Identification of time-varying cortico-cortical and cortico-muscular coherence during motor tasks with multivariate autoregressive models

Neural populations coordinate at fast subsecond time-scales during rest and task execution. As a result, functional brain connectivity assessed with different neuroimaging modalities (EEG, MEG, fMRI) may also change over different time scales. In addition to the more commonly used sliding window techniques, the General Linear Kalman Filter (GLFK) approach has been proposed to estimate time-varying brain connectivity. In the present work, we propose a modification of the GLFK approach to model time-varying connectivity. We also propose a systematic method to select the hyper-parameters of the model. We evaluate the performance of the method using MEG and EMG data collected from 12 young subjects performing two motor tasks (unimanual and bimanual hand grips), by quantifying time-varying cortico-cortical and cortico-muscular coherence (CCC and CMC). The CMC results revealed patterns in accordance with earlier findings, as well as an improvement in both time and frequency resolution compared to sliding window approaches. These results suggest that the proposed methodology is able to unveil accurate time-varying connectivity patterns with an excellent time resolution

Cortical network and connectivity underlying hedonic olfactory perception

Objective. The emotional response to olfactory stimuli implies the activation of a complex cascade of events triggered by structures lying in the limbic system. However, little is known about how this activation is projected up to cerebral cortex and how different cortical areas dynamically interact each other. Approach. In this study, we acquired EEG from human participants performing a passive odor-perception task with odorants conveying positive, neutral and negative valence. A novel methodological pipeline integrating global field power (GFP), independent component analysis (ICA), dipole source localization was applied to estimate effective connectivity in the challenging scenario of single-trial low-synchronized stimulation. Main results. We identified the brain network and the neural paths, elicited at different frequency bands, i.e. θ (4-7Hz), α (8-12Hz) and β (13-30Hz), involved in odor valence processing. This brain network includes the orbitofrontal cortex (OFC), the cingulate gyrus (CgG), the superior temporal gyrus (STG), the posterior cingulate cortex/precuneus (PCC/PCu) and the parahippocampal gyrus (PHG). It was analyzed using a time-varying multivariate autoregressive model to resolve time-frequency causal interactions. Specifically, the OFC acts as the main node for odor perception and evaluation of pleasant and unpleasant stimuli, whereas no specific path was observed for a neutral stimulus. Significance. The results introduce new evidences on the role of the OFC during hedonic perception and underpin its specificity during the odor valence assessment. Our findings suggest that, after the odor onset different, bidirectional interactions occur between the OFC and other brain regions associated with emotion recognition/categorization and memory according to the stimulus valence. This outcome unveils how the hedonic olfactory network dynamically changes based on odor valence

Study of cortical rhythmic activity and connectivity with magnetoencephalography

Intracranial recordings in animals and neuroimaging studies on humans have indicated that oscillatory activity and its modulations may play a fundamental role in large-scale neural information processing. Furthermore, rhythmic interactions between cortical areas have been detected across a variety of tasks with electroencephalography (EEG) and magnetoencephalography (MEG). This kind of coupling has been proposed to be a key mechanism through which information is integrated across segregated areas. So far, rhythmic interactions have been analyzed primarily at the EEG/MEG sensor level, without explicit knowledge of cortical areas involved. In this thesis work we developed new methods that can be used to image oscillatory activity and coherence at the cortical level with MEG. Dynamic Imaging of Coherent Sources (DICS) enables localization of interacting areas both using external reference signals and directly from the MEG data. When the interacting areas have been determined it is possible to use additional measures beyond coherence to further quantify interactions within the networks. DICS was originally designed for study of continuous data; its further development into event-related DICS (erDICS) adds the possibility to image modulations of rhythmic activity that are locked to stimulus or movement timing. Furthermore, permutation testing incorporated into erDICS allows the evaluation of the statistical significance of the results. Analysis of simulated and real data showed that DICS and erDICS yield accurate localization and quantification of oscillatory activity and coherence. Comparison of DICS to other methods of localizing oscillatory activity revealed that it is equally accurate and that it can better separate the activity originating from two nearby areas. We applied DICS to two datasets, recorded from groups of subjects while they performed slow finger movements and when they were reading continuously. In both cases, we were able to systematically identify interacting cortico-cortical networks and, using phase coupling and causality measures, to quantify the manner in which the nodes within these networks influenced each other. Furthermore, we compared the identified reading network to results reported in neurophysiological and hemodynamic activation studies. In addition to areas typically detected in activation studies of reading the network included areas that are normally found in language production rather than perception tasks, indicating more extensive networking of neural systems than usually observed in activation studies

The role of oscillation population activity in cortico-basal ganglia circuits.

The basal ganglia (BG) are a group of subcortical brain nuclei that are anatomically situated between the cortex and thalamus. Hitherto, models of basal ganglia function have been based solely on the anatomical connectivity and changes in the rate of neurons mediated by inhibitory and excitatory neurotransmitter interactions and modulated by dopamine. Depletion of striatal dopamine as occurs in Parkinson's Disease (PD) however, leads primarily to changes in the rhythmicity of basal ganglia neurons. The general aim of this thesis is to use frontal electrocorticogram (ECoG) and basal ganglia local field potential (LFP) recordings in the rat to further investigate the putative role for oscillations and synchronisation in these structures in the healthy and dopamine depleted brain. In the awake animal, lesion of the SNc lead to a dramatic increase in the power and synchronisation of P-frequency band oscillations in the cortex and subthalamic nucleus (STN) compared to the sham lesioned animal. These results are highly similar to those in human patients and provide further evidence for a direct pathophysological role for p-frequency band oscillations in PD. In the healthy, anaesthetised animal, LFPs recorded in the STN, globus pallidus (GP) and substantia nigra pars reticulata (SNr) were all found to be coherent with the ECoG. A detailed analysis of the interdependence and direction of these activities during two different brain states, prominent slow wave activity (SWA) and global activation, lead to the hypothesis that there were state dependant changes in the dominance of the cortico-subthalamic and cortico-striatal pathways. Multiple LFP recordings in the striatum and GP provided further evidence for this hypothesis, as coherence between the ECoG and GP was found to be dependent on the striatum. Together these results suggest that oscillations and synchronisation may mediate information flow in cortico-basal ganglia networks in both health and disease

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Multimodal (EEG-fMRI) functional connectivity study of levodopa effect in Parkinson’s disease

Aim: To assess if the intake of levodopa in patients with Parkinson’s Disease (PD) changes cerebral connectivity, as revealed by simultaneous recording of hemodynamic (functional MRI, or fMRI) and electric (electroencephalogram, EEG) signals. Particularly, we hypothesize that the strongest changes in FC will involve the motor network, which is the most impaired in PD. Methods: Eight patients with diagnosis of PD “probable”, therapy with levodopa exclusively, normal cognitive and affective status, were included. Exclusion criteria were: moderate-severe rest tremor, levodopa induced dyskinesia, evidence of gray or white matter abnormalities on structural MRI. Scalp EEG (64 channels) were acquired inside the scanner (1.5 Tesla) before and after the intake of levodopa. fMRI functional connectivity was computed from four regions of interest: right and left supplementary motor area (SMA) and right and left precentral gyrus (primary motor cortex). Weighted partial directed coherence (w-PDC) was computed in the inverse space after the removal of EEG gradient and cardioballistic artifacts. Results and discussion: fMRI group analysis shows that the intake of levodopa increases hemodynamic functional connectivity among the SMAs / primary motor cortex and: sensory-motor network itself, attention network and default mode network. w-PDC analysis shows that EEG connectivity among regions of the motor network has the tendency to decrease after the intake the levodopa; furthermore, regions belonging to the DMN have the tendency to increase their outflow toward the rest of the brain. These findings, even if in a small sample of patients, suggest that other resting state physiological functional networks, beyond the motor one, are affected in patients with PD. The behavioral and cognitive tasks corresponding to the affected networks could benefit from the intake of levodopa

EEG and TMS-EEG Studies on the Cortical Excitability and Plasticity associated with Human Motor Control and Learning

More than half of the activities of daily living rely on upper limb functions (Ingram et al., 2008). Humans perform upper limb movements with great ease and flexibility but even simple tasks require complex computations in the brain and can be affected following stroke leaving survivors with debilitating movement impairments. Hemispheric asymmetries related to motor dominance, imbalances between contralateral and ipsilateral primary motor cortices (M1) activity and the ability to adapt movements to novel environments play a key role in upper limb motor control and can affect recovery. Motor learning and control are critical in neurorehabilitation, however to effectively integrate these concepts into upper limb recovery treatments, a deeper understanding of the basic mechanisms of unimanual control is needed. This thesis aimed to investigate hemispheric asymmetries related to motor dominance, to evaluate the relative contribution of the contralateral and ipsilateral M1 during unilateral reaching preparation and finally to identify the neural correlates underlying the formation of a predictive internal model enabling to adapt movements to new environments. To this end electroencephalography (EEG), transcranial magnetic stimulation (TMS), simultaneous TMS-EEG were employed during a simple motor and a highly standardised robot-mediated task. The first study used TMS-EEG to examine differences in cortical excitability related to motor dominance by applying TMS over the dominant and non-dominant M1 at rest and during contraction. No hemispheric asymmetries related to hand dominance were found. The second study assessed the temporal dynamics of bi-hemispheric motor cortical excitability during right arm reaching preparation. TMS was applied either to the ipsilateral or contralateral M1 during different times of movement preparation. Significant bilateral M1 activation during unilateral reaching preparation was observed, with no significant differences between the contralateral and ipsilateral M1. Unimanual reaching preparation was associated with significant interactions of excitatory and inhibitory processes in both motor cortices. The third study investigated the neural correlates of motor adaptation. EEG was recorded during a robot-mediated adaptation task involving right arm reaching movements and cortical excitability was assessed by applying TMS over the contralateral M1 and simultaneously recording TMS responses with EEG before and after motor adaptation. It was found that an error-related negativity (ERN) over fronto-central regions correlated with performance improvements during adaptation, suggesting that this neural activity reflects the formation of a predictive internal model. Motor adaptation underlay significant modulations in cortical excitability (i.e. neuroplasticity) in sensorimotor regions. Finally, it was shown that native cortical excitability was linked to motor learning improvements during motor adaptation and explained the variability in motor learning across individuals. These experiments demonstrated that even unimanual motor control relies on interactions between excitatory and inhibitory mechanisms not only in the contralateral M1 but in a wider range of brain regions, shown by a bi-hemispheric activity during movement preparation, the formation of a predictive model in fronto-central regions during motor adaptation and neuroplastic changes in sensorimotor regions underlying motor adaptation during unimanual reaching