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Current workflows for studying hydrocephalus in rodent models rely on manual segmentation or qualitative assessment of ventricular size on small animal magnetic resonance imaging, which are both inefficient and prone to variability. Atlas-based methods enable more streamlined segmentation, but their analysis is limited to morphologically normal samples. This study aimed to develop and internally validate a deep learning model that performs automated segmentation of lateral ventricles in rodent brain MRIs, allowing for 3D ventricle reconstruction, morphological analysis, and ventriculomegaly detection. Four U-Net++ neural networks, each with different encoder backbones, were trained using 343 rodent brain MRIs (298 rats, 45 mice), each with manually segmented lateral ventricles serving as the ground truth. Model performance was evaluated using the Dice coefficient and 95th percentile Hausdorff distance (HD95). The most optimal model was evaluated further for its ability to quantify ventricle volume, convexity, surface area, and symmetry. The U-Net++ model with an EfficientNet-B1 encoder achieved high accuracy (Dice: 0.819 ± 0.121; HD95: 2.493 ± 3.984). Further assessment of its morphological predictions found strong correlations with manual measurements of ventricular morphology, with Pearson and interclass correlation coefficients exceeding 0.95 across all metrics. The full validated pipeline was packaged into a publicly available application, hosted at https://ava-tar.org. This study introduces a deep learning tool for automated segmentation and morphological analysis of lateral ventricles in rodent MRIs. The tool's efficiency and accuracy in quantifying ventricle morphology offers significant utility in preclinical hydrocephalus research with potential future application in the clinical setting.

The steady-state visual evoked potential (SSVEP), the brain's oscillatory response to repetitive visual stimulation (RVS), has emerged as a powerful tool in neuroscience with wide-ranging applications in multiple disciplines. This review provides a scoping, narrative roadmap of SSVEP applications organized into three primary domains: fundamental research in vision and cognition, clinical neuroscience, and neural engineering. Although these fields differ in focus, they often converge in their use of similar research questions, stimulation paradigms, analysis techniques, and application scenarios. At the same time, specialization may have created knowledge silos that limit cross-disciplinary transfer of methods and insights. By bridging findings from seemingly disparate domains, this review highlights the versatility of SSVEPs in investigating neural mechanisms, supporting diagnosis and treatment of neurological and psychiatric conditions, and advancing brain-computer interface technology. We conclude with cross-field insights on how stimulus and analysis choices affect interpretation and usability, and we outline directions for improving the comparability and transferability of SSVEP research and applications.

Colony-stimulating factor 1 receptor-related adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (CSF1R-ALSP) is a rare, fatal, autosomal-dominant neurodegenerative disorder caused by pathogenic CSF1R variants and characterized by progressive cognitive, neuropsychiatric, and motor dysfunction, white matter lesions on brain imaging, and white matter demyelination, swollen axons, and pigmented glial cells on pathology. Limited data regarding clinical, biofluid or radiological biomarkers of disease severity are available, and no clinical trial endpoints have yet been validated. The objectives of this first-of-its-kind, prospective, observational natural history study were to characterize the clinical trajectory of CSF1R-ALSP and to identify and evaluate key biomarkers and clinical endpoints indicative of disease severity and progression. ILLUMINATE (NCT05020743) was a multicentre, noninterventional natural history study of adults with CSF1R-ALSP and prodromal carriers of CSF1R variants. Participants were followed for up to 36 months, with clinical assessments, fluid biomarkers and volumetric MRI assessments of brain atrophy collected at screening and every 6 months. This study was terminated early (4 June 2025). The analyses reported here include data collected through 19 February 2025. Of 53 participants, 19 were prodromal and 34 were symptomatic (11 of whom had a history of haematopoietic stem cell transplant and 23 who did not). Mean participant age was 47.8 (standard deviation, 4.5) years, and 36.4% were female. Prodromal participants remained relatively stable over 36 months, with little change in neurological function, neurodegeneration biomarkers or radiological disease burden. Impaired neurological function, MRI characteristics of CSF1R-ALSP, and elevated NfL (neurofilament light chain; neurodegeneration biomarker) and GFAP (glial fibrillary acidic protein; astrogliosis biomarker) levels were more pronounced at baseline and often showed progression over time among symptomatic participants who had not previously received haematopoietic stem cell transplant compared with participants who had previously received haematopoietic stem cell transplant. Significant correlations were observed at baseline and longitudinally between MRI measures of brain atrophy and clinical outcome measures. Based on the fluid biomarkers, MRI measures, and clinical outcome assessments evaluated here, active neurodegeneration, widespread changes visualized on brain MRI, and impaired cognitive and motor function were observed in symptomatic patients with CSF1R-ALSP. The neurological impairment can be assessed using the Montreal Cognitive Assessment and Cortical Basal ganglia Functional Scale. Our data suggest that quantification of brain atrophy using MRI volumetry is a potential biomarker of disease severity and progression in CSF1R-ALSP. It is hoped that this report will contribute to the understanding of disease progression in CSF1R-ALSP and inform future drug development.

Artificial intelligence (AI) has not seen the clinical uptake that might be expected from a technology that has received so much attention and investment. Coupled to neuroimaging, it is conceivable that AI algorithms can provide better performance in diagnosis and prognosis as well as optimize treatments in a precision medicine regimen, all of which is focused on improving both the patient experience and clinical outcomes. But in practice, little impact has been seen. Why might this be the case? This overview focuses on the possible reasons. First, technical concerns: the way in which AI algorithms are developed and validated in the research setting does not adequately prepare them for deployment in clinics and hospitals. Second, the importance of asking clinical questions with AI algorithms that have meaning and value is often underplayed or not considered. The outputs of AI algorithms mostly, but not always, also need to explain how decisions have been made. Thirdly, operational and ethical considerations loom over the integration of AI algorithms into electronic health record systems, clinical pathways, and legal frameworks. Above all these considerations is the motivation for deployment and particularly whether it is primarily for patient benefit or service economics.

Bipolar disorder (BD) follows a neuro-progressive trajectory, yet current clinical staging models lack robust biological markers. Based on recent evidence, we hypothesized that BD stages are associated with alterations in the functional architecture of the Triple Network Model. We analyzed resting-state fMRI data from 123 individuals spanning five clinically defined BD stages. Using a hierarchical approach, we investigated how functional connectivity features characterize these stages, transitioning from coarse- to fine-grained analyses in both clinical classification (binary early-versus-late grouping to five-stage discrimination) and connectivity metrics (mean and standard deviation of connectivity to topological dynamic features). First, low-level functional connectivity features, combined with gene expression data, discriminated early- from late-stage illness, reaching higher performance than unimodal approaches. Next, evaluating the full staging spectrum revealed a non-linear trajectory in whole-brain connection density, which was lower at each successive stage from 0 to 3 but unexpectedly higher at stage 4. Network topology analysis contextualized this finding, revealing a reorganization of functional hubs in the final stage. Specifically, three hubs within the Salience Network exhibited decreased centrality despite the overall increase in connection density. This shift corresponded to a loss of temporal synchronization between these hubs and default-mode hubs, an axis of communication mediated by the salience network that is known to be important in healthy subjects and disrupted in psychiatric disorders. Overall, our findings suggest that late-stage BD is characterized by a subtle but significant reorganization of brain architecture, accompanied by alterations in the dynamics of the salience network which loses its coordinating role.

Advanced brain imaging studies have been scarcely reported in neurodevelopmental encephalopathies. In this study we assess structural brain alterations in three rare neurogenetic disorders primarily affecting glutamatergic neurotransmission, aiming to identify shared and disease-specific neuroanatomical patterns and their clinical correlations. A cohort of patients with SYNGAP1 (n = 19), GRIN gene family (n = 19), and STXBP1 (n = 10) mutations underwent magnetic resonance imaging. Advanced segmentation tools were used to extract regional brain volumes. Volumetric differences between patient and age-matched normative reference templates were assessed using parametric and non-parametric tests, depending on data distribution, and ANCOVA was used to adjust between covariates. Associations with clinical symptoms were evaluated using the appropriate correlation tests. In our cohort, we found that patients exhibited statistically significant and consistent shared differences in brain tissue volumes compared to age-matched templates, including larger volumes in the basal ganglia, thalamus, ventricles, and certain cortical regions, alongside reductions in total white matter, cerebellar, and limbic structures (amygdala and parahippocampal gyrus). Clinically, ventricular enlargement correlated positively with the severity of intellectual disability, language impairment, and motor dysfunction, while total intracranial volume showed negative correlations with these same domains. Distinctive trends included supplementary motor cortex enlargement and cerebellar volume deficit in STXBP1, while amygdala volume deficit was most prominent in SYNGAP1 and GRINpathies. In conclusion, the study suggests the presence of shared and disease-specific brain alterations in SYNGAP1, GRINpathies, and STXBP1 disorders. Overall, brain volumetry may represent a useful exploratory tool, contributing to a more detailed characterization of these diseases while offering insights beyond conventional radiological assessment.

Progressive supranuclear palsy (PSP) is a 4-repeat tauopathy characterized by clinicopathological heterogeneity. The complex interplay between tau deposition, structural changes, and disease spread are unclear. To investigate the temporospatial patterns of tau propagation and neurodegeneration using multimodal imaging with MRI and flortaucipir (FTP) PET, and determine relationship with clinical heterogeneity. 150 PSP patients (n = 66 Richardson's syndrome [PSP-RS], n = 26 parkinsonism [PSP-P], n = 25 speech-language [PSP-SL], n = 13 progressive gait freezing (PSP-PGF), n = 10 corticobasal syndrome [PSP-CBS], n = 3 frontal, n = 1 oculomotor and n = 6 postural instability) underwent 3 T-MRI and FTP-PET. Forty-three patients died and underwent autopsy. Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that separates data-driven disease phenotypes distinguished by diverse temporal progression patterns, was applied to both MRI and FTP-PET W-scores (adjusted for age, sex and scanner, using 102 controls). Longitudinal MRI (n = 76) and FTP (n = 56) data, analysed with linear mixed models, were used to assess regional progression patterns and compared with the machine learning predictions. Two subtypes emerged across modalities. Subtype 1 exhibited initial subcortical involvement, mainly included PSP-RS and PSP-P patients and mostly featured PSP pathology, while subtype 2 exhibited early cortical involvement, PSP-SL patients and CBD pathology. FTP-PET stages preceded MRI stages, suggesting tau deposition anticipates atrophy. MRI stages were better in capturing clinical progression and predicting longitudinal disease evolution. These findings suggest the existence of subcortical and cortical subtypes of PSP, with distinct clinicopathological features. Tau PET and MRI provide complementary insights into disease progression, with MRI more closely reflecting clinical evolution.

Grey matter volume loss (GM-VL) is an accurate marker of multiple sclerosis (MS)-related progression. However, long-term comparisons of GM-VL between MS and healthy controls (HC) are rare, and the brain regions with most significant GM-VL and their clinical importance still need robust and longitudinal validation. This multi-cohort longitudinal observational study used two relapsing remitting MS (RRMS) cohorts (N&#x202f;=&#x202f;386, T1w-scans=940) sampled for up-to-12 years to localise grey matter volume loss (GM-VL) and disease progression (Expanded Disability Status Scale (EDSS), Paced Auditory Serial Addition Test (PASAT), Fatigue Severity Scale (FSS)). The identified region-specific significant GM-VL was compared with 2163 HCs (T1w-scans=4326). The strongest, replicable, significant patterns of brain GM-VL in RRMS were found in the frontal lobes, specifically, in the superior frontal cortex (SFC, &#x3b2;age&#x2264;-0.27), pars orbitalis (&#x3b2;age&#x2264;-0.25), and thalami (&#x3b2;age&#x2264;-0.20). Compared with healthy controls (HCs) >20 years older, MS showed greater GM-VL in the right SFC, caudal-middle frontal cortex, caudate, and left frontal pole (all Z&#x202f;>&#x202f;2.08, p&#x202f;<&#x202f;0.019). The overlap of associations between volumetric and clinical outcome changes was limited to EDSS, which was significantly related to left hippocampal volumes &#x3b2;EDSS&#x2264;-0.19 in both cohorts. Our findings indicate GM-VL in people with RRMS comparable to 20-year older HC, and stronger in the SFC and thalamus, and cohort-specific relationships with disability-progression.

Multiple Sclerosis (MS) disrupts white matter (WM) tract organization, affecting brain networks and neurological function. Current methods lack the capacity to directly quantify tract- and network specific myelin-sensitive measures with clinical outcomes. This cross-sectional study (122 MS patients, 72 females, median age 48.5 years, median EDSS 3.0) applied multi-compartment Myelin Streamline Decomposition (MySD), an advanced MRI technique for assessing myelin-sensitive measures of WM bundles and networks in the presence of focal lesions. Patients underwent 3T MRI with magnetization transfer (MT)-weighted and multi-shell diffusion imaging. These data enabled multi-compartment MySD reconstruction to generate myelin-sensitive tract measures for the corticospinal tract (CST) and cingulum bundle (CB) and MVF-derived myelin-sensitive network properties. Associations were tested between tract-specific measures and five network measures with disability (Expanded Disability Status Scale (EDSS)), information processing speed (Symbol Digit Modalities Test) and neuroaxonal damage (serum neurofilament light chain), adjusting for covariables. Primary FDR-corrected analyses were considered for the left/right CST with EDSS and left/right CB with SDMT. All remaining analyses were considered exploratory. While primary CST-EDSS associations did not reach significance, a subgroup analysis revealed an association between myelin-sensitive tract measure of the left CST with SDMT z-scores (&#x3b2;&#x202f;=&#x202f;0.57, p&#x202f;=&#x202f;0.001, R2 Adj&#x202f;=&#x202f;0.21). Sensitivity analysis demonstrated correlations between myelin-sensitive tract measure of the left CST with mean strength (&#x3c1;&#x202f;=&#x202f;0.38, p&#x202f;<&#x202f;0.001) and efficiency (&#x3c1;&#x202f;=&#x202f;0.33, p&#x202f;<&#x202f;0.001). Lower myelin-sensitive tract measures of the CST related to poorer cognitive performance. Multi-compartment MySD enables exploration of myelin-sensitive proxies and clinical outcomes in MS.

Infra-Low-Frequency Neurofeedback (ILF-NFB) combines classic frequency-band (FB) and infra-low-frequency (ILF) EEG components in implicit training protocols and is increasingly applied in clinical contexts. Yet, the neurophysiological mechanisms underlying ILF-NFB remain to be further elucidated. In this randomized, sham-controlled and double-blind study, we explored the online impact of a one-session ILF-NFB application on EEG correlates in healthy participants (39 analyzed datasets). Continuous 31-channel EEG was recorded during verum and sham feedback in a double-blind, randomized crossover design. In this exploratory analysis approach, functional connectivity was estimated using the debiased weighted phase-lag index (dwPLI) and analyzed with graph-theoretical measures. The results revealed higher global efficiency during verum compared to sham in the Beta1 band (12-15 Hz), reaching significance in the primary comparison but not surviving Bonferroni correction across the five tested bands; block-wise follow-ups showed a significant verum-sham difference in the first half of the neurofeedback session and a directionally consistent pattern in the second half. The Condition&#x202f;&#xd7;&#x202f;Block interaction was not significant. No consistent differences were observed in other frequency bands, nor for betweenness centrality. While preliminary, these exploratory results point to possible network-level effects during ILF-NFB and motivate further confirmatory work in extended training protocols and clinical populations.

Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneity, complicating diagnosis, subtype characterization, and prediction of disease progression. We introduce PatientSpace, a multimodal graph-based latent representation framework designed to model neurodegenerative disease heterogeneity using T1-weighted MRI and FDG-PET. PatientSpace is built upon a structured variational autoencoder that integrates multimodal neuroimaging features while organizing patients within a latent space constrained by age, diagnosis, and a consistency regularization term encouraging similarity between neuroimaging phenotypes. This design enables the construction of an interpretable patient graph in which neighborhood relationships reflect biological similarity. Applied to cohorts of cognitively normal individuals, AD, and FTD patients, PatientSpace revealed multiple disease clusters associated with distinct neuroimaging patterns and clinical severity. Diagnostic classification achieved performance comparable to state-of-the-art deep learning models, while graph-based neighborhood inference enabled prediction of structural volumes, metabolic activity, and cognitive severity. Projection of mild cognitive impairment (MCI) subjects from an independent cohort further showed that cluster membership was associated with differential risks of dementia conversion and distinct longitudinal trajectories. Together, these results demonstrate that PatientSpace provides an interpretable framework linking multimodal neuroimaging representations to disease subtypes, patient-level characterization, and progression modeling in neurodegenerative disorders.

Tumefactive demyelinating lesions (TDLs) are a rare form of idiopathic inflammatory demyelinating disease of the central nervous system. The clinical manifestations of TDLs are nonspecific, the initial symptoms are diverse, and the imaging often shows isolated space-occupying lesions, rarely multiple lesions, which are hard to be distinguished with neoplasms. We here report a case of a male adult presented with unilateral limb weakness as the initial symptom, who shows multiple lesions located at the top of the fourth ventricle with typical 'open ring' enhancement characteristics, space-occupying effect and tissue edema, with no central vein sign (CVS). PCT-CT indicated significant hypometabolism in the affected areas. Cerebrospinal fluid oligoclonal bands, both serum and CSF demyelinating antibodies were negative. The final diagnosis of TDLs was confirmed by brain biopsy. After a clear diagnosis and treatment with glucocorticoids, the neurological dysfunction of patient was gradually improved. Two months later, the follow-up cranial MRI showed that the intracranial lesion was significantly smaller than before. One year later, the patient's symptoms do not recur and muscle strength was completely restored. Misdiagnosis between TDLs and neoplastic masses such as brain tumors is common in clinical practice. Timely and accurate diagnosis of TDLs can prevent unnecessary surgical interventions, radiation therapy, and chemotherapy. We expect that this representative case will enhance clinicians understanding of TDLs and improve their ability to diagnose and manage this condition.

Microvascular remodeling and blood-brain barrier dysfunction (BBBD) are increasingly recognized as contributors to epilepsy. However, commonly used vascular imaging markers are often state-dependent and lack spatial specificity. We aimed to (1) validate plasma volume fraction (v&#x209a;) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a sensitive marker of microvascular changes; (2) characterize the v&#x209a; alterations in patients with drug-resistant epilepsy (DRE); and (3) assess the spatial relationship between v&#x209a; abnormalities and BBBD. We analyzed DCE-MRI images from 49 people with epilepsy (PWE) and 68 healthy controls across two sites. v&#x209a; and BBB permeability were quantified using BBBdetect software. Voxel-wise v&#x209a; was estimated using the extended Tofts model, and BBBD was quantified using slope-based permeability mapping. Both measures were summarized using modified z-scores, with suprathreshold abnormality defined as modified z-score&#xa0;>&#xa0;2. We performed region-wise and lobe-wise analyses restricted to gray matter and trained supervised classifiers to distinguish PWE from controls using regional z-v&#x209a; and z-BBBD features. Compared with controls, PWE showed increased voxel-wise and region-wise v&#x209a; abnormality burden, with a non-uniform spatial pattern that includes prominent fronto-temporal elevations and frequent involvement of limbic regions. BBBD was common and spatially diffuse. Restricting analysis to regions with co-occurring suprathreshold v&#x209a; and BBBD reduced spatial diffuseness relative to BBBD alone. Multivariate classification achieved encouraging test performance using vascular and barrier features (balanced accuracy&#xa0;=&#xa0;0.81), with feature importance suggesting complementary contributions from v&#x209a; and BBBD. v&#x209a; is a sensitive DCE-MRI-derived marker of microvascular abnormalities in DRE. Integrating v&#x209a; with BBBD enhances the spatial specificity of abnormality patterns and shows encouraging concordance with the clinically suspected epileptogenic territories, warranting prospective validation against clinical reference standards.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established treatment for Parkinson's disease (PD). Beyond basic omnidirectional, monopolar stimulation, advanced stimulation settings (AS), such as directional or vertical current steering, variation of pulse width and frequency, bipolar and interleaving stimulation are increasingly available. Our aim was to summarize current evidence on AS in STN-DBS in PD and systemically report their application and potential benefits in clinical routine. In this retrospective single-center observational study, we analyzed stimulation settings of 145 patients with bilateral STN-DBS 3, 6, and 12&#xa0;months postoperatively. Secondary outcomes included preoperative levodopa response, lead positions, and postoperative reduction of levodopa-equivalent daily dose (LEDD) in patients staying with basic stimulation settings (BS) compared to those initially with AS at 3-months follow-up and those with a change from BS to AS. AS were applied in 40.7%, 55.9%, and 73.8% of patients at 3, 6, and 12&#xa0;months respectively. LEDD reduction after three months was higher in patients remaining with BS or initially AS than in patients with a switch to AS after three months, while there was no difference at 12&#xa0;months. Median distance of leads to the center of gravity of the motor-STN was slightly larger when AS were applied. AS are frequently employed in clinical routine at a specialized DBS center. They may compensate for deviant lead placement in terms of stimulation efficacy measured by postoperative LEDD reduction. Prospective studies are warranted, focusing on specific AS indications in chronic DBS to optimize individual patient outcomes.

Clinical trajectories in patients with functional neurological disorder (FND) are variable, and the neural mechanisms underlying this heterogeneity remain poorly understood. This longitudinal brain imaging study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND. Thirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and functional MRI (fMRI), followed by naturalistic treatment for 6.8 &#xb1; 0.8 months. All participants completed follow-up questionnaires; 28 completed follow-up fMRI. At each timepoint, three graph-theory network metrics of resting-state functional connectivity were computed: whole-brain weighted-degree (centrality), cortical integration (between-network connectivity), and cortical segregation (within-network connectivity). All analyses adjusted for age, sex, antidepressants, head motion, time between sessions and baseline score of interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs). Based on patient-reported Clinical Global Impression of Improvement ratings, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Core FND symptom (i.e. Screening for Somatoform Symptoms-7 Subscale for Conversion Disorder) and non-core physical symptom (Patient Health Questionnaire-15) scores showed variable trajectories, with no group-level changes. For whole-brain weighted-degree analyses, baseline centrality in right middle frontal, precentral, and left cerebellar regions was positively associated with core FND symptom change; longitudinally, centrality decreases in right precentral, superior parietal, lateral occipital, and cerebellar regions were associated with symptom improvement. For cortical integration analyses, baseline between-network connectivity in ventral attention, frontoparietal, and default mode network regions was positively associated with core FND symptom change; longitudinally, decreases in between-network connectivity for regions of these same networks were associated with symptom improvement. For cortical segregation analyses, baseline within-network connectivity in frontoparietal network regions was positively associated with core FND symptom change; no regions showed longitudinal segregation changes associated with symptom change. The right anterior insula emerged as a convergent site across baseline and longitudinal integration analyses, with the most improved participants showing elevated baseline between-network connectivity relative to HCs that normalized at follow-up. More modest functional connectivity associations were observed with non-core physical symptom change, spanning baseline within-network connectivity in dorsal attention network regions and longitudinal between-network connectivity increases in visual network regions. Findings remained significant adjusting for FND phenotype, although several attenuated when accounting for baseline affective symptoms or trauma burden. In conclusion, this study identified baseline and longitudinal resting-state functional connectivity features linked to symptom change in FND, highlighting the potential of large-scale network interactions as prognostic markers and providing mechanistic insights that set the stage for novel, biologically informed interventions.

Progressive supranuclear palsy (PSP) shows a characteristic but incompletely defined pattern of neurodegeneration, in part because prior imaging studies have been limited by small and heterogeneous cohorts. Here, we consolidated evidence for PSP-related gray matter (GM) loss using a coordinate-based meta-analysis and interpreted the resulting atrophy pattern in a network and molecular framework to infer disease-relevant mechanisms. We conducted an Anatomical Likelihood Estimation (ALE) meta-analysis of whole-brain morphometry studies investigating atrophy in PSP, followed by functional decoding to evaluate the functions recruiting the atrophied regions and meta-analytic connectivity to delineate co-activation-based connectivity profiles. Finally, we explored potential neurochemical underpinnings by correlating the atrophy map with PET-derived neurotransmitter density distributions. ALE meta-analysis identified clusters of robust gray matter (GM) atrophy in PSP in the bilateral thalamus & midbrain, left anterior-dorsal insula as well as bilateral caudate nucleus. These regions were shown to be functionally associated with language, body perception, somatosensation and emotion processing. Connectivity analyses indicated coupling with fronto-insular salience-network circuitry and with key subcortical nodes, consistent with a distributed systems-level disturbance. At the molecular level, PSP-related atrophy aligned with higher densities of dopaminergic, serotonergic, and-most prominently-cholinergic markers, suggesting multi-transmitter-system vulnerability with a critical role of the cholinergic architecture. Together, these findings identify an interconnected set of cortical-subcortical targets in PSP whose functional and molecular profiles map onto core clinical features, supporting a network-based view of PSP neurodegeneration beyond isolated local atrophy with critical roles of the insula and the cholinergic system.

Sleep is a dynamic process involving complex changes in brain activity across distinct stages, yet the neurobiology of these transitions remains poorly understood. Simultaneous electroencephalography (EEG) and neuroimaging provide a unique window into stage-specific brain activity by combining the temporal precision of EEG with the spatial resolution of functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and the signal specificity of Near-Infrared Spectroscopy (NIRS). We conducted the first systematic review of simultaneous EEG-neuroimaging studies of human sleep. Systematic searches across five databases identified 205 eligible studies. Eligible studies recorded simultaneous EEG and fMRI, PET, or NIRS during sleep in humans, with EEG-based staging; sequential acquisition and awake-only studies were excluded. Findings were grouped by imaging modality, sleep state (NREM, REM, transitions), sleep manipulation type, and population (healthy vs. clinical). The majority used EEG-fMRI (n=141), with smaller numbers employing EEG-PET (n=34), EEG-NIRS (n=26), and multimodal combinations (n=4). Despite heterogeneity in protocols and analyses, convergent findings highlight robust thalamic, precuneus and cingulate involvement across non-REM (NREM) stages, with additional modality-specific insights. REM sleep showed increased activity in limbic and paralimbic networks, alongside reduced activity in executive control regions. Studies varied in design (49% overnight-only, 20% nap-only), populations (74% healthy adults only), and staging approaches (61% using 30-sec epochs), with methodological challenges including small sample sizes (median N=18) and heterogeneity in sleep manipulation protocols (28% used sleep restriction). This systematic review synthesizes three decades of simultaneous EEG-neuroimaging, mapping convergent and divergent findings across modalities to advance understanding of sleep-state brain function.

Well-characterized cohorts are essential for advancing neuroimaging biomarkers and refining models of brain aging and dementia across diverse populations. Despite growing neuroimaging research in Latin America, additional multimodal cohorts integrating imaging, genomic, and environmental data are needed to capture population diversity. We present GeNED.ar (Genetics and Neuroimaging of Aging and Dementia in Argentina), a multimodal cohort established in the Metropolitan Area of Buenos Aires to investigate brain aging in a population with genetic admixture and socioeconomic heterogeneity. The dataset combines two complementary recruitment strategies, community-based healthy participants and Memory Clinic attendees, including 3T MRI, genome-wide genotyping, and detailed sociodemographic data from 367 individuals aged 18-94 years. Participants comprise healthy individuals (n&#x202f;=&#x202f;235) and Memory Clinic attendees classified as cognitively unimpaired (n&#x202f;=&#x202f;65), mild cognitive impairment (n&#x202f;=&#x202f;37), Alzheimer's or mixed dementia (n&#x202f;=&#x202f;24), and vascular dementia (n&#x202f;=&#x202f;6). Genetic ancestry analysis (n&#x202f;=&#x202f;191) indicated a predominantly admixed population (65% European, 28.3% Native American) with significant differences across recruitment sources. Brain age gap (BAG), estimated from T1-weighted MRI, increased progressively along the clinical continuum, where people with dementia exhibited older-appearing brains relative to cognitively unimpaired participants, and intermediate values were observed in mild cognitive impairment. No independent associations were observed between BAG and individual genetic or environmental risk factors. By integrating multimodal MRI and genomic data across complementary recruitment settings, GeNED.ar provides a unique regional resource to evaluate neuroimaging biomarkers, facilitate cross-cohort validation, and strengthen the generalizability of aging and dementia models in genetically and socially diverse populations.

Semantic processing is one of the core functions of human higher-level cognition. The neural mechanism of the decoding process is attached to great significance for understanding language, cognition, and clinical applications. The key issue in decoding semantic representation is how to enhance the sensitivity of task-related neural signals to effectively distinguish the spatial patterns under different stimuli. However, current studies are limited by the sensitivity to task-irrelevant noise, the trade-offs in spatial and temporal resolution inherent in neuroimaging modalities. Therefore, this study proposed a novel representation similarity analysis method based on spatial projection. By integrating optically pumped magnetometer magnetoencephalography (OPM-MEG) with multi-channel electroencephalography (EEG), we conducted a semantic congruity decoding analysis. The results showed that the optimized RSA framework significantly delineated the deflection of neural representations to different stimuli. Notably, semantic processing exhibited clear frequency-band specificity, where low-frequency bands (particularly &#x3b4; and &#x3b8;) accounted for a substantially larger proportion of semantic representation variance compared to higher frequencies. Further analysis revealed that semantic processing involved the refined spatiotemporal evolution of neural patterns, and semantic incongruity significantly enhanced the similarity of patterns. In addition, the source localization results not only verified the classical language network, but also found the involvement of the limbic system (such as the parahippocampal gyrus and insula), suggesting that semantic processing involved a wider range of memory and regulatory networks. This study not only expands the understanding to semantic neural representations from the multi-dimensional aspects, but also highlights the potential of spatial projection optimization combined with multivariate analysis for high-precision neural decoding.