CNS resilience in the progression of MS.

Resilience; Progressive multiple sclerosis; Genomics.

Machine learning classification of multiple sclerosis in children using optical coherence tomography.

BACKGROUND: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. OBJECTIVE: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. METHODS: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. RESULTS: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. CONCLUSION: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.

Patterns of white and gray structural abnormality associated with paediatric demyelinating disorders.

The impact of multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) - associated disorders (MOGAD) on brain structure in youth remains poorly understood. Reductions in cortical mantle thickness on structural MRI and abnormal diffusion-based white matter metrics (e.g., diffusion tensor parameters) have been well documented in MS but not in MOGAD. Characterizing structural abnormalities found in children with these disorders can help clarify the differences and similarities in their impact on neuroanatomy. Importantly, while MS and MOGAD affect the entire CNS, the visual pathway is of particular interest in both groups, as most patients have evidence for clinical or subclinical involvement of the anterior visual pathway. Thus, the visual pathway is of key interest in analyses of structural abnormalities in these disorders and may distinguish MOGAD from MS patients. In this study we collected MRI data on 18 MS patients, 14 MOGAD patients and 26 age- and sex-matched typically developing children (TDC). Full-brain group differences in fixel diffusion measures (fibre-bundle populations) and cortical thickness measures were tested using age and sex as covariates. Visual pathway analysis was performed by extracting mean diffusion measures within lesion free optic radiations, cortical thickness within the visual cortex, and retinal nerve fibre layer (RNFL) and ganglion cell layer thickness measures from optical coherence tomography (OCT). Fixel based analysis (FBA) revealed MS patients have widespread abnormal white matter within the corticospinal tract, inferior longitudinal fasciculus, and optic radiations, while within MOGAD patients, non-lesional impact on white matter was found primarily in the right optic radiation. Cortical thickness measures were reduced predominately in the temporal and parietal lobes in MS patients and in frontal, cingulate and visual cortices in MOGAD patients. Additionally, our findings of associations between reduced RNFLT and axonal density in MOGAD and TORT in MS patients in the optic radiations imply widespread axonal and myelin damage in the visual pathway, respectively. Overall, our approach of combining FBA, cortical thickness and OCT measures has helped evaluate similarities and differences in brain structure in MS and MOGAD patients in comparison to TDC.

Memory, processing of emotional stimuli, and volume of limbic structures in pediatric-onset multiple sclerosis.

OBJECTIVE: The limbic system is involved in memory and in processing of emotional stimuli. We measured volume of the hippocampus, amygdala, and thalamus, and assessed their relative contribution to episodic memory and emotion identification in POMS. METHOD: Sixty-five POMS participants (Mage = 18.3 ± 3.9 years; 48 female (73.8%)), average disease duration = 3.8 ± 3.8 years) and 76 age- and sex-matched controls (Mage = 18.1 ± 4.6 years; 49 female (64.5%)) completed the Penn Computerized Neurocognitive Battery (PCNB); 59 of 65 POMS participants and 69 out of 76 controls underwent 3 T MRI scanning. We derived age-adjusted Z-scores on accuracy and response time (RT) measures of episodic memory and emotion identification of the PCNB. Magnetic resonance imaging (MRI) volumetrics were normalized using the scaling factor computed by SIENAx. On PCNB tests that differed between groups, we used multiple linear regression to assess relationships between regional brain volumes and either episodic memory or emotion identification outcomes controlling for age, sex, accuracy/RT, and parental education. RESULTS: POMS participants were slower and less accurate than controls on the episodic memory domain but did not differ from controls on emotion outcomes. At the subtest level, POMS participants showed reduced accuracy on Word Memory (p = .002) and slower performance on Face Memory (p = .04) subtests. POMS participants had smaller total and regional brain volumes of the hippocampus, amygdala, and thalamus (p values ≤ 0.01). Collapsing across groups, both hippocampal and thalamic volume were significant predictors of Word Memory accuracy; hippocampal volume (B = 0.24, SE = 0.10, p = .02) was more strongly associated with Word Memory performance than thalamic volume (B = 0.16, SE = 0.05, p = .003), though the estimate with was less precise. CONCLUSIONS: POMS participants showed reduced episodic memory performance compared to controls. Aspects of episodic memory performance were associated with hippocampal and thalamic volume. Emotion identification was intact, despite volume loss in the amygdala.

No regional gray matter atrophy differences between pediatric- and adult-onset relapsing-remitting multiple sclerosis.

OBJECTIVE: To investigate differences in region-specific gray matter (GM) damage between adults with pediatric-onset (PO) multiple sclerosis (MS) and adult-onset (AO) MS. METHODS: Twenty-four relapsing-remitting (RR) adults with POMS (mean age = 35 years, mean disease duration = 18.4 years) were compared to 23 age-matched (AOA, mean age = 33.9 years, mean disease duration = 2.4 years) and 24 disease-duration matched (AOD, mean age = 45.9 years, mean disease duration = 18.5 years) RRMS adults who developed MS after the age of 18. Three-dimensional-T1-weighted images were acquired on a 1.5 T MRI. Image analysis was conducted using voxel-based morphometry (Statistical Parametric Mapping 8). RESULTS: There were no regional GM atrophy differences between POMS and AODMS groups. No regional GM atrophy differences were found between POMS and AOAMS patients when disease duration was included as a covariate. CONCLUSIONS: Regional GM differences were not found between POMS adults and MS controls matched for age or disease duration. Although of limited sample size, these findings suggest that there are no regional GM atrophy differences between RR POMS and AOMS.