NeuroImage: Clinical

White matter hyperintensities (WMH) are a highly prevalent finding on FLAIR MRI scans and a prominent feature of white matter pathology across cerebrovascular and neurodegenerative diseases. Currently, WMH are assessed with visual rating scales such as the Fazekas scale or with their volume, as calculated from automatic or manual segmentations. Both methods have limitations: Visual rating scales are rater-dependent and coarse, while WMH volume does not take the confluence of lesions into account and thus disregards their spatial organisation. As an alternative, here we propose a novel automated method for quantifying the confluence of white matter hyperintensities on a continuous standardised scale between 0 and 1. The metric is based on WMH segmentations from routine MRI and quantifies the extent to which individual WMH merge into coherent lesions, independently of total lesion volume. We apply the method to QMIN-MC, a large UK memory clinic cohort, and show associations of the confluence metric with age, cognitive performance across domains, and Fazekas ratings. Participants with vascular and mixed dementia showed higher confluence than other diagnostic groups, whereas cognitively unimpaired participants showed lower confluence. However, confluence did not explain additional cognitive variance after accounting for log-transformed WMH volume. Findings were validated in DELCODE, an independent cohort of individuals with neurodegenerative disorders, replicating our original results. In this validation cohort, periventricular WMH confluence remained associated with cognition after adjustment for WMH volume. These findings introduce WMH confluence as a reproducible, automated, and fine-grained measure of lesion spatial organisation. It provides complementary information about morphological WMH severity beyond volume and is an alternative to visual rating scales. Although related to WMH volume in memory-clinic populations, confluence captures clinically interpretable information and may complement existing WMH measures for improved lesion characterisation in studies of white matter disease, ageing, and cognitive impairment.

Background: Quantitative susceptibility mapping (QSM) exhibits phase cancellation between paramagnetic (e.g. iron) and diamagnetic (e.g. myelin/protein) sources, obscuring microstructural lesion heterogeneity in multiple sclerosis (MS). We evaluated whether biophysical {chi}-separation identifies clinically meaningful lesion phenotypes and whether reduced echo sampling preserves microstructural sensitivity. Methods: In this cross-sectional study of 183 people with multiple sclerosis (PwMS), we analyzed 3327 quality-controlled white matter lesions using 3T MRI with a five-echo gradient-recalled echo sequence (GRE5) and a subsampled two-echo dataset (GRE2). A hybrid framework combining unsupervised clustering on whole-lesion paramagnetic ({chi}para) and diamagnetic ({chi}dia) features with rule-based compartmental edge-core gradient assignment resolved MS lesion phenotypes. Intralesional microstructural properties were evaluated using linear mixed-effects models. Multivariable linear regressions assessed associations with physical disability (Expanded Disability Status Scale, EDSS; n = 158), cognitive processing speed (Symbol Digit Modalities Test, SDMT; n = 79), and disease duration (DD; n = 161), with sensitivity models adjusting for total lesion volume, count, or DD. Results: The framework identified four reproducible lesion phenotypes (Adjusted Rand Index = 0.924): Type 1 (22.7%; preserved {chi}dia), Type 2 (32.9%; high {chi}para without positive edge-core gradient), Type 3 (35.8%; attenuated {chi}dia), and Type 4 (8.6%; high {chi}para with a positive edge-core paramagnetic gradient). Higher EDSS was selectively associated with more negative signed {chi}dia within Type 4 lesions (whole: {beta} = -0.37, q = 0.020; core: {beta} = -0.37, q = 0.006; edge: {beta} = -0.34, q = 0.030) and Type 2 edge ({beta} = -0.33, q = 0.044). Type 4 core associations remained robust across sensitivity models adjusting for total lesion volume ({beta} = -0.35, q = 0.004), total lesion count ({beta} = -0.37, q = 0.008), and DD ({beta} = -0.32, q = 0.033). Abbreviated GRE2 attenuated Type 4 compartmental associations to non-significance (all q greater than 0.05; core: {beta} = -0.20, q = 0.215). Conclusions: Quantitative {chi}-separation identified four internally stable susceptibility-defined MS lesion phenotypes, revealing disability-associated tissue heterogeneity beyond total lesion burden. Higher EDSS was associated with more negative diamagnetic measures within Type 4 cores after adjustment for lesion burden, pointing toward complex intralesional non-myelin tissue remodeling or reactive astrogliosis. Two-echo subsampling may influence compartment-specific clinical associations. By uncoupling susceptibility sources, {chi}-separation provides a quantitative framework for characterizing disability-associated lesion heterogeneity.

Background: Neuromelanin-MRI enables in vivo assessment of the substantia nigra (SN) and locus coeruleus (LC) in individuals with Parkinson's disease (PD), yet longitudinal studies rely on cross-sectional processing that may introduce measurement variability and confound estimates of change over time. Objectives: In this paper, a longitudinal neuromelanin-MRI processing framework is presented that is designed and validated to improve measurement stability and reduce processing-related variability across repeated scans. Methods: Imaging and clinical data from the Quebec Parkinson Network were analyzed in 268 participants (199 PD, 69 controls), including a longitudinal subset of 74 participants (49 PD, 25 controls) scanned approximately one year apart. Validation experiments evaluated slice-by-slice intensity normalization for slice dependent intensity variation, bias field correction for LC signal asymmetry, and the effects of longitudinal registration on measurement stability and PD-control discrimination. Results: Slice-by-slice intensity normalization significantly reduced brainstem intensity variability by 3.6%. A systematic leftward signal asymmetry was observed in the LC and persisted following N4 bias field correction, suggesting a scanner-related effect not captured by conventional bias field modeling. Longitudinal registration reduced annualized change variability by 25-36% for SN_CR and 27-34% for LC_CR metrics in controls, indicating improved within-subject measurement stability. Residual variability was also reduced for contrast-based metrics by up to 28%. Longitudinal registration generally produced larger PD-control effect sizes at baseline and follow-up, particularly for SN volume metrics. However, no significant method x group x time interactions were observed, indicating that estimated longitudinal trajectories did not differ significantly between longitudinal and conventional cross-sectional processing. Conclusions: Longitudinal registration reduced technical variability and improved the precision of NM-MRI measurements. Although it did not significantly enhance detection of longitudinal PD-control differences over the follow-up interval examined here, it provides a more robust framework for longitudinal NM-MRI studies and may improve sensitivity to subtler biological effects in future investigations.

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.

White matter hyperintensities on brain magnetic resonance imaging are established markers of cerebral small vessel disease and typically associate inversely with brain volume in the general population, reflecting irreversible tissue loss. In Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy, a monogenic small vessel disease caused by NOTCH3 cysteine-altering mutations, white matter hyperintensities may instead reflect tissue water accumulation and perivascular space dilation rather than net structural damage. Whether this distinct pathophysiological relationship is detectable at population level among carriers of NOTCH3 cysteine-altering variants without clinically manifest disease remains unknown. We conducted a cross-sectional neuroimaging study using UK Biobank data acquired between 2014 and 2023. Among 44,943 participants with complete brain magnetic resonance imaging data (mean age 64 years +/- 8 years), 96 carried heterozygous NOTCH3 cysteine-altering variants within epidermal growth factor-like repeat domains 7 to 34 and 44,847 were non-carriers. White matter hyperintensity volume was automatically quantified and normalized to total intracranial volume. Brain parenchymal fraction was defined as combined gray and white matter volumes divided by total intracranial volume. Lacunes were manually assessed in all carriers. Linear regression and generalized additive models examined the association between white matter hyperintensity volume and brain parenchymal fraction stratified by genotype, adjusting for age, sex, lacune count, and vascular risk factors. NOTCH3 variant carriers showed markedly higher normalized white matter hyperintensity volume than non-carriers (0.83% versus 0.36%; P<10^-8) but only marginally higher brain parenchymal fraction (81.1% versus 80.5%; P=0.03). In non-carriers, white matter hyperintensity volume was inversely associated with brain parenchymal fraction (beta=-0.116% per 1% increase; P=3.10^-6). In contrast, NOTCH3 variant carriers demonstrated a positive association (beta=+0.51%; P=0.019), strengthened after adjusting for lacune count (beta=+0.61%; P=0.006). A significant genotype-specific interaction confirmed this divergence (P=0.036), persisting across age groups and after vascular risk factor adjustment. NOTCH3 cysteine-altering variant carriers identified at population level exhibit a fundamentally distinct white matter hyperintensity-brain volume relationship compared with non-carriers, consistent with lesions reflecting tissue water accumulation rather than irreversible structural loss at this early, predominantly preclinical disease stage. These findings establish that genetic context modifies neuroimaging biomarker interpretation in cerebral small vessel disease and underscore the need for genotype-aware approaches when evaluating white matter hyperintensities as markers of disease severity or progression.

Background. Dynamic susceptibility contrast MRI with capillary-function post-processing exports a relative maximum cerebral metabolic rate of oxygen, formed from blood flow and a transit-time-derived extraction term. The share each contributes to an observed contrast is unquantified. Methods. In a retrospective single-centre cohort with untreated glioblastoma, six perfusion maps normalised to normal-appearing white matter were sampled in automatically segmented enhancing tumour and peritumoral brain. The paired compartment contrast in the oxygen-metabolism index was partitioned into flow, extraction and residual terms and examined against tumour-core volume. Results. Of 131 patients, 122 were analysable. Flow-linked maps were about twice as high in enhancing tumour, the transit and extraction maps only modestly (all q < 0.05). Flow accounted for 92.6% (95% CI 85.9-98.8) of the contrast and extraction for 6.6% (0.7-12.9). Across volume tertiles the flow share rose from 67.8% to 104.0%, a gradient arising peritumorally: every map changed with volume there, none in enhancing tumour. Conclusion. The compartment contrast in the oxygen-metabolism index is largely accounted for by blood flow and varies with lesion size, that dependence originating peritumorally. It should be read within the complete perfusion panel, not as independent metabolic evidence.

Background: Corticobasal syndrome (CBS) is a neurodegenerative syndrome that often results from a 4-repeat tauopathy. Abnormalities on tau PET, structural MRI, and diffusion MRI (dMRI) have been observed in CBS, although no prior work has systematically compared the relative sensitivity of these modalities. In addition, the relationships between tau uptake, volume loss, and white matter degeneration remain incompletely understood. Objective: To compare the sensitivity of tau PET, structural volume, and dMRI metrics to differentiate CBS from controls, and to characterize relationships between regional tau uptake, volume loss, and white matter tract abnormalities. Methods: Thirty-two participants meeting criteria for possible or probable CBS and 35 healthy controls underwent standardized 3T dMRI, 18F-flortaucipir tau PET, and detailed neurologic assessment. Diffusion data were processed using complementary diffusion tensor, free-water, and Neurite Orientation Dispersion and Density Imaging (NODDI) pipelines, with tract-level metrics extracted using the Johns Hopkins University-Eve (JHU EVE) White Matter atlas. Regional gray matter volumes and flortaucipir standardized uptake value ratios (SUVRs) were calculated. Global tau PET uptake, represented by the first principal component (PC1), was removed in a secondary tau analysis. Modality-level discrimination was assessed using area under the curve (AUC) analyses. Cross-modality relationships were evaluated using regional correlations and covariate-adjusted models comparing volume- and tau-related predictors of white matter abnormalities. Results: Relative to controls, CBS showed widespread higher mean diffusivity (MD) and lower intracellular volume fraction (ICVF), especially in sensorimotor and projection white matter pathways. Structural volume reductions were most prominent in precentral cortex and subcortical regions including putamen, thalamus, and pallidum, whereas tau PET abnormalities were weaker and less spatially extensive. MD showed the strongest overall discrimination between CBS and controls, followed by ICVF, structural volume, PC1-removed tau PET, and original tau PET. In targeted sensorimotor analyses, higher DTI-MD was most strongly associated with lower gray matter volume. PC1-removed sensorimotor tau uptake was also associated with higher sensorimotor DTI-MD, whereas original tau uptake and broader cortical or subcortical tau measures were not significant predictors. Conclusions: White matter microstructural disruption is a dominant imaging signature of CBS and is more closely linked to gray matter volume loss than to measurable uptake on tau PET. dMRI, particularly MD and ICVF, may provide greatest sensitivity for detecting disease-related changes in CBS.

Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.

Background and purpose: Gliomas are characterised by a complex tumour microenvironment (TME) that contributes to treatment resistance and tumour heterogeneity. Therefore, the non-invasive interrogation of both the intracellular and extracellular compartments of gliomas remains a key unmet need. We investigated the feasibility and complementarity of combining diffusion-weighted MRI (DW-MRI) with biophysical modelling Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours (VERDICT) and diffusion-weighted MR spectroscopy (DW MRS) for simultaneous characterisation of glioma tumour cells and the glioma TME. Methods: 14 patients with newly diagnosed glioma (WHO grades 2 to 4: 4 IDH wildtype and 10 IDH mutant) underwent DW-MRI at 3 T; DW-MRS was additionally acquired in 10 patients. Tumours were automatically segmented into enhancing, non-enhancing, and oedema regions using a validated pipeline. VERDICT models were fitted to multi-shell DW-MRI data to estimate intracellular volume fraction (fIC), cell radius, extracellular diffusivities, and free-water fraction (fFW). Single voxel DW-MRS provided metabolite-specific apparent diffusion coefficients (ADCs) for total N-acetylaspartate (tNAA), Creatine (tCr), and choline (tCho). T-tests assessed DW-MRI and descriptive statistics assessed DW-MRS parameters in tumour regions compared to normal appearing white matter (NAWM) and IDH mutation status. Pearsons correlations assessed associations between DW-MRS metabolite ADCs and DW-MRI parameters. Results: VERDICT-MRI distinguished high grade IDH-wildtype from lower grade IDH-mutant gliomas, with significantly higher fIC and lower extracellular diffusivities; in enhancing and non-enhancing regions of IDH-wildtype lesions. DW-MRS demonstrated a trend towards reduction in tNAA ADC in tumour versus contralateral NAWM, consistent with neuronal loss, and a trend towards increased tCho ADC, suggesting glial activation. A descriptive trend towards decreased tNAA ADC in IDH-wildtype tumours was observed. Significant positive correlations were identified between tumoral tCho ADC and VERDICT parameters fEES, extracellular diffusivity; and negative correlations for ADC and fFW, in non-enhancing tumour regions. Conclusion: This proof-of-concept study demonstrates the feasibility of combining multi b- value DW-MRI and DW-MRS within a clinically feasible protocol to simultaneously probe the extracellular and intracellular compartments of the glioma TME. VERDICT captured cell-level and extracellular matrix differences in IDH mutation status, while DW-MRS provided metabolite-specific indices of neuronal and glial compartment integrity. The correlation between tCho ADC and VERDICT metrics in infiltrative tumour regions supports the complementarity of these modalities. With this combined approach, it is possible to simultaneously characterise the tumour compartment and the tumour microenvironment in gliomas.

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.

Focal lesions are common after complicated mild-to-severe traumatic brain injury (cmsTBI) in children, yet it is unclear whether the location of focal damage relates to cognitive outcome beyond overall lesion burden. Combining 13 international cohorts through the ENIGMA Pediatric cmsTBI working group, we conducted voxelwise lesion-symptom mapping (VLSM) to test whether focal lesion location relates to cognitive outcome, and how this compares with overall lesion volume. From 357 patients with imaging that passed quality assurance, 67 had a focal lesion visible on T1-weighted MRI along with at least one performance-based cognitive outcome (D-KEFS Trail Making conditions and WISC/WAIS Coding). We tested whether lesion location related to cognitive outcome using VLSM, adjusting for age, sex, and time since injury, and separately tested total lesion volume as a predictor of each outcome. Lesions in the right middle frontal gyrus were associated with poorer performance on number sequencing (D-KEFS Trail Making condition 2), a timed visuomotor sequencing task (false discovery rate corrected). In separate regression analyses, greater lesion volume was associated with poorer number-letter switching and motor speed. The switching association remained significant after adjusting for injury severity, whereas the motor-speed association did not. In the largest lesion sample assembled in pediatric cmsTBI to date, focal lesion location and injury burden index separable contributions to cognitive outcome, with number sequencing linked to focal prefrontal damage and switching and motor speed to overall lesion volume, underscoring the value of precise lesion characterization for large-scale multi-site neuroimaging.

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.

Paramagnetic rim lesions are a subset of focal white matter lesions specific to multiple sclerosis that are chronically inflamed and are associated with increased tissue injury, brain atrophy, and clinical disability. The molecular mechanisms linking paramagnetic rim lesions to these progressive biological outcomes remain unclear. Glutamatergic dysregulation has been hypothesized as a mechanism of multiple sclerosis progression potentially via excitotoxicity, but lesion-specific involvement is unknown. Here, 7T MRI was used to investigate glutamate-related metabolic alterations in paramagnetic rim lesions. Glutamate-weighted chemical exchange saturation transfer, together with T1 mapping and quantitative susceptibility mapping, was evaluated across paramagnetic rim lesions, non-paramagnetic rim lesions, and normal-appearing tissues in participants with multiple sclerosis (n=20) and healthy controls (n=11). Glutamate-weighted chemical exchange saturation transfer contrast was significantly higher in paramagnetic rim lesions compared to non- paramagnetic rim lesions (+10.7%) and normal-appearing white matter (+13%), while no differences were observed in normal-appearing tissue between multiple sclerosis and healthy controls. Additionally, reduced glutamate-weighted chemical exchange saturation transfer contrast in normal-appearing tissues was associated with worse motor and dexterity performance, linking observed metabolic abnormalities to clinical disability. These results identify a distinct metabolic phenotype of paramagnetic rim lesions marked by elevated glutamate-weighted signal consistent with localized excitotoxic stress. This work also implicates lesion-specific glutamatergic dysregulation in paramagnetic rim lesion-related neurodegeneration and demonstrates the potential of metabolic MRI to probe pathogenic mechanisms in multiple sclerosis.

Background: MRI has increasingly been explored as a biomarker for detecting structural and functional brain changes. For clinical decision-making, it is crucial to validate observed changes in MRI indices at the individual level. The uncertainty in longitudinal MRI indices can be quantified using the repeatability coefficient (RC). Methods: Twenty healthy controls (10 males, 10 females) underwent two test-retest sessions of structural magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) on the same day, separated by a 30-minute interval. RC values and their 95% confidence intervals (CI) were estimated for subcortical volumes, cortical thickness, and within-network functional connectivity. Additionally, 33 patients with mental health disorders underwent MRI before and after 20 sessions of transcranial magnetic stimulation (TMS). Percentage changes in MRI-derived indices were assessed at the individual level, with changes exceeding the RC threshold considered indicative of true change beyond measurement uncertainty. Results: The RC showed measurement variability in subcortical volumetric in the range of 8% to 17.5% for caudate and left amygdala, respectively. For cortical thickness, the RC was measured between 3.5% and 16.5% for the left occipital pole and the left temporal pole, respectively. The RC% for fractional anisotropy (FA) measures were variable between 11.3% (the left middle cingulum) and 62.9% (the right anterior cingulum). For within-network connectivity, the RC was measured in a range of 9.7% and 29.4% for sensorimotor and visual networks, respectively. TMS-treated patients exhibited no changes beyond the RC in almost all subcortical volumes and within-network connectivity. The most frequent changes beyond the uncertainty were observed in FA measures, particularly in the posterior cingulum, where 17 out of 23 patients exhibited clinically meaningful alterations. Conclusion: Structural brain features extracted from MRI demonstrated high reliability. Among all measures, FA, reflecting white matter integrity, was most sensitive in detecting neural changes following TMS, highlighting its potential utility as a treatment-responsive biomarker.

Background and Objective: For their acceptance as endpoints in clinical trials, biomarkers and clinical outcome assessment (COA) measures should show reliability by being reproducible (precise) and responsive to change. We evaluated precision and responsiveness of MRI-derived brain volume metrics and various COAs in ceroid-lipofuscinosis, neuronal-3 (CLN3) disease. Methods: This longitudinal observational study (NCT03307304) included 44 individuals with CLN3-related disease (ages 0.5-29.1 years, 48% female). Brain volumetric measures were computed using SynthSeg-v2.0 from T1-weighted 3D MPRAGE images acquired at 3T (Median [Q1, Q3] total MRI follow-up duration from baseline: 2.04 [0.00, 3.69] years). Compartmental tissue volumes were normalized to a model-adjusted total intracranial volume and scaled to the tissue-specific volume fraction expected in healthy adolescent brains to extract measures of brain atrophy. COAs included the Vineland Adaptive Behavior Scales, verbal IQ, and the Unified Batten Disease Rating Scale (UBDRS). Analysis of linear mixed-effects models included only CLN3 participants with syndromic presentation, having three or more visits per measurement to estimate slope, its relative standard error (RSE), and the minimum inter-visit interval (Tmin) for statistically significant progression for each participant. Tmin to detect progression in 75% of the population (Tmin,75%) was computed for each metric. Results: Among brain volumetric measures, brain parenchyma volume (BPV) and gray matter volume (GMV) were the most precise and responsive metrics. BPV showed an overall intra-individual decline with age of -1.32%/year (95% CI: -1.64% to -0.99%), low RSE of 12.5%, and short Tmin,75% of 0.85 years (95% CI: 0.47-1.24). Amongst COAs, the UBDRS Clinical Global Impression (CGI) score was the most precise and responsive with an overall intra-individual increase of 1.26 units/year (95% CI: 1.03-1.49), RSE of 24.10%, and Tmin,75% of 2.56 years (95% CI: 1.73-3.46), though with lower precision (RSE higher) and lower responsiveness (Tmin,75% longer) than BPV. Discussion: MRI-derived metrics, in particular BPV and GMV, detected change in brain atrophy with high precision and responsiveness suggesting that they would be informative for evaluating potential neuroprotective therapies in CLN3 disease. These findings support incorporating MRI-derived volumetric measurements alongside clinical assessments in future clinical trials in CLN3.

Importance: Brain MRIs are routinely acquired following moderate or severe traumatic brain injury (TBI) because of their high sensitivity for detecting traumatic brain lesions. However, there is no standard framework for their interpretation, and their clinical utility is unproven. Objective: Here, we test whether MRI-based quantification of traumatic brain lesions improves outcome prediction relative to the best tools currently available to clinicians. Design, Setting and Participants: We analyzed hemorrhagic contusions and axonal injury in four prospective studies of moderate or severe TBI, validating results in a fifth held-out dataset. Exposure: Contusion volume was measured via manual segmentation. Axonal injury volume was quantified with diffusion tensor imaging, by measuring the volume of skeletonized fractional anisotropy significantly lower than that of healthy controls. Main Outcomes and Measures: Functional outcomes were assessed at 6 months with the Glasgow Outcome Scale-Extended; values [&le;] 4 were considered unfavorable. Results: Among 201 patients with MRI (mean age: 41, median Glasgow Coma Scale (GCS) score on admission: 7, MRI obtained median 14 days post-injury), MRI-measured contusion volume discriminated outcomes better than CT Marshall score (AUC 0.81 vs 0.64; p=0.001), and remained associated with unfavorable outcomes controlling for multiple commonly-measured clinical variables. Quantitative axonal injury volume discriminated outcomes better than manual MRI grading (AUC 0.82 vs 0.62; p<0.001) and remained associated with unfavorable outcomes independent of other clinical predictors. The prognostic discrimination of traumatic lesion volumes was not improved by accounting for the location or normative connectivity of either lesion type. In a multivariate model, contusion and axonal injury volumes were independently predictive, with age-dependent volume thresholds for 90% probability of unfavorable outcome, and strong discrimination in an external cohort (N=27; AUC 0.83, p<0.001). Replacing all CT-based measures with MRI lesion volumes in the IMPACT model significantly improved out-of-sample prognostic performance (AUC 0.85 vs 0.64; p<0.05). Conclusions and Relevance: MRI-based lesion quantification improves prognostic discrimination relative to measures currently available to clinicians treating patients with TBI. Global functioning at 6-months is driven more by the volume than the location or connectivity of traumatic lesions. These findings support integrating MRI-based lesion quantification into clinical workflows and updated TBI classification frameworks.

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.

Multiple sclerosis (MS) white matter lesion (WML) automated segmentation on ultra-high-field 7T MRI remains challenging due to the domain shift from lower-field acquisitions, limited annotated data, and specific imaging artifacts. Fine-tuning is an effective and practical strategy for adapting deep learning WML segmentation algorithms to 7T MRI, even with limited annotated data. This study evaluates fine-tuning as a domain adaptation strategy to leverage a pre-trained deep learning model for automated WML segmentation on 7T MRI. We fine-tuned a U-Net-based model, originally trained on approximately 35,000 heterogeneous lower-field (1T, 1.5T, 3T) multi-contrast MRI scans of people with MS for T2-hyperintense WML (T2-WML) segmentation, using a 7T dataset. Multiple approaches were evaluated, including standard fine-tuning on 3D FLAIR images, low-rank adaptation (LoRA) and training from scratch (nnU-Net). Models were evaluated on an external multi-center 7T test dataset. Additionally, a separate model was fine-tuned for T1-hypointense WML (T1-WML) segmentation on 7T MP2RAGE images. The original model showed substantial performance degradation on 7T data compared to 3T (Dice score decreased from 0.69 to 0.31), confirming the need for domain adaptation. Fine-tuning markedly improved T2-WML segmentation, with the fine-tuned model achieving a median Dice score of 0.57. Lesion-wise sensitivity and F1-score were 0.78 and 0.75, respectively, and the lesion volume agreement with manual segmentation was 0.93. For T1-hypointense WML, fine-tuning showed lower lesion detection performance compared to the T2-hyperintense WML segmentation (sensitivity and F1 of 0.58 and 0.50, respectively); however, incorporating multi-center data into the training set substantially reduced false positives by 23% and improved lesion detection by 14%. Multi-center fine-tuning further improved performance, particularly for the more challenging task of T1-WML segmentation. The models presented here may be included in MS research workflows to facilitate multi-center collaborations with 7T MRI.

Gambling disorder (GD) is a behavioural addiction that may help identify addiction-related neural features without the direct neurobiological effects of a primary substance of dependence. We examined regional grey matter volume (GMV) and resting-state functional connectivity (rsFC) in the same well-characterised sample. Eighteen men with GD and 21 matched healthy controls underwent high-resolution structural and resting-state functional MRI. GMV was quantified across 214 cortical and subcortical regions, and seed-based rsFC analyses focused on striatal subdivisions and mesocorticolimbic regions. Group differences were evaluated using permutation testing and cluster-corrected mixed-effects modelling. GD was associated with lower GMV in the ventromedial prefrontal cortex, orbitofrontal regions and other cortical and subcortical areas, alongside higher GMV in a subset of limbic and default-mode regions. Participants with GD also showed lower connectivity between the limbic striatum and the hippocampus, thalamus and putamen. In exploratory analyses, somatomotor connectivity was positively associated with gambling severity (Problem Gambling Severity Index: Spearman's rho = 0.71, p = 0.003, false-discovery-rate-adjusted q = 0.016). Structural and functional findings overlapped spatially in regions associated with valuation, memory, reward and habit formation, but regional GMV did not mediate group differences in rsFC. These findings are broadly consistent with corticostriatal models of GD and identify candidate circuit-level differences for independent replication. Larger, more diverse and longitudinal samples are required to establish their reproducibility, temporal direction and clinical relevance.

Disorders of consciousness (DoC) are characterised by marked reductions in cerebral glucose metabolism, but the specific cellular and neurochemical processes driving this impairment remain unclear. To elucidate these mechanisms, a multimodal imaging approach combining [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance spectroscopy (MRS) was applied to the posterior medial cortex (PMC) in 15 DoC patients and 14 healthy controls. Regional FDG metabolic index (FDG-MI) values and MRS concentration estimates of glutamate plus glutamine (Glx), {gamma}-aminobutyric acid (GABA+), total N-acetylaspartate (tNAA), total creatine (tCr), and total choline (tCho) were compared between groups and evaluated against Coma Recovery Scale-Revised (CRS-R) scores at baseline and follow-up (mean 127 days). Patients demonstrated reductions within the PMC in Glx, tCr, and tNAA concentrations, as well as a reduced Glx/GABA+ ratio. Local FDG-MI was substantially reduced in patients and correlated positively with tNAA and tCr. Furthermore, baseline tCho concentrations positively predicted long-term clinical recovery, as indexed by changes in CRS-R scores at follow-up. These findings suggest that macroscopic glucose hypometabolism in DoC may be linked to disrupted intracellular mitochondrial energy production pathways and a shift towards relative synaptic inhibition in the PMC. Moreover, the association between choline levels and behavioural recovery may point to the relevance of retained structural synthesis capacity for functional outcomes.