Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

Neurofilament light chain (NfL) has been demonstrated to correlate with multiple sclerosis disease severity as well as treatment response. nevertheless, additional serum biomarkers are still needed to better differentiate disease activity from disease progression. The aim of our study was to assess serum glial fibrillary acid protein (s-GFAP) and neurofilament light chain (s-NfL) in a cohort of 129 multiple sclerosis (MS) patients. Eighteen primary progressive multiple sclerosis (PPMS) and 111 relapsing remitting MS (RRMS) were included. We showed that these 2 biomarkers were significantly correlated with each other (R = 0.72, p < 0.001). Moreover, both biomarkers were higher in PPMS than in RRMS even if multivariate analysis only confirmed this difference for s-GFAP (130.3 ± 72.8 pg/ ml vs 83.4 ± 41.1 pg/ml, p = 0.008). Finally, s-GFAP was correlated with white matter lesion load and inversely correlated with WM and GM volume. Our results seem to confirm the added value of s-GFAP in the context of multiple sclerosis. Multiple sclerosis (MS) is a complex autoimmune neurological disease 1. Despite progresses in the management of MS, reliable and easy-to-use biomarkers are needed to accurately identify patients at risk of future disease progression 2. The recent development of highly sensitive immunoassay platforms has enabled the measurement in the serum of several biomarkers of interest in MS. Notably, serum neurofilament light chain (s-NfL) is correlated with disease activity, treatment response, risk of disease progression and MRI markers of disease activity/severity 3-8. Serum Glial Fibrillary Acid Protein (s-GFAP), an intermediate astrocytes cytoskeletal protein, has been only more recently shown to be higher in progressive MS than in RRMS and correlate with disability 9-11 .

ObjectiveQuantitative and semi-quantitative MRI (qMRI) metrics provide complementary specificity and differential sensitivity to pathological brain changes compatible with brain inflammation, degeneration, and repair. Moreover, advanced magnetic resonance imaging (MRI) metrics with overlapping elements amplify the true tissue-related information and limit measurement noise. In this work, we combined multiple advanced MRI parameters to assess focal and diffuse brain changes over 2 years in a group of early-stage relapsing-remitting MS patients.MethodsThirty relapsing-remitting MS patients with less than 5 years disease duration and nine healthy subjects underwent 3T MRI at baseline and after 2 years including T1, T2, T2* relaxometry, and magnetization transfer imaging. To assess longitudinal changes in normal-appearing (NA) tissue and lesions, we used analyses of variance and Bonferroni correction for multiple comparisons. Multivariate linear regression was used to assess the correlation between clinical outcome and multiparametric MRI changes in lesions and NA tissue.ResultsIn patients, we measured a significant longitudinal decrease of mean T2 relaxation times in NA white matter (p = 0.005) and a decrease of T1 relaxation times in the pallidum (p < 0.05), which are compatible with edema reabsorption and/or iron deposition. No longitudinal changes in qMRI metrics were observed in controls. In MS lesions, we measured a decrease in T1 relaxation time (p-value < 2.2e−16) and a significant increase in MTR (p-value < 1e−6), suggesting repair mechanisms, such as remyelination, increased axonal density, and/or a gliosis. Last, the evolution of advanced MRI metrics—and not changes in lesions or brain volume—were correlated to motor and cognitive tests scores evolution (Adj-R2 > 0.4, p < 0.05). In summary, the combination of multiple advanced MRI provided evidence of changes compatible with focal and diffuse brain repair at early MS stages as suggested by histopathological studies.

Background To assess the performance of plasma neurofilament light (NfL) and phosphorylated tau 181 (p-tau181) to inform about cerebral Alzheimer’s disease (AD) pathology and predict clinical progression in a memory clinic setting. Methods Plasma NfL and p-tau181, along with established cerebrospinal fluid (CSF) biomarkers of AD pathology, were measured in participants with normal cognition (CN) and memory clinic patients with cognitive impairment (mild cognitive impairment and dementia, CI). Clinical and neuropsychological assessments were performed at inclusion and follow-up visits at 18 and 36 months. Multivariate analysis assessed associations of plasma NfL and p-tau181 levels with AD, single CSF biomarkers, hippocampal volume, and clinical measures of disease progression. Results Plasma NfL levels were higher in CN participants with an AD CSF profile (defined by a CSF p-tau181/Aβ1–42 > 0.0779) as compared with CN non-AD, while p-tau181 plasma levels were higher in CI patients with AD. Plasma NfL levels correlated with CSF tau and p-tau181 in CN, and with CSF tau in CI patients. Plasma p-tau181 correlated with CSF p-tau181 in CN and with CSF tau, p-tau181, Aβ1–42, and Aβ1–42/Aβ1–40 in CI participants. Compared with a reference model, adding plasma p-tau181 improved the prediction of AD in CI patients while adding NfL did not. Adding p-tau181, but not NfL levels, to a reference model improved prediction of cognitive decline in CI participants. Conclusion Plasma NfL indicates neurodegeneration while plasma p-tau181 levels can serve as a biomarker of cerebral AD pathology and cognitive decline. Their predictive performance depends on the presence of cognitive impairment.

In a prospective observational study, patients with TIA confirmed by acute perfusion imaging experienced a significant reduction in global gray matter and focal structural atrophy related to the area of acute ischemia. The atrophy also resulted in a proportional decreased cognitive performance on the Montreal Cognitive Assessment. Further studies are required to identify the mechanisms of this atrophy.

• Cerebral vasoreactivity does not differ between multiple sclerosis patients and controls. • Cerebral vasoreactivity measure is linked to cognitive impairment in multiple sclerosis. • Cerebral vasoreactivity is linked to level of education in multiple sclerosis.

Background and Purpose— Poststroke fatigue affects a large proportion of stroke survivors and is associated with a poor quality of life. In a recent trial, modafinil was shown to be an effective agent in reducing poststroke fatigue; however, not all patients reported a significant decrease in fatigue with therapy. We sought to investigate clinical and radiological predictors of fatigue reduction with modafinil therapy in a stroke survivor cohort. Methods— Twenty-six participants with severe fatigue (multidimensional fatigue inventory–20 ≥60) underwent magnetic resonance imaging at baseline and during the last week of a 6-week treatment period of 200 mg modafinil taken daily. Resting-state functional magnetic resonance imaging and high-resolution structural imaging data were obtained, and functional connectivity and regional brain volumes within the fronto-striato-thalamic network were obtained. Linear regression analysis was used to identify predictors of modafinil-induced fatigue reduction. Results— Multiple regression analysis showed that baseline multidimensional fatigue inventory–20 score (β=0.576, P =0.006) and functional connectivity between the dorsolateral prefrontal cortex and the caudate nucleus (β=−0.424, P =0.008) were significant predictors of modafinil-associated decreases in poststroke fatigue (adjusted r 2 =0.52, area under the receiver operator characteristic curve=0.939). Conclusions— Fronto-striato-thalamic functional connectivity predicted modafinil response for poststroke fatigue. Fatigue in other neurological disease has been attributed to altered function of the fronto-striato-thalamic network and may indicate that poststroke fatigue has a similar mechanism to other neurological injury related fatigue. Self-reported fatigue in patients with normal fronto-striato-thalamic functional connectivity may have a different mechanism and require alternate therapeutic approaches. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: ACTRN12615000350527.

SynopsisStandard MR parameters, notably spatial resolution, contrast and image filtering, systematically bias results of automated brain MRI morphometry by up to 4.8%. This is in the same range as early disease-related brain volume alterations, for example in Alzheimer's disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR-parameter-related bias of brain morphometry results.