NeuroImage: Reports

Introduction Language functional MRI (fMRI) is a valuable tool for presurgical planning in epilepsy. Functional MRI can be challenging in children, and head motion can compromise its utility. The candidacy of patients with ADHD for fMRI is sometimes queried regarding concerns about possible head motion. In 2020, we implemented an fMRI task training program, via telehealth and/or mock MRI. We aimed to determine whether training increased language lateralisation success and/or reduced head motion in all patients, and in those with ADHD. We also aimed to determine whether patients with ADHD exhibited more head motion during fMRI than those without ADHD. Methods We retrospectively identified 223 epilepsy (85%) and other neurosurgery patients, (241 scans including repeats) with language fMRI at Royal Children's Hospital, Melbourne, Australia, 2016-2024. There were 24 individuals with ADHD listed in the Electronic Medical Record, five of whom had diagnoses of both ADHD and autism; and nine with autism. Language lateralisation success was determined by clinician description recorded as left/right/bilateral in the medical record. 99 patients were provided the training including fMRI task practise. Head motion was quantified by maximum Framewise Displacement (FDmax; mm). Results ADHD was associated with lower language lateralisation success. Training was associated with greater language lateralisation success, across all patients, and in those with ADHD. Regarding ADHD and head motion, outliers in FDmax were seen in 5 young patients with ADHD. Data were trimmed to allow separate investigation of FDmax for the sample with and without extremes of head motion. In untrimmed data, FDmax was significantly higher in patients with ADHD than in those without. In trimmed data, FDmax was on average lower in patients with ADHD than those without, however this was not statistically supported. Regarding training and head motion, across all patients, FDmax was significantly lower for scans with training than without. In patients with ADHD, FDmax was on average lower for scans with training, however training was not associated with FDmax. Conclusions Language fMRI training was associated with higher language lateralization success, particularly in patients with ADHD. Training was associated with reduced head motion across all patients. Although some young patients with ADHD had substantial head motion, most in our sample did not move more than those without ADHD. We conclude that the training program increases success of language fMRI, and that an ADHD diagnosis should not be a contraindication to language fMRI.

Background: Transcranial direct current stimulation (tDCS) is increasingly used as an adjunct to rehabilitation for young people with cerebral palsy (CP), yet considerable variability exists in clinical response. Individualised electric field modelling provides an opportunity to estimate the distribution of electrical fields generated by the stimulation delivered to the brain and explore potential relationships with functional outcomes. Methods: Structural MRI scans from nineteen participants (10-16 years) from a previously published randomised controlled trial (ISRCTN74235136) investigating the effects of tDCS combined with motor training, underwent participant-specific finite element modelling using SimNIBS. Electric field strength was quantified within anatomically defined motor regions of interest, including the primary motor cortex (M1), dorsal premotor cortex (PMd), supplementary motor area (SMA), and a combined motor network. Global grey matter electric field metrics and stimulation focality were also extracted. Results: Estimated electric field strength differed significantly across motor regions (p<0.001), with PMd receiving significantly greater stimulation than both M1 and SMA. Electric field strength within a control region (primary visual cortex) was significantly lower than within M1 (p<0.001). Despite inter-individual variability in regional and global electric field metrics, no significant associations were observed between estimated electric field strength or focality and changes in function following intervention. Conclusion: Individualised electric field modelling demonstrated that an M1-targeted tDCS montage preferentially stimulated PMd rather than M1 in young people with CP. These findings highlight the importance of subject-specific modelling when characterising current distribution and suggest that variability in electric field strength alone does not explain variability in behavioural response.

Background: Large language models have been proposed to improve patient comprehension of radiology reports. However, whether they improve objective understanding remains unproven. Purpose: To evaluate the effect of appending an LLM-generated lay summary to brain MRI reports on objective and subjective patient comprehension in a randomized controlled trial. Materials and Methods: In this randomized controlled trial, 2,727 adult participants from the ComPaRe e-cohort were randomly assigned to interpret six standardized brain MRI reports for headache, presented either in their native format (control; n = 1,401) or appended with a lay summary generated by an open-weights LLM (Mistral Small 3.2) (intervention; n = 1,326). The primary outcome was objective comprehension, defined as the rate of correct classification of whether the report provided a probable explanation for the headache, with ground truth established by four-radiologist consensus. Secondary outcomes included satisfaction, subjective comprehension, anxiety, and willingness to contact a healthcare professional. Generalized estimating equations accounted for repeated within-participant observations. Results: A total of 2,727 participants (mean age, 52 years +/- 15; 75.2% women) were evaluated. Objective comprehension did not differ between arms (58.3% vs 59.4%; odds ratio (OR) 0.97; 95% CI: 0.90-1.06; P = .54). The intervention significantly improved overall satisfaction (64.9% vs 36.7%; OR 3.26; 95% CI: 2.93-3.64; P < .001) and subjective comprehension (50.3% vs 24.0%; OR 3.17; 95% CI: 2.82-3.56; P < .001). High anxiety was modestly reduced (25.1% vs 26.6%; OR 0.92; P = .037). The effect on objective comprehension varied by report type (P for interaction < .001): summaries improved comprehension of symptom-explaining reports (42.4% vs 37.4%; P < .001) but reduced it for normal reports (72.5% vs 76.6%; P = .001). Conclusion: LLM-generated lay summaries appended to brain MRI reports improved patient satisfaction and subjective comprehension but did not improve objective comprehension, indicating a gap between perceived and actual understanding that should be addressed before clinical integration.

INTRODUCTION: Early detection of Alzheimer's disease (AD) remains challenging. EEG offers a scalable, non-invasive tool for patient stratification, but its relationship to ATN-defined staging is poorly understood. METHODS: EEG was recorded in 60 participants (SCD, MCI--, MCI+, N=20 per group) using a battery of tasks (N-back, auditory oddball, 40 Hz ASSR, resting-state). Event-related, spectral, connectivity, and complexity features were extracted, compared across groups, correlated with CSF and plasma biomarkers, and evaluated for classification performance. RESULTS: Multiple EEG features showed discriminatory power among groups and correlated with amyloid and tau biomarkers. MCI-- showed a cortical hyperexcitability profile. EEG added no value for ATN-based discrimination where plasma pTau217 performed near ceiling (AUC=0.96--0.99), but uniquely separated SCD from MCI (EEG AUC {approx} 0.72--0.75) where plasma biomarkers failed (AUC{approx} 0.32--0.33). DISCUSSION: EEG biomarkers capture ATN-stage-dependent neurophysiological signatures, support a non-monotonic model of AD progression, and show promise as a first screening tool where plasma biomarkers are uninformative.

Moderate-to-severe traumatic brain injury (msTBI) is recognized as a chronic and evolving neurological condition characterized by progressive structural brain changes and persistent cognitive impairment. While prior studies have demonstrated widespread atrophy following msTBI, less is known regarding the longitudinal trajectory of gray matter (GM) changes during recovery and post-rehabilitation. The current study used longitudinal voxel-based morphometry (VBM) to characterize GM volume changes over a period of 9 months, in individuals with msTBI relative to healthy controls (HC). Associations between regional GM volume and neuropsychological functioning were examined. Twenty-eight participants (14 msTBI, 14 HC) completed MRI and neuropsychological assessments across three timepoints spanning outpatient rehabilitation and follow-up. Longitudinal VBM analyses revealed significant group and time interactions within subcortical and limbic regions. Relative to HC, individuals with msTBI showed lower GM volume in these regions at baseline, with trajectories that converged toward HC values (right hippocampus) or increased relative to HC over the rehabilitation period (bilateral pulvinar), whereas the right amygdala and inferior cerebellar vermis remained persistently reduced. Significant longitudinal improvements in memory and psychomotor speed during the rehabilitation period were demonstrated in msTBI. Greater (preserved) GM volume within the right hippocampus, thalamus, and bilateral pulvinar was associated with better performance across measures of verbal memory, processing speed, executive functioning, and cognitive flexibility. These findings suggest that msTBI is associated with dynamic structural brain changes involving subcortical, limbic, and cerebellar networks, and that the rehabilitation period was accompanied by relative volumetric stabilization in these regions and by meaningful cognitive improvement.

Disruptions in somatosensory processing have been observed in cognitive impairment (CI), suggesting that alterations in sensory processing may emerge earlier during cognitive decline than previously recognized. Somatosensory gating (SG) is an automatic inhibitory mechanism that protects neural resources by suppressing responses to redundant, non-behaviorally relevant stimuli. Prior work has demonstrated exaggerated gamma SG and response amplitudes in the primary somatosensory cortex (S1) of individuals with Alzheimer's disease-confirmed pathology, and these effects were masked by variability in attention/executive function performance. However, whether similar relationships are present during earlier stages of cognitive decline, such as CI, remains unclear. Herein, 63 cognitively healthy older adults (CH; mean age = 59.9 {+/-} 8.6 years) and 32 individuals with CI (mean age = 62.4 {+/-} 8.8 years) underwent magnetoencephalography (MEG) while completing a paired-pulse SG paradigm designed to probe inhibitory sensory processing. MEG oscillatory responses were source-imaged using a beamformer. Time series data were extracted from the peak voxel to quantify oscillatory dynamics and SG. Neuropsychological testing was conducted to assess attention/executive function. After controlling for attention/executive function variance, exaggerated gamma SG was observed in adults with CI compared with CH adults (p < 0.05). Additionally, adults with CI exhibited increased beta peak frequency following the second stimulation (p < 0.01) and a group-by-age interaction for theta SG in S1 (p < 0.05). Together, these results suggest somatosensory abnormalities are present in earlier stages of cognitive decline and highlight a dynamic interaction between sensory processing and cognitive systems during this decline.

Objectives: Non-invasive brain stimulation (NIBS) holds significant promise for treating neurological and neuropsychiatric conditions, yet translation into routine clinical practice remains limited. We aimed to explore stakeholder perceptions of NIBS and barriers to its clinical adoption. Methods: We conducted focus-group interviews with 33 participants across three key stakeholder groups in the UK: (1) people with lived experience of brain injury, depression, or dementia; (2) healthcare professionals; and (3) researchers. Reflexive thematic analysis was used to identify themes in the data. Findings: Seven key themes emerged spanning preferences, hope and disappointment, communication, accessibility, infrastructure, ethical/regulatory uncertainty, and the evidence base. Across groups, NIBS was viewed positively and with cautious optimism, but substantial barriers were highlighted, including limited public and clinical awareness, challenges in demonstrating cost-effectiveness, infrastructure constraints, and difficulties navigating regulatory and translational pathways. Participants emphasised the importance of clear communication, improved education, and stronger interdisciplinary collaboration to support adoption. Notably, stakeholders prioritised evidence of clinical efficacy and usability over detailed mechanistic understanding. Conclusions: These findings provide actionable insights into the translational gap in NIBS and highlight priorities for facilitating its integration into clinical care.

SignificancefNIRS is highly suitable for the study of reading development, however, the reliability of its signals is not well understood during reading tasks. AimTherefore, this study assessed the test-retest reliability of the fNIRS signal during a common event-related reading paradigm. ApproachEnglish-speaking adults (n = 30) completed a lexical decision task during fNIRS recording twice, one week apart. ResultsOur results demonstrated contrast effects partially consistent with prior neuroimaging literature, insofar as for each contrast, at least one predicted region of interest was activated. However, we did not identify significant activation in all predicted brain areas. Regarding group level test-retest reliability, we observed poor reliability across predicted brain regions for most conditions, with the exception of fair test-retest reliability in the left posterior temporal lobe for coarse lexical tuning. At the single-subject level, test-retest reliability ranged from poor to excellent across subjects, but was poor for most subjects. ConclusionThese results suggest fNIRS can detect changes in brain activation during a fast event-related reading task at the group level. However, reliability does not appear sufficient to interpret individual-level data. Further research should explore reliability across a wider range of designs to assess the generalizability of these findings.

Can computational analysis of a brief voice recording classify depression-related anhedonia as effectively as task-based fMRI? We address this question through a pre-registered cross-modal benchmarking study (osf.io/bsvrj) that evaluates two independent classification pipelines against depression-related anhedonia operationalized via self-report. Anhedonia-- the diminished capacity to experience pleasure or motivation to pursue rewards--is a transdiagnostic marker of reward-system dysfunction that predicts treatment resistance in major depressive disorder, yet current assessment requires either expensive functional neuroimaging or subjective self-report scales, neither of which scales to routine screening. We benchmarked two independent classification pipelines: Stream A extracted 88 acoustic prosody features from DAIC-WOZ clinical interviews (n = 142) using the ClinicalWhisper pipeline (Whisper Large-v3, pyannote diarization, OpenSMILE eGeMAPS v02); Stream B extracted nucleus accumbens BOLD activation during a reward task from the UCLA ds000030 dataset (n = 272). Three classifiers (logistic regression, random forest, gradient-boosted trees) were evaluated under stratified 5-fold cross-validation with fixed, pre-registered hyperparameters. Stream A achieved a best AUC-ROC of 0.63 (random forest; permutation p = .049), confirming H1 that acoustic prosody classifies PHQ-8-defined anhedonia above chance at = .05 (uncorrected), though this result does not survive Bonferroni correction for three classifiers (/3 = .017). Bootstrap analysis of {Delta}AUC confirmed non-inferiority relative to Stream B (H2: 95% CI lower bound > -0.10). However, Stream B itself did not achieve above-chance classification (p = .057), so this non-inferiority finding reflects comparable, modest performance across both modalities rather than equivalence to a validated neural biomarker. Pitch variability features (F0) ranked among the top 5 predictors by SHAP value in the gradient-boosted trees model (H3: partially supported). An exploratory combined model (eGeMAPS + recurrence quantification analysis) reached AUC = 0.65 (GBT; p = .032), though this lift was not statistically significant by DeLong test (z = -0.44, p = .66). These results provide initial evidence that vocal prosody, acquired through a standardized, open-source pipeline, carries depression-related information comparable to fMRI-derived ventral striatal activation for binary classification. However, neither streams operationalization isolates anhedonia from general depressive symptomatology, and the cross-modal design compares different constructs across different cohorts. We refer to the classification target throughout as the "anhedonia composite" to acknowledge that PHQ-8 Items 1+2 conflate anhedonia with dysphoria. We discuss these constraints and their implications for the causal framework motivating this work.

Transcranial magnetic stimulation (TMS) is widely used in both clinical and research settings to study and treat neurological and neuropsychiatric disorders, yet its underlying neural mechanisms remain unclear; particularly how stimulation influences both local and distant regional activity in relation to behavior. In this study, we combined single-pulse TMS with concurrent whole-head functional near-infrared spectroscopy (fNIRS) to examine hemodynamic and behavioral responses during a working memory task, with a focus on behavioral variability. Single TMS pulses were delivered to the left dorsolateral prefrontal cortex (DLPFC) while healthy participants rested; additionally, single pulses were delivered online to the left DLPFC while participants performed the working memory task. Across all participants, we observed a reliable load-dependent increase in hemodynamic activity associated with task. However, behavioral responses to TMS varied during the task. When participants were stratified into subgroups based on performance, a distinct topographic pattern emerged. During the task, TMS systematically modulated hemodynamic responses in regions including DLPFC, superior medial gyrus, precuneus, and parietal lobule, which are areas belonging to the default mode network. Moreover, the hemodynamic response during the single pulse alone sessions without any task was also found to be associated with the behavioral responses in a coherent pattern involving DLPFC, precuneus and parietal lobules. These findings suggest that variable behavioral outcomes during online TMS task are linked to distinct hemodynamic responses in a topographic pattern of local and distant regional areas.

Fast periodic visual stimulation (FPVS) has recently demonstrated an ability to index multiple cognitive domains such as facial expression processing, working memory, and more recently, semantic categorization in just a few minutes of recording time. The present work investigates the effects of low semantic distance and how this modulates semantic categorization responses. Twenty-seven healthy young adults completed an FPVS oddball paradigm in order to determine whether comparing fruits and vegetables of the same color would elicit semantic categorization responses. Strong oddball responses were observed up to the 10th frequency harmonic, and responses showed statistically significant right occipito-temporal lateralization that was not present in a low-level color change condition completed by participants and is opposite the pattern observed in recent word-based semantic categorization FPVS studies. The findings suggest that image-based FPVS paradigms should be investigated further as candidate tools to study conditions that affect semantic categorization such as Alzheimers disease.

Background Chemotherapy-related cognitive impairment is a well-documented concern among cancer survivors, yet the neural mechanisms underlying deficits in cognitive control remain poorly understood. This study examined group differences in brain activation during a flanker task using functional MRI (fMRI) between chemotherapy-exposed participants and healthy controls. Methods Participants (21 survivors (24.9 years old; 71.4 % female; 15 years from diagnosis) and 21 healthy controls (26.7 years old; 61.9 % female) completed a flanker task during fMRI, with congruent and incongruent conditions. Reaction time, accuracy, and Flanker scores were collected. Whole-brain group comparisons were performed for congruent, incongruent, and incongruent > congruent contrasts. Associations between the incongruent > congruent contrast and cognitive performance were examined. Results Compared to controls, the Chemo group had longer reaction times in both congruent and incongruent conditions (p < .001) and lower NIH Flanker scores (p = .01), with no differences in accuracy. They showed reduced activation in the bilateral inferior frontal gyri, supplementary motor area, and bilateral caudate, but greater activation in the right inferior temporal and cerebellar regions. The incongruent > congruent contrast correlated with increased activation in the orbitofrontal cortex, inferior temporal gyri, and fusiform gyrus with cognitive performance. Conclusions Chemotherapy-exposed participants showed cognitive control deficits and altered neural activation during a flanker task, indicating disrupted recruitment of frontoparietal and subcortical regions key for conflict processing. These findings improve understanding of neural causes of chemotherapy-related cognitive impairment and may help identify at-risk survivors and guide personalized rehabilitation.

Background. Scores on neuropsychological assessments are typically corrected for the influences of age, education, and gender (AEG). However, other demographic factors, such as crystallized ability and race/ethnicity, independently affect test performance. As a result, standard scores systematically over- or under-classify impairment in patients whose demographic profile differs from that of the reference population. Methods. We developed a Comprehensive (C-) model scoring algorithm that added vocabulary, age-squared, race/ethnicity, Latino background, a coarse socioeconomic status proxy, computer use, and daily prescription medications to the standard AEG predictor pool. The model was developed using data from 1,914 community-dwelling adults assessed with the California Cognitive Assessment Battery (CCAB; Woods et al., 2024). For each of 118 individual cognitive measures, stability-selection LASSO identified robust predictors in 300 random 80/20 splits retained at >=80% frequency and then estimated mean coefficients and confidence intervals in 1,000 bootstrap OLS samples. Cross-sample frozen-coefficient validation was used to evaluate scoring model generalization in two subgroups: Group 1 (n = 1,033, older, first enrolled cohort) and Group 2 (n = 881, a recently recruited younger cohort). Results. Stability selection retained a mean of 2.81 predictors per measure (range 1-6). Compared to the AEG model, the C-model approximately doubled variance explained (r2 = 0.50 vs 0.25; mean across cognitive domains r2 = 0.32 vs 0.18) and outperformed AEG in 98.8% of individual measures with non-trivial demographic signal. Racial disparities in MCI classification (the bottom-7th-percentile) were substantially reduced: Black-vs-White ratios fell from 5.6 (AEG) to 1.8 (C). Conversely, sensitivity was improved in individuals with elevated premorbid function: MCI classification ratios in low-vs-high vocabulary quartiles fell from 11.3 to 2.1. AIC favored the C-model in 88.1% of measures (mean delta-AIC = -167), ruling out overfitting. Frozen-coefficient validation preserved the C-model's r2 advantage in every cognitive domain. Conclusions. By correcting scores for race, premorbid cognitive functioning (vocabulary), and other demographic predictors, the C-model explains substantially more variance than the AEG model, reduces racial bias, and increases sensitivity to cognitive decline in high-functioning participants. C and AEG models can be used in parallel: model concordance increases diagnostic confidence, while disagreement carries diagnostic information.

Objective: This computational modeling study quantified the influence of sex and race-related cranial anatomy on predicted brain-wide current flow during electroconvulsive therapy (ECT) across conventional (bifrontal (BF), bitemporal/bilateral (BL), right unilateral (RUL)) and experimental (focal electrically administered seizure therapy (FEAST) and frontomedial (FM)) electrode montages. The objective was to determine whether race-associated variability meaningfully contributes to differences in ECT stimulation metrics across montages. Methods: Finite element head models of Chinese, Black, and Caucasian subjects were developed using high-resolution magnetic resonance imaging and analyzed using the Realistic vOlumetric- Approach-based Stimulator for Transcranial electric stimulation (ROAST) pipeline (N = 150 total; n = 50 per cohort, comprising 25 M and 25 F, age range: 20-30 years). Five ECT montages were simulated under a constant-current condition (900mA). Stimulation strength (Ebrain/Eth) was quantified as 90th percentile of brain-wide E-field magnitude (Ebrain) relative to neuronal activation threshold (Eth = 0.25 V/cm) quantified stimulation strength. Overall focality was evaluated as a percentage of brain volume stimulated above the neural activation threshold (Ebrain [&ge;] Eth), while laterality was quantified as the median right-to-left hemispheric E-field magnitude ratio. The effects of race, sex, and montage on stimulation strength, focality, and hemispheric laterality were statistically analyzed. Results: Substantial race- and sex-related differences observed in cranial anatomy resulted in systematic variation in predicted ECT-induced E-field intensity. Brain-wide E-field magnitude varied by both race and montage, with the largest fields generally observed in Caucasian head models and during BL stimulation. Montage exerted the strongest effect on stimulation strength (Ebrain/Eth) with BL and FEAST producing the highest stimulation strengths, followed by RUL and FM, while BF produced the lowest. Caucasian subjects generally predicted higher stimulation strengths than Black and Chinese subjects, whereas females predicted modestly higher stimulation strengths than males. Laterality was primarily determined by montage, with FEAST producing the greatest hemispheric asymmetry, followed by RUL. Chinese subjects demonstrated higher laterality ratios than both Black and Caucasian subjects. BL, RUL, and FEAST stimulated substantially larger brain volumes above neural activation threshold (less focal stimulation) than BF. Lower focality was observed in Caucasian subjects relative to Black and Chinese subjects, and in females relative to males. Conclusions: Electrode montage was the primary determinant of predicted ECT stimulation strength, focality, and laterality. Race-related anatomical differences and, to a lesser extent, sex-related differences systematically altered stimulation patterns, supporting consideration of individualized anatomy in ECT dosing and treatment optimization.

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.

Compared to traditional gross volumetrics, surface- based models provide greater spatial precision for understanding brain alterations related to developmental, neurological, and psychiatric disorders. Large-scale brain initiatives are combining data from around the world to discover and improve illness- related brain markers. Here, we present a toolkit for 3D brain geometry analysis aimed at addressing key challenges facing large- scale neuroimaging studies. Our framework incorporates scalable methods for multisite data integration, site-specific confound correction, accelerated statistical modeling, interpretable machine learning, and interactive results visualization. The toolkit was tested on data from 21 independently collected study samples participating in the ENIGMA Bipolar Disorder Working Group (N = 3,373). Compared to traditional volume features, we show how subcortical shape measures can be combined across study sites to capture spatially complex differences between diagnostic groups and associations with common treatments. Statistical modeling was accelerated using the Fast and Efficient Mixed- Effects Algorithm (FEMA) and achieved a 16-fold reduction in computation time compared to traditional approaches. Machine learning models showed shape features may provide greater predictive performance over traditional volumes for both diagnostic and treatment prediction tasks, with interpretable weight maps providing insights into the local features driving model performance.

Background: Choroid plexus (CP) volume is an emerging magnetic resonance imaging (MRI) biomarker in various disorders of the central nervous system (CNS). However, clinical translation is hindered by methodological heterogeneity and inconsistent anatomical coverage. Double inversion recovery (DIR) - a sequence providing dual-tissue suppression - is a promising candidate to improve CP segmentation. Methods: The dataset included 93 scans across healthy subjects and individuals with multiple sclerosis (MS), divided into a training set (n = 63), an internal test set (n = 20), and an external test set (n = 10). First, relative CP signal intensity and tissue contrast ratios on DIR were compared against fluid-attenuated inversion recovery (FLAIR) and T1-weighted (T1w) sequences (pre- and post-contrast). Reproducibility of manual CP segmentations was assessed via intraclass correlation coefficients (ICCs). Subsequently, we developed a 3D nnU-Net model for CP segmentation based on manually labeled DIR masks. Model performance was evaluated against manual segmentation using spatial overlap and volumetric error metrics. Finally, we compared our DIR-based model against three publicly available T1w- or FLAIR-based tools by assessing slice-wise volume distributions and voxel-wise density maps. Results: DIR demonstrated the highest CP signal intensity and most consistent tissue contrast among evaluated MRI sequences (p < 0.001). Intra- and inter-rater agreement for manual CP segmentations was robust (ICC = 0.92 and 0.83, respectively). The trained nnU-Net achieved high internal accuracy (Dice = 0.82) independent of scanner, diagnosis, or absolute CP volume, and generalized well to the external test set (Dice = 0.75). Compared to public T1w- and FLAIR-based models, DIR-based approaches (nnU-Net and manual) yielded significantly larger CP volumes (p < 0.01). Axial volume distribution analysis attributed this difference to a distinct bimodal profile in DIR segmentations, more fully capturing the CP inside the temporal horn of the lateral ventricle (p < 0.001 against T1w- and FLAIR-based models). Conclusions: By leveraging the superior tissue contrast of DIR, our nnU-Net model achieves highly accurate CP segmentation that generalizes across scanners and captures the inferior extent of the C-shaped structure often missed by conventional models. This may improve standardization of CP volumetry and allow for more reliable studies in CNS disorders.

Purpose: To develop and evaluate an anatomy-aware deep learning framework for enhancement of neonatal 64mT T2-weighted MRI that improves anatomical visibility while preserving native ultra-low-field contrast and enabling quantitative structural analysis. Methods: A multitask network, jointly performing image enhancement and tissue segmentation, was trained on 75 and evaluated on 20 paired neonatal 64mT/3T MRI datasets spanning a broad range of gestational ages and pathologies. To preserve native 64mT contrast, 3T images were locally harmonized before training. The framework also generated quality-control maps and regional volumetric measurements. Volumetric agreement was further assessed in 40 paired term-born control datasets. Results: Enhanced 64mT images showed improved image quality metrics and better delineation of cortical, deep gray matter, ventricular, white matter, and posterior fossa structures while maintaining native contrast characteristics. Tissue segmentations demonstrated good agreement with reference 3T labels. Volumetric measurements showed excellent correspondence with 3T across major tissue compartments, with only small systematic regional biases. Conclusions: Anatomy-aware enhancement enables automated tissue segmentation and volumetric analysis directly from neonatal 64mT MRI while preserving native image contrast. These findings support the feasibility of quantitative neonatal neuroimaging at ultra-low field.

Attention-Deficit/Hyperactivity Disorder (ADHD) is often accompanied by attentional challenges, sleep difficulties and increased reports of daytime sleepiness, indicating potential links between attention and arousal mechanisms. Electroencephalographic (EEG) studies have shown that sleep-like wake slow wave (SW) activity during wakefulness, characterised by reduced cortical activity, corresponds with periods of inattention in both ADHD and neurotypical populations. However, it remains unclear whether group differences in waking SWs are consistent across different types of sustained attention tasks or may be context-dependent. The present study compared sustained attention performance and wake SW activity between adults with and without ADHD during a continuous visual target detection task. EEG data were collected while adults with (n = 52) and without ADHD (n = 49) completed a sustained attention paradigm. The task presented a continuous stream of rotating (anti-clockwise or clockwise) black-and-white checkerboard stimuli, and participants were required to detect infrequent targets that were marginally longer in duration than non-targets. Behavioural and neural measures (power spectra, steady-state visually evoked potentials (SSVEP), event-related potentials and SW density during wake) were analysed. No significant group differences were observed for task performance and wake SW activity. However, electrophysiological analyses revealed the ADHD group showed reduced P300, elevated beta-band power, and lower SSVEPs to target stimuli compared with the neurotypical group. These neural differences in the context of comparable performance suggest compensatory cortical recruitment in ADHD. The absence of SW differences further supports this, suggesting comparable performance and maintained arousal may reflect successful neural compensation in ADHD in this cognitive task. Significance StatementIndividuals with ADHD commonly experience attention differences, but emerging evidence suggests these difficulties may depend on task context. Using clinical diagnostic interviews, medication washout, and Bayesian statistics, we found strong evidence for comparable performance between ADHD and neurotypical adults on a sustained attention task. Notably, slow wave activity during wakefulness did not differ between groups, consistent with maintained arousal. Nevertheless, differences in brain activity, including altered target processing, increased cortical activation and reduced visual entrainment, suggested compensatory neural recruitment in ADHD. This context-dependent pattern has important theoretical and clinical implications: identifying task features that challenge or support performance in ADHD can reveal how individuals compensate neurally, and guide interventions that leverage task features to support attention in ADHD.

PURPOSE: The UK7T travelling head dataset was used to characterise the reproducibility of 7T measurements of the susceptibility of the hippocampal subfields, focusing on the Cornu Ammonis (CA1, CA2 and CA3), dentate gyrus (DG), subiculum (SUB), tail of the hippocampus (TAIL) and entorhinal cortex (ERC). METHODS: Susceptibility maps were created from whole-brain 3D single-echo GRE data (TE=20 ms; 0.7 mm isotropic resolution) using Multi-Scale Dipole Inversion. Automatic Segmentation of Hippocampal Subfields (ASHS) was applied to high resolution T1- and T2-weighted images for segmentation. The mean magnetic susceptibility and volume of hippocampal subfields was evaluated in 50 data sets, comprising 5 repeat acquisitions on 10 healthy participants (age 32 + or -6 years; 3 female). RESULTS: Averaging over subjects, susceptibility values spanned an 18ppb range over the hippocampus (ranging from -13.3ppb in DG to 4.7ppb in ERC). Susceptibility values in the larger hippocampal subfields showed a consistent pattern of variation across subjects, being generally more positive in ERC and SUB than in CA1 and more positive in CA1 than in DG and TAIL. The standard deviation of subfield susceptibilities over subjects ranged from 8.2ppb in the TAIL to 1.7ppb in CA1, and the average standard deviation across repeated measurements, which ranges from 1.7 to 4 ppb, was less than half of the inter-participant standard deviation in all subfields. Susceptibility values in the smaller subfields (CA2 and CA3) were more variable, but ICC(2,k) values for all subfields were >0.82. CONCLUSION: The reported data characterises the variation and reproducibility of hippocampal subfield susceptibility measurements at 7T.