the Reference Ability Neural Network (RANN) and Cognitive Reserve (CR) studies, we examined the impact of Sleep PGI on brain measures (total cortical thickness, gray matter volume, white matter volume, WMH volume) over time. Generalized Estimated Equations were used for the statistical analysis. Analysis was performed in the total sample (n = 94) and in three age-groups (young, middle, old). Results: Across age, higher Sleep PGI was associated with higher temporal WMH volumes over time. In models considering an interaction of age between Sleep PGI and time in study, age emerged as a significant moderator for outcomes of hippocampal volume, cortical white matter volume, and WMH volume (total, parietal) as outcomes. Conclusions: Sleep duration polygenic score was associated with changes in the brain in cognitively healthy adults. Genetic predisposition for longer sleep duration was associated with more favorable longitudinal trajectories against brain decline, a result mostly driven by younger adults. These findings underscore the importance of maintaining optimal sleep duration and the potential for personalized interventions to improve sleep and brain health. Full article

Background/Objectives: Recurrence of glioblastoma (GBM) mostly occurs in close vicinity to the resection cavity. Therefore, our group has previously designed an implant to locally apply repetitive photodynamic therapy to mitigate tumor recurrence. The penetration depths of different wavelengths in brain tissue were exhaustively studied before. However, the PDT-induced biological effects of 5-ALA-based PDT against GBM cells at different depths have not been evaluated yet. Methods: Therefore, a synthetic brain substitute material of 1–10 mm thickness and with optical properties comparable to the white or gray matter of pig brain was developed. Tumor cell viability was assessed in spheroids from six GBM cell lines using disks of varying thickness prepared from pig brain substitute material to mimic in vivo radiation attenuation. Results: Using an artificial brain tissue optical model based on material science, we have established a relationship between the PDT-induced effect of our PDT implant and the distance of migrating GBM cells from the resection cavity wall. Conclusions: This model may be helpful to aid optimization of the irradiation doses and fractionation required to attain the maximal therapeutic effect by long-term PDT applications. Full article

Background: Action video games—particularly first-person-shooter (FPS), real-time-strategy (RTS), multiplayer-online-battle-arena (MOBA), and battle-royale (BR) titles—have been linked to enhanced visuospatial skills, yet their impact on brain structure remains unclear. Purpose: To examine, using a cross-sectional design, whether long-term exposure to high-speed genres is associated with variations in cortical thickness and white matter microstructure. Methods: Structural and diffusion MRI were acquired from 27 video-game players (VGPs) and 19 non-video-game players (NVGPs). FreeSurfer-derived cortical thickness and DSI-Studio quantitative anisotropy (QA) were compared between groups, co-varying for intracranial volume. All p-values were Holm–Bonferroni- and FDR-corrected; bootstrap 95% CIs are reported. Results: VGPs showed greater cortical thickness in right inferior and superior parietal, supramarginal, and precuneus cortices (η_p² = 0.12–0.21) and higher QA along right SOG–SPL and left SOG–IPL tracts. Conclusions: Frequent action gaming is associated with greater cortical thickness in the dorsal stream and enhanced occipito-parietal connectivity. However, causal inference is precluded; longitudinal work is warranted. Full article

Human memory is inherently susceptible to errors, including the formation of false memories—instances where individuals mistakenly recall information they were never exposed to. While prior research has largely focused on neural activity associated with false memory, the structural brain correlates of this phenomenon remain relatively unexplored. This study bridges that gap by investigating gray matter structure as it relates to individual differences in false memory performance. Using publicly available magnetic resonance imaging datasets, we analyzed cortical thickness (CT) in neural regions implicated in memory processes. To assess false memory, we applied signal detection theory, which provides a robust framework for differentiating between true and false memory. Our findings reveal that increased CT in the parietal lobe and middle occipital gyrus correlates with greater susceptibility to false memories, highlighting its role in integrating and manipulating memory information. Conversely, CT in the middle frontal gyrus and occipital pole was associated with enhanced accuracy in memory recall, emphasizing its importance in perceptual processing and encoding true memories. These results provide novel insights into the structural basis of memory errors and offer a foundation for future investigations into the neural underpinnings of memory reliability. Full article

Introduction: Advanced magnetic resonance imaging (MRI) techniques in neuroplasticity evaluations provide important information on stroke disease and the underlying mechanisms of neuronal recovery. It has been observed that gray matter density or volume in brain regions closely related to motor function can be a valuable indicator of the response to treatment. Objective: To compare structural MRI-evaluated gray matter volume changes in patients with post-stroke upper limb spasticity for >1 year between those undergoing surgery and those treated with botulinum toxin A (BoNT-A) and to relate these findings to upper limb function and quality of life outcomes. Materials and Methods: Design. A two-arm controlled and randomized clinical trial in patients with post-stroke upper limb spasticity. Participants. Thirty post-stroke patients with spastic upper limbs. Intervention. Participants were randomly assigned (1:1 allocation ratio) for surgery (experimental group) or treatment with BoNT-A (control group). Main outcome measures. The functional parameters were analyzed with Fugl-Meyer, Zancolli, Keenan, House, Ashworth, pain visual analogue, and hospital anxiety and depression scales. Quality of life was evaluated using SF-36 and Newcastle stroke-specific quality of life scales. The carer burden questionnaire was also applied. Clinical examinations and MRI scans were performed at baseline and at six months post-intervention. Correlations between brain volume/thickness and predictors of interest were examined across evaluations and groups. Results: Five patients were excluded due to the presence of intracranial implants. Eleven patients were excluded from analyses since they were late dropouts. Changes were observed in the experimental group but not in the control group. Between baseline and six months, gray matter volume was augmented at the hippocampus and gyrus rectus and cortical thickness was increased at the frontal pole, occipital gyrus, and insular cortex, indicating anatomical changes in key areas related to motor and behavioral adaptation These changes were significantly related to subjective pain, Ashworth spasticity scale, and Newcastle quality of life scores, and marginally related to the carer burden score. Conclusions: The structural analysis of gray matter by MRI revealed differences in patients with post-stroke sequelae undergoing different therapies. Gray matter volume and cortical thickness measurements showed significant improvements in the surgery group but not in the BoNT-A group. Volume was increased in areas associated with motor and sensory functions, suggesting a neuroprotective or regenerative effect of upper limb surgery. Full article

Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic resonance imaging (sMRI) as input data often fail to provide the objectivity and sensitivity needed for early and accurate diagnosis. sMRI is well known to be capable of detecting anatomical changes, such as reduced gray matter volume in SCH or cortical thickness alterations in BD. However, advanced techniques are required to capture subtle neuroanatomical patterns critical for distinguishing these disorders in sMRI. Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. Building on its success in other domains, including autism spectrum disorder and Alzheimer’s disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. In this context, this study introduces a novel deep learning framework for distinguishing BD and SCH using sMRI data. The model is specifically designed to address subtle neuroanatomical differences, offering three key contributions: (1) a tailored DL model that leverages explainability to extract features that boost psychiatric MRI analysis performance, (2) a comprehensive evaluation of the model’s performance in classifying BD and SCH using both spatial and morphological analysis together with classification metrics, and (3) detailed insights, which are derived from both quantitative (performance metrics) and qualitative analyses (visual observations), into key brain regions most relevant for differentiating these disorders. The results have achieved an accuracy of 78.84%, an area under the curve (AUC) of 83.35%, and a Matthews correlation coefficient (MCC) of 59.10% using the proposed framework. These metrics significantly outperform traditional machine learning models. Furthermore, the proposed method demonstrated superior precision and recall for both BD and SCH, with notable improvements in identifying subtle neuroanatomical patterns. Depending on the acquired result, it can be said that the proposed method enhances the application of DL in psychiatry, paving the way for more objective, non-invasive diagnostic tools with the potential to improve early detection and personalized treatment. Full article

Background and Objective: Brain morphometry is shaped by a complex interplay of genetic and environmental factors, including physiological and neuropsychiatric conditions. These influences can vary across distinct brain regions, yet the precise contributions of genetics and environment to regional variation in healthy brains remain poorly understood. This study examines the heritability of specific brain structures to provide deeper insights into their development. Materials and Methods: We studied 118 healthy adult twins from the Hungarian Twin Registry using T1-weighted magnetic resonance imaging (T1W MRI) and the volBrain pipeline for structural measurements. Results: In all regions, monozygotic (MZ) twins showed a higher resemblance than dizygotic (DZ) twins in total brainstem and cerebellar volumes, with significant heritability (A: 90.5–92.6%) and minimal unique environmental effects (E: <1%). For supratentorial regions, regarding the total gray matter volume, all regions exhibited high heritability (A: 74.5–92.4%) and minimal environmental influence (E: <1.5%). In average cortical thickness analysis, the frontal lobe, temporal lobe, and pre-central gyrus were influenced by shared and unique environmental factors (C: 63–66.5%; E: 33.4–37%), whereas genetics were more prominent in the parietal lobe, occipital lobe, and post-central gyrus (A: 67.7–85%; E: 15–32.3%). Conclusions: Genetics strongly influence cortical gray matter volume in supratentorial regions (both total and regional), as well as the total brainstem volume and the total and cortical gray matter volumes of the cerebellum in infratentorial regions. This genetic influence extends to the average cortical thickness of the parietal lobe, post-central gyrus, and occipital lobe, while the frontal lobe, temporal lobe, and pre-central gyrus are more affected by environmental factors. These findings emphasize the importance of understanding region-specific genetic and environmental contributions to brain structure, which could guide personalized therapeutic and preventive strategies for neurological conditions. Full article

Background: Pathophysiological models of pediatric bipolar disorder (PBD) are lacking. Multimodal approaches may provide a comprehensive description of the complex relationship between the brain and behavior. Aim: To assess behavioral, neuropsychological, neurophysiological, and neuroanatomical alterations in youth with PBD. Methods: Subjects with PBD (n = 23) and healthy controls (HCs, n = 23) underwent (a) clinical assessments encompassing the severity of psychiatric symptoms, (b) neuropsychological evaluation, (c) analyses of event-related potentials (related to the passive viewing of fearful, neutral, and happy faces during electroencephalography recording, and (d) cortical thickness and deep gray matter volume measurement using magnetic resonance imaging. Canonical correlation analyses were used to assess the relationships between these dimensions. Results: Youth with PBD had higher levels of anxiety (p < 0.001) and borderline personality features (p < 0.001), greater commission errors for negative stimuli (p = 0.003), delayed deliberation time (p < 0.001), and smaller risk adjustment scores (p = 0.002) than HCs. Furthermore, they showed cortical thinning in the frontal, parietal, and occipital areas (all p < 0.001) and greater P300 for happy faces (p = 0.29). In youth with PBD, cortical thickening and P300 amplitude positively correlated with more commission errors for negative stimuli, longer deliberation times, reduced risk adjustment, higher levels of panic and separation anxiety, and greater levels of negative relationships, whereas they negatively correlated with levels of depression (overall loadings > or <0.3). Limitations: Small sample size, cross-sectional design, and limited variables investigated. Conclusions: This preliminary work showed that multimodal assessment might be a viable tool for providing a pathophysiological model that unifies brain and behavioral alterations in youth with PBD. Full article

Background: Sleep plays a crucial role in cognitive performance and cognitive changes in aging. In the current study, we investigated the role of sleep duration genetics in cognitive changes over time and the moderating effect of age. Methods: Participants were drawn from the Reference Abilities Neural Network and the Cognitive Reserve studies of Columbia University. Each participant underwent an evaluation of sleep function and an extensive neuropsychological assessment. Published GWAS summary statistics from a polygenic score for sleep duration (Sleep PGI) were used to derive Sleep PGI in our study. We examined whether this Sleep PGI is associated with cognitive changes over a 5-year follow-up and if age moderates this effect. Analysis was performed after first being adjusted for age group (young: 20–44; middle: 45–64; old: 65–80), sex, education, the first four principal components, intracranial volume (ICV), mean cortical thickness, and total gray matter volume. We included ICV, mean thickness, and total gray matter volumes as time-varying covariates. We further included interactions of time with age and the first four PCs. Results: A total of 96 white-only participants were included, aged 24 to 78 years old. In the fully adjusted model, age-specific analysis showed that in younger individuals, higher Sleep PGI was associated with lower rates of cognitive decline in speed of processing. Conclusion: Genetic variants associated with sleep duration significantly influence performance in speed of processing, with age playing a critical moderating role, over and above brain morphometry. A genetic predisposition for longer sleep duration can work as a protective factor against decline in the speed of processing in young adults. Full article

Background: Perinatal asphyxia is a major cause of neonatal morbidity and mortality, often resulting in hypoxic-ischemic encephalopathy (HIE) with long-term neurodevelopmental impairments. While therapeutic hypothermia has emerged as a promising intervention to reduce brain damage, its specific impact on key brain structures and long-term neurodevelopmental outcomes remains underexplored. This study aims to evaluate the effects of therapeutic hypothermia on brain volumetry, cortical thickness, and neurodevelopment in term neonates with perinatal asphyxia. Methods: This prospective cohort study enrolled 34 term neonates with perinatal asphyxia, with 12 receiving therapeutic hypothermia and 22 serving as controls without hypothermia. Brain MRI data were analyzed using Infant FreeSurfer to quantify the basal ganglia volumes, gray matter, white matter, cerebellum, cortical gyri, and cortical thickness. Neurodevelopmental outcomes were assessed at 18 and 24 months, using the Bayley Scale III, evaluating the motor, cognitive, and language domains. Genetic analyses, including next-generation sequencing (NGS) and microarray testing, were performed to investigate potential neurodevelopmental markers and confounding factors. Results: Neonates treated with hypothermia demonstrated significantly larger gray and white matter volumes, with a 3.7-fold increase in gray matter (p = 0.025) and a 2.2-fold increase in white matter (p = 0.025). Hippocampal volume increased 3.4-fold (p = 0.032) in the hypothermia group. However, no significant volumetric differences were observed in the cerebellum, thalamus, or other subcortical regions. Moderate correlations were found between white matter volume and cognitive outcomes, but these associations were not statistically significant. Conclusions: Therapeutic hypothermia appears to have region-specific neuroprotective effects, particularly in gray and white matter and the hippocampus, which may contribute to improved neurodevelopmental outcomes. However, the impact was not uniform across all brain structures. Further research is needed, to investigate the long-term benefits and to optimize therapeutic strategies by integrating advanced neuroimaging techniques and genetic insights. Full article

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications. Full article

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified. Full article

Early life substance use, including cannabis and nicotine, may result in deleterious effects on the maturation of brain tissue and gray matter cortical development. The current study employed linear regression models to investigate the main and interactive effects of past-year nicotine and cannabis use on gray matter cortical thickness estimates in 11 bilateral independent frontal cortical regions in 223 16–22-year-olds. As the frontal cortex develops throughout late adolescence and young adulthood, this period becomes crucial for studying the impact of substance use on brain structure. The distinct effects of nicotine and cannabis use status on cortical thickness were found bilaterally, as cannabis and nicotine users both had thinner cortices than non-users. Interactions between nicotine and cannabis were also observed, in which cannabis use was associated with thicker cortices for those with a history of nicotine and tobacco product (NTP) use in three left frontal regions. This study sheds light on the intricate relationship between substance use and brain structure, suggesting a potential modulation of cannabis’ impact on cortical thickness by nicotine exposure, and emphasizing the need for further longitudinal research to characterize these interactions and their implications for brain health and development. Full article