Brain Sciences

21 pages, 674 KB

Open AccessArticle

Algorithmic Habituation: A Neurocognitive and Systems-Based Framework for Human–AI Co-Adaptation

by Narcisa Carmen Mladin, Dana Rad, Dumitru Ștefan Coman, Miron Gavril Popescu, Maria Iulia Felea, Radiana Marcu and Gavril Rad

Brain Sci. 2026, 16(5), 473; https://doi.org/10.3390/brainsci16050473 - 28 Apr 2026

Abstract

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given [...] Read more.

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

(This article belongs to the Special Issue Trends and Challenges in Neuroengineering)

► Show Figures

Figure 1

14 pages, 1016 KB

Open AccessArticle

Data-Driven Neurocognitive Clustering Predicts Virtual Reality Task Performance in Children: A Pilot Study

by Yumi Ju, Jihye Kim, Sura Kang and HyunJu Park

Brain Sci. 2026, 16(5), 472; https://doi.org/10.3390/brainsci16050472 - 28 Apr 2026

Abstract

Background: Traditional diagnosis-based classifications often fail to capture neurocognitive heterogeneity among children with developmental disabilities (DD). Establishing function-based subtyping is essential for developing individualized education frameworks that move beyond categorical labels. Methods: This pilot study employed a data-driven clustering approach integrating [...] Read more.

Background: Traditional diagnosis-based classifications often fail to capture neurocognitive heterogeneity among children with developmental disabilities (DD). Establishing function-based subtyping is essential for developing individualized education frameworks that move beyond categorical labels. Methods: This pilot study employed a data-driven clustering approach integrating neurophysiological and cognitive indices to identify functional subtypes in 18 school-aged children (8 typically developing; 10 with DD). Input features included EEG-derived theta/beta ratio (TBR) and cognitive variables from the CANTAB Multitasking Test (MTT). Ecological validity was evaluated using the Virtual Kitchen Errand Task for Children (VKET-C). Results: K-means clustering revealed three distinct groups. In terms of MTT performance, Cluster 1 exhibited high accuracy and short response latencies. Cluster 2 demonstrated a “Slow but Accurate” pattern, with prolonged reaction times irrespective of diagnosis. Cluster 3 presented a “Fast but Error-prone” profile, showing significantly higher TBR values and increased error rates, indicative of cognitive impulsivity. Notably, clusters did not align with diagnostic boundaries. The three identified clusters significantly differentiated commission errors on the VKET-C task and showed greater explanatory power for VR task performance than diagnosis-based classifications. Conclusions: Cluster-based classification better differentiated VR task performance, particularly commission errors, than traditional diagnosis-based grouping. Integrating diagnosis with neurocognitive deep phenotyping approaches may enable more individualized intervention and educational support for children. Full article

(This article belongs to the Special Issue Educational Neuroscience: The Translation of Research Between Neuroscience and Education)

► Show Figures

Figure 1

13 pages, 1229 KB

Open AccessArticle

Differences in Nursing Complexity and Intensity Across Stroke Subtypes: A Retrospective Study Using Standardized Nursing Language

by Manuele Cesare, Augusto Fusco, Gianfranco Damiani and Antonello Cocchieri

Brain Sci. 2026, 16(5), 471; https://doi.org/10.3390/brainsci16050471 - 28 Apr 2026

Abstract

Background/Objectives: Ischemic stroke, hemorrhagic stroke, and transient ischemic attack (TIA) differ in terms of medical severity and prognosis; however, it remains unclear whether these differences are reflected in nursing complexity and nursing intensity when assessed using standardized nursing language. Methods: This [...] Read more.

Background/Objectives: Ischemic stroke, hemorrhagic stroke, and transient ischemic attack (TIA) differ in terms of medical severity and prognosis; however, it remains unclear whether these differences are reflected in nursing complexity and nursing intensity when assessed using standardized nursing language. Methods: This retrospective study analyzed routinely collected nursing and administrative data from an acute care hospital. Hospitalizations were classified as ischemic stroke, hemorrhagic stroke, or TIA using ICD-9-CM codes. Nursing complexity was measured as the number of nursing diagnoses (NDs) documented within 24 h of admission, while nursing intensity was measured as the number of nursing actions (NAs) recorded during hospitalization. Group differences were tested using ANOVA and Kruskal–Wallis tests, as appropriate. Results: A total of 728 hospitalizations were included: 429 ischemic strokes, 236 hemorrhagic strokes, and 63 TIAs. Overall, 4136 NDs and 27,528 NAs were recorded. Distinct patterns emerged across stroke categories. ND counts differed significantly (F = 5.81, p = 0.003), with TIA showing lower counts than both ischemic and hemorrhagic stroke, while no significant difference was observed between ischemic and hemorrhagic stroke. NA counts also differed significantly (H = 16.73, p < 0.001), with the highest counts in hemorrhagic stroke, intermediate counts in ischemic stroke, and the lowest counts in TIA. In a sensitivity analysis standardized by length of stay, nursing intensity also differed significantly across stroke categories (H = 12.999, p = 0.002), although the pattern differed from that observed for cumulative counts. Conclusions: Nursing complexity and nursing intensity showed distinct patterns across stroke categories. While complexity was comparable between ischemic and hemorrhagic stroke and lower in TIA, intensity followed a clear gradient, highest in hemorrhagic stroke, intermediate in ischemic stroke, and lowest in TIA. Standardized nursing data may complement medical indicators by capturing additional dimensions of patient needs and care delivery in people with stroke. Full article

(This article belongs to the Section Neurorehabilitation)

► Show Figures

Figure 1

14 pages, 11353 KB

Open AccessArticle

Intracranial Pressure Reduction Is Associated with Mitochondrial OPA1 and Cytochrome c Release in the Retinas of AQP1-Null Mice

by Zheng Zhang, Shen Wu, Kegao Liu, Jingxue Zhang, Qian Liu, Ningli Wang and Hai Xue

Brain Sci. 2026, 16(5), 470; https://doi.org/10.3390/brainsci16050470 - 28 Apr 2026

Abstract

Background: Recent studies strongly suggest that low intracranial pressure (ICP) may be involved in the pathogenesis of glaucomatous optic neuropathy. As retinal ganglion cells (RGCs) are highly susceptible to mitochondrial dysfunction, mitochondrial injury may be associated with optic neuropathy related to reduced ICP. [...] Read more.

Background: Recent studies strongly suggest that low intracranial pressure (ICP) may be involved in the pathogenesis of glaucomatous optic neuropathy. As retinal ganglion cells (RGCs) are highly susceptible to mitochondrial dysfunction, mitochondrial injury may be associated with optic neuropathy related to reduced ICP. In this study, aquaporin-1 (AQP1)-null mice were used to investigate whether reduced ICP is associated with alterations in mitochondrial structure and the release of optic atrophy type 1 (OPA1) and cytochrome c from mitochondria. Methods: Intraocular pressure (IOP) and ICP were measured in AQP1-null mice, and mitochondrial structural changes were examined using transmission electron microscopy (TEM). Total OPA1 and cytochrome c protein levels were evaluated using immunocytochemistry and Western blotting. Cytosolic and mitochondrial fractions were extracted from retinal tissues, and the subcellular distribution of OPA1 and cytochrome c was further analyzed by Western blotting. Bax and Bcl-2 expression levels were also detected. Results: TEM revealed mitochondrial fission, matrix swelling, and abnormal cristae depletion in the retinas of 1-, 3-, and 6-month-old AQP1-null mice. Morphometric quantification further confirmed significantly reduced mitochondrial length across all age groups and increased mitochondrial width at 1 and 6 months in AQP1-null mice compared with wild-type controls. Decreased retinal OPA1 immunoreactivity and protein expression were observed across all age groups of AQP1-null mice compared with age-matched C57BL/6 control mice. Subcellular fractionation showed increased mitochondrial release of OPA1 (at 3 and 6 months) and cytochrome c (at 1, 3, and 6 months) in the retinas of AQP1-null mice. Altered Bax expression was also detected in the retinas of AQP1-null mice with reduced ICP at all examined ages. Conclusions: Mitochondrial ultrastructural abnormalities, including fission and cristae depletion, altered OPA1 distribution, increased mitochondrial release of OPA1 and cytochrome c, and upregulated Bax expression were observed in the retinas of AQP1-null mice with reduced ICP. These concurrent changes indicate a close association between reduced ICP and retinal mitochondrial dysfunction. Maintaining mitochondrial integrity may therefore serve as a potential protective strategy against optic nerve degeneration in patients with chronic low ICP. Full article

(This article belongs to the Special Issue Molecular and Cellular Research in Neurodegenerative Diseases)

► Show Figures

Figure 1

26 pages, 798 KB

Open AccessReview

Mechanisms and Therapeutic Targets of Ischemia—Reperfusion Injury in Stroke: A Narrative Review Focusing on Blood—Brain Barrier Dysfunction

by Suqin Guo, Rui Liu, Si Cheng, Xia Liu and Jianping Wu

Brain Sci. 2026, 16(5), 469; https://doi.org/10.3390/brainsci16050469 - 27 Apr 2026

Abstract

Ischemic stroke remains a leading cause of death and disability worldwide. While thrombolysis and endovascular thrombectomy are current mainstays of treatment, their clinical efficacy is often undermined by ischemia–reperfusion injury (I/R). This injury induces secondary brain damage, primarily via disruption of the blood–brain [...] Read more.

Ischemic stroke remains a leading cause of death and disability worldwide. While thrombolysis and endovascular thrombectomy are current mainstays of treatment, their clinical efficacy is often undermined by ischemia–reperfusion injury (I/R). This injury induces secondary brain damage, primarily via disruption of the blood–brain barrier (BBB). No approved therapies directly target BBB protection. This review reinterprets the pathophysiological mechanism of BBB disruption after stroke through a dynamic spatiotemporal framework. The pathological cascade reaction is clearly divided into two core stages: the ischemic phase is mainly driven by energy failure and calcium overload; the reperfusion phase is further divided into four consecutive and progressive sub-stages, namely, oxidative stress burst, amplification of inflammatory response, matrix metalloproteinase 9 (MMP-9)-mediated barrier degradation and programmed cell death. This review critically assesses current therapies and identifies major clinical translation gaps, including a temporal mismatch between preclinical and clinical windows, unacceptable toxicity, lack of durable efficacy and biphasic effects. Matching specific interventions to the different pathophysiological stages of blood–brain barrier disruption is essential for optimizing clinical outcomes. Full article

44 pages, 1241 KB

Open AccessSystematic Review

Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI

by Simona Aresta, Cinzia Palmirotta, Muhammad Asim, Petronilla Battista, Gaia C. Santi, Gianvito Lagravinese, Claudia Cava, Pietro Fiore, Andrea Santamato, Paolo Vitali, Isabella Castiglioni, Gennaro D’Anna, Leonardo Rundo and Christian Salvatore

Brain Sci. 2026, 16(5), 468; https://doi.org/10.3390/brainsci16050468 - 27 Apr 2026

Abstract

Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this [...] Read more.

Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this systematic review, PubMed and Scopus were searched for articles published from 2022 to March 2025. Authors independently identified eligible studies based on predefined inclusion criteria and extracted data. The study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist. Results: Thirty-one studies met the inclusion criteria from 310 records, with eight addressing both segmentation and classification. Most segmentation studies used publicly available multiparametric MRI datasets. Performance varied by architecture and tumor region, with whole-tumor segmentation achieving the highest Dice Similarity Coefficient (DSC). Classical U-Nets reported DSC values ranging 80–87%, while models with residual or attention mechanisms exceeded 90%. Classification focused on tumor type and glioma grading, using features learned from multiparametric MRI. Reported accuracy ranged from 91.3% to 99.4%, with sensitivity and specificity often above 95%. However, variability across tumor subregions, limited external validation, reliance on public datasets, and heterogeneous preprocessing raise concerns about robustness and real-world generalizability. Evidence on the use of explainability methods for both tasks remains limited. Conclusions: DL models for glioma segmentation and classification demonstrate promising performance. However, standardized validation protocols, multi-center datasets, and the integration of explainable artificial intelligence techniques are needed to improve transparency, robustness, and clinical applicability. Full article

(This article belongs to the Special Issue Artificial Intelligence in Neurological Disorders)

32 pages, 436 KB

Open AccessReview

Amblyopia in 2026: A State-of-the-Art Review of Multidimensional Phenotyping, Response Heterogeneity, and Clinical Considerations

by Danjela Ibrahimi and José R. García-Martínez

Brain Sci. 2026, 16(5), 467; https://doi.org/10.3390/brainsci16050467 - 27 Apr 2026

Abstract

Amblyopia is increasingly conceptualized as a neurodevelopmental visual disorder that often arises from discordant binocular visual experience during early life and is associated with abnormal binocular interactions, interocular suppression, orientation-dependent developmental abnormalities in selected refractive phenotypes, and experience-dependent plasticity, consistent with a distributed-network [...] Read more.

Amblyopia is increasingly conceptualized as a neurodevelopmental visual disorder that often arises from discordant binocular visual experience during early life and is associated with abnormal binocular interactions, interocular suppression, orientation-dependent developmental abnormalities in selected refractive phenotypes, and experience-dependent plasticity, consistent with a distributed-network perspective rather than a purely monocular acuity deficit. We performed a structured state-of-the-art narrative synthesis of peer-reviewed reviews, randomized controlled trials, and key mechanistic human studies indexed in PubMed/MEDLINE, Web of Science, and Scopus (1 January 2016–28 February 2026; last search 28 February 2026), prioritizing recent evidence from 2021–2026. Literature supports consideration of clinically trackable constructs beyond best-corrected visual acuity (BCVA), including quantified suppression/imbalance, binocular function, and functionally meaningful outcomes such as reading-related limitation and broader functional impact. Across established and emerging intervention classes, treatment effects are heterogeneous across ages and etiologies. Evidence is strongest for conventional penalization and selected active training-based approaches, whereas newer protocol-standardized approaches remain investigational and require prospective evaluation with transparent exposure/dose reporting. Based on these findings, we outline a clinically oriented, core outcome set for amblyopia and strabismus (COSAMS)-aligned framework that combines quantified binocular imbalance with multidimensional phenotyping and a hypothesis-driven, prospectively testable therapeutic model intended to structure (not replace) clinical decision-making. Priorities for precision-oriented amblyopia care include standardization of suppression metrics, adoption of core outcome sets, transparent reporting of ‘not measurable’ outcomes and missingness, and prospective validation of phenotype-driven, prediction-ready frameworks. Full article

(This article belongs to the Special Issue Brain Plasticity in Health and Disease: From Molecules to Circuits)

► Show Figures

Figure 1

12 pages, 214 KB

Open AccessArticle

Knowledge and Support Needs of School-Based Speech and Language Pathologists in Addressing Dyslexia

by Mazen Almethen

Brain Sci. 2026, 16(5), 466; https://doi.org/10.3390/brainsci16050466 - 27 Apr 2026

Abstract

Background/Objectives: This study aimed to investigate school-based speech–language pathologists’ (SLPs) perceptions of their ability and needs to identify and remediate students with dyslexia. Methods: A qualitative approach was employed, utilizing semi-structured interviews with school-based SLPs. The findings were thematically analyzed by [...] Read more.

Background/Objectives: This study aimed to investigate school-based speech–language pathologists’ (SLPs) perceptions of their ability and needs to identify and remediate students with dyslexia. Methods: A qualitative approach was employed, utilizing semi-structured interviews with school-based SLPs. The findings were thematically analyzed by following six steps; (a) familiarization with the data; (b) initial code generation; (c) theme identification; (d) theme review; (e) defining and naming themes; and (f) report writing. Results: The findings revealed that participants had a very limited knowledge of dyslexia, and most did not consider reading difficulties within their scope of practice. Professional development opportunities, collaboration with teachers, and administrative support were identified as crucial factors to enhance SLPs’ practice supporting dyslexic cases in school settings. Conclusions: The study showed that school-based SLPs lack the necessary knowledge to work with students with dyslexia, which aligns with results observed in other countries. This finding highlights the importance of empowering their abilities to effectively support students with dyslexia. Full article

(This article belongs to the Special Issue Educational Neuroscience: The Translation of Research Between Neuroscience and Education)

21 pages, 1486 KB

Open AccessReview

The Effect of Metabolic Syndrome on Alzheimer’s Disease: Physical Activity as a Preventive and Therapeutic Measure

by Eleazar Ramírez Hernández, Citlalli Netzahualcoyotzi, Gabriela Hurtado-Alvarado, José Luis Sánchez, Ali Pereyra Morales, David Arredondo-Zamarripa, Luis Fernando Hernández-Zimbrón, Dulce Papy-Garcia, Jorge Guevara, Natalia Gutiérrez Ponce, Wilton Gomez-Henao, Yonathan Garfias, Gustavo Ortiz Chavez and Edgar Zenteno

Brain Sci. 2026, 16(5), 465; https://doi.org/10.3390/brainsci16050465 - 26 Apr 2026

Abstract

Epidemiological and clinical research on neurodegenerative diseases has shown that metabolic dysregulations increase the risk of developing Alzheimer’s Disease (AD). Many metabolic changes can be grouped into metabolic syndrome (MetS), which is defined as the presence of three or more risk factors, including [...] Read more.

Epidemiological and clinical research on neurodegenerative diseases has shown that metabolic dysregulations increase the risk of developing Alzheimer’s Disease (AD). Many metabolic changes can be grouped into metabolic syndrome (MetS), which is defined as the presence of three or more risk factors, including insulin resistance, hyperglycemia, hypertension, central obesity, and dyslipidemia. These changes cause systemic effects that are crucial in triggering neuroinflammation and neurodegeneration, key factors in AD development. All these factors impair energy metabolism in peripheral tissues and the brain by decreasing glucose utilization, leading to alterations in O-GlcNAcylation, glycosylation, mitochondrial function, oxidative stress, chronic inflammation, synaptic dysfunction, autophagy impairment, and blood–brain barrier (BBB) dysfunction. However, these factors are modified and largely influenced by lifestyle choices. A newer perspective emphasizes that regular exercise is vital for maintaining brain metabolism as we age. Current evidence suggests that engaging in physical activity for individuals with metabolic syndrome reduces their risk of Alzheimer’s disease, enhances prognosis, and improves cognitive abilities. This review explores how metabolic syndrome relates to Alzheimer’s and highlights possible strategies for prevention and treatment. Full article

(This article belongs to the Special Issue Neural–Immune Interactions: Understanding the Bidirectional Relationship between the Brain and Immune Cells)

► Show Figures

Figure 1

3 pages, 146 KB

Open AccessEditorial

New Advances in Neuroimmunology and Neuroinflammation

by Junhui Wang

Brain Sci. 2026, 16(5), 464; https://doi.org/10.3390/brainsci16050464 - 26 Apr 2026

Abstract

The field of neuroimmunology has changed dramatically over the last ten years [...] Full article

(This article belongs to the Special Issue New Advances in Neuroimmunology and Neuroinflammation)

20 pages, 9647 KB

Open AccessArticle

CCL2 and PAK6 as Candidate Biomarkers of Neuroinflammation in Parkinson’s Disease: An Integrated Machine Learning and Single-Nucleus Transcriptomic Study

by Qixin Zhu, Zhen Zhang, Leiming Zhang, Qian Li, Ting Zhang and Fei Yang

Brain Sci. 2026, 16(5), 463; https://doi.org/10.3390/brainsci16050463 - 25 Apr 2026

Abstract

Background: Neuroinflammation is recognized as a key contributor to Parkinson’s disease (PD), but the relationships between inflammatory signaling, immune-state alterations, and cell-type-specific transcriptional programs remain unclear. Methods: Public transcriptomic datasets, including GSE20141 (discovery cohort) and the substantia nigra subset of GSE114517 (external validation [...] Read more.

Background: Neuroinflammation is recognized as a key contributor to Parkinson’s disease (PD), but the relationships between inflammatory signaling, immune-state alterations, and cell-type-specific transcriptional programs remain unclear. Methods: Public transcriptomic datasets, including GSE20141 (discovery cohort) and the substantia nigra subset of GSE114517 (external validation cohort), were analyzed. Genes identified by exploratory differential-expression screening in the discovery cohort were intersected with predefined inflammation- and chemokine-related gene sets to define a candidate space for downstream prioritization. Protein–protein interaction, Gene Ontology, KEGG, and immune-signature analyses were performed, followed by machine learning-based feature prioritization using Elastic Net, support vector machine-recursive feature elimination, and random forest. Prioritized candidates were further evaluated by cross-platform validation, single-nucleus transcriptomic mapping, and a hypothesis-generating in silico perturbation analysis in PD astrocytes. Results: Seventeen genes were retained at the intersection of PD-related differentially expressed genes and inflammation-/chemokine-associated gene sets. These candidates formed a response module enriched in mitochondrial organization, oxidative phosphorylation, and mitophagy pathways. Immune-signature analysis suggested an altered transcriptome-derived immune landscape in PD, with changes in NK cell-related signatures and significant correlations between immune-state scores and the candidate genes. Machine learning-based prioritization yielded five shared candidates, of which only CCL2 and PAK6 showed same-direction support with nominal significance in the external validation cohort. Single-nucleus transcriptomic analysis localized CCL2 predominantly to astrocytes, whereas PAK6 was more strongly associated with neuronal populations, particularly OTX2-positive ventral midbrain neurons. In silico perturbation analysis further predicted that CCL2 suppression in PD astrocytes may be associated with translational- and ribosome-related regulatory programs. Conclusions: CCL2 and PAK6 emerged as prioritized candidate biomarkers associated with PD-related inflammatory and chemokine-linked transcriptional alterations in the substantia nigra. More broadly, this study provides a multi-layered framework for candidate prioritization, cross-platform validation, and cell-type-level contextualization in PD neuroinflammation. Because the study is computational and the perturbation analysis is predictive, orthogonal experimental validation will be required to determine whether CCL2 and PAK6 are biomarkers of disease-associated transcriptional states, functional contributors to PD pathogenesis, or both. Full article

(This article belongs to the Section Neurodegenerative Diseases)

14 pages, 938 KB

Open AccessArticle

Bimanual Force Production at 90-Degree Relative Phase with Lissajous Feedback

by Naoki Hamada, Shiho Fukuda, Han Gao, Hitoshi Oda, Hiroshi Kunimura, Taku Kawasaki and Koichi Hiraoka

Brain Sci. 2026, 16(5), 462; https://doi.org/10.3390/brainsci16050462 - 25 Apr 2026

Abstract

Background/Objectives: Bimanual movements with a 90° relative phase are typically unstable but can be facilitated by Lissajous visual feedback, which integrates the movements of the two hands into a single visual representation. We examined whether such visual integration leads to a unified sensorimotor [...] Read more.

Background/Objectives: Bimanual movements with a 90° relative phase are typically unstable but can be facilitated by Lissajous visual feedback, which integrates the movements of the two hands into a single visual representation. We examined whether such visual integration leads to a unified sensorimotor representation by testing whether unilateral tactile stimulation suppresses motor output bilaterally during bimanual force production. Methods: Fifteen healthy participants produced rhythmic bimanual index finger flexion with a 90° relative phase under two feedback conditions: Lissajous feedback and individual visual feedback. In each trial, vibrotactile stimulation was applied to either hand or not applied at one of four phases of the force cycle. Force trajectory error and post-stimulus electromyographic (EMG) activity of the first dorsal interosseous muscle were analyzed. Results and Discussion: Lissajous feedback reduced force trajectory error compared with individual feedback. Tactile stimulation did not produce bilateral suppression of motor output. This indicates that visual integration of bimanual movements does not lead to global bilateral suppression of motor output induced by unilateral tactile stimulation. A significant reduction in post-stimulus EMG amplitude was observed only when the right hand was stimulated during one phase of the Lissajous feedback task. This suppression may reflect the unmasking of the tactile stimulus-induced inhibition within sensorimotor processes in the left hemisphere when visual feedback of the two hands is merged into a single representation. Full article

(This article belongs to the Special Issue New Insights into Movement Generation: Sensorimotor Processes—2nd Edition)

13 pages, 456 KB

Open AccessReview

Regenerative Medicine Strategies for Spinal Cord Injury: Advances in Stem Cell Therapy

by Ahmed I. Anwar, Alaa Abd-Elsayed and Alan D. Kaye

Brain Sci. 2026, 16(5), 461; https://doi.org/10.3390/brainsci16050461 - 25 Apr 2026

Abstract

Spinal cord injuries disrupt the motor, sensory, and autonomic functions routinely carried out by the spinal cord, with injury progressing from primary mechanical damage from the initial trauma to a secondary phase driven by inflammation and cellular cascades. This disruption significantly impacts the [...] Read more.

Spinal cord injuries disrupt the motor, sensory, and autonomic functions routinely carried out by the spinal cord, with injury progressing from primary mechanical damage from the initial trauma to a secondary phase driven by inflammation and cellular cascades. This disruption significantly impacts the patient’s ability to perform basic physiological and voluntary functions seen in a normal spinal cord, which often results in long-term disability and dependence on supportive care. Stem cell therapies, including mesenchymal stem cells, neural stem cells, and induced pluripotent stem cells, have been investigated as potential regenerative approaches that may promote repair through neuroprotection, remyelination, and axonal regeneration. Preclinical studies have demonstrated encouraging results in motor and sensory recovery following injury; however, clinical evidence remains limited and variable. Some studies report improvements in motor and sensory function post-injury, with improvements in bladder and bowel management, tissue repair, and other functions. Overall, the outcomes vary based on cell type, delivery method, and the stage of spinal cord injury. The key challenges of stem cell therapy include safety concerns and the limited number of small-scale studies currently available. Additionally, understanding the variability in therapeutic outcomes and identifying optimal treatment conditions are critical steps toward advancing stem cell therapies in spinal cord injury repair. This review aims to characterize and summarize the stem cell approach to the treatment of spinal cord injuries, while also critically highlighting the limitations of current preclinical and clinical evidence, as well as the importance of continued investigation into the long-term and functional recovery processes and possibilities, as well as the patient’s quality of life following treatment with stem cells for spinal cord injury. Full article

(This article belongs to the Special Issue Spinal Cord Injury: Biomarkers, Neuroimaging and Therapeutic Approaches)

► Show Figures

Figure 1

6 pages, 158 KB

Open AccessEditorial

Sleep, Circadian Rhythms and Cognitive Function: Translational and Real-World Perspectives

by Maria Comas

Brain Sci. 2026, 16(5), 460; https://doi.org/10.3390/brainsci16050460 - 25 Apr 2026

Abstract

Sleep is a fundamental biological process with broad implications for physical recovery, emotional regulation, and cognitive functioning [...] Full article

(This article belongs to the Special Issue Sleep, Circadian Rhythms and Cognitive Function)

16 pages, 1382 KB

Open AccessArticle

The Effects of Mental Fatigue on Psychophysiological Responses, Mood States, and Archery Shooting Performance Under a Simulated Archery Competition: A Randomized Cross-Over Study

by Sevval Soylu, Ersan Arslan, Bulent Kilit and Yusuf Soylu

Brain Sci. 2026, 16(5), 459; https://doi.org/10.3390/brainsci16050459 (registering DOI) - 24 Apr 2026

Abstract

Background/Objective: Mental fatigue (MF) significantly impairs psychomotor performance in dynamic sports; however, its specific impact on closed-skill precision-demanding tasks remains underexplored. This study investigated the acute effects of experimentally induced MF exposure on psychophysiological responses, mood states, and archery shooting performance. Methods: Fifteen [...] Read more.

Background/Objective: Mental fatigue (MF) significantly impairs psychomotor performance in dynamic sports; however, its specific impact on closed-skill precision-demanding tasks remains underexplored. This study investigated the acute effects of experimentally induced MF exposure on psychophysiological responses, mood states, and archery shooting performance. Methods: Fifteen well-trained male compound-bow archers participated in a randomized crossover study. Participants completed an MF condition (30 min modified Stroop task) and a control condition (CON; passive viewing of a neutral documentary), separated by a 72 h washout period. Continuous heart rate (HR), archery shooting accuracy, ratings of perceived exertion (RPE), rating scale of mental effort (RSME), state anxiety (VAS-A), mood states, and exercise enjoyment scale (EES) were assessed. Results: The Stroop task successfully induced subjective MF. Consequently, shooting accuracy significantly deteriorated in the MF condition compared to that in the CON condition (p < 0.001; g = 0.731). While HR and VAS-A remained consistent across conditions, the MF condition elicited a significant increase in RPE (p = 0.007; g = 0.836) and RSME (p = 0.010; g = 0.794). Furthermore, MF significantly increased feelings of anger and fatigue while drastically reducing PACES (p < 0.001; g = 1.530). Conclusions: Acute MF significantly degrades fine motor accuracy in precision sports. The pronounced dissociation between elevated RPE and stable peripheral physiological strain suggests that performance decline is driven by top-down cognitive burden rather than physiological limitations. Therefore, systematic monitoring of cognitive load is crucial for optimizing performance in precision sports. Full article

(This article belongs to the Special Issue Mind in Motion: Exploring Neuropsychophysiological Aspects of Sports Performance, Health and Physical Activity: 2nd Edition)

► Show Figures

Figure 1

16 pages, 963 KB

Open AccessArticle

Reduced Clinical Target Volume Margins in Glioblastoma: Exploratory Evidence Supporting Further Margin Reduction Independent of MGMT Status

by Flavio Donnini, Giuseppe Minniti, Salvatore Chibbaro, Giulio Bagnacci, Armando Perrella, Giuseppe Battaglia, Giovanni Rubino, Pierpaolo Pastina, Tommaso Carfagno, Marta Vannini, Maria Antonietta Mazzei, Alfonso Cerase and Paolo Tini

Brain Sci. 2026, 16(5), 458; https://doi.org/10.3390/brainsci16050458 (registering DOI) - 24 Apr 2026

Abstract

Background: Clinical target volume (CTV) delineation in glioblastoma remains debated, particularly in the era of modern chemoradiation and image-guided radiotherapy. Whether reduced CTV margins can preserve oncological outcomes without increasing marginal or out-of-field failures remains uncertain. We evaluated the association of the gross [...] Read more.

Background: Clinical target volume (CTV) delineation in glioblastoma remains debated, particularly in the era of modern chemoradiation and image-guided radiotherapy. Whether reduced CTV margins can preserve oncological outcomes without increasing marginal or out-of-field failures remains uncertain. We evaluated the association of the gross tumor volume (GTV)-to-CTV margin with survival, patterns of failure, and its interaction with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Materials and Methods: We retrospectively analyzed a single-center cohort of patients with glioblastoma treated with conventionally fractionated chemoradiation (58–60 Gy in 29–33 fractions). Patients were categorized into two predefined margin groups: <1.5 cm and 1.5 cm. The primary endpoint was overall survival (OS); secondary endpoints included progression-free survival (PFS) and patterns of failure. Survival was assessed using Kaplan–Meier estimates and Cox regression, including an interaction term with MGMT status. Results: Among 102 eligible patients, 95 were included in the margin-based OS analysis. Reduced margins (<1.5 cm; applied range 1.0–1.4 cm) were not associated with worse OS, either overall or within MGMT subgroups. No significant differences were observed in PFS or recurrence patterns, with overlapping distributions and no increase in marginal or out-of-field recurrences. MGMT methylation and gross total resection were independently associated with improved survival, while no statistically significant interaction between margin and MGMT status was detected. Conclusions: In this retrospective exploratory cohort, reduced GTV-to-CTV margins were not associated with a clear signal of worse survival or less favorable recurrence patterns. These findings are consistent with the oncological adequacy of margins around 15 mm and justify cautious prospective evaluation of whether further reduction can be achieved safely, including formal assessment of toxicity, neurocognitive outcomes, and quality of life. Full article

(This article belongs to the Special Issue Brain Tumors: From Molecular Basis to Therapy)

11 pages, 999 KB

Open AccessArticle

Artificial Intelligence for STN-DBS Surgical Planning in Parkinson’s Disease: A Multicenter Study Comparing Conventional Targeting Versus Supervised Statistical Machine Learning

by Feifei Wu, Raffaella Buonanno, Valentina Baro, Vincenzo Levi, Giulia Melinda Furlanis, Mariasole Gagliano, Andrea Guerra, Alberto D’Amico, Carlo Giorgio Giussani, Roberto Eleopra, Luca Denaro, Angelo Antonini and Andrea Landi

Brain Sci. 2026, 16(5), 457; https://doi.org/10.3390/brainsci16050457 - 24 Apr 2026

Abstract

Objective: Deep Brain Stimulation (DBS) has been consolidated as a valid therapeutic option for advanced Parkinson’s disease (PD). The identification of specific targets can be achieved through different methods, including conventional direct and indirect methods. The aim of our multicentric study is [...] Read more.

Objective: Deep Brain Stimulation (DBS) has been consolidated as a valid therapeutic option for advanced Parkinson’s disease (PD). The identification of specific targets can be achieved through different methods, including conventional direct and indirect methods. The aim of our multicentric study is to provide a comparison between these traditional methods and artificial intelligence (AI) in the ascertainment of the ideal targets. Materials and Methods: A total of eight patients, who received bilateral STN (subthalamic nucleus) DBS implantation between 2022 and 2023 were analyzed. Target coordinates were calculated based on the Schaltenbrand and Wahren atlases and the AI using the RebrAIn system during the planning phase; intraoperatively, the targets were either confirmed or adjusted according to microelectrode recordings (MERs). The differences in the three Cartesian axes of stereotactic coordinates (X, Y, and Z) according to these methods were evaluated and compared through non-parametric ANOVA Friedman test. Results: The results revealed significant agreement in the lateral–lateral coordinates (X, X′, X″), indicating stability in target determination along this axis across the methods. However, more substantial discrepancies were observed in the antero-posterior and cranio-caudal coordinates, suggesting lower consistency between the examined methodologies. Conclusions: Our preliminary study results suggest that, despite the challenges posed by interindividual anatomical variability and the limitations of imaging techniques, artificial intelligence has shown comparable values on the lateral–lateral X coordinates. The accuracy of predictive targeting using machine learning models needs to be validated by further studies, but the preliminary results appear to indicate a potential promising role for artificial intelligence in integrating the preoperative workflow. Full article

(This article belongs to the Special Issue New Advances in Functional Neurosurgery—2nd Edition)

► Show Figures

Figure 1

13 pages, 555 KB

Open AccessCase Report

Augmentative and Alternative Communication as an Ecological Window on Neglect-Related Spatial Asymmetry After Hemorrhagic Stroke: A Longitudinal Case Report

by Carmela Rifici, Rosaria De Luca, Francesco Corallo, Sabrina Miceli, Santina Caliri, Andrea Calderone, Rosalia Calapai, Alessio Mirabile, Maria Pagano, Angelo Quartarone and Rocco Salvatore Calabrò

Brain Sci. 2026, 16(5), 456; https://doi.org/10.3390/brainsci16050456 (registering DOI) - 24 Apr 2026

Abstract

Background/Objectives: Spatial neglect after stroke may be difficult to characterize in patients with severe motor, cognitive, and communication impairment. Augmentative and alternative communication interfaces require visual scanning and intentional selection and may therefore provide an ecological context in which lateralized visuospatial behavior becomes [...] Read more.

Background/Objectives: Spatial neglect after stroke may be difficult to characterize in patients with severe motor, cognitive, and communication impairment. Augmentative and alternative communication interfaces require visual scanning and intentional selection and may therefore provide an ecological context in which lateralized visuospatial behavior becomes clinically observable. Methods: A 58-year-old man with a unilateral right-hemisphere hemorrhagic stroke underwent serial assessment at baseline before training, at the end of 24 AAC sessions delivered over 2 months in addition to standard neurorehabilitation, and at 1-month follow-up. Measures included cognitive functioning, behavioral responsiveness, global disability, bedside communication status, and P300 latency. The AAC/eye-tracking intervention also generated process data across 21 analyzable sessions, including calibration quality, free-exploration heatmaps, and performance in the Stars and Bow-Target tasks. Results: Global measures showed modest early improvement followed by stabilization. Cognitive functioning improved from 2 to 3 and remained stable, behavioral responsiveness increased from 7 to 10 and then to 11, bedside communication increased from 7 to 9 and remained stable, and P300 latency decreased from 393 to 350 and then to 351 ms, whereas global disability remained unchanged at 25 throughout. Calibration was at least good in all quadrants and never scored 0. Performance was lower and more unstable in Stars than in Bow-Target. Heatmaps showed rightward clustering, reduced left-sided exploration, and limited whole-screen scanning. Conclusions: AAC/eye-tracking did not provide formal diagnostic proof of neglect, but it supported ecological recognition of a neglect-like lateralized exploratory pattern under less guided conditions. Full article

(This article belongs to the Topic Advances in Neurorehabilitation)

25 pages, 2769 KB

Open AccessArticle

Spec-RWKV: A Spectrum-Guided Multi-Scale Recurrent Modeling Framework for Multi-Center Resting-State fMRI-Assisted Diagnosis

by Sihang Peng and Qi Xu

Brain Sci. 2026, 16(5), 455; https://doi.org/10.3390/brainsci16050455 (registering DOI) - 24 Apr 2026

Abstract

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. [...] Read more.

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article

(This article belongs to the Section Computational Neuroscience, Neuroinformatics, and Neurocomputing)

Journal Description

Brain Sciences

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI