Biology subjects

Moezzi, B.

Publications and source records attributed to Moezzi, B..

4 recordsLinked to original sources

bioRxiv2019

Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation

ObjectiveTo develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS). MethodsWe combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP. ResultsA MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input-output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction. ConclusionsThe model reproduces effects seen in common TMS paradigms. SignificanceThe model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures. HighlightsO_LIA model of motor-evoked potential formation gives a realistic electromyogram in response to TMS. C_LIO_LIThe model reproduces effects of SICI, ICF and LICI. C_LIO_LIA link between existing neural field modeling and realistic outcome measures of TMS is provided. C_LI

biophysics↗

bioRxiv2019

Associations between EEG functional brain connectivity and a cognitive reserve proxy in healthy older adults

Cognitive reserve is a concept that explains individual differences in vulnerability to cognitive impairment due to age and dementia-related brain changes. Mechanisms underlying the cognitive reserve effect are poorly understood. We investigated associations between a comprehensive cognitive reserve proxy (Lifetime Experiences Questionnaire/LEQ) and functional connectivity of the prefrontal cortex across the whole scalp, covarying for the level of current cognitive functioning (Addenbrookes Cognitive Examination Revised/ACE-R), using multiblock parallel and orthogonalized partial least squares regression. EEG data were collected from 34 healthy older adults (63 to 83 years) in eyes-open and eyes-closed resting-states, and during 0-back and 1-back tasks. Functional connectivity was estimated using imaginary coherence in the theta and alpha frequency bands, as these bands have been heavily implicated in cognitive ageing, attention and executive function. We found three clusters of electrodes where the absolute values of the regression coefficient were above threshold when covarying for ACE-R: (1) a cluster approximating the right frontocentral region during the eyes-open condition in the theta band with seed electrodes approximating the left prefrontal cortex with positive associations of medium effect size; (2) a cluster approximating the right parietotemporal region during the 0-back task in the theta band with seed electrodes approximating the right prefrontal cortex with negative associations of medium to large effect sizes; and (3) a cluster approximating the occipitoparietal region in the eyes-closed condition in the alpha band with seed electrodes approximating the left prefrontal cortex with negative associations of medium effect size. These relationships between a cognitive reserve proxy and functional connectivity, within key networks and frequency bands associated with attention and executive function, may reflect greater neural capacity and efficiency.

bioengineering↗

bioRxiv2018

Characterization of young and old adult brains: An EEG functional connectivity analysis

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age. Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.

neuroscience↗

bioRxiv2018

Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory

Metrics of brain network organization can be derived from neuroimaging data using graph theory. We explored the test-retest reliability of graph metrics of functional networks derived from resting-state electroencephalogram (EEG) recordings. Data were collected within two designs: (1) within sessions (WS) design where EEG data were collected from 18 healthy participants in four trials within a few hours and (2) between sessions (BS) design where EEG data were collected from 19 healthy participants in three trials on three different days at least one week apart. Electrophysiological source activity was reconstructed and functional connectivity between pairs of sensors or brain regions was determined in different frequency bands. We generated undirected binary graphs and used intra-class correlation coefficient (ICC) to estimate reliability. We showed that reliabilities ranged from poor to good. Reliability at the sensor-level was significantly higher than source-level. The most reliable graph metric at the sensor-level was cost efficiency and at the source-level was global efficiency. At the sensor-level: WS reliability was significantly higher than BS reliability; high beta band in WS design had the highest reliability; in WS design reliability in gamma band was significantly lower than reliability in low and high beta bands. At the source-level: low beta band in BS design had the highest reliability; there was no significant main effect of frequency band on reliability; reliabilities of WS and BS designs were not significantly different. These results suggest that these graph metrics can provide reliable outcomes, depending on how the data were collected and analysed.

neuroscience↗