Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity

BACKGROUND: Local 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 FINDINGS: 18 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/SIGNIFICANCE: We 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

2010. Resting state networks change in clinically isolated syndrome

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

Impact of APOE-epsilon 4 and family history of dementia on gray matter atrophy in cognitively healthy middle-aged adults

The apolipoprotein E ε4 allele (APOE4) and family history of dementia (FH) are well-known risk factors for the development of sporadic Alzheimer's disease. We assessed the effects of these risk factors on gray matter (GM) volume in 295 cognitively healthy middle-aged community-dwelling subjects. Voxel-based morphometry was used to study GM volume differences between high- and low-risk subjects, based on APOE4 carriership (n = 74), first-degree FH (n = 228), or both (n = 62). No significant results were found using a corrected p value. Using a more lenient threshold (p < 0.001 and minimum cluster size of 100 voxels), APOE4 carriers had reduced GM in the striatum compared to noncarriers. Subjects with FH had reduced GM in right precuneus compared to subjects without FH. Maternal and paternal FH provided similar atrophy patterns. APOE4 carriers with FH had GM reductions in bilateral insula compared to subjects with neither APOE4 nor FH. We conclude that a family history of dementia and APOE4 carriership are both associated with regional GM decreases in cognitively healthy middle-aged subjects, with differential effects on brain regions typically affected in Alzheimer's disease

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Purpose To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. Materials and Methods Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. Results Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P .05). Conclusion With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values. (©) RSNA, 2016

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

PURPOSE: To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. MATERIALS AND METHODS: Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. RESULTS: Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P .05). CONCLUSION: With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values

Sleep benefits subsequent hippocampal functioning.

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. © 2009 Nature America, Inc. All rights reserved

Gender-related differences in functional connectivity in multiple sclerosis

Background: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contribute to limit disability, and possible gender differences in this process may have important clinical implications.Objectives: The aim of this study was to explore gender-related changes in functional connectivity and network efficiency in MS patients. Additionally, we explored the association of functional changes with cognitive function.Methods: Sixty subjects were included in the study, matched for age, education level and intelligence quotient (IQ). Male and female patients were matched for disability, disease duration and white matter lesion load. Two cognitive domains often impaired in MS, i.e. visuospatial memory and information processing speed, were evaluated in all subjects. Functional connectivity between brain regions and network efficiency was explored using resting-state functional magnetic resonance imaging and graph analysis. Differences in cognitive and functional characteristics between groups, and correlations with cognitive performance, were examined.Results: Male patients showed worse performance on cognitive tests than female and male controls, while female patients were cognitively normal. Decreases in functional connectivity and network efficiency, observed in male patients, correlated with reduced visuospatial memory (r = -0.6 and r = -0.5, respectively). In the control group, no cognitive differences were found between genders, despite differences in functional connectivity between healthy men and women.Conclusions: Functional connectivity differences were found in male patients only and were related to impaired visuospatial memory. These results underline the importance of gender in MS and require further investigation in larger and longitudinal studies