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[This corrects the article DOI: 10.3389/fnhum.2025.1598598.].

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Growing evidence suggests that corticospinal tract (CST) damage and microstructural integrity are key predictors of post-stroke motor impairment. However, their combined clinical utility-particularly in CST sub-pathways originating from non-primary motor cortical areas-remains underexplored. This study aimed to determine whether microstructural integrity and lesion load (LL) of each CST sub-pathway at 2 weeks predict motor outcomes at 2, 6, and 12 weeks post-stroke. Fifty seven participants completed motor and neuroimaging evaluations at 2 weeks post-stroke and underwent follow-up motor assessments at 6 ( = 37) and 12 weeks ( = 34). The integrity of the CSTs was quantified using diffusion spectrum imaging (DSI), while CST-LL was measured using structural magnetic resonance imaging, both based on the sensorimotor area tract template atlas. Stepwise multiple linear regression models were used to assess the predictive value of CST microstructural integrity and CST-LL in each sub-pathway at 2 weeks for motor function at 2, 6, and 12 weeks post-stroke. The results indicated CST integrity and CST-LL were both the main determinants of motor deficit at 2 weeks post-stroke. Specifically, the integrity of CSTs from the primary motor cortex (M1), reflected by fractional anisotropy, emerged as a significant predictor of post-stroke motor deficit at 2 weeks, whereas CST integrity from the dorsal premotor cortex (PMd), reflected by generalized fractional anisotropy, quantitative anisotropy, and radial diffusivity. CST-LL originating from non-M1 motor areas, such as primary sensory cortex (S1), were also the main determinants for motor impairment at 2 weeks post-stroke. However, compared to CST integrity, CST-LL from non-M1 motor areas, including both the PMd and S1, were more dominant predictors, explaining 68.3% ( = 0.683, < 0.001) and 79.5% ( = 0.795, < 0.001) of the variance in motor outcomes at 6 and 12 weeks. The microstructural integrity of the PMd tracts and CST-LL from the non-M1 motor areas may be promising biomarker for post-stroke motor impairment. These findings highlight the pivotal role of non-M1 tracts in post-stroke motor function, particularly the PMd tracts, as a potential intervention target to enhance motor recovery.

The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Despite the critical importance of close replications in strengthening and advancing scientific knowledge, there are inherent challenges to conducting replications of lesion-based studies. In the present study, we conducted a close conceptual replication of a study (i.e., Hope et al., 2016) that found that fluency and naming scores in post-stoke aphasia were more strongly associated with a binary measure of structural white matter integrity (tract disconnection) than a graded measure (lesion load). Using a different sample of stroke patients (N = 128) and four language deficit measures (aphasia severity, picture naming, and composite scores for speech production and semantic cognition), we examined tract disconnection and lesion load in three white matter tracts that have been implicated in language processing: arcuate fasciculus, uncinate fasciculus, and inferior fronto-occipital fasciculus. We did not find any consistent evidence that binary tract disconnection was more strongly associated with language impairment over and above lesion load, though individual deficit measures differed with respect to whether lesion load or tract disconnection was the stronger predictor. Given the mixed findings, we suggest caution when using such indirect estimates of structural white matter integrity, and direct individual measurements (for example, using diffusion weighted imaging) should be preferred when they are available. We end by highlighting the complex nature of replication in lesion-based studies and offer some potential solutions.

ObjectiveThe putative effect of lesion-induced brain damage on post-stroke upper limb motor impairment can be estimated by overlaying a patient's lesion or its surrogate with key motor areas. We assessed the predictive value of imaging-based brain damage measures for cross-sectional upper limb motor impairment and subsequent upper limb motor outcome after stroke.MethodsIn 47 stroke patients, upper limb motor impairment was evaluated with the Upper-Extremity Fugl-Meyer Assessment (UE-FMA) at 2 weeks (2W) and 3 months (3M) post-stroke. Given each patient’s lesion identified at 2W, we considered the disconnectome, estimated as an ensemble of structural and functional connections passing through the lesion, as a surrogate of the lesion. The lesion load and the disconnectome load were measured by overlaying the lesion and disconnectome with the corticospinal tract (CST) and motor cortex (MC), and their association with the UE-FMA score at 2W and 3M was assessed.ResultsWhereas the disconnectome loads on the CST and MC were better in predicting the UE-FMA score at 2W, the lesion load on the CST was better in predicting the UE-FMA score at 3M. Furthermore, when the CST lesion load was combined with the UE-FMA score at 2W, the UE-FMA score at 3M was better predicted, with smaller generalization error, than by using either measure alone.ConclusionsThe combination of the CST lesion load and baseline upper limb motor impairment would provide a tailored fusion of imaging and clinical measures for more accurate motor outcome prediction.

While the impact of multiple sclerosis (MS) on various cognitive functions has been well documented, social memory - memory of other individuals - remains unexplored. In this study, 26 MS patients and 23 healthy controls underwent task-based fMRI during a social navigation paradigm simulating interpersonal interactions, followed by an episodic social memory recall questionnaire. T1-weighted, T2-FLAIR and diffusion tensor imaging were also acquired. As social navigation metrics have been associated with activity in the precuneus/posterior cingulate cortex (PPCC), we examined this region’s functional and volumetric correlates, as well as T2-FLAIR hyperintense white matter lesion burden and microstructural abnormalities in normal-appearing white matter. MS patients showed preserved social navigation but impaired episodic social memory ( p  = 0.003). No task-based functional differences were observed between groups. However, impaired recall in MS was associated with reduced right PPCC volume ( p  = 0.032), greater volume ( p  = 0.002) and number ( p  = 0.017) of T2-FLAIR hyperintense lesions, and altered integrity of normal-appearing white matter, indexed by lower fractional anisotropy ( p  < 0.001) and higher mean diffusivity ( p  = 0.019). These findings suggest a novel cognitive deficit in MS and underscore the potential relevance of regional brain volume and white matter pathology to social memory, informing future studies of social cognition in MS and other neuropsychiatric disorders.

BackgroundIn relapsing–remitting multiple sclerosis (RRMS) the connection between cognitive impairment (CI) and white matter lesion load (WM-LL) and location is still unclear. This study aimed to identify the relationship between CI in RRMS patients and WM-LL and locations using a fully automated platform. CI and WM-LL were evaluated in 90 patients with RRMS using the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and Automated MRI volumetric measures of WM-LL and lesion distribution. Regression analysis of BICAMS as a dependent variable with different clinical and radiological parameters was performed.ResultsData were obtained from 90 patients with RRMS who had a mean age of 32.74 ± 8.43 years and a female-to-male ratio of 3:1. The mean (± SD) cognitive rating scores for the BICAMS subtests were 28.07 ± 11.78 for the Symbol Digit Modalities Test (SDMT), 42.32 ± 12.46 for the California Verbal Learning Test-II (CVLT-II), and 16.13 ± 8.17 for the Brief Visuospatial Memory Test-Revised (BVMT-R). According to the BICAMS criteria, 29 cases (32.2%) had CI. BICAMS scores were significantly correlated with age, education level, relapse frequency, disease duration, and time to start disease-modifying therapies. Whole WM-LL and periventricular lesion load were significantly associated with CI. After controlling for age, sex, and education, logistic regression analysis revealed that total WM-LL was the best predictor for CI together with duration of illness and years of education. The cut-off value of 12.85 cc for total WM-LL predicted CI.ConclusionsWhole WM-LL and periventricular lesion load are the best anatomical predictors for CI probably due to the effect on the anterior commissural fibers while years of education and duration of disease are the best demographic predictors for CI.

BackgroundCognitive deficits are common in early multiple sclerosis (MS), however, spatial navigation changes and their associations with brain pathology remain poorly understood.ObjectiveTo characterize the profile of spatial navigation changes in two main navigational strategies, egocentric (self-centred) and allocentric (world-centred), and their associations with demyelinating and neurodegenerative changes in early MS.MethodsParticipants with early MS after the first clinical event (n = 51) and age-, gender- and education-matched controls (n = 42) underwent spatial navigation testing in a real-space human analogue of the Morris water maze task, comprehensive neuropsychological assessment, and MRI brain scan with voxel-based morphometry and volumetric analyses.ResultsThe early MS group had lower performance in the egocentric (p = 0.010), allocentric (p = 0.004) and allocentric-delayed (p = 0.038) navigation tasks controlling for age, gender and education. Based on the applied criteria, lower spatial navigation performance was present in 26–29 and 33–41% of the participants with early MS in the egocentric and the allocentric task, respectively. Larger lesion load volume in cortical, subcortical and cerebellar regions (ß ≥ 0.29; p ≤ 0.032) unlike brain atrophy was associated with less accurate allocentric navigation performance.ConclusionLower spatial navigation performance is present in up to 41% of the participants with early MS. Demyelinating lesions in early MS may disrupt neural network forming the basis of allocentric navigation.

Background: Understanding long-term disability in multiple sclerosis (MS) is a key goal of research; it is relevant to how we monitor and treat the disease. Objectives: The Magnetic Imaging in MS (MAGNIMS) collaborative group sought to determine the relationship of brain lesion load, and brain and spinal cord atrophy, with physical disability in patients with long-established MS. Methods: Patients had a magnetic resonance imaging (MRI) scan of their brain and spinal cord, from which we determined brain grey (GMF) and white matter (WMF) fractional volumes, upper cervical spinal cord cross-sectional area (UCCA) and brain T2-lesion volume (T2LV). We assessed patient disability using the Expanded Disability Status Scale (EDSS). We analysed associations between EDSS and MRI measures, using two regression models (dividing cohort by EDSS into two and four sub-groups). Results: In the binary model, UCCA (p < 0.01) and T2LV (p = 0.02) were independently associated with the requirement of a walking aid. In the four-category model UCCA (p < 0.01), T2LV (p = 0.02) and GMF (p = 0.04) were independently associated with disability. Conclusions: Long-term physical disability was independently linked with atrophy of the spinal cord and brain T2 lesion load, and less consistently, with brain grey matter atrophy. Combinations of spinal cord and brain MRI measures may be required to capture clinically-relevant information in people with MS of long disease duration.