Brain Sciences最新文献

Background/Objectives: Repetitive Transcranial Magnetic Stimulation (rTMS) can enhance post-stroke aphasia recovery. The right Inferior Frontal Gyrus is the most common target in rTMS studies for inhibitory stimulation. However, lexicosemantic processes involve a large brain network including the Anterior Temporal Lobe (ATL). We hypothesize that rTMS targeting the ATL will improve lexicosemantic impairments in people with post-stroke aphasia. Methods: In a Single-Case Experimental Design, three people with post-stroke aphasia with lexicosemantic impairments performed Picture and Auditory Naming and Semantic Decision tasks five times a week for one or two weeks to establish baseline scores. Then, each participant received continuous inhibitory Theta Burst Stimulation targeting the right ATL, five times a week for two weeks. After each rTMS session, participants performed all linguistic tasks. A follow-up measurement was performed one month after the end of the study. Results: All participants showed significant improvement in the Picture Naming task, while only P1 improved in Auditory Naming accuracy. In the Semantic Decision task, only P2 showed improvement in both accuracy and RT, while P1 showed improvement in RT alone and P3 showed no improvement. Conclusions: The results suggest that ATL could be a potential target for future brain stimulation studies in aphasia involving lexicosemantic impairments. RTMS targeting the ATL may modulate the connected ventral semantic stream, leading to improvements in lexical access. This preliminary study highlights the possibility of selecting the cortical target for rTMS based on the clinical profile of the participant, an approach that will need further investigation in larger sham-controlled studies.

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive-motor integration, making it an ideal model for investigating neural efficiency-the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999-2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle-Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69-1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen's d = 0.13-1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations.

Background and Objectives: Epilepsy is a chronic neurological disorder characterized by recurrent seizures caused by abnormal brain activity. Reliable near-real-time seizure detection is essential for preventing injuries, enabling early interventions, and improving the quality of life for patients with drug-resistant epilepsy. This study presents a near-real-time epileptic seizure detection framework designed for low-latency operation, focusing on improving both clinical reliability and patient comfort through electrode reduction. Method: The framework integrates bidirectional long short-term memory (BiLSTM) networks with wavelet-based feature extraction using Electroencephalogram (EEG) recordings from the EPILEPSIAE dataset. EEG signals from 161 patients comprising 1032 seizures were analyzed. Wavelet features were combined with raw EEG data to enhance temporal and spectral representation. Furthermore, electrode reduction experiments were conducted to determine the minimum number of strategically positioned electrodes required to maintain performance. Results: The optimized BiLSTM model achieved 86.9% accuracy, 86.1% recall, and an average detection delay of 1.05 s, with a total processing time of 0.065 s per 0.5 s EEG window. Results demonstrated that reliable detection is achievable with as few as six electrodes, maintaining comparable performance to the full configuration. Conclusions: These findings demonstrate that the proposed BiLSTM-wavelet approach provides a clinically viable, computationally efficient, and wearable-friendly solution for near-real-time epileptic seizure detection using reduced EEG channels.

Background: Spasticity is a complex and multifactorial condition resulting from upper motor neuron injury. It manifests through muscle contractions, pain, limited range of motion, and clonus, which significantly impair daily activities and quality of life. High-frequency spinal cord stimulation (HF SCS) has shown optimal results in treating chronic neuropathic pain, but its potential role in spasticity remains underexplored. This study aimed to evaluate the efficacy of HF SCS in patients with spasticity. Methods: From April 2021 to July 2024, six patients with spasticity from various etiologies underwent SCS implantation at our institution. Clinical evaluations including the use of the Visual Analog Scale (VAS), Douleur Neuropathique 4 (DN4), and the Ashworth score, as well as ambulation ability and clonus episodes, were performed preoperatively and at a minimum of six months post-surgery. Subjective assessments of motor function, including coordination, movement efficiency, and postural transitions, were also recorded. Results: The mean age of patients was 50.12 ± 9.41 years, with follow-up averaging 24.32 ± 10.83 months. Statistically significant improvements were observed in VAS (p = 0.0412) and DN4 (p = 0.0422) scores, alongside a reduction in clonus episodes. All patients reported subjective improvements in coordination, movement efficiency, and postural transitions. Ambulation remained stable or improved in all cases. No perioperative complications or sensory/motor side effects were noted. Conclusions: HF SCS offers a promising approach to managing spasticity, with improvements in motor function, ambulation, and postural transitions. These findings support further investigation into HF SCS for spasticity, with multicenter trials needed to optimize treatment protocols and identify the most responsive patient populations.

Background: By exploring the principles of Gestalt psychology, the neural mechanisms of perception, and computational models, scientists aim to unravel the complex processes that enable us to perceive a coherent and organized world. This multidisciplinary approach continues to advance our understanding of how the brain constructs a perceptual world from sensory inputs. Objectives and Methods: This study investigates the nature of visual perception through an experimental paradigm and method based on a comparative analysis of human and artificial intelligence (AI) responses to a series of modified square images. We introduce the concept of a "phenomenal gradient" in human visual perception, where different attributes of an object are organized syntactically and hierarchically in terms of their perceptual salience. Results: Our findings reveal that human visual processing involves complex mechanisms including shape prioritization, causal inference, amodal completion, and the perception of visible invisibles. In contrast, AI responses, while geometrically precise, lack these sophisticated interpretative capabilities. These differences highlight the richness of human visual cognition and the current limitations of model-generated descriptions in capturing causal, completion-based, and context-dependent inferences. The present work introduces the notion of a 'phenomenal gradient' as a descriptive framework and provides an initial comparative analysis that motivates testable hypotheses for future behavioral and computational studies, rather than direct claims about improving AI systems. Conclusions: By bridging phenomenology, information theory, and cognitive science, this research challenges existing paradigms and suggests a more integrated approach to studying visual consciousness.

The German IQVIA Disease Analyzer (DA) database has become an increasingly important source of real-world evidence for psychiatric research. Over the past decade, and particularly since 2020, DA-based studies have addressed a broad spectrum of psychiatric outcomes including depression, anxiety disorders, schizophrenia, bipolar disorder, dementia, sleep disorders, and the mental health consequences of chronic somatic diseases and of contracting COVID-19. Using large, representative outpatient cohorts, these studies have examined factors associated with the incidence of psychiatric disorders, patterns of psychiatric and somatic comorbidity, treatment trajectories, and long-term outcomes under routine care conditions. The DA database's longitudinal structure, nationwide coverage, and inclusion of multiple medical specialties enable it to capture psychiatric disorders throughout patient lifetimes and across different clinical contexts. This narrative review summarizes psychiatric research using the DA database that has been published since 2020, focusing on study design, main findings, methodological strengths and limitations, and implications for future psychiatric epidemiology and clinical research.

Spinal cord injury (SCI) remains one of the most formidable challenges in regenerative medicine, often resulting in permanent loss of motor, sensory, and autonomic function. Cell-based therapies offer a promising path toward repair by providing donor neurons and glia capable of integrating into host circuits, modulating the injury environment, and restoring function. Early studies employing fetal neural tissue and neural progenitor cells (NPCs) have demonstrated proof-of-principle for survival, differentiation, and synaptic integration. More recently, pluripotent stem cell (PSC)-derived donor populations and engineered constructs have expanded the therapeutic repertoire, enabling precise specification of interneuron subtypes, astrocytes, and oligodendrocytes tailored to the injured spinal cord. Advances in genetic engineering, including CRISPR-based editing, trophic factor overexpression, and immune-evasive modifications, are giving rise to next-generation donor cells with enhanced survival and controllable integration. At the same time, biomaterials, pharmacological agents, activity-based therapies, and neuromodulation strategies are being combined with transplantation to overcome barriers and promote long-term recovery. In this review, we summarize progress in designing and engineering donor cells and tissues for SCI repair, highlight how combination strategies are reshaping the therapeutic landscape, and outline opportunities for next-generation approaches. Together, these advances point toward a future in which tailored, multimodal cell-based therapies achieve consistent and durable restoration of spinal cord function.

Background: Approximately 20-30% of ultra-high risk (UHR) individuals transition to psychosis within 2-3 years. Neurobiological markers predicting conversion remain critical for precision prevention strategies.

Objective: To systematically identify and evaluate structural and functional neuroimaging biomarkers at UHR baseline that predict subsequent conversion to psychosis.

Methods: Following PRISMA 2020 guidelines, we searched five databases from January 2000 to February 2025. Two independent reviewers screened studies and assessed quality using the Newcastle-Ottawa Scale. Eligible studies examined baseline neuroimaging measures (structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy) as predictors of psychosis conversion in UHR cohorts.

Results: Twenty-five studies comprising 2436 UHR individuals (627 converters, 25.7%) were included (80.0% high quality). Reduced baseline gray matter volume in medial temporal structures (hippocampus: Cohen's d = -0.45 to -0.68; parahippocampal gyrus: d = -0.52 to -0.71) and prefrontal cortex (d = -0.41 to -0.68) consistently predicted conversion. Progressive gray matter loss in superior temporal gyrus distinguished converters (d = -0.72). Reduced prefrontal-temporal functional connectivity predicted conversion (AUC = 0.73-0.82). Compromised white matter integrity in uncinate fasciculus (fractional anisotropy: d = -0.47 to -0.71) and superior longitudinal fasciculus predicted transition. Elevated striatal glutamate predicted conversion (d = 0.52-0.76). Thalamocortical dysconnectivity showed large effects (Hedges' g = 0.66-0.88). Multimodal imaging models achieved 78-85% classification accuracy.

Conclusions: Neuroimaging biomarkers, particularly medial temporal and prefrontal structural alterations, functional dysconnectivity, and white matter abnormalities, demonstrate moderate-to-large effect sizes in predicting UHR conversion. Multimodal approaches combining structural, functional, and neurochemical measures show promise for individualized risk prediction and early intervention targeting in precision prevention strategies.

The "Neuroimaging and Pathology Biomarkers in Parkinson's Disease" course held on 12-13 September 2025 in Milan, Italy, convened an international faculty to review state-of-the-art biomarkers spanning neurotransmitter dysfunction, protein pathology and clinical translation. Here, we synthesize the four themed sessions and highlights convergent messages for diagnosis, stratification and trial design. The first session focused on neuroimaging markers of neurotransmitter dysfunction, highlighting how positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) provided complementary insights into dopaminergic, noradrenergic, cholinergic and serotonergic dysfunction. The second session addressed in vivo imaging of protein pathology, presenting recent advances in PET ligands targeting α-synuclein, progress in four-repeat tau imaging for progressive supranuclear palsy and corticobasal syndromes, and the prognostic relevance of amyloid imaging in the context of mixed pathologies. Imaging of neuroinflammation captures inflammatory processes in vivo and helps study pathophysiological effects. The third session bridged pathology and disease mechanisms, covering the biology of α-synuclein and emerging therapeutic strategies, the clinical potential of seed amplification assays and skin biopsy, the impact of co-pathologies on disease expression, and the "brain-first" versus "body-first" model of pathological spread. Finally, the fourth session addressed disease progression and clinical translation, focusing on imaging predictors of phenoconversion from prodromal to clinically overt stages of synucleinopathies, concepts of neural reserve and compensation, imaging correlates of cognitive impairment, and MRI approaches for atypical parkinsonism. Biomarker-informed pharmacological, infusion-based, and surgical strategies, including network-guided and adaptive deep brain stimulation, were discussed as examples of how multimodal biomarkers may inform personalized management. Across all sessions, the need for harmonization, longitudinal validation, and pathology-confirmed outcome measures was consistently emphasized as essential for advancing biomarker qualification in multicentre research and clinical practice.