Poster No:
1725
Submission Type:
Abstract Submission
Authors:
Brandon Ingram1, Stephen Mayhew2, Andrew Bagshaw1
Institutions:
1University of Birmingham, Centre for Human Brain Health, Birmingham, West Midlands, 2Aston University, Institute of Health and Neurodevelopment (IHN), Birmingham, West Midlands
First Author:
Brandon Ingram
University of Birmingham, Centre for Human Brain Health
Birmingham, West Midlands
Co-Author(s):
Introduction:
The thalamus is the largest subcortical structure in the human brain and is widely involved in sensory processing, with visual, auditory, somatosensory, and gustatory signals all passing through their associated thalamic nuclei before being relayed to their respective cortical areas. Due to this, the thalamus has often been viewed as a simple sensory relay station, and as a result has been understudied within human neuroscience research (Shine et al., 2023). However, the region has since been associated with more complex functions, including visual attention (Rees, 2009) and the generation and regulation of the alpha rhythm (Becker et al., 2015; Suffczynski et al., 2001). One such function is the modulation of visual responses, with recent animal literature demonstrating that the pulvinar nucleus modulates the firing rate of V1 neurons during visual perception (De Souza et al., 2020). In addition, fMRI research in humans has shown that the thalamus, as well as other subcortical regions such as the putamen, exhibit unique BOLD patterns when different resting-state networks are active (Greene et al., 2020), suggesting a link with the control of resting-state networks (Hwang et al., 2017). This demonstrates that the thalamus is implicated in a wide range of functions and that the relay station hypothesis represents an oversimplification of the region. This study employed EEG-fMRI to investigate the dynamics of the thalamus and their involvement with visual response modulation and the occipital alpha rhythm.
Methods:
Thirty control participants were displayed full contrast visual checkerboards to the left visual field to evoke a lateralised visual BOLD response, VEP and alpha ERD/S. EEG (BrainProducts) and fMRI (Siemens Prisma, TR=1010 ms, 2.5 × 2.5 × 2.5 mm) were recorded simultaneously. The thalamus was masked, and a group-level ICA (10 components) performed to obtain a functional parcellation of the thalamus. This was used as input within a Hidden Markov Model (HMM-MAR toolbox) (Vidaurre et al, 2016). We evaluated the impact of thalamic states when they were active at the time of visual stimulation on subsequent BOLD, VEP and alpha ERD/S responses. Additionally, we investigated the relationship between the thalamic states and occipital alpha power by calculating an alpha power time course and correlating it with the thalamic state time courses.
Results:
Hidden Markov Modelling identified a total of six thalamic states. Thalamic state at pre-stimulus baseline significantly impacted the magnitude of subsequent BOLD responses and the amplitude of the P250 component of the VEP. Two of the thalamic states exhibited a specific temporal relationship with occipital alpha power, with alpha power increasing or decreasing approximately six to eight seconds after the onset of the thalamic state.
Conclusions:
This study demonstrates that the thalamus exhibits complex BOLD dynamics during a visual task, with different states showing specific relationships with the rest of the brain and with posterior alpha power. Further, these thalamic states significantly modulated responses to visual stimuli when they were active at pre-stimulus baseline, influencing the amplitude of the visual BOLD response peak and the amplitude of the P250 VEP component. The results presented here confirm the complex role of the thalamus, beyond what would be expected of a simple sensory relay. HMM is able to identify multiple thalamic BOLD states, with strong relationships with the rest of the brain and posterior alpha power. These states modulate visual responses, highlighting the involvement of the thalamus in both the generation of the alpha rhythm and visual attention.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Electroencephaolography (EEG)
FUNCTIONAL MRI
Modeling
Thalamus
Vision
1|2Indicates the priority used for review
Provide references using author date format
Becker, R. et al. (2015). Relating alpha power and phase to population firing and hemodynamic activity using a thalamo-cortical neural mass model. PLoS computational biology, 11(9), e1004352.
de Souza, B. O. F. et al. (2020). Pulvinar modulates contrast responses in the visual cortex as a function of cortical hierarchy. Cerebral Cortex, 30(3), 1068-1086.
Greene, D. J. et al. (2020). Integrative and network-specific connectivity of the basal ganglia and thalamus defined in individuals. Neuron, 105(4), 742-758.
Hwang, K. et al. (2017). The human thalamus is an integrative hub for functional brain networks. Journal of Neuroscience, 37(23), 5594-5607.
Rees, G. et al. (2009). Visual attention: the thalamus at the centre?. Current biology, 19(5), R213-R214.
Shine, J. M., et al. (2023). The impact of the human thalamus on brain-wide information processing. Nature Reviews Neuroscience, 1-15.
Suffczynski, P. et al. (2001). Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention. International Journal of Psychophysiology, 43(1), 25-40.
Vidaurre, D. et al. (2016). Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage, 126, 81-95.