We found that cortical thickness, but not cortical surface area, is affected in early RRMS. We identified specific structural correlates to the main clinical symptoms in early RRMS.

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10 −6 ). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging ( p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10 −15 ) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

New treatment options may make “no evidence of disease activity” (NEDA: no relapses or disability progression and no new/enlarging MRI lesions, as opposed to “evidence of disease activity” (EDA) with at least one of the former), an achievable goal in relapsing-remitting multiple sclerosis (RRMS). The objective of the present study was to determine whether early RRMS patients with EDA at one-year follow-up had different disability, cognition, treatment and gray matter (GM) atrophy rates from NEDA patients and healthy controls (HC). RRMS patients (mean age 34 years, mean disease duration 2.2 years) were examined at baseline and one-year follow-up with neurological (n = 72), neuropsychological (n = 56) and structural MRI (n = 57) examinations. Matched HC (n = 61) were retested after three years. EDA was found in 46% of RRMS patients at follow-up. EDA patients used more first line and less second line disease modifying treatment than NEDA (p = 0.004). While the patients groups had similar disability levels at baseline, they differed in disability at follow-up (p = 0.010); EDA patients progressed (EDSS: 1.8–2.2, p = 0.010), while NEDA patients improved (EDSS: 2.0–1.7, p<0.001). Cognitive function was stable in both patient groups. Subcortical GM atrophy rates were higher in EDA patients than HC (p<0.001). These results support the relevance of NEDA as outcome in RRMS and indicate that pathological neurodegeneration in RRMS mainly occur in patients with evidence of disease activity.

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

No abstract

IntroductionIn early multiple sclerosis (MS) patients, cognitive changes and fatigue are frequent and troublesome symptoms, probably related to both structural and functional brain changes. Whether there is a common cause of these symptoms in MS is unknown. In theory, an altered regulation of central neuropeptides can lead to changes in regulation of autonomic function, cognitive difficulties, and fatigue. Direct measurements of central neuropeptides are difficult to perform, but measurements of the eye pupil can be used as a reliable proxy of function.MethodsThis study assesses pupil size during problem‐solving in early MS patients versus controls. A difference in pupil size to a cognitive challenge could signal altered activity within the autonomic system because of early functional brain changes associated with cognitive load. We recruited MS patients (mean disease duration: 2.6 years, N = 41) and age‐matched healthy controls (N = 43) without eye pathology. Neurological impairment, magnetic resonance imaging, visual evoked potentials, depression, and fatigue were assessed in all of the patients. In both groups, we assessed processing speed and retinal imaging. Pupil size was recorded with an eye‐tracker during playback of multiplication tasks.ResultsBoth groups performed well on the cognitive test. The groups showed similar pupillary responses with a mean of 0.55 mm dilation in patients and 0.54 mm dilation in controls for all the tasks collapsed together. However, controls (N = 9) with low cognitive scores (LCS) had an increased pupillary response to cognitive tasks, whereas LCS MS patients (N = 6) did not (p < .05). There was a tendency toward a smaller pupillary response in patients with fatigue.ConclusionsThis is the first study to investigate pupillary responses to cognitive tasks in MS patients. Our results suggest that MS‐related changes in cognition and fatigue may be associated with changes in arousal and the autonomic regulation of task‐related pupillary responses. This supports the theory of a link between cognition and fatigue in MS.

DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.