The role of ipsilateral motor network in upper limb movement.

The execution of voluntary movements is primarily governed by the cerebral hemisphere contralateral to the moving limb. Previous research indicates that the ipsilateral motor network, comprising the primary motor cortex (M1), supplementary motor area (SMA), and premotor cortex (PM), plays a crucial role in the planning and execution of limb movements. However, the precise functions of this network and its interplay in different task contexts have yet to be fully understood.

Modular segregation drives causality of the dynamic oscillatory network responses during threat processing.

Physiological responses to threat and stress stimuli entrain synchronized neural oscillations among cerebral networks. Network architecture and adaptation may play a critical role in achieving optimal physiological responses, while alteration can lead to mental dysfunction. We reconstructed cortical and sub-cortical source time series from high-density electroencephalography, which were then fed into community architecture analysis.

Inhibitory and excitatory responses in the dorso-medial prefrontal cortex during threat processing.

Objective: To evaluate cortical excitability during instructed threat processing.

Methods: Single and paired transcranial magnetic stimulation (TMS) pulses were applied to the right dorsomedial prefrontal cortex (dmPFC) during high-density electroencephalography (EEG) recording in young healthy participants ( = 17) performing an instructed threat paradigm in which one of two conditioned stimuli (CS+ but not CS-) was paired with an electric shock (unconditioned stimulus [US]). We assessed TMS-induced EEG responses with spectral power (both at electrode and source level) and information flow (effective connectivity) using Time-resolved Partial Directed Coherence (TPDC).

Treadmill training in Parkinson's disease is underpinned by the interregional connectivity in cortical-subcortical network.

Treadmill training (TT) has been extensively used as an intervention to improve gait and mobility in patients with Parkinson's disease (PD). Regional and global effects on brain activity could be induced through TT. Training effects can lead to a beneficial shift of interregional connectivity towards a physiological range.

Machine learning aided classification of tremor in multiple sclerosis.

Background: Tremors are frequent and disabling in people with multiple sclerosis (MS). Characteristic tremor frequencies in MS have a broad distribution range (1-10 Hz), which confounds the diagnostic from other forms of tremors. In this study, we propose a classification method for distinguishing MS tremors from other forms of cerebellar tremors.

Reduced Cross-Frequency Coupling and Daytime Sleepiness in Obstructive Sleep Apnea Patients.

Obstructive sleep apnea (OSA) is associated with sleep-stage- and respiratory-event-specific sensorimotor cortico-muscular disconnection. The modulation of phase−amplitude cross-frequency coupling (PACFC) may influence information processing throughout the brain. We investigated whether sleep-stage-specific PACFC is impaired at the sensorimotor areas in OSA patients.

Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.

Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS.

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study.

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients.

Alterations in White Matter Network and Microstructural Integrity Differentiate Parkinson's Disease Patients and Healthy Subjects.

Parkinson's disease (PD) is a neurodegenerative disease, neuropathologically characterized by progressive loss of neurons in distinct brain areas. We hypothesize that quantifiable network alterations are caused by neurodegeneration. The primary motivation of this study was to assess the specific network alterations in PD patients that are distinct but appear in conjunction with physiological aging.

Cerebello-cortical network fingerprints differ between essential, Parkinson's and mimicked tremors.

Cerebello-thalamo-cortical loops play a major role in the emergence of pathological tremors and voluntary rhythmic movements. It is unclear whether these loops differ anatomically or functionally in different types of tremor. We compared age- and sex-matched groups of patients with Parkinson's disease or essential tremor and healthy controls (n = 34 per group).

Primary Sensorimotor Cortex Drives the Common Cortical Network for Gamma Synchronization in Voluntary Hand Movements.

Gamma synchronization (GS) may promote the processing between functionally related cortico-subcortical neural populations. Our aim was to identify the sources of GS and to analyze the direction of information flow in cerebral networks at the beginning of phasic movements, and during medium-strength isometric contraction of the hand. We measured 64-channel electroencephalography in 11 healthy volunteers (age: 25 ± 8 years; four females); surface electromyography detected the movements of the dominant hand.

Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS.

Functional near infrared spectroscopy (fNIRS) is a promising neuroimaging method for investigating networks of cortical regions over time. We propose a directed effective connectivity method (TPDC) allowing the capture of both time and frequency evolution of the brain's networks using fNIRS data acquired from healthy subjects performing a continuous finger-tapping task. Using this method we show the directed connectivity patterns among cortical motor regions involved in the task and their significant variations in the strength of information flow exchanges.

Changes of Functional and Directed Resting-State Connectivity Are Associated with Neuronal Oscillations, ApoE Genotype and Amyloid Deposition in Mild Cognitive Impairment.

The assessment of effects associated with cognitive impairment using electroencephalography (EEG) power mapping allows the visualization of frequency-band specific local changes in oscillatory activity. In contrast, measures of coherence and dynamic source synchronization allow for the study of functional and effective connectivity, respectively. Yet, these measures have rarely been assessed in parallel in the context of mild cognitive impairment (MCI) and furthermore it has not been examined if they are related to risk factors of Alzheimer's disease (AD) such as amyloid deposition and apolipoprotein ε4 (ApoE) allele occurrence.

Complex network analysis of resting-state fMRI of the brain.

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected.

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network.

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters.

Essential and aging-related tremor: Differences of central control.

Background: For essential tremor, the distribution of age of onset is bimodally distributed, with peaks in adolescence and another in late adulthood. The latter is here referred to as aging-related tremor, and it is considered to be associated with earlier aging and increased mortality. We hypothesize that different tremor networks detected by multichannel electroencephalography (EEG) underlie these two tremor groups.

Multi-modal causality analysis of eyes-open and eyes-closed data from simultaneously recorded EEG and MEG.

Owing to the recent advances in multi-modal data analysis, the aim of the present study was to analyze the functional network of the brain which remained the same during the eyes-open (EO) and eyes-closed (EC) resting task. The simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) were used for this study, recorded from five distinct cortical regions of the brain. We focused on the 'alpha' functional network, corresponding to the individual peak frequency in the alpha band.

Differences in hemispherical thalamo-cortical causality analysis during resting-state fMRI.

Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis.

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures.

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities.

Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks.

Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC).

Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG.

Several studies demonstrated that resting-state EEG power differs tremendously between school-aged children and adults. Low-frequency oscillations (delta and theta, <7 Hz) are dominant in children but become less prominent in the adult brain, where higher-frequency alpha oscillations (8-12 Hz) dominate the mature brain rhythm. However, this assessment of developmental effects with EEG power mapping is restricted to the scalp level and blind to the information flow between brain regions, thus limiting insights about brain development.