Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves

The neural mechanisms behind human voluntary motion are not fully characterized yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement. This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz. Using the GraphVar toolbox, inter-task and inter-group comparisons were performed. In inter-task comparisons PL matrices showed encouraging results unlike GC matrices. Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively. This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior

Mapping multiplex hubs in human functional brain network

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.Comment: 11 pages, 8 figures, 2 table

Brain connectomics' modification to clarify motor and nonmotor features of myotonic dystrophy type 1

The adult form of myotonic dystrophy type 1 (DM1) presents with paradoxical inconsistencies between severity of brain damage, relative preservation of cognition, and failure in everyday life. This study, based on the assessment of brain connectivity and mechanisms of plasticity, aimed at reconciling these conflicting issues. Resting-state functional MRI and graph theoretical methods of analysis were used to assess brain topological features in a large cohort of patients with DM1. Patients, compared to controls, revealed reduced connectivity in a large frontoparietal network that correlated with their isolated impairment in visuospatial reasoning. Despite a global preservation of the topological properties, peculiar patterns of frontal disconnection and increased parietal-cerebellar connectivity were also identified in patients' brains. The balance between loss of connectivity and compensatory mechanisms in different brain networks might explain the paradoxical mismatch between structural brain damage and minimal cognitive deficits observed in these patients. This study provides a comprehensive assessment of brain abnormalities that fit well with both motor and nonmotor clinical features experienced by patients in their everyday life. The current findings suggest that measures of functional connectivity may offer the possibility of characterizing individual patients with the potential to become a clinical tool

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI