Poster No:
1676
Submission Type:
Abstract Submission
Authors:
Sorenza Bastiaens1,2, Davide Momi1, Taha Morshedzadeh1,2, Parsa Oveisi1,3, Kevin Kadak1,2, John Griffiths1,2,3,4
Institutions:
1Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada, 2Institute of Medical Science, University of Toronto, Toronto, Canada, 3Institute of Biomedical Engineering, University of Toronto, Toronto, Canada, 4Department of Psychiatry, University of Toronto, Toronto, Canada
First Author:
Sorenza Bastiaens
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)|Institute of Medical Science, University of Toronto
Toronto, Canada|Toronto, Canada
Co-Author(s):
Davide Momi
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)
Toronto, Canada
Taha Morshedzadeh
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)|Institute of Medical Science, University of Toronto
Toronto, Canada|Toronto, Canada
Parsa Oveisi
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)|Institute of Biomedical Engineering, University of Toronto
Toronto, Canada|Toronto, Canada
Kevin Kadak
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)|Institute of Medical Science, University of Toronto
Toronto, Canada|Toronto, Canada
John Griffiths
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)|Institute of Medical Science, University of Toronto|Institute of Biomedical Engineering, University of Toronto|Department of Psychiatry, University of Toronto
Toronto, Canada|Toronto, Canada|Toronto, Canada|Toronto, Canada
Introduction:
Understanding the underlying mechanisms driving resting-state alpha oscillations is of major importance due to their role in a number of neurocognitive processes and pathologies. This study aims to compare and contrast current prominent alpha rhythmogenesis theories within a novel framework. The approach involves identifying large-scale resting-state spatial feature variations associated with the alpha rhythm. Subsequently, a corticothalamic model is fitted, and parameters are explored to elucidate the key circuits believed to shape alpha rhythms. The rationale for examining spatial variations arises from the hypothesis that alpha oscillations in rostral and caudal regions originate from distinct neural processes1.
Methods:
Using the publicly available Cam-CAN dataset2 (N=631, ages 18-88), resting-state MEG source analysis resulted in time series from 8196 source locations per subject, from which the power spectrum was computed. Periodic features (alpha peak frequency and power) and aperiodic components (low and high-frequency slope), were estimated using the FOOOF toolbox3. Cortical parcellation into 200 regions-of-interest with the Schaefer brain atlas4 facilitated the investigations of spatial variations within different age groups. Finally, the significance of the spatial and age-related empirical feature variations were determined with pearson correlations and linear mixed models. Source power spectra from occipital and frontal regions were then fitted with the BrainTrak toolbox5, which estimates the Robinson corticothalamic model6 parameters, including cortico-cortical, corticothalamic and intrathalamic connection strengths (gains).
Results:
A significant Pearson correlation with the MNI y coordinate value, indicating a posterior-anterior gradient, was observed in 73.81% of subjects for alpha peak frequency, 96.98% for alpha power, 89.37% for the high-frequency aperiodic slope, and 80.79% for the low-frequency aperiodic exponent (all p<0.005). Subject-averaged feature values within each decile (18-27, 28-37,…, 78-88) also showed a posterior-anterior gradient, with significant negative correlations observed in all age groups between y and alpha peak frequency (r=-0.8877 to r=-0.5382, p<0.0001), alpha power (r=-0.9583 to r=-0.9105, p<0.0001), high-frequency aperiodic exponent (r=-0.8388 to -0.5417, p<0.0001), and low-frequency exponent (r=-0.5534 to -0.8659, p<0.0001). Within these, the strength of correlation with y was weaker in older subjects for the high-frequency aperiodic exponent, and stronger for the low-frequency aperiodic exponent. These consistent variations over space were observed for all spectral features, but markedly stronger in alpha power. Furthermore, linear mixed model results found a significant effect of space and a significant effect of age for the alpha peak (ROI coeff.=-4.199, z=-54.373, p<0.0001; Age coeff.=-0.006, z=-5.490, p<0.0001; ROI:Age coeff.=0.052, z=38.187, p<0.0001). Between the anterior vs posterior regions, our physiological model parameter fitting results indicated a higher cortico-cortical activity in frontal areas than occipital regions (t=19.1398, p<0.0001), but a greater corticothalamic (t=-9.0176, p<0.0001) and intrathalamic (t=-12.0461, p<0.0001) activity in occipital regions.
Conclusions:
Our results confirm previously-reported spatial and age-related variations in oscillatory brain activity7,8,9, separating out the contribution of different spectral features with greater precision than earlier work. Moreover, physiologically-based modelling of these data was able to capture these effects - suggesting that activity in occipital areas is more strongly driven by corticothalamic interactions than frontal areas, and conversely that the observed frontal features reflect relatively stronger cortico-cortical interactions than in occipital areas.
Lifespan Development:
Aging
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Keywords:
Aging
Computational Neuroscience
Cortex
Data analysis
MEG
Modeling
Other - Resting-state
1|2Indicates the priority used for review
Provide references using author date format
1: Hoshi, H. (2020), 'Age-and gender-specific characteristics of the resting-state brain activity: a magnetoencephalography study', Aging, vol. 12(21), pp. 21613-21637
2: Taylor, J.R. (2017), 'The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample', NeuroImage, vol. 144, pp.262-269
3: Donoghue T (2020). 'Parameterizing neural power spectra into periodic and aperiodic components'. Nature Neuroscience, vol. 23, pp. 1655-1665
4: Schaefer, A. (2018), 'Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI', Cerebral cortex, vol. 28(9), pp. 3095-3114
5: Abeysuriya R.G. (2016), 'Real-time automated EEG tracking of brain states using neural field theory', Journal of Neuroscience Methods, vol 258, pp. 28-45
6: Roberts, J. A. (2012), 'Corticothalamic dynamics: structure of parameter space, spectra, instabilities, and reduced model', Physical Review E, vol. 85(1), pp. 011910.
7: Mahjoory, K. (2020), 'The frequency gradient of human resting-state brain oscillations follows cortical hierarchies', Elife, vol. 9, pp. e53715.
8: Lew, B. J. (2021), 'Three-year reliability of MEG resting-state oscillatory power', NeuroImage, vol. 243, pp. 118516
9: Donoghue, T. (2020), 'Parameterizing neural power spectra into periodic and aperiodic components', Nature neuroscience, vol. 23(12), pp. 1655-1665