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.

A central priority in stroke recovery research is the development of useful biomarkers that can reveal underlying disease states to aid diagnosis, prognosis, stratification, and treatment, ideally using tools that are readily available in clinical settings. Large-scale neural slowing has the potential to be such a biomarker, but uncertainty about its nature and underlying causes currently limits its usefulness. In this work, we sought to address these gaps by parameterizing abnormal resting state electroencephalography (EEG) spectral features across the scalp, and investigating their relationship to thalamic atrophy and dysfunction, using structural magnetic resonance imaging (MRI) and a computational model of corticothalamic circuit dynamics, respectively. As predicted, stroke patients (n=25) exhibited widespread spectral abnormalities, including significantly increased aperiodic exponent and offset, lower alpha frequency, and reduced beta power, which together can account for the frequently observed shift in power towards low frequencies after stroke. Furthermore, corticothalamic models fit to power spectra across the scalp inferred broad thalamic disinhibition. Crucially, these abnormalities (except beta reductions) were strongly predicted by ipsilesional thalamic atrophy measured with MRI, despite the lack of direct thalamus damage in this sample. Together, these findings highlight secondary thalamic injury as an important consequence of stroke and potential cause of widespread neural dysfunction, helping to clarify the underlying causes of post-stroke neural slowing, and demonstrating its feasibility as a clinically-accessible biomarker.

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

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.

Background: Brain age gap (BAG) is increased in multiple sclerosis (MS), but whether it reflects microstructural pathology beyond conventional atrophy remains unclear. Objective: To test whether BAG is elevated in MS and correlates with conventional and diffusion tensor imaging (DTI) abnormalities relative to healthy controls. Methods: A case-control study of 43 people with MS and 18 healthy controls was performed. BAG was estimated from T1-weighted MRI using brainageR. Controls were used as MRI reference distributions. MRI values were expressed as deviation z-scores and correlated with BAG within MS. Conventional MRI and DTI domains were analysed using age/sex-adjusted partial correlations with domain-wise Benjamini-Hochberg FDR correction, where appropriate. Results: BAG was higher in MS than controls (4.79 vs -2.58 years; p<0.001; Cohen's d=0.84). Within MS, BAG correlated with EDSS (partial r=0.38, p=0.014), disease duration (r=0.39, p=0.011), and lesion volume (r=0.67, p<0.001). Control-referenced conventional MRI abnormalities correlated strongly with BAG, including lower peripheral grey matter volume (r=-0.71, q<0.001), higher CSF volume (r=0.69, q<0.001), and lower grey matter volume (r=-0.67, q<0.001). DTI associations were robust, including higher NAWM mean diffusivity (r=0.66, q<0.001), higher radial diffusivity (r=0.65, q<0.001), and lower fractional anisotropy (r=-0.52, q<0.001). Conclusions: BAG was elevated in MS and correlated with clinical severity, conventional MRI abnormality, and DTI-derived microstructural injury. These findings support BAG as a biologically relevant MS phenotype extending beyond volumetric atrophy.

Introduction- Choroid plexus (CP) enlargement on brain MRI has been identified as an emerging neuroinflammatory biomarker in multiple sclerosis (MS), yet its relationship to downstream parenchymal neurochemical abnormalities remains unknown. Proton magnetic resonance spectroscopy (1H MRS) enables non-invasive in vivo quantification of neurometabolites, making it well-suited to probe downstream consequences of CP pathology in MS. Methods- Ultra-high-field 7T 1H MRS was performed in 45 people with MS (pwMS) (28 Relapsing Remitting MS, RRMS; 17 Progressive MS, PMS) and 43 age- and sex-matched healthy controls (HCs) in the posterior cingulate cortex (PCC) and centrum semiovale white matter (CSWM). CP volume, EDSS, and MS Functional Composite measures were also acquired. Group differences in metabolite concentrations were evaluated using Mann-Whitney U tests with correction for multiple comparisons, and associations between CP volume, altered metabolites, and clinical disability and functional measures were investigated. Results- Myo-inositol (mI) was significantly elevated and total N-acetylaspartate was reduced in both MS subtypes, in the CSWM. In PMS, CP volume was positively associated with CSWM mI/total creatine (tCr) ({rho} = 0.63, p = 0.008), an association absent in RRMS. Across the combined MS cohort, CP volume correlated significantly with EDSS ({rho} = 0.40, p = 0.006). Conclusions- WM mI/tCr was elevated and tNAA/tCr was reduced across MS phenotypes compared with controls, reflecting a dual metabolic signature consistent with concurrent glial overactivation and neuroaxonal compromise. Increased CP volume was associated with greater neurological disability across MS phenotypes. The association of CP enlargement with CSWM mI/tCr in PMS suggests a potential link between CP-mediated periventricular inflammation and progressive WM glial pathology. Collectively, these findings support CP volume as a clinically relevant, non-invasive biomarker and restoring CP integrity as a potential therapeutic target in PMS, where effective treatments remain limited.

Background: Cognitive impairment in Parkinson's disease (PD) is linked to degeneration of the cholinergic basal forebrain, particularly cholinergic nucleus 4 (Ch4) in the nucleus basalis of Meynert. Structural and diffusion MRI separately detect this degeneration, but few studies have combined these modalities across the PD cognitive spectrum. Methods: We analyzed 92 participants: 14 healthy controls (HC), 35 PD with normal cognition (PD-NC), 33 with mild cognitive impairment (PD-MCI), and 10 with dementia (PDD). For Ch4 and cholinergic nuclei 1, 2, and 3 (Ch1-3) in the medial septal/diagonal band complex, we determined TIV-normalized gray matter density (GMD) and free-water (FW) fraction. We evaluated group differences, cognitive correlations, adjusted multivariable regression, and exploratory ROC discrimination. Results: Ch4 GMD was significantly lower in PDD compared to PD-MCI (p=0.007), PD-NC (p<0.001), and HC (p<0.001). Ch4 GMD was also lower in PD-MCI versus HC (p=0.028); the PD-MCI versus PD-NC difference was not significant after correction (p=0.074). Ch1-3 GMD was lower in PDD versus PD-NC (p=0.008) and HC (p=0.009). Ch4 and Ch1-3 FW were elevated in PDD versus all other groups (all p<0.01). Among PD patients (n=78), MoCA was positively correlated with Ch4 GMD ({rho}=0.49) and Ch1-3 GMD ({rho}=0.42) and negatively correlated with Ch4 FW ({rho}=-0.51) and Ch1-3 FW ({rho}=-0.40; all p<0.001). In the full four-metric model, Ch4 GMD and Ch4 FW were the only independent basal forebrain predictors (Ch4 GMD {beta}=+2.04, p<0.001; Ch4 FW {beta}=-1.46, p=0.005) of MoCA score. The combined Ch4 GMD + Ch4 FW model showed high discrimination for PDD versus non-demented PD (AUC=0.934; optimism-corrected AUC=0.925). Conclusions: Structural and free-water diffusion MRI provide complementary information about Ch4 degeneration in PD. The combined Ch4 model showed promising exploratory discrimination of PDD; validation in larger independent samples is needed.

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. 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: 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.

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age {+/-} SD: 48 {+/-} 13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated. DSC scores with pULF reference WML masks from PLAn-FL (DSC mean {+/-} SD: 0.50 {+/-} 0.24) outperformed MIMoSA (0.24 {+/-} 0.20, p < 0.0001), WMH-SynthSeg (0.30 {+/-} 0.18, p < 0.0001), nnU-Net-FL (0.41 {+/-} 0.24, p < 0.0001), and nnU-Net-FL/T1 (0.41 {+/-} 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores. nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI's mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

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: 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.

Alzheimer's disease (AD) is clinically marked by difficulty retaining newly learned information, yet routine memory scores often conflate poor initial encoding with failure to stabilise information after encoding. This ambiguity limits the mechanistic interpretability of cognitive assessment during the transition from mild cognitive impairment to AD. Here we propose a Hippocampal Cortical Consolidation Bottleneck (HCCB) model to computationally separate these two components of new memory failure. The model represents newly presented information as a rapidly formed hippocampal trace and a slowly stabilised cortical trace, predicting a residual bottleneck when delayed recall falls below the level expected from immediate recall. We operationalised this prediction as Consolidation Bottleneck Index*(CBI*), a cognitively normal reference normalised residual index, and evaluated it using Alzheimer's Disease Neuroimaging Initiative (ADNI) cognitive and MRI data, with independent dynamical support from OpenNeuro EEG. Simulations showed recent memory vulnerability when hippocampal vulnerability exceeded cortical vulnerability. In ADNI, CBI* increased from cognitively normal participants to mild cognitive impairment nonconverters, reached Alzheimer like levels in mild cognitive impairment converters, and was associated with hippocampal atrophy. CBI* added minimal discrimination beyond established clinical and structural predictors, supporting its role as a mechanistic phenotype rather than a replacement prognostic model. OpenNeuro EEG further showed increased neurodynamic rigidity in AD. Our findings provide a computational framework for quantifying failed stabilisation of newly encoded information in AD progression.

Focal cortical dysplasia (FCD) is a principal cause of pharmacoresistant focal epilepsy, yet its structural MRI signature, subtle cortical thickening, blurring of the gray-white matter junction, is frequently undetected even by experienced neuroradiologists, delaying or precluding referral for curative surgical resection. Here we develop a machine learning pipeline for FCD detection that prioritizes mechanistic interpretability over model complexity. In a subsample of 50 subjects (25 FCD, 25 age-matched controls) drawn from a public structural MRI cohort, we register all scans to a common stereotactic template and derive hemispheric asymmetry features across 48 cortical regions, exploiting the characteristic unilaterality of FCD pathology. Among four classifiers evaluated under leave-one-out cross-validation, an L1-regularized logistic regression achieves the highest accuracy (78\%, permutation p=0.02), substantially outperforming tree-based ensembles, which perform at or below chance in this feature-to-sample regime. The fitted model selects a sparse subset of 21 of 96 features, with the largest-magnitude contributions localized to inferior and middle frontal gyri and temporal pole and superior temporal gyrus, regions consistent with the known anatomical distribution of FCD. These findings indicate that hemispheric asymmetry, combined with a sufficiently regularized, interpretable classifier, captures a modest but statistically robust and anatomically grounded signal for FCD detection, offering a transparent complement to existing deep learning approaches for presurgical evaluation.

Comprehensive biomarkers of multiple sclerosis (MS) capable of simultaneously diagnosing the condition, capturing symptom severity and predicting treatment efficacy remain elusive. Although several studies have highlighted the pivotal role played by demyelinating lesions in determining MS structural pathology, their relationship with symptom severity is limited. Here, we combined personalized computational brain modeling with magnetoencephalography (MEG) recordings from 17 MS patients and 20 healthy controls (CTR) to derive personalized brain network excitability parameters, which we tested as MS biomarkers. Personalized parameters discriminated between CTR and MS participants with high accuracy, also classifying between progressing and remitting MS patients. Notably, they also predicted MS clinical scales across multiple domains. In all clinical tasks, personalized parameters consistently outperformed standard clinical measures and total lesion loads. Together, these results highlight the potential of personalized brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying MS subtypes and predicting symptom severity. d brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying between MS subtypes and predicting the severity of symptomatology.