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

Mental fatigue correlates with depression of task-related network and augmented DMN activity but spares the reward circuit

Long-lasting and demanding cognitive activity typically leads to mental fatigue (MF). Indirect evidence suggests that MF may be caused by altered motivational processes. Here, we hypothesized that if MF consists in an alteration of motivational states, brain functional changes induced by MF could specifically affect the brain motivation circuit. In order to test this hypothesis, we devised a functional neuroimaging protocol to detect altered brain activity in reward-related brain regions in relation to cognitively induced mental fatigue. Twenty-five healthy participants underwent a FATIGUE and a CONTROL session on different days. In the FATIGUE session, MF was induced by performing a demanding cognitive task (adapted Stroop task) during 90 min, whereas in the CONTROL session, participants were asked to read magazines for the same period of time. We measured the neural consequences of the MF induction during a working memory task (Missing Number task) while modulating extrinsic motivation with block-wise variations in monetary reward. We also tracked participants’ momentary fatigue, anxiety state and intrinsic motivation prior to and following the MF inducement and measurement. Accuracy on the Missing Number Task was lower in the FATIGUE than in the CONTROL condition. Furthermore, subjective MF, but not its behavioral manifestations, was associated with hypoactivity of the task-evoked neural responses. Importantly, activity in regions modulated by reward showed no differences between FATIGUE and CONTROL sessions. In parallel, subjective MF correlated with increased on-task activity and resting-state functional connectivity in the default mode network. These results indicate that subjective mental fatigue is not associated with altered activity in the brain motivation circuit but rather with hypoactivity in task-specific brain regions as well as relative increases of activity and connectivity in the default mode network during and after the task.Une nouvelle théorie du coût de la cognition basée sur la théorie de l'information: validation expérimental

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

The bidirectional relation between emotional reactivity and sleep: From disruption to recovery

Sleep disturbances are highly prevalent and greatly affect consecutive emotional reactivity, while sleep quality itself can be strongly affected by reactions to previous emotional events. In this review, we shed light on this bidirectional relation through examples of pathology: insomnia and bipolar disorder. We show that both experimental sleep deprivation and insomnia are related to increased emotional reactivity and increased amygdala activation upon emotional stimuli presentation, and that particularly Rapid Eye Movement (REM) sleep is important for emotional processing and reorganization of emotion-specific brain activity. Increased emotional reactivity affects REM sleep quality and sleep spindles, while REM sleep is particularly affected in insomnia, possibly related to condition-specific hyperarousal levels. Normal sleep onset deactivation of brain regions important for emotional processing (amygdala, anterior cingulate cortex (ACC)) is further affected in insomnia. In bipolar disorder, sleep disturbances are common in both symptomatic and nonsymptomatic phases. Both amygdala and ACC volume and function are affected in bipolar disorder, with the ACC showing phase-dependent resting state activity differences. Deficient Gamma-aminobutyric acid (GABA) GABA-ergic activity of this region might play a role in sleep disturbances and their influence on emotional reactivity, given the inhibitory role of GABA on brain activity during sleep and its deficiency in both bipolar disorder and insomnia. Promising findings of normalizing brain activity in both insomnia and bipolar disorder upon treatment may inspire a focus on treatment studies investigating the normalization of sleep, emotional reactivity, and their corresponding brain activity patterns

Sleep

Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. We used 3 Tesla functional magnetic resonance imaging (fMRI) to determine neural reactivity during the presentation of standardized short, 10- to 40-seconds, humorous films in patients with insomnia (n = 20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n = 20, 19 females, aged 26.7 +/- 7.0 years) and assessed humor ratings through a visual analog scale. Seed-based functional connectivity was analyzed for the left and right amygdalas (lAMYG and rAMYG, respectively) networks: group-level mixed-effects analysis (FLAME; FMRIB Software Library [FSL]) was used to compare amygdala connectivity maps between groups. fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in patients with insomnia than in controls in several brain network clusters within the reward brain network, without humor rating differences between groups (p = 0.6). For lAMYG connectivity, cluster maxima were in the left caudate (Z = 3.88), left putamen (Z = 3.79), and left anterior cingulate gyrus (Z = 4.11), whereas for rAMYG connectivity, cluster maxima were in the left caudate (Z = 4.05), right insula (Z = 3.83), and left anterior cingulate gyrus (Z = 4.29). Cluster maxima of the rAMYG network were correlated with hyperarousal scores in patients with insomnia only. The presentation of humorous films leads to increased brain activity in the neural reward network for patients with insomnia compared with controls, related to hyperarousal features in patients with insomnia, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions. The Sleepless Brain: Neuroimaging Support for a Differential Diagnosis of Insomnia (SOMNET). ClinicalTrials.gov identifier: NCT02821234; https://clinicaltrials.gov/ct2/show/NCT02821234.Bordeaux Region Aquitaine Initiative for Neuroscienc

Differentiation of edema and glioma infiltration: Proposal of a DTI-based probability map

Conflicting results on differentiating edema and glioma by diffusion tensor imaging (DTI) are possibly attributable to dissimilar spatial distribution of the lesions. Combining DTI-parameters and enhanced registration might improve prediction. Regions of edema surrounding 22 metastases were compared to tumor-infiltrated regions from WHO grade 2 (12), 3 (10) and 4 (18) gliomas. DTI data was co-registered using Tract Based Spatial Statistics (TBSS), to measure Fractional Anisotropy (FA) and Mean Diffusivity (MD) for white matter only, and relative changes compared to matching reference regions (dFA and dMD). A two-factor principal component analysis (PCA) on metastasis and grade 2 glioma was performed to explore a possible differentiating combined factor. Edema demonstrated equal MD and higher FA compared to grade 2 and 3 glioma (P < 0.001), but did not differ from glioblastoma. Differences were non-significant when corrected for spatial distribution, since reference regions differed strongly (P < 0.001). The second component of the PCA (PCA-C2) did differentiate edema and low-grade tumor (sensitivity 91.7 %, specificity 86.4 %). PCA-C2 scores were plotted voxel-wise as a probability-map, discerning distinct areas of presumed edema or tumor infiltration. Correction of spatial dependency appears essential when differentiating glioma from edema. A tumor-infiltration probability-map is presented, based on supplementary information of multiple DTI parameters and spatial normalization

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 &apos;at rest&apos; 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