Brain Connectivity

Introduction The superior longitudinal fasciculus (SLF) is a major association fiber bundle implicated in cognition, visuospatial attention, language, and motor control, and its impairment is linked to several neurological and neuropsychiatric disorders. This proof-of-concept study was performed with three main objectives in healthy adults. First, to fuse whole brain spectroscopic (WBSI) and diffusion MRI (dMRI) derived parametric maps along the SLF I and II segments to quantify their spatial concordance, second, to evaluate regional metabolite concentrations and microstructural properties along these trajectories and finally, to determine the relationships between the WBSI and dMRI parameters within these segments. Methods Ten healthy adults (4F, 6M; mean age 31.4 {+/-} 7.53 years) underwent 3T MRI including multi-shell high angular resolution diffusion imaging and WBSI. After preprocessing and non-linear co-registration, WBSI-derived white matter metabolite maps and neurite orientation dispersion and density imaging (NODDI) / diffusion tensor imaging (DTI) derived parametric maps were spatially aligned and projected along the centroid of reconstructed SLF I and II segments divided into 20 discrete, anatomically contiguous sections. Results A strong spatial alignment between WBSI and dMRI imaging modalities was confirmed by mutual information and Pearson's correlation analyses. Intra-subject repeatability, as assessed from a single participant scanned three times, demonstrated high tract reconstruction reliability (mean Dice similarity coefficients >0.79; track density-weighted Dice >0.97) and acceptable intra-subject coefficients of variation. Inter-subject coefficients of variation were within acceptable ranges ({approx}3-17%) for most parameters, with free water fraction (fiso) exhibiting relatively higher variability. Single and multivariate regression analyses revealed significant associations between WBSI and dMRI tract profiles: choline/creatine (Cho/Cr) and choline/ N-acetyl aspartate (Cho/NAA) ratios showed positive linear associations with intra-cellular volume fraction (ficvf) and fractional anisotropy (FA), and negative associations with mean diffusivity (MD) along bilateral SLF I, with ficvf and MD identified as the strongest combined predictors of metabolite ratios. Conclusion Co-localization/fusion of WBSI and NODDI/DTI data into one framework offers a reliable, user-independent way for mapping regional metabolite and microstructural alterations along the path of SLF. Moving forward, this image processing pipeline has the potential to enhance diagnosis and clinical assessment of neurological disorders linked to SLF damage.

This study explores the nonlinear developmental trajectories of brain networks in neurotypically developing infants during their first six months. Using a longitudinal dataset of 137 resting-state functional MRI scans from 74 infants, we analyzed five spatial metrics across 13 intrinsic connectivity networks, including motor, visual, subcortical, and prefrontal networks. A cubic model was specifically employed to capture distinct linear and non-linear trends in the networks developmental patterns, allowing for the identification of significant differences in cubic, quadratic, and linear slope parameters across networks. This model choice was driven by the need to examine how specific non-linear components (e.g., inflection points and acceleration rates) uniquely characterize each networks trajectory, which a generalized approach might smooth out without pinpointing such network-specific features. Notably, the subcortical network exhibited a distinct cubic growth pattern, while secondary motor and visual networks showed pronounced quadratic variations, suggesting network-specific shifts in spatial organization and connectivity. These findings highlight the unique maturation timelines and interactions between functional systems, such as early sensory-motor coordination and later cognitive integration. The results underscore the importance of network-specific growth patterns, providing deeper insights into how infant brain networks evolve and interact to support emerging cognitive and behavioral functions.

Tourette Syndrome (TS) is a neurodevelopmental hyperkinetic disorder characterised by involuntary tics. These tics are thought to arise due to hyperexcitability of the motor cortex due to disinhibition within the cortico-striato-thalamo-cortical pathway. Given the involvement of motor circuits in this disorder, we explored whether there is a difference in the oscillatory dynamics of voluntary movements in people with tic disorders. We recorded optically pumped magnetometer magnetoencephalography during cued voluntary finger abductions in individuals with tic disorders and age- and sex-matched neurotypical controls. We analysed the data both using conventional time frequency analysis and using hidden Markov modelling to explore the difference in beta burst characteristics and dynamics. Whilst no differences were seen between groups during conventional analysis, we demonstrated an increase in beta burst duration during the post-movement beta rebound within the contralateral motor cortex in individuals with TS suggestive of increased inhibition following movement. We also show evidence of increased contralateral sensorimotor functional connectivity and reduced connectivity from and between frontal regions, as measured using coincident beta bursts. This is suggestive of disrupted functional connectivity within frontal control networks in individuals with TS.

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

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.

A single injection of a subanesthetic dose of ketamine, an NMDA receptor antagonist, induces schizophrenia-like behavior in mice. Previous studies investigated the effect of single injection of ketamine on behavior and c-Fos expression in mice dose-dependently. However, there has not been functional magnetic resonance imaging (fMRI) study to investigate the effect of ketamine in mice. Here, we used awake fMRI and investigated the dose-dependent effect of single injection of ketamine in awaked mice. The lower-dose (5 mg/kg) ketamine increased functional connectivity (FC) with the thalamus and the hypothalamic nuclei. Higher-dose (50 mg/kg) ketamine increased widespread FC with cerebral cortex as well as hippocampus and thalamic nuclei. Graph theory analysis reveals that several regions of hypothalamus and cerebral cortex are essential for the network change by ketamine injection. These results indicate that sub-anesthetic ketamine dose-dependently alters the brain and indues the abnormal behavior in mice.

Background: Neuroimaging studies implicate network alterations in functional motor disorder (FND-motor), yet white matter remains poorly characterized. Objectives: To characterize white matter microstructure in FND-motor relative to healthy (HCs) and psychiatric (PCs) controls and examine symptom associations. Methods: Fifty individuals with FND-motor, 50 age- and sex-matched HCs, and 50 PCs matched on age, sex, depression, anxiety, and post-traumatic stress disorder severity underwent multi-shell diffusion MRI. Voxel-based analyses examined whole-brain white matter using diffusion tensor imaging (fractional anisotropy [FA], mean diffusivity [MD]) and neurite orientation dispersion and density imaging (NODDI) (neurite density index [NDI], orientation dispersion index, and free water fraction [FWF]) metrics. Cross-metric convergence was characterized using atlas-based tract overlap analyses and probabilistic tractography. Associations with FND symptoms and transdiagnostic physical symptoms were also evaluated. Results: Compared with HCs, FND-motor showed higher FA/NDI and lower MD/FWF, predominantly in the middle cerebellar peduncle. Compared with PCs, differences were limited to lower MD/FWF, involving the corpus callosum, middle cerebellar peduncle, and left inferior longitudinal fasciculus. Greater FND symptom severity was associated with a lower FA/NDI and higher MD/FWF in the corpus callosum and right-lateralized association and projection pathways, whereas greater transdiagnostic physical symptom burden across FND-motor and PCs was associated with higher FA and lower MD/FWF in the middle cerebellar peduncle. Conclusions: This study provides a comprehensive multi-metric diffusion-weighted characterization of white matter microstructure in FND-motor relative to both HCs and PCs - highlighting cortico-cerebellar connections via the middle cerebellar peduncle as distinct in FND-motor and associated transdiagnostically with physical symptom burden.

Resting-state functional MRI (rs-fMRI) studies in schizophrenia commonly rely on normalization to volumetric templates and fixed atlas parcellations derived from neurotypical populations. While these approaches enable group-level comparisons, they may obscure individual variation in cortical organization and intrinsic brain dynamics. In this study, we compared four preprocessing and parcellation strategies across two independent schizophrenia cohorts (MRI site 1, n=159; MRI site 2, n=255) to evaluate how analytic choices affect static functional connectivity and dynamic quasi-periodic pattern (QPP) measures, including default mode-dorsal attention network opposition, QPP component rank, explained variance, event rate, and associations with PANSS symptom severity. Across datasets, individualized surface-based parcellation (IndiPar) consistently detected more pronounced QPP dynamics, stronger default mode / dorsal attention network opposition, and greater explained variance of QPPs relative to atlas-based pipelines. IndiPar also produced larger and more reproducible patient-control differences in functional connectivity and QPP event-rate measures, suggesting improved sensitivity through preservation of subject-specific organization. IndiPar additionally detected a significantly increased QPP event rate and more symptom associations in patients in the larger dataset. However, associations between fMRI measures and symptom severity showed limited stability across cohorts. These findings extend previous reports of altered resting-state activity in schizophrenia, and demonstrate that preprocessing and parcellation choices substantially influence both static and dynamic rs-fMRI results. Individualized surface-based parcellation appears to better preserve subject-specific variability and improves detection of intrinsic brain dynamics. At the same time, the limited cross-dataset replication of symptom associations highlights the challenges of deriving stable brain-symptom relationships from heterogeneous psychiatric cohorts.

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.

Typical brain network maturation involves an increase in network flexibility and hemispheric specialization. Tourette syndrome (TS) disrupts these trajectories, with tic severity potentially modulating deviations. This study examined theta-band EEG source connectivity states in typically developing children and children with TS. We assessed age-related trajectories and the impact of tic severity using generalized linear modeling, accounting for sex and multiple comparisons. K-means clustering identified four recurrent source connectivity states (A-D), with metrics including Coverage, representing state prevalence (proportion of time spent in each state), Average Dwell Time, an index of state stability (mean duration of stable persistence of each state), and Transition Rate Per Minute, reflecting global network flexibility (frequency of state switches per minute). In healthy controls (HC), typical maturation was characterized by increased left intra-hemisphere connectivity state stability and prevalence, decreased diffuse connectivity state stability, and rising network flexibility. TS patients exhibited deviant trajectories, including age-dependent decreasing global network flexibility across subgroups stratified by tic severity and marginally divergent diffuse activity patterns, with high-severity cases showing increased diffuse connectivity state stability. The normative patterns suggest typical motor development requiring dynamic network reconfiguration and hemispheric specialization, processes that appear altered in TS. TS patients exhibit age-dependent network rigidity across severity subgroups, as reflected by decreased transition rates, alongside severity- modulated network imbalances, indicating that tic disorders disrupt mechanisms of brain network maturation underlying motor control. These findings suggest that atypical trajectories of network stability and flexibility represent a key feature of tic pathophysiology, highlighting the role of altered network dynamics in TS during maturation.

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.

In-vivo examination of neurites to understand microstructural properties of white matter tissue utilizing neurite orientation dispersion and density imaging (NODDI) has shown sensitivity to healthy aging as well as disease biomarkers and status. Neurite density index (NDI), which is a proxy for the amount of neurites, in white matter tissue typically decreases with age. However, orientation dispersion index (ODI), which is a proxy for neurite dispersion or fanning, has been mixed with studies finding both increases and decreases with age. Furthermore, white matter tracts are not uniform and hold its own unique spatial pattern or gradient in microstructural properties. In addition to the spatial pattern of the microstructural property, age-related effects have also shown spatial patterns with stronger age effects in the medial, anterior, and dorsal portions of white matter tissue. However, spatial gradients along cardinal axes within an individual's tract have yet to be examined with age in an adult lifespan sample. The current aim of the study was to examine whether average and spatial gradients of neurite microstructural properties within tracts related to the cortico-striato-pallido-thalamic (CSPT) loop were age-sensitive. An adult lifespan sample aged 20-90 years old was recruited from the Dallas-Fort Worth metroplex (N = 104, 62% females) as part of the Dallas Area Longitudinal Lifespan Area Study (DALLAS). Participants completed an MRI session that included a structural T1-weighted image as well as multi-shell diffusion weighted imaging (MS-DWI). MS-DWI were preprocessed and tracts of interest related to the CSPT loop were obtained using probabilistic tractography. For most tracts, a significant inverted-U association with age was found for both average NDI and ODI. Most tracts revealed a reliable spatial gradient of NDI and ODI in the medial-to-lateral, posterior-to-anterior, and ventral-to-dorsal direction. Tracts related to CSPT loop were age-sensitive such that the spatial gradient was becoming more homogenous with age. This loss of spatial gradients with age is analogous to network-level dedifferentiation observed in BOLD functional connectivity. These findings highlight that age effects in a fundamental circuit for both basic and higher-order function is significantly age sensitive and while organized into spatial gradients, these gradients are also vulnerable to aging.

Multisite functional Magnetic Resonance Imaging (fMRI) studies are rapidly becoming the norm to allow the acquisition of large samples required to perform advanced statistical techniques, e.g. machine learning. However, acquiring data at multiple sites includes methodological and technical challenges due to the difference in the facilities of each site, such as scanner manufacturer and model. These new challenges add to the already well-known challenges of fMRI data, including scanner-related noise, participant movement, and physiological confounds. To ensure the optimal quality of data, a balance needs to be achieved between standardizing data preprocessing across sites and optimizing within-site data preprocessing. To define the optimal preprocessing pipeline including standard steps and additional denoising technique for our multisite dataset, we first tested 3 commonly used preprocessing pipelines, i.e. fMRIPrep, FSL, and CONN. These pipelines all include standard steps, e.g. motion correction, temporal filter, etc. In a second step, to further improve signal quality, we tested the efficiency of 3 additional denoising techniques on the output of the data preprocessed with the previously defined pipeline. These techniques included aCompCor, FSL FIX and ICA-AROMA. Signal quality was quantified as temporal signal-to-noise ratio (tSNR) across and within sites. Our results show that the performance of the preprocessing pipeline and denoising technique varies between sites. FSL yielded the highest tSNR at two sites and fMRIPrep at the third, revealing a significant site x pipeline interaction. An FSL-based pipeline with minimal adjustment to accommodate site-specific parameters and followed by denoising using a single FSL FIX classifier, which was custom-trained across sites, achieved the highest quality of signal in our data and yielded the greatest consistency in improved data quality across sites. Because of the potential of this pipeline to adequately identify and address site-specific noise, while remaining constant across sites, we selected it as optimal preprocessing pipeline for our dataset.

Background: Multiple sclerosis subpial cortical lesions are prevalent and associated with disability but difficult to detect on MRI. Inversion recovery susceptibility weighted imaging with enhanced T2 weighting (IR-SWIET) and T1/T2 ratio imaging have been proposed for cortical lesion detection on 3 tesla (T) MRI. Objectives: To assess cortical lesion detection using IR-SWIET and T1/T2 ratio imaging. Methods: Cortical lesions were identified in 20 persons with MS (pwMS) independently on six image sets: T1 weighted (w) magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) + T2w fluid attenuated inversion recovery (FLAIR) alone or with T1/T2, IR-SWIET single acquisition (x1), average of two (x2) or median of four (x4) acquisitions, or denoised single acquisition (IR-SWIETx1DN). In 10 additional pwMS with 7T-based cortical lesion segmentations, lesions were identified on MP2RAGE + FLAIR + IR-SWIETx1DN. Results: Median subpial lesions identified on MP2RAGE + FLAIR was 0 (interquartile range (IQR) 2) vs 0 with T1/T2 (IQR 1, p=0.07), 1 with IR-SWIETx1 (IQR 6, p=0.42), 5 with IR-SWIETx2 (IQR 5, p=0.008), 4 with IR-SWIETx4 (IQR 6, p=0.008), and 4 with IR-SWIETx1DN (IQR 6, p=0.008). Versus 7T, IR-SWIETx1DN detected subpial lesions with similar sensitivity to IR-SWIETx2. Conclusions: IR-SWIET, but not T1/T2, improves subpial cortical lesion detection. Denoising may be an efficient and sensitive alternative to multi-acquisition averaging.