BackgroundLocal network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data.Methodology/Principal Findings18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions.Conclusions/SignificanceWe present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

No abstract

Task-functional magnetic resonance imaging studies have shown that early cortical recruitment exists in multiple sclerosis, which can partly explain the discrepancy between conventional magnetic resonance imaging and clinical disability. The study of the brain 'at rest' may provide additional information, because task-induced metabolic changes are relatively small compared to the energy use of the resting brain. We therefore questioned whether functional changes exist at rest in the early phase of multiple sclerosis, and addressed this question by a network analysis of no-task functional magnetic resonance imaging data. Fourteen patients with symptoms suggestive of multiple sclerosis (clinically isolated syndrome), 31 patients with relapsing remitting multiple sclerosis and 41 healthy controls were included. Resting state functional magnetic resonance imaging data were brought to standard space using non-linear registration, and further analysed using multi-subject independent component analysis and individual time-course regression. Eight meaningful resting state networks were identified in our subjects and compared between the three groups with non-parametric permutation testing, using threshold-free cluster enhancement to correct for multiple comparisons. Additionally, quantitative measures of structural damage were obtained. Grey and white matter volumes, normalized for head size, were measured for each subject. White matter integrity was investigated with diffusion tensor measures that were compared between groups voxel-wise using tract-based spatial statistics. Patients with clinically isolated syndrome showed increased synchronization in six of the eight resting state networks, including the default mode network and sensorimotor network, compared to controls or relapsing remitting patients. No significant decreases were found in patients with clinically isolated syndrome. No significant resting state synchronization differences were found between relapsing remitting patients and controls. Normalized grey matter volume was decreased and white matter diffusivity measures were abnormal in relapsing remitting patients compared to controls, whereas no atrophy or diffusivity changes were found for the clinically isolated syndrome group. Thus, early synchronization changes are found in patients with clinically isolated syndrome that are suggestive of cortical reorganization of resting state networks. These changes are lost in patients with relapsing remitting multiple sclerosis with increasing brain damage, indicating that cortical reorganization of resting state networks is an early and finite phenomenon in multiple sclerosis.

These findings indicate that thalamic changes in structure and function are highly informative regarding overall cognitive performance in MS. Increased thalamic FC only became apparent in SCI, possibly as a sign of maladaption.

Sleep before learning benefits memory encoding through unknown mechanisms. We found that even a mild sleep disruption that suppressed slow-wave activity and induced shallow sleep, but did not reduce total sleep time, was sufficient to affect subsequent successful encoding-related hippocampal activation and memory performance in healthy human subjects. Implicit learning was not affected. Our results suggest that the hippocampus is particularly sensitive to shallow, but intact, sleep.

No abstract

Objectives: Gray matter (GM) atrophy is common in multiple sclerosis (MS), as is cognitive dysfunction. Understanding the exact relationship between atrophy and cognition requires further investigation. The aim of this study was to investigate the relationship between subcortical GM atrophy and cognition in early relapsing onset MS.Methods: Structural MRI and neuropsychological evaluations were performed in 120 patients (80 women) and 50 controls (30 women), part of an early inception cohort, 6 years postdiagnosis. Deep GM volumes were segmented automatically. Cognition was assessed in 7 domains.Stepwise linear regression was used to predict average cognition in the patient group.Results: Most deep GM volumes were reduced in patients, with larger effects on average in men (Ϫ11%) than in women (Ϫ6.3%). Only the bilateral hippocampus, amygdala, and right nucleus accumbens in men, and right hippocampus and nucleus accumbens, bilateral amygdala, and putamen in women, showed no atrophy compared to controls. All cognitive domains except visuospatial memory were affected in men; none were significantly affected in women. In the MS group, average cognition was best predicted by thalamic volume, sex, and education (adjusted R 2 ϭ 0.31), while lesion volume was not a significant predictor in the model. Conclusions:Six years postdiagnosis, almost all subcortical structures were affected by MS, especially in men. Cognition was most severely affected in male patients. Thalamic volume, sex, and education best predicted average cognition. These results underline the relevance of specific subcortical structures to cognition, as well as the relevance of (sex-specific) atrophy in MS. Neurology ® 2012;79:1754-1761 GLOSSARY BRB-N ϭ Brief Repeatable Battery for Neurological disease; DGM ϭ deep gray matter; DMT ϭ disease-modifying therapy; EDSS ϭ Expanded Disability Status Scale; GLM ϭ general linear model; GM ϭ gray matter; MS ϭ multiple sclerosis; NBV ϭ normalized whole brain volume; NCGMV ϭ normalized cortical gray matter volume; NDGMV ϭ normalized deep gray matter volume; NGMV ϭ normalized total gray matter volume; NWMV ϭ normalized total white matter volume; RRMS ϭ relapsingremitting multiple sclerosis; WM ϭ white matter.Atrophy of the gray matter (GM) and white matter (WM) is frequently found in multiple sclerosis (MS), 1,2 and can be reliably measured with MRI.3 Although already present in early stages, GM atrophy becomes much more dominant in the progressive phase of the disease. 4 As the clinical relevance and the specific histopathologic substrate of GM atrophy in MS is not well known, it is the subject of many recent studies. 5,6 While the relationship between brain atrophy and clinical measures has been investigated extensively in the past, 7 how atrophy relates to cognition is only recently becoming clearer. -11As cognitive dysfunction is common and present in all stages of the disease, 12-15 exploring the relationship between atrophy and cognition could provide valuable new information.As most studies that focus...

Cognitive decline is common in multiple sclerosis and strongly affects overall quality of life. Despite the identification of cross-sectional MRI correlates of cognitive impairment, predictors of future cognitive decline remain unclear. The objective of this study was to identify which MRI measures of structural damage, demographic and/or clinical measures at baseline best predict cognitive decline, during a 5-year follow-up period. A total of 234 patients with clinically definite multiple sclerosis and 60 healthy control subjects were examined twice, with a 5-year interval (mean = 4.9 years, standard deviation = 0.9). An extensive neuropsychological evaluation was performed at both time points and the reliable change index was computed to evaluate cognitive decline. Both whole-brain and regional MRI (3 T) measures were assessed at baseline, including white matter lesion volume, diffusion-based white matter integrity, cortical and deep grey matter volume. Logistic regression analyses were performed to determine which baseline measures best predicted cognitive decline in the entire sample as well as in early relapsing-remitting (symptom duration <10 years), late relapsing-remitting (symptom duration ≥10 years) and progressive phenotypes. At baseline, patients with multiple sclerosis had a mean disease duration of 14.8 (standard deviation = 8.4) years and 96/234 patients (41%) were classified as cognitively impaired. A total of 66/234 patients (28%) demonstrated cognitive decline during follow-up, with higher frequencies in progressive compared to relapsing-remitting patients: 18/33 secondary progressive patients (55%), 10/19 primary progressive patients (53%) and 38/182 relapsing-remitting patients (21%). A prediction model that included only whole-brain MRI measures (Nagelkerke R2 = 0.22, P < 0.001) showed cortical grey matter volume as the only significant MRI predictor of cognitive decline, while a prediction model that assessed regional MRI measures (Nagelkerke R2 = 0.35, P < 0.001) indicated integrity loss of the anterior thalamic radiation, lesions in the superior longitudinal fasciculus and temporal atrophy as significant MRI predictors for cognitive decline. Disease stage specific regressions showed that cognitive decline in early relapsing-remitting multiple sclerosis was predicted by white matter integrity damage, while cognitive decline in late relapsing-remitting and progressive multiple sclerosis was predicted by cortical atrophy. These results indicate that patients with more severe structural damage at baseline, and especially cortical atrophy, are more prone to suffer from cognitive decline. New studies now need to further elucidate the underlying mechanisms leading to cortical atrophy, evaluate the value of including cortical atrophy as a possible outcome marker in clinical trials as well as study its potential use in individual patient management.