Brain Connectivity

Cognitive impairment is a prevalent, disabling manifestation of Long COVID, but whether cognitive rehabilitation alters neural network organization is unknown. We examined whether a 12-week cognitive rehabilitation intervention modified resting-state connectivity within the central-executive (CEN), default-mode (DMN), and salience (SN) networks, and whether connectivity changes associated with cognitive performance. Participants were adults with prior SARS-CoV-2 infection, persistent Long COVID symptoms ([&ge;]3 months), and objectively confirmed mild-to-moderate cognitive impairment. Of 58 participants randomized, 39 (mean age 46.4; 69% women) completed neuroimaging (19 intervention, 20 control). The 12-week intervention combined virtual cognitive rehabilitation and small-group and individual sessions with computerized cognitive training. No significant group, time, or group-by-time interaction effects were observed for within- or between-network connectivity (all P > 0.22). Exploratory analyses identified nominal associations (P < 0.05) between connectivity changes and cognition; improved processing speed associated with increased CEN-DMN connectivity, greater SN-DMN connectivity associated with worse performance across processing speed, working memory, and executive function. None survived correction for multiple comparisons. Cognitive rehabilitation was not associated with detectable changes in resting-state network connectivity in long COVID-related cognitive impairment. SN-DMN connectivity may represent a marker of reduced network segregation underlying cognitive dysfunction, warranting study in larger, multimodal trials. Trial registration: NCT05498493.

The glymphatic system is the waste clearance pathway in the central nervous system. Although its dysfunction has been associated with several neurological disorders, its role in multiple sclerosis (MS) remains poorly understood. This system is thought to be particularly active during sleep, yet the relationship between sleep metrics and MRI-derived markers of glymphatic activity has been scarcely explored. In this study, we aimed to perform a multimodal magnetic resonance imaging (MRI) assessment of glymphatic function in MS and investigate its association with sleep parameters, clinical disability, disease duration and MRI-derived structural markers. The study included 22 patients (34.3{+/-}10.1 years; 16 female) with relapsing-remitting MS and 26 healthy controls (32.2{+/-}5.7 years; 18 female), all of whom underwent 3.0 Tesla MRI. Glymphatic function was assessed using diffusion tensor image analysis along the perivascular space (DTI-ALPS) index and global blood-oxygen-level-dependent (BOLD) signal-cerebrospinal fluid (CSF) coupling. In addition, sleep micro- and macrostructural features, clinical disability, disease duration, along with mean diffusivity (MD), lateral ventricular, choroid plexus volumes and lesion load were evaluated. Patients showed reduced DTI-ALPS index, though this difference became only a trend after controlling for MD. No significant group differences were observed in global BOLD signal-CSF coupling. However, in patients, the weaker coupling was associated with longer total sleep time, lower arousal index, greater disability and lateral ventricle enlargement, whereas the DTI-ALPS index showed no significant correlations with any of the measures. Together, these findings challenge the assumption that DTI-ALPS can serve as a reliable marker of glymphatic function in demyelinating diseases such as MS, where disease-related alterations in white matter microstructure can confound diffusion-based measures. In contrast, gBOLD-CSF coupling showed associations with sleep, clinical disability and structural changes in patients, despite the absence of a significant group-level difference. This finding suggests that gBOLD-CSF coupling may capture individual variability in glymphatic-related dynamics relevant to sleep and the clinical expression of MS.

Background: Behavioral variant frontotemporal dementia (bvFTD) is clinically defined by progressive changes in personality, executive function, and language, yet the structural neural correlates of symptom severity remain unclear. This study aimed to assess the association between clinical findings and neuroimaging biomarkers. Methods: We included 87 patients with bvFTD. Clinical functioning was covering behavior and language. Spearman rank correlations were used to assess univariate associations, and Bonferroni correction was applied for multiple comparisons. Principal component analysis (PCA) was conducted on regional volumes, and multivariable logistic regression was used to model high behavioral impairment (FBI total [&ge;]30), adjusting for age, sex, and education. Results: No individual regional volume was significantly correlated with clinical scores after Bonferroni correction (all adjusted p>0.05), and PCA-derived imaging components failed to predict behavioral severity in multivariable models. Age emerged as the only significant predictor (OR=1.11 per year; p=0.009). Model discrimination was modest (AUC=0.735) and did not improve with the inclusion of imaging features (p=0.453 vs. demographics-only model). Conclusions: In this bvFTD cohort, structural volumes of key frontotemporal and perisylvian regions did not predict behavioral or language impairments after correction for multiple comparisons. These findings show the limitations of regional volumetric analysis in isolation and stress the need for network-based, multimodal, and longitudinal approaches.

The validation of objective neurophysiological monitoring of concussion from diagnosis to return to play has the potential to improve the clinical care of athletes. Extracted from event-related potentials (ERP), brain vital signs offer a non-invasive objective neurophysiological measure of three well established ERP responses that complement existing clinical assessments: 1) the N100 response related to auditory sensation; 2) the P300 response related to basic attention; and 3) the N400 response related to cognitive processing. The N400, in particular, has increasingly been demonstrated to be sensitive to head impact related cognitive impairment. Accordingly, brain vital sign measures were collected in n =165 rugby league players from two Australian professional National Rugby League clubs (NRL), with concussed players (n =26) being subsequently assessed over a total of five separate time points (Baseline, Day 2, Day 5, Day 10, and Day 42). This timeline aligned with acute (Day 2), NRL mandatory minimum return-to-play (RTP) guidelines (Day 10), and an extended post RTP period. Relative to baseline, the results revealed significant N400 cognitive processing latency delays at Day 2 post-concussion (p =0.002). The N400 latency delays persisted past day 5, significantly returning to baseline values at Day 10, consistent with the RTP mandatory minimum requirement. The findings indicated that neurophysiological recovery occurred in this cohort within an approximate 10-day time frame. Future studies monitoring brain vital sign changes across the trajectory of injury to recovery are needed in sports related concussion management.

Background and Objectives Transient ischemic attack (TIA) diagnosis is challenging because it relies mainly on clinical assessment and exclusion of common differential. Functional MRI (fMRI) may provide a diagnostic biomarker by detecting transient brain dysfunction not visible on conventional imaging. This pilot study aimed to characterize acute motor-task BOLD activation in patients with suspected DWI-negative TIA and compare functional patterns between confirmed TIA and alternative diagnoses. Secondary objectives were to assess longitudinal changes in activation and connectivity at 3 months. Methods In this prospective single-center study, adults presenting within 24 h of transient anterior-circulation neurological symptoms underwent conventional MRI and additional motor-task fMRI. Patients with acute DWI lesions were excluded. Final diagnosis was established after 3-month follow-up. Whole-brain analyses, individualized motor cortex region-of-interest (ROI) analyses, and generalized psychophysiological interaction (gPPI) connectivity analyses were performed during the acute phase and at 3 months. Results Eight patients were enrolled; six had complete longitudinal imaging data and were included in the final analyses. Final diagnoses were TIA (n=2), migraine aura (n=2), functional neurological disorder (n=1), and hypoglycemia (n=1). Whole-brain and ROI analyses showed reduced bilateral motor-related BOLD responses during the acute phase in both TIA patients and in the patient with hypoglycemia. In contrast, patients with migraine and functional neurological disorder exhibited less marked longitudinal changes. In TIA patients, motor cortex activation increased at 3 months, tending to normalize toward values observed in non-TIA participants. Connectivity analyses revealed heterogeneous patterns, but the 2 TIA patients demonstrated increased longitudinal connectivity between the motor cortex contralateral to symptoms and other sensorimotor regions, including supplementary motor, premotor, parietal, and postcentral cortices. Discussion These preliminary findings suggest that TIA may be associated with transient bilateral reductions in motor-network activity. Functional abnormalities appeared to improve over time, supporting the hypothesis of reversible network dysfunction. Although limited by the very small sample size, this study provides the first longitudinal assessment of emergency task-based fMRI in rigorously defined TIA and supports further investigation of acute functional MRI as a potential biomarker of transient cerebral dysfunction.

Abstract Background The neuropsychiatric sequelae (cognitive deficits, anxiety and depression) post-COVID-19 exist as part of the spectrum of the multisystemic long COVID. People with these symptoms have been shown to have worse functional outcomes. Hence, this significantly impacts the economy, primarily due to productivity losses. Yet, the precise underlying pathogenesis remains unknown, as do any diagnostic or prognostic biomarkers. Aim This study aims to identify functionally impacted brain regions in those with post-COVID-19 neurological sequelae and to evaluate how alterations in brain function relate to acute serum inflammatory mediators and brain injury biomarkers. Methods The study will utilise data from the COVID-19 Clinical Neuroscience Study (CNS), a large UK multicentre study conducted during the pandemic. This sub-study will be a secondary analysis of the COVID-19 CNS, a prospective observational study that compared COVID-19-positive participants with negative controls. Participants had an MRI, blood tested for acute serum inflammatory mediators and brain injury biomarkers, and neuropsychiatric assessment (cognitron test, PHQ-9 and GAD-7) post-acutely and at follow-up. The DPABI (Data Processing & Analysis for Brain Imaging) toolbox will be used for pre-processing and analysis of the neuroimaging data (resting state functional MRI) to obtain regional homogeneity (ReHo). The association between ReHO and neuropsychiatric symptoms, acute inflammatory mediators and brain injury biomarkers will be explored. Regression models would be used to determine the risk and protective factors for developing these symptoms. Conclusion It is anticipated that the study will provide new insights into the functional basis of post-acute COVID-19 neuropsychiatric manifestations and identify new diagnostic biomarkers. Key words: Regional Homogeneity, COVID-19, functional MRI, neuropsychiatry, biomarkers

BACKGROUND: One in three adolescents with a concussion history report experiencing long-lasting somatic, sleep, emotional, and/or cognitive disturbances (persisting symptoms after concussion (PSaC)). Cognitive difficulties, such as attentional problems, are common symptoms and can interfere with ongoing development. Clinically, psychiatric symptoms also present in youth with PSaC. A concussion causes diffuse functional and microstructural alterations to neural networks. To develop targeted treatments for PSaC (e.g., neurostimulation), we need to understand how and where structural alterations and neural dysfunction intersect to cause long-lasting symptoms. OBJECTIVES: 1) Identify functional and microstructural properties in intrinsic connectivity networks, as well as neuropsychological outcomes, that differentiate adolescents with concussion with PSaC from those who do not have PSaC. 2) Determine whether neural activity within intrinsic connectivity networks mediates the relationship between network microstructure and specific neuropsychological features in adolescents with concussion. METHODS: CAMBY is a prospective study of adolescents aged 12 to 18 years with a history of concussion. Participants are allocated based on recovery status (PSaC vs. No-PSaC (recovered controls)). Advanced structural (magnetic resonance imaging; MRI) and functional (magnetoencephalography; MEG) neuroimaging and neuropsychological measures will be collected one month or more after concussion. Microstructural and functional features will be computed within intrinsic connectivity networks, then compared between PSaC and recovered control groups and related to symptom domains and neuropsychological profiles. IMPACT: This will be the first study to integrate MRI, MEG, and neuropsychology in adolescents with PSaC to identify disrupted networks underlying PSaC. This observational study is a necessary step towards future clinical trials that accurately target promising therapeutic neurostimulation protocols.

The discrepancy between chronological age and predicted brain age, derived from neuroimaging data, serves as a biomarker for neurological health. However, the exact electroencephalography (EEG) mechanisms contributing to the variation of brain age are yet to be understood. This paper comprehensively investigated the relationship between brain age estimated from EEG-based functional connectivity and cognitive or mental manifestations of healthy adults. A mean absolute error (MAE) of 5.97 and R-squared (R2) of 0.85 were reported for training data, while an MAE of 10.73 and R2 of 0.56 were reported for the prediction data. Increased brain age was found to be negatively related to the scores of memory and attention tasks. Most young adults (19 of 26) suffering from mental symptoms such as addictions, depression, phobia, and anorexia nervosa demonstrated higher brain age than their chronological age. Alcohol consumption was also significantly correlated to the difference between brain and chronological ages. The functional connectivity of frontal and central motor regions was found to be most distinguishing between individuals with high and low brain ages. These brain regions were significantly correlated with brain age but not chronological age, suggesting a relatively stronger relationship with biological age. Ablation studies further confirmed the importance of the frontal brain and all bands EEG in the prediction of brain age. This work advances the understanding of EEG mechanisms contributing to brain age and could provide an interpretation for physiological or psychological conditions associated with brain age.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by persistent symptoms of inattention, hyperactivity, and impulsivity that significantly affect cognitive, academic, and social functioning. Although electroencephalography (EEG) has emerged as a promising non-invasive tool for identifying objective neurophysiological biomarkers of ADHD because of its high temporal resolution and cost-effectiveness, many existing machine learning studies rely on single-domain EEG features, which may not adequately capture the complex functional organization of the brain. Methods: This study proposes a subject-level EEG classification framework that integrates three complementary feature domains: spectral characteristics, weighted Phase Lag Index (wPLI)-based functional connectivity, and topological descriptors derived from persistent homology. EEG recordings from 121 subjects were preprocessed and segmented into overlapping windows for feature extraction. Window-level features were aggregated to generate subject-level representations. Recursive Feature Elimination (RFE) was employed to select the 110 most informative features from an initial set of 1,043 descriptors. Five machine learning classifiers, including Support Vector Machine (SVM), Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron (MLP), were evaluated using stratified five-fold cross-validation. An ablation study was further conducted to assess the contribution of each feature domain. Model interpretability was investigated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), two widely adopted Explainable Artificial Intelligence techniques for interpreting machine learning models. Results: Among the evaluated classifiers, the SVM achieved the highest classification performance, yielding an accuracy of 80.17%, an F1-score of 80.95%, a balanced accuracy of 80.14%, a receiver operating characteristic area under the curve (ROC-AUC) of 86.72%, and a precision recall area under the curve (PR-AUC) of 87.34%. The ablation study demonstrated that combining spectral, functional connectivity, and topological features consistently outperformed individual feature domains, highlighting the complementary nature of these representations. Explainable AI analyses further identified functional connectivity features as the primary contributors to classification performance while confirming the additional value of spectral and topological descriptors. Conclusions: The proposed framework demonstrates that integrating spectral analysis, functional connectivity, and topological data analysis provides an effective and interpretable approach for subject-level ADHD classification from EEG recordings. The combination of recursive feature selection, multi-domain feature fusion, ablation analysis, and Explainable Artificial Intelligence establishes a transparent and reproducible pipeline that may facilitate the development of reliable EEG-based clinical decision-support systems for ADHD.

Collision and contact sport athletes are repeatedly exposed to subconcussive head impacts or subclinical head acceleration events (HAEs), which are impacts that do not result in a clinical diagnosis of mild traumatic brain injury but may induce subtle neurological alterations. These effects may include structural, functional, and metabolic changes within the brain. Various exposure quantification techniques have been developed to characterise HAE exposure, while diffusion MRI enables assessment of brain microstructural organisation, with changes in diffusion-derived metrics associated with pathophysiological processes. This systematic review was conducted in accordance with PRISMA guidelines to evaluate the use of various HAE exposure quantification techniques, exposure weighting approaches, and diffusion MRI in characterising the relationship between HAE exposure and brain microstructure in athletes. Across studies, longitudinal findings for fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) were inconsistent, and substantial heterogeneity in exposure quantification methods and analyses was observed. However, quantitative analysis demonstrated a significant negative association between total subclinical HAE count across the season and FA, and a positive association with MD, suggesting cumulative microstructural alteration. Exposure metrics, including risk-weighted, time-weighted, and finite-element-derived strain measures, showed stronger associations with diffusion MRI alterations than simple impact counts or peak acceleration measures. However, HAE exposure risk models are sport-specific and require broader validation for applicability across sporting cohorts. Future studies should reduce methodological heterogeneity, control for confounding variables, and refine exposure quantification and weighting approaches to better understand the relationship between repetitive subclinical HAE exposure and WM microstructural integrity.

Background: Schizophrenia is increasingly viewed as a disorder of multi-regional brain networks, but molecular and pathophysiological hypotheses are often studied at the level of single brain regions or neurotransmitter systems. A multi-regional, multi-neurotransmitter analysis is needed to clarify how molecular abnormalities relate to circuit-level hypotheses of the disorder. Methods: Transcriptomic data from 2,209 post-mortem brain samples spanning five brain regions in 1,037 individuals were integrated. Directed connectivity strengths between regions were estimated using co-expression between presynaptic and postsynaptic neurotransmission-related genes. These estimates constitute an "interregional molecular connectome". Macaque transcriptomic data were used to benchmark the estimation pipeline against reference macaque connectomes. Connectivity differences between healthy controls and patients with schizophrenia were calculated, and a directional consistency filter was applied to identify candidate dysregulations. An LLM-augmented literature screening framework was used to assess the level of literature support for our candidates. Results: Our estimation pipeline showed concordance with reference connectomes, outperformed whole-transcriptome and random-gene co-expression measures, and remained stable when applied to macaque data. A total of 10 connections were marked as candidate dysregulations in schizophrenia at a nominal permutation threshold. Three of the candidates align with established anatomical connectivity, and another three with anatomical connections through relay regions. These candidates were characterized by bidirectional abnormalities in the cortico-striatal circuitry. The remaining four candidates had no well-established monosynaptic connection between two regions. These candidates were interpreted as co-regulated co-expression coordination changes. Literature screening quantified the number of articles discussing each candidate and how many of the articles directly support or refute each candidate. Our candidates collectively highlight the role of the cortico-striato-thalamo-cortical circuit in the pathophysiology of schizophrenia. Conclusions: Interregional co-expression of neurotransmission-related genes carries more anatomical information than whole-transcriptome co-expression. Applied to schizophrenia, our analysis recapitulates known hypotheses and also proposes hypothesis-generating data-driven findings for experimental follow-up. This approach complements conventional region-wise analyses. Our approach could support a unified, multi-regional, and multi-neurotransmitter perspective of schizophrenia and point to candidate molecular mechanisms for further studies.

Multi-echo functional magnetic resonance imaging (fMRI) acquires signals with distinct contrast profiles across echo times (TEs). Empirical evidence suggests that different TEs capture distinct signal contributions with varying sensitivity to BOLD and non-BOLD processes from long to short TEs (Stroman, 2002; Krishnamurthy, 2023; Dong, 2024). Previous literature has not yet examined how brain states extracted from temporal dynamic functional connectivity (dFNC) evolve across TEs. Here we present a methodological and empirical study of TE-dependent evolution of dynamic connectivity states, with a phenomenological two-component model for interpretation. Multiple echoes were acquired with an echo planar time-resolved imaging (EPTI) sequence. TE-specific components were extracted via atlas-based group ICA (Neuromark 1.0), and brain connectivity states were generated via windowed dFNC and spatial ICA clustering of windows. Within-subject and group-level states evolved across TEs in network organization and variance. States exhibited TE-dependent variance profiles, with distinct peak echo times; and group-level points of state transition were independently mapped to TE-dependent transitions in variance. We modified a bi-exponential model to simulate and model changes in the dFNC time-course across TEs. The bi-exponential model revealed high consistency with subject states (R2 fit=0.86, res. error=0.55, 86% model convergence rate), suggesting a two-component signal system contributing to dynamic TE-dependent connectivity changes in state. A significant correlation between relative model terms was estimated on a cell-wise basis across states (r(24) = -0.43, p=0.03, 95% CI [-0.70, -0.04]). This suggests that specific bi-exponential signal parameters (i.e., short FNC1,TE=0, long FNC2,TE=0, and long FNC2,decay) were associated with the observed TE-dependent variations in connectivity states across TEs. In sum, our analyses provide downstream implications for TE-aware state analysis dependent on connectivity magnitude (FNCTE=0) and rate of connectivity decay (FNCdecay), in a two-component signal model, with future implications for TE-dependent state biomarkers.

Identifying regional brain atrophy is critical for understanding neurodegenerative disorders. Traditionally, group comparisons rely on frequentist statistics, utilising $p$-values and point estimates that offer limited characterization of uncertainty. Bayesian methods provide an alternative by estimating effect sizes and credible intervals while incorporating prior knowledge and uncertainty. This study evaluated the feasibility of Bayesian hierarchical modelling to assess regional brain atrophy in Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS) compared to healthy controls. Participants meeting diagnostic criteria for CBS or PSP, together with healthy controls, were recruited from the Cambridge Centre for Parkinson-plus. Grey matter volume, cortical thickness, and subcortical volumes were derived using FreeSurfer for 210 cortical and 36 subcortical regions defined by the Human Brainnetome Atlas. Participants with PSP were classified into Richardson syndrome (PSP-RS), corticobasal syndrome variant (PSP-CBS), frontal variant (PSP-F), and other presentations (PSP-Other). Bayesian hierarchical ANOVA models with appropriate priors were used to estimate group differences. Regions of practical equivalence (ROPE) were calculated to assess evidence of meaningful group effects. A total of 428 people were included (113 PSP-RS, 30 PSP-CBS, 23 PSP-Other, 19 PSP-F, 81 CBS, and 162 healthy controls). For grey matter volume, there was evidence of whole brain volume loss compared to controls for all disease groups except PSP-Other, which showed evidence of no difference. For cortical thickness, there was evidence of global cortical thinning in CBS compared to controls, but for no other group. For subcortical volume all groups showed evidence of volume loss compared to controls. Patterns of regional atrophy were identified in keeping with previous literature, with the largest effect observed in the left pre-motor thalamus (30\% reduction in CBS). There was greater loss of subcortical volume in PSP-RS compared to other PSP subgroups. Bayesian hierarchical modelling provides a feasible framework for quantifying regional brain atrophy, with interpretable effect size estimates and uncertainty measures. This approach complements conventional statistical methods, particularly in the assessment of rare neurodegenerative and neuropsychiatric disorders.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

BackgroundThe balance between excitatory and inhibitory neuronal signalling (E/I balance), proposed as a key mechanism controlling neuronal activity, is disrupted in many neurodevelopmental conditions. One such condition is Neurofibromatosis Type 1, a monogenic neurodevelopmental disorder characterised by increased inhibitory tone. While previous research suggests that E/I balance may be altered in infancy, it is unknown whether E/I balance remains altered during adolescence in NF1. Here we investigated this question using the aperiodic exponent of the resting electroencephalogram (EEG) power spectrum, a proposed electrophysiological index of E/I balance. MethodThe aperiodic exponent of the EEG power spectrum ( 1/f) was estimated for 16 adolescents with NF1 diagnosis aged 11-16, and 16 age- and sex-matched neurotypical controls. General linear models (GLM) were fit to estimate effects of NF1 diagnosis, age and their interaction on aperiodic exponent. We also estimated the peak dominant frequency and power in oscillatory activity of the EEG signal and fitted the same structure of GLM models. Cognitive assessments across domains of memory, executive function and attention were also analysed with GLM to examine brain-cognition associations. ResultsThe NF1 group had higher aperiodic exponent compared to neurotypical controls, but the aperiodic exponent was not associated with cognitive performance. After accounting for aperiodic activity, the NF1 group also had lower dominant oscillatory frequency than the control group. There was no significant interaction between NF1 diagnosis and age on PAF. There were no group differences in peak alpha power. ConclusionsWe demonstrate for the first time that the alterations to aperiodic activity in NF1 are consistent with the proposed mechanism of increased inhibitory tone. These findings show the potential of this paradigm for future pharmacological and brain stimulation trials investigating treatment-related changes in cortical function.

Background. CNM-Au8, a catalytic nanocrystalline gold suspension, is proposed to support neuronal bioenergetics, but its effect on chronic lesion evolution in multiple sclerosis (MS) has not been reported. Objectives. To evaluate the effect of CNM-Au8 on chronic lesion tissue expansion (CLTE). Results. In this exploratory post hoc analysis of the VISIONARY randomised placebo-controlled trial, CLTE was quantified from serial FLAIR MRI. Fifty-five participants were included (CNM-Au8, n=40; placebo, n=15). Compared with placebo, CNM-Au8 was associated with lower lesion expansion rate (p=0.001; Cliffs =-0.58) and volume (p<0.001; Cliffs =-0.65). Conclusions. CNM-Au8 treatment was associated with substantially reduced chronic lesion tissue expansion.

Introduction: Major depressive disorder (MDD) is a highly prevalent and disabling psychiatric disorder. Human neuroimaging studies increasingly frame its neurobiological substrate in terms of alterations of large-scale brain network organization. Resting-state fMRI findings broadly align with this view, yet remaining highly heterogeneous, reflecting both clinical and analytic variability. Predominant approaches largely characterize functional organization in terms of pairwise relationships between regions, which may limit the ability to capture more global features of brain organization associated with depression and its course. Here we use novel methods from Topological Data Analysis to provide new perspectives on these brain-behavior associations Objectives: The main objective of this study was to determine whether whole-brain topological descriptors of functional connectivity capture alterations associated with MDD. In particular, we examined whether these features vary along clinically relevant dimensions of heterogeneity, focusing on illness course (single-episode vs. recurrent MDD) and current antidepressant medication status. To this end, we analyzed the area under the curve (AUC) of the zeroth and first Betti numbers (B and B), which index global network integration and higher-order cycle structure across scales, respectively. Methods: Neuroimaging (resting state fMRI) and phenotypic data were obtained from 1,490 participants (776 individuals with MDD and 714 never-depressed controls) included in the REST-meta-MDD project of the DIRECT consortium. Functional connectivity matrices were computed using Pearson correlations between regions defined by the Power-264 atlas and harmonized across sites using CovBat. Topological Data Analysis was applied to characterize whole-brain network organization across connectivity thresholds using B and B curves and their areas under the curve (AUC). B associated cycle counts and their coarse anatomical configurations were further examined. Group differences and effects of illness course and medication status were assessed using multiple linear and generalized linear regression models. Results: Compared with controls, individuals with MDD showed significantly higher B AUC, indicating altered global integration profiles across connectivity thresholds. B AUC did not differ by illness course, illness duration, or symptom severity. In contrast, B AUC showed a graded increase across illness courses, with the highest values observed in recurrent depression. This effect was driven by an increased number of one-dimensional cycles rather than greater cycle persistence. Medication status further modulated B alterations, with elevated values observed in unmedicated single-episode and medicated recurrent MDD. Anatomical decomposition revealed that higher-order alterations were primarily driven by inter-network configurations spanning multiple large-scale functional systems. Discussion:These findings indicate that MDD is associated with altered multiscale functional organization, combining less compact global integration with an increased prevalence of cross-network hole-defining cycles, with these features varying systematically with illness course and current medication, suggesting that topology-informed measures capture clinically relevant variability in brain organization in depression.

Language alterations are recognized as early markers of psychopathological vulnerability, yet genetic and environmental factors underlying language-related brain regions remain unknown. We applied univariate twin analyses and risk-discordant comparisons to multimodal dataset from 31 healthy twin pairs (9-31 years) and 17 pairs discordant for psychopathological risk (10-27 years). Gray matter volume (GMV), cortical thickness (CT), and functional interactions, specifically effective connectivity (EC), were assessed in eight bilateral language-related regions, alongside linguistic, emotional-behavioural, temperamental, and personality-related phenotypes. In healthy twin pairs (27-31 pairs, depending on data completeness), genetic and environmental determinants were estimated for all phenotypes. In 17 pairs discordant for psychopathological risk, intra-pair neuroimaging differences were tested for association with psychopathological discordance and zygosity, and individual brain measures were compared across high-risk twins, their low-risk co-twins, and healthy controls. Environmental factors predominated in structural and functional brain variability, while genetic contributions were limited, region-specific. Narrative fluency and psychological traits showed stronger heritability. Findings converged on the right middle frontal gyrus, where GMV and CT alterations reflected non-shared environment and risk expression. Despite the small sample, preliminary results suggest neurobiological vulnerability in language networks arises from environmental rather than genetic factors, informing environmental susceptibility to adolescent mental illness.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

Introduction: Cognitive intraindividual variability (IIV), a measure of fluctuation in performance across time or tasks, is an emerging marker of cognitive decline. However, little is known about within-domain IIV in Latino adults. To address this gap, we examined memory IIV and hippocampal integrity in older Latino individuals. Methods: Sixty-seven participants from the Boston Latino Aging Study underwent cognitive testing and MRI. Linear regressions assessed whether memory IIV was associated with hippocampal volume and hippocampus-cingulum tract fractional anisotropy. Additional analyses evaluated associations using mean memory performance as the outcome. Results: Higher mean performance was associated with greater hippocampal volume (=0.24, p=.03), while memory IIV was not (=0.028, p=.84). Higher memory IIV was associated with lower fractional anisotropy in the hippocampus-cingulum tract (=-0.34, p<.01), while mean performance was not (=-0.07, p=.52). Discussion: Memory IIV may be more closely linked to hippocampal white matter, while mean memory performance may better reflect hippocampal volume.