osl-dynamics: A toolbox for modelling fast dynamic brain activity

Gohil, Chetan; Huang, Ruobing; Roberts, Evan; Es, Mats W.J. van; Quinn, Andrew; Vidaurre, Diego; Woolrich, Mark W.

doi:10.1101/2023.08.07.549346

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…It has been proposed that brain network dynamics are fundamental to healthy brain function. 65 Future iterations of this model might seek to include brain dynamics combined with important static features in larger, unseen datasets.…”

Section: Discussionmentioning

confidence: 99%

The cortical neurophysiological signature of amyotrophic lateral sclerosis

Trubshaw,

Gohil,

Yoganathan

et al. 2024

Brain Communications

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The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterise the cortical oscillatory signature of amyotrophic lateral sclerosis for potential as a pharmacodynamic biomarker. Eight-ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n=36) and healthy age-matched controls (n=51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutations-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a Random Forest Regression model was trained and evaluated using nested Leave-One-Out Cross-Validation. Amyotrophic lateral sclerosis was characterised by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta, and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The Random Forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.

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Section: Discussionmentioning

confidence: 99%

The cortical neurophysiological signature of amyotrophic lateral sclerosis

Trubshaw,

Gohil,

Yoganathan

et al. 2024

Brain Communications

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“…While we focus here on the estimation of dynamic FC using the HMM on MEG data, this approach can be readily adapted to other methods that estimate static or dynamic FC, and on other modalities including EEG and fMRI. Source code for the HIVE model is available in the osl-dynamics toolbox ( [24]) The x-axis encodes whether a word is for animals or non-animals and the y-axis encodes whether the object can fly or not. b) Illustration of possible variability captured by embedding vectors in brain networks found in electrophysiological data such as M/EEG.…”

Section: Introductionmentioning

confidence: 99%

Modelling variability in functional brain networks using embeddings

Huang,

Gohil,

Woolrich

2024

Preprint

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Neuroimaging techniques offer unprecedented insights into the dynamic neural processes underlying cognitive functions. With recent studies, data driven models like the Hidden Markov Model (HMM) are getting more attention due to their ability to infer fast temporal dynamics in functional networks in an unsupervised manner. However, these dynamic network models are typically trained at the group level. Whilst it is possible to post-hoc estimate the session-specific networks with the so-called dual estimation, this does not allow the model to discover and benefit from subpopulation structure in the group. We propose an extension to the HMM model that incorporates embedding vectors (c.f. word embedding in Natural Language Processing) to explicitly model individual sessions while training on the entire group. This effectively infers a "fingerprint" for each individual session, which can group together those with similar spatio-temporal patterns. With simulated data, we show that the model can recover the underlying subpopulation structure, achieve higher accuracy than dual estimation on session-specific quantities and can make use of increasing number of sessions to benefit the inference of individual sessions. Applying this model to resting-state and task MEG data, we show the learnt embedding vectors capture meaningful sources of variation across a population. This includes subpopulations related to demographics and systematic differences, such as scanner types or measurement sites. The proposed model offers a powerful new technique for modelling individual sessions while leveraging information from an entire group.

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osl-dynamics: A toolbox for modelling fast dynamic brain activity

Cited by 2 publications

References 51 publications

The cortical neurophysiological signature of amyotrophic lateral sclerosis

The cortical neurophysiological signature of amyotrophic lateral sclerosis

Modelling variability in functional brain networks using embeddings

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