Upregulation of cortico-cerebellar functional connectivity after motor learning

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity

Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

International audienceBrain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and elec-troencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed interregional differences with very good interhemi-spheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of " functional cytoarchitectonics " , that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts

Spatiotemporal brain dynamics induced by propofol and ketamine in humans

Human brain dynamics are radically altered under the influence of anaesthetics. However, despite their widespread clinical use, the whole-brain mechanisms by which anaesthetics alter consciousness are still not fully understood and clinical translation of existing insights is limited. This thesis presents several lines of investigation aimed to improve our understanding of spatiotemporal brain states under the anaesthetics propofol and ketamine. First, slow-wave activity saturation (SWAS) was studied across the brain and in relation to existing depth of anaesthesia markers. Local propofol concentration needed to achieve SWAS in healthy volunteers correlated with GABA-A receptor density (Spearman ρ=-0.69, P=0.0018), providing more evidence for the importance of the neurophysiological state of SWAS. The average Bispectral Index at SWAS across volunteers was 49±4, but its value varied significantly over time. Second, relevant cortico-cardiac interactions were studied. A slow propofol infusion increased heart rate in a dose-dependent manner (increase of +4.2±1.5 bpm / (μg ml-1), P<0.001). Individual cortical slow waves were coupled to the heartbeat (P<0.001), with heartbeat incidence peaking about 450ms before slow-wave onset. A ketamine case study showed decreased amplitude of heartbeat-evoked potentials, suggesting impaired interoceptive signalling may have a part in dissociative phenomenology. Third, novel methodology was developed, validated, and applied throughout the thesis. Iterated Masking Empirical Mode Decomposition was used to identify three types of low-frequency propofol waves with different spatiotemporal maps and dose-responses. Hidden Markov Modelling of propofol showed a shift to anterior alpha states and a reduced switching rate (P<0.01); with ketamine states exhibiting low alpha power and decreased connectivity became more prominent (P<0.001). Fourth, the potential of translating electroencephalographic markers from high- to low- density montages was studied. Posterior montages were best at capturing the reduced state switching under propofol. A patient study of antidepressant ketamine treatment demonstrated reduced temporal lobe alpha and theta power were associated with dissociation (P=0.0109)

Low-frequency connectivity is associated with mild traumatic brain injury

AbstractMild traumatic brain injury (mTBI) occurs from a closed-head impact. Often referred to as concussion, about 20% of cases complain of secondary psychological sequelae, such as disorders of attention and memory. Known as post-concussive symptoms (PCS), these problems can severely disrupt the patient's quality of life. Changes in local spectral power, particularly low-frequency amplitude increases and/or peak alpha slowing have been reported in mTBI, but large-scale connectivity metrics based on inter-regional amplitude correlations relevant for integration and segregation in functional brain networks, and their association with disorders in cognition and behaviour, remain relatively unexplored. Here, we used non-invasive neuroimaging with magnetoencephalography to examine functional connectivity in a resting-state protocol in a group with mTBI (n = 20), and a control group (n = 21). We observed a trend for atypical slow-wave power changes in subcortical, temporal and parietal regions in mTBI, as well as significant long-range increases in amplitude envelope correlations among deep-source, temporal, and frontal regions in the delta, theta, and alpha bands. Subsequently, we conducted an exploratory analysis of patterns of connectivity most associated with variability in secondary symptoms of mTBI, including inattention, anxiety, and depression. Differential patterns of altered resting state neurophysiological network connectivity were found across frequency bands. This indicated that multiple network and frequency specific alterations in large scale brain connectivity may contribute to overlapping cognitive sequelae in mTBI. In conclusion, we show that local spectral power content can be supplemented with measures of correlations in amplitude to define general networks that are atypical in mTBI, and suggest that certain cognitive difficulties are mediated by disturbances in a variety of alterations in network interactions which are differentially expressed across canonical neurophysiological frequency ranges

Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research

The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this ‘living’ set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.Peer reviewe

How to build a functional connectomic biomarker for mild cognitive impairment from source reconstructed MEG resting-state activity: the combination of ROI representation and connectivity estimator matters

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal and cingulo-opercular network. Our analysis supports the notion of analysing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings

EEG and MEG data analysis in SPM8.

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools