Global brain function is predicted by the brain's anatomical connectivity and myelination
Poster No:
1636
Submission Type:
Abstract Submission
Authors:
Parul Verma1, Benjamin Sipes2, Joline Fan1, Srikantan Nagarajan1, Ashish Raj1
Institutions:
1University of California San Francisco, San Francisco, CA, 2University of California, San Francisco, San Francisco, CA
First Author:
Co-Author(s):
Introduction:
Understanding how the dynamic brain functional patterns arise despite the brain's structural constraints is a prevailing question in neuroscience. We address this question by quantifying brain function in terms of spectral patterns and the alpha frequency band (8-13 Hz) spatial patterns as observed in magnetoencephalography (MEG). The brain structural constraints are quantified in terms of the anatomical connectivity and regional myelination. Subsequently, we investigate how the spectral and spatial patterns arise in different states of consciousness.
Methods:
We use whole-brain biophysical modeling that incorporates the brain's anatomical connectivity and regional myelination to predict the spectral and spatial patterns as observed with MEG. It is a global analytic model that describes the coupled excitatory and inhibitory activity of local neuronal subpopulations, and the long-range excitatory macroscopic activity, for every brain region. The local neuronal excitatory and inhibitory time constants are modulated by the extent of myelination in that region. The model is parameterized by a small set of global parameters that we inferred for two healthy cohorts (N=36, 18). We investigate the following three different states of consciousness: wakefulness, non-rapid eye movement sleep (N2 stage), and propofol-induced anesthesia.
Results:
We show that the spectral and spatial patterns are predictable from the brain's anatomical connectivity and regional myelin map in the resting wakefulness state, using the biophysical model (Fig 1A and B). We confirm this finding in two healthy cohorts (N=36, 18; figures shown only for the N=18 cohort). Further, the model can capture the transition of the spectral peak in the alpha band in wakefulness to the delta band in the N2 stage (Fig 1A). Simultaneously, it can capture the disappearance of the alpha band spectral power's posterior-anterior spatial gradient when transitioning from wakefulness to N2 stage (Fig 1B). Comparing the model parameters show that the overall global coupling is increased in N2, in addition to changes in the excitatory signals (not shown). Last, increasing the inhibitory neuronal time constant to simulate the effect of propofol anesthesia in the model results in arousal of delta waves, as observed in empirical EEG-based studies previously (Fig 1C). Simultaneously, it results in an anteriorization of the alpha band's spatial patterns, as observed in prior empirical studies.
Conclusions:
Together, these results suggest a parsimonious explanation for the spectral and spatial patterns in wakefulness, N2 sleep, and general anesthesia. In addition, comparing the biophysical parameters when transitioning from resting state to N2 sleep allows us to infer the biophysical mechanisms underlying this transition.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis 1
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 2
Keywords:
Computational Neuroscience
Consciousness
MEG
Modeling
Myelin
Sleep
1|2Indicates the priority used for review
Provide references using author date format
Purdon, Patrick L., et al. "Clinical electroencephalography for anesthesiologists: part I: background and basic signatures." Anesthesiology 123.4 (2015): 937-960.