Poster No:
1783
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, Birmingham, West Midlands
First Author:
Brandon Ingram
University of Birmingham, Centre for Human Brain Health
Birmingham, West Midlands
Co-Author(s):
Introduction:
Functional magnetic resonance imaging (fMRI) is one of the most widely applied methods for non-invasively investigating human brain function. Unlike other neuroimaging techniques, fMRI can measure changes in the BOLD signal across the entirety of the human brain at millimetre resolution. Thus, with fMRI, it is possible to investigate the functions of deeper brain structures (e.g. the thalamus) and to identify large-scale networks (Biswal et al., 1995; Smith et al., 2009). Research has demonstrated that the BOLD signal exhibits a large amount of variability within individuals, between sessions (Aguirre et al., 1998) and within sessions, with the BOLD response varying to repeated presentations of the same stimulus (Bianciardi et al., 2009; Duann et al., 2002; Schölvinck et al., 2012; Mayhew et al., 2013). A proportion of this variability in BOLD responses results from the ongoing BOLD dynamics within the region, with the baseline activity becoming linearly summated with the evoked BOLD activity (Fox & Raichle, 2007; Fox et al., 2006). However, these studies generally focussed on the spontaneous BOLD signal within the sensory cortex associated with the stimulus, neglecting the impact of BOLD signals within more distal brain regions. Recent research in rodents has demonstrated that spontaneous BOLD activity across the whole brain (including areas that do not respond to the stimulus) explained approximately 30% of the variability in visual BOLD responses, indicating that the spontaneous activity across the whole brain also explains a large amount of the variance in visually evoked responses (Zhang et al., 2022). Therefore, this study applied Hidden Markov Modelling to EEG-fMRI data with the goal of identifying whole-brain states, and evaluating their impact on visually evoked BOLD responses, visually evoked potentials (VEPs) and alpha synchronisation / desynchronisation (alpha ERD/S).
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. A group level ICA (20 components) was performed to obtain a functional parcellation of the resting state networks. This was used as input within a Hidden Markov Model (HMM-MAR toolbox) (Vidaurre et al, 2016). We then evaluated how whole-brain states influenced visual BOLD responses, VEPS and alpha ERD/S responses when the states were active at the time of visual stimulation.
Results:
The HMM approach found five whole-brain states, with three significantly modulating the amplitude of subsequent BOLD responses and the P100 VEP component when active at the time of visual stimulation. Specifically, whole-brain states associated with high BOLD activity within visual cortex resulted in smaller BOLD responses and P100 component amplitudes (and vice versa for whole-brain states associated with low BOLD activity in visual cortex). Additionally, the amplitude of the visual BOLD responses was significantly increased when a DMN state was active at the time of visual stimulation. The ERD/ERS was not impacted significantly by pre-stimulus brain state.
Conclusions:
Overall, the results of this study demonstrated that the active whole-brain state during the pre-stimulus baseline period significantly influences BOLD responses and P100 amplitudes. This finding shows that baseline BOLD activity defined across the whole brain affects subsequent BOLD responses and electrophysiological responses to visual stimuli, highlighting the importance of accounting for baseline BOLD activity within neuroimaging research. HMMs are an effective way of identifying brain states to examine their impact on stimulus responses.
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
Vision
1|2Indicates the priority used for review
Provide references using author date format
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