Biology subjects

Muthukumaraswamy, S. D.

Publications and source records attributed to Muthukumaraswamy, S. D..

3 recordsLinked to original sources

bioRxiv2017

Multi-band component analysis for EEG artifact removal and source reconstruction with application to gamma-band activity

Independent component analysis (ICA) has been used extensively for artifact removal and reconstruction of neuronal time-courses in electroencephalography (EEG). Typically, ICA is applied on wide-band EEG (for example 1 to 100 Hz or similar ranges). Since EEG captures the activities of a large number of sources and the fact that number of the components separated by the ICA is limited by the number of the sensors, only the stronger sources (in terms of magnitude and duration) will be detected by the ICA, and the activity of weaker sources will be lost or scattered amongst the stronger components. Because of the 1/f nature of the EEG spectra this biases components to the lower frequency ranges. Here we used a versatile combination of a filter bank, PCA and ICA, calling it multi-band ICA, to both increase the number of the ICA components substantially and improve the SNR of the separated components. Using band-pass filtering we break the original signal mixture into several subbands, and using PCA we reduce the dimensionality of each subband, before applying ICA to a matrix containing all the principal components from each band. Using simulated sources and real EEG of participants, we demonstrate that multi-band ICA is able to outperform the traditional wide-band ICA in terms of both signal-noise ratio of the separated sources and the number of the identified independent components. We successfully separated the gamma-band neuronal components time-locked to a visual stimulus, as well as weak sources which are not detectable by wide-band ICA.

neuroscience↗

bioRxiv2017

Modification of scale-free electrophysiological activity induced by changes in excitatory-inhibitory balance is governed by the dynamics of multiple oscillatory relaxation processes

Neurophysiological recordings are dominated by arhythmical activity whose spectra can be characterised by power-law functions, and on this basis are often referred to as reflecting scale-free brain dynamics (1/f {beta}). Relatively little is known regarding the neural generators and temporal dynamics of this arhythmical behaviour compared to rhythmical behaviour. Here we used Irregularly Resampled AutoSpectral Analysis (IRASA) to quantify {beta}, in both the high (5-100 Hz, {beta}hf) and low frequency bands (0.1-2.5 Hz, {beta}lf in EEG/MEG/ECoG recordings and to separate arhythmical from oscillatory modes of activity, such as, alpha rhythms. In MEG/EEG/ECoG data, we demonstrate that oscillatory alpha power dynamically correlates over time with {beta}hf and similarly, participants with higher rhythmical alpha power have higher {beta}hf). In a series of MEG investigations using the GABA reuptake inhibitor tiagabine, the glutamatergic AMPA receptor antagonist perampanel, the NMDA receptor antagonist ketamine and the mixed partial serotonergic agonist LSD we reveal systematic effects of excitation-inhibition balance on both {beta}hf and {beta}lf. Additionally, strong modulations of {beta}hf are seen in monkey ECoG data during general anaesthesia using propofol and ketamine. Surrogate data analysis demonstrates that arhythmical activity is generated by both linear and non-linear schemes, with non-linear effects emerging at critical boundaries. We develop and test a unifying model which can explain, the 1/f nature of electrophysiological spectra, their dynamic interaction with oscillatory rhythms as well as the sensitivity of 1/f activity to excitation-inhibition balance by considering electrophysiological spectra as being generated by a collection of stochastically perturbed damped oscillators having a distribution of relaxation rates.

neuroscience↗

bioRxiv2017

Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian Learning mechanisms in the cerebral cortex

HighlightsO_LIERP and DCM study of two sensory plasticity paradigms: roving MMN and visual LTP\nC_LIO_LIFirst demonstration of multiple learning mechanisms under different task demands\nC_LIO_LIEvidence for both Predictive Coding and Hebbian learning mechanisms\nC_LIO_LIThe BDNF Val66Met polymorphism modulates ERPs for both paradigms\nC_LIO_LIHowever, the polymorphism only modulates MMN network connectivity\nC_LI\n\nThe Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aims of the current study were to 1) compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain, and 2) assess whether the paradigms identify similar group differences in plasticity. Forty participants were split into two groups based on the BDNF Val66Met polymorphism and were presented with both paradigms. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. Genetic differences were identified in the ERPs for both paradigms, but were only apparent in backward connections of the MMN network. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. Additionally, both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

neuroscience↗