An Open Dialogue Between Neuromusicology and Computational Modelling Methods

Music perception, cognition, and production research have progressed significantly from examining neural correlates of musical components to a better understanding of the interplay of multiple neural pathways that are both unique and shared among other higher neurocognitive processes. The interactions between the neural connections to perceive an abstract entity like music and how musicians make music are an area to be explored in greater depth. With the abstract nature of music and cultural differences, carrying out research studies using ecologically valid stimuli is becoming imperative. Artificial intelligence (AI) and machine learning (ML) models are data-driven approaches that can investigate whether our current understanding of the neural substrates of musical behaviour can be translated to teach machines to perceive, decode, and produce music akin to humans. AI algorithms can extract features from human-music interaction. Training ML models on such features can help in information retrieval to look at the brain\u27s natural music processing, recognizing the patterns concealed within it, deciphering its deeper meaning, and, most significantly, mimicking human musical engagements. The question remains how these models can be generalized for knowledge representation of human musical behaviour and what would be applications in a more ecologically valid manner

The perturbational map of low frequency repetitive transcranial magnetic stimulation of primary motor cortex in movement disorders

Background: Repetitive transcranial magnetic stimulation (rTMS) is applied to the primary motor cortex (M1) for the treatment of different movement disorders like Writer's Cramp (WC), Essential tremor (ET), and Spinocerebellar ataxia (SCA). However, the benefits vary, ranging from no effect to significant improvement in tremor. The variation in the benefits obtained from rTMS might be due to the difference in the spread of the stimuli across the brain areas associated with tremor. The spread of stimuli can be evaluated by combining rTMS with resting state functional magnetic resonance imaging (rsfMRI). Aim: To determine the spread of low frequency rTMS for WC, ET and SCA after stimulation of M1. Method: The rsfMRI was collected from the participants with WC (n = 27), ET (n = 30) and SCA (n = 28) at two time points, i.e., before and after the delivery of 1 Hz rTMS. Two measures from dynamic systems theory were calculated to understand how the system interacts with exogenously applied input (rTMS), namely entropy and frustration. While entropy measures the disorder from the rsfMRI time series, frustration quantifies it by assessing the change in sign (positive to negative, and vice-versa) of the functional connections.  The two quantities outlined the spread of perturbation due to rTMS. Result: For WC, a dense architecture of functional connections facilitated the spread across the bilateral areas of the five regions namely- the frontal cortex (frontal-Mid and Sup), motor cortex (supplemental motor area, precentral- and postcentral gyrus), parietal cortex (precuneus), subcortical regions (caudate, thalamus, posterior and mid-cingulate cortex) and the cerebellum (Crus-I and II, Cerebellum III and VI, and Vermis VI). For ET, though the areas belong to the above-mentioned 5 regions, the spread was narrower (i.e., lesser areas in the region). For SCA, the sparse connection between the areas led to minimal spread with no propagation of rTMS perturbation to the cerebellar regions. Interestingly for the three disorders, rTMS reduced the disorderliness (frustration and entropy) of the neural circuit (motor, subcortical and cerebellar regions) but increased the disorderliness in the default mode network (frontal and parietal regions). Conclusion: The spread of perturbation (due to TMS on M1) in the functional circuit varies in the three movement disorders. The current rTMS protocol achieves a wider spread for WC but not for ET and SCA. Findings from the present study suggest that pathology-specific stimulation protocols are required in movement disorders

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

peer reviewedBackground and Purpose: Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise"

A Single Session of rTMS Enhances Small-Worldness in Writer’s Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph

peer reviewedBackground and Purpose: Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise"

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy

Purpose Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients with Temporal Lobe Epilepsy (TLE) in the absence of an interictal discharge (IED). Method 4 Classes of microstates were computed from 2 min artefact free EEG epochs in 42 subjects (21 TLE and 21 controls). The percentage of time coverage, frequency of occurrence and duration for each of these microstates were computed and redundancy reduced using feature selection methods. Subsequently, Fishers Linear Discriminant Analysis (FLDA) and logistic regression were used for classification. Result FLDA distinguished TLE with 76.1% accuracy (85.0% sensitivity, 66.6% specificity) considering frequency of occurrence and percentage of time coverage of microstate C as features. Conclusion Microstate alterations are present in patients with TLE. This feature might be useful in the diagnosis of epilepsy even in the absence of an IED

Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy

Objectives Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state “epilepsy networks,” we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. Methods Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain “rsfMRI epilepsy networks.” Results SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. Conclusions IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these “rsfMRI epilepsy networks” could reflect epileptogenesis in TLE