Korhan Buyukturkoglu

Background: Pregnancy is a distinct neuroendocrine and immunological state in women with multiple sclerosis (MS). Relapse activity typically decreases during gestation and increases after delivery, but the relationship between peri-pregnancy brain volumetric measures and disability remains unclear. Methods: This retrospective longitudinal pilot study included 14 women with relapsing-remitting MS who underwent MRI examinations before pregnancy and after delivery. Thirty-eight MRI scans were analyzed, with a mean of 2.71 ± 0.99 timepoints per patient. Global and regional brain volumes were measured using the longitudinal FreeSurfer pipeline. Choroid plexus (CP) volume was evaluated as an exploratory neuroimmune imaging marker. Associations between volumetric measures and Expanded Disability Status Scale (EDSS) scores were assessed using linear mixed-effects modeling with forward stepwise selection. Results: The final EDSS model retained bilateral ventral diencephalon (ventral DC) volumes, left amygdala volume, right pallidum volume, left nucleus accumbens volume, and cerebral cortex volume. The most prominent association involved the left ventral DC, where lower volume was associated with higher EDSS scores (p = 0.0008). CP volume showed no significant longitudinal change across peri-pregnancy MRI timepoints (p = 0.21) and was not retained in the final EDSS model. Conclusion: Regional brain volumetric measures, particularly ventral DC volumes, were associated with disability across the peri-pregnancy period despite low EDSS levels. To our knowledge, this is the first longitudinal evaluation of CP volume across the peri-pregnancy period in women with MS; no significant change was observed over time. These exploratory findings require confirmation in larger prospective studies using standardized serial MRI protocols.

Background: Language dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with multiple sclerosis (MS). Objectives: To establish a network-level model of language dysfunction in MS. Methods: Cognitive data and 3T structural and functional brain magnetic resonance imaging (MRI) were acquired from 54 MS patients and 54 matched healthy controls (HCs). Summary measures (anteriority, segregation, betweenness, withinness) of the extended language network (ELN) were calculated, and structural imaging metrics were derived. Group differences in ELN connectivity were evaluated. Associations between ELN connectivity and language performance were assessed; in the MS group, dimensionality reduction was used to assess relationships between language performance and multimodal functional, structural, and diffusion-based neuroimaging features derived from ELN-related areas. Results: The MS group performed worse on semantic fluency and rapid automized naming tests ( p < 0.005) compared with HCs. Regarding ELN measures, the MS group exhibited higher within-ELN connectivity than HCs ( p < 0.05). Principal component analysis (PCA) yielded a multimodal latent component that uniquely correlated with language performance in the MS group ( p < 0.05). Conclusion: We identified network-level functional and structural measures to potentially characterize language dysfunction in MS. Further studies leveraging these features may reveal mechanisms and predictors of language dysfunction specific to MS.

Background and Purpose: Radiomics extracts imaging features that may not be detectable through conventional volumetric analyses. Given their role in multiple sclerosis (MS), we applied radiomics to thalamic nuclei and examined their associations with cognitive performance. Methods: A total of 601 individuals were included (342 people with MS [PwMS] from two cohorts and 259 healthy controls [HC]). Radiomic features (RF) and volumes were extracted from the whole thalamus, five thalamic nuclei, and the putamen segmented on three‐dimensional T1‐weighted images. Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT) and Paced Auditory Serial Addition Test (PASAT) in PwMS and the Digit Symbol Substitution Test (DSST) in HC. In the first MS cohort, multivariate linear regression in a discovery set ( N = 103) identified thalamus‐derived RF associated with SDMT, which were retested in a replication set ( N = 63). Their associations with PASAT in a second MS cohort ( N = 176) and DSST in HC were also evaluated. We then tested whether the same RFs, when extracted from the putamen, was associated with SDMT. Least Absolute Shrinkage and Selection Operator (LASSO) models assessed the combined predictive value of RF and volumes. Results: Twenty‐eight RF–region of interest (ROI) pairs were associated with SDMT in the replication set (false discovery rate [FDR] < 0.05). Of these, 24 were also associated with PASAT (FDR ≤ 0.03), and 2 with DSST. Only ventral nuclei volume showed replicated associations among volumetrics. Only four putamen‐derived pairs were associated with SDMT (FDR = 0.04). LASSO results confirmed RF outperformed volumes. Conclusion: RF extracted from the thalamus is strongly associated with cognitive performance in PwMS, outperforming volumetric measures and supporting their potential as sensitive imaging biomarkers.

Background: Memory decline is common in multiple sclerosis (MS), although pathophysiological mechanisms are not fully understood. Objective: The objective was to investigate the relationship of changes in structural and functional neuroimaging markers to memory decline over 3-year follow-up. Methods: Participants with MS underwent cognitive evaluation and structural, diffusion, and functional 3T magnetic resonance imaging (MRI) scans at baseline and 3-year follow-up. Changes in neuroimaging metrics from baseline to follow-up were compared between memory stable and memory decline groups. Our hypothesis that structural and functional connectivity changes mediate the association of atrophy to memory decline was tested. Results: A total of 249 MS patients completed baseline visit; 169 (67.8%) returned at 3-year follow-up. Based on ⩾10% decline, memory decline was observed in 44.4% ( n = 75). Those with memory decline showed marginally greater whole-brain volume loss over time compared with those with stable memory performance ( p = 0.08). In those with memory decline, changes in white matter tract integrity were related to regional cortical thinning ( p < 0.01). Exploratory mediation analysis revealed structural and functional connectivity to mediate the relationship of atrophy to verbal and visual memory decline. Conclusion: Further work is needed to characterize complex interrelationships of atrophy and structural/functional connectivity changes to memory decline in MS.

Background: Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported predictive external validity, i.e., relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationships, using the Symbol Digit Modalities Test (SDMT), the most reliable and sensitive cognitive measure in MS, measured from patients seen via telehealth versus in-person. Methods: SDMT was administered to individuals with MS either in-person (N=60, mean age=39.7) or remotely via video conference (N=51, mean age=47.4). Magnetic resonance imaging (MRI) data was collected in 3-Tesla scanners. Using 3D-T1 images cerebral, cortical, deep gray, cerebral white matter and thalamic nuclei volumes were calculated. Using a meta-analysis approach with an interaction term for participant group, individual regression models were run for each MRI measure having SDMT as the outcome variable in each model. In addition, the correlation and average difference between In-person and Remote group effect sizes across the MRI measures were calculated. Finally, for each MRI variable I ² score was quantified to test the heterogeneity between the groups. Results: Administration modality did not affect the association of SDMT with MRI measures. Brain tissue volumes showing high associations with the SDMT in one group also showed high associations in the other ( r = 0.83; 95% CI = [0.07, 0.86]). The average difference between the In-person and the Remote group effect sizes was not significant (β _Remote - β _In-person = 0.14, 95% CI = [-0.04, 0.34]). Across MRI measures, the average I ² value was 14%, reflecting very little heterogeneity in the relationship of SDMT to brain volume. Conclusion: We found consistent relationships to neural correlates across in-person and remote SDMT administration modalities. Hence, our study can be considered a step towards providing predictive external validity to remote administration of the SDMT in MS. Highlights: Symbol Digit Modalities Test (SDMT) was administered to individuals with MS either in-person or remotely via video conference. Administration modality did not affect the association of SDMT with MRI measures. Brain tissue volumes showing high associations with the SDMT in one group also showed high associations in the other. The average difference between the In-person and the Remote group effect sizes was not significant. This study can be considered a step towards providing predictive external validity to remote administration of the SDMT in MS.

Background: Memory dysfunction is common in multiple sclerosis (MS); mechanistic understanding of its causes is lacking. Large-scale network resting-state functional connectivity (RSFC) is sensitive to memory dysfunction. Objective: We derived and tested summary metrics of memory network RSFC. Methods: Cognitive data and 3T magnetic resonance imaging (MRI) scans were collected from 235 MS patients and 35 healthy controls (HCs). Index scores were calculated as RSFC within (anteriority index, AntI) and between (integration index, IntI) dorsomedial anterior temporal and medial temporal memory subnetworks. Group differences in index expression were evaluated. Associations between index scores and memory/non-memory cognition were evaluated; relationships between T2 lesion volume (T2LV) and index scores were assessed. Results: Index scores were related to memory and T2LV in MS patients, who showed marginally elevated AntI relative to HC ( p = 0.06); no group differences were found for IntI. Better memory was associated with higher AntI (β = 0.15, p = 0.018) and IntI (β = 0.16, p = 0.014). No associations were found for non-memory cognition. Higher T2LV was associated with higher AntI and IntI; exploratory mediation analysis revealed significant inconsistent mediation, that is, higher index scores partially suppressed the negative association between T2LV and memory. Conclusion: Summary, within-subject metrics permit replication and circumvent challenges of traditional (incommensurate) RSFC variables to advance development of mechanistic models of memory dysfunction in MS.

Background and Purpose: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC).Methods: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age‐ and sex‐matched HC. Thickness of 68 cortical regions and resting‐state functional‐connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank‐sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1‐SWM, 2‐DWM, 3‐SWM and DWM, 4‐cortical thickness, or 5‐RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5‐folds cross‐validation. Area under the receiver operating characteristic curve (AUC_roc) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUC_roc values.Results: Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre‐ and post‐central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUC_roc = 0.75). The SWM model provided higher AUC_roc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all‐features (0.68) models (p < .001 for all).Conclusion: Our results reveal a non‐random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.

Background: Humans are inherently social, biologically programmed to connect with others. Social connections are known to impact mental and physical health. Objective: The aim of this study was to test whether social network structure is linked to cognition, mood, fatigue, and regional brain volumes in persons with multiple sclerosis (MS). Methods: A questionnaire quantifying individual-level social network structure (size, density, effective size, and constraint), a comprehensive battery of neuropsychological tests, and magnetic resonance imaging (MRI) was administered to 51 persons with relapsing–remitting MS. Linear regressions assessed associations of network variables to cognition, depression, fatigue, and structural brain volumes. Results: Higher network density and constraint, indicating stronger connections among network members, were associated with worse language functions. Conversely, larger network effective size, a measure of non-redundant network members, was associated with better language functions. No relationships of network structure to depression or fatigue were found. Larger network size was related to larger amygdala volume. Conclusion: Findings suggest that social network structure is linked to language function and amygdala volume in persons with MS. Patients with close-knit networks showed worse language function than those with open networks. Longitudinal studies with larger samples are warranted to evaluate potential causal links between social network structure and MS-related cognitive impairment.

Objective: To build a model to predict cognitive status reflecting structural, functional, and white matter integrity changes in early multiple sclerosis (MS). Methods: Based on Symbol Digit Modalities Test (SDMT) performance, 183 early MS patients were assigned “lower” or “higher” performance groups. Three-dimensional (3D)-T2, T1, diffusion weighted, and resting-state magnetic resonance imaging (MRI) data were acquired in 3T. Using Random Forest, five models were trained to classify patients into two groups based on 1—demographic/clinical, 2—lesion volume/location, 3—local/global tissue volume, 4—local/global diffusion tensor imaging, and 5—whole-brain resting-state-functional-connectivity measures. In a final model, all important features from previous models were concatenated. Area under the receiver operating characteristic curve (AUC) values were calculated to evaluate classifier performance. Results: The highest AUC value (0.90) was achieved by concatenating all important features from neuroimaging models. The top 10 contributing variables included volumes of bilateral nucleus accumbens and right thalamus, mean diffusivity of left cingulum-angular bundle, and functional connectivity among hubs of seven large-scale networks. Conclusion: These results provide an indication of a non-random brain pattern mostly compromising areas involved in attentional processes specific to patients who perform worse in SDMT. High accuracy of the final model supports this pattern as a potential neuroimaging biomarker of subtle cognitive changes in early MS.