Functional network dynamics in a neurodevelopmental disorder of known genetic origin

Abstract

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9‐associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data‐driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting‐state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

Transient resting-state network dynamics in cognitive ageing

“…Conversely, the least common network, with lowest FO and NO and with greatest MIL, was frontotemporoparietal network FTP3. These findings largely agree with Hawkins et al 23 and (to a lesser extent) with other previous studies 21,22 , though now based on a much larger sample with a much larger age range. Our next step was to apply CCA to relate the 32 temporal characteristics of the HMM states (4 metrics for each of the 8 states) to age (such a multivariate analysis is important in the presence of multicollinearity, given the co-dependence between the 4 metrics of HMM state dynamics).…”

“…The states include three distributed frontotemporoparietal networks (FTP1, FTP2, FTP3), a higher-order visual network (HOV), two early visual networks (EV1, EV2) and two sensorimotor networks (SM1, SM2). They are similar to those obtained from young adults in previous studies [21][22][23] . Each map shows the partial correlation between the state time course and the parcelwise amplitude envelopes.…”

“…The partial correlation values have been thresholded to show correlation values above 50% of the maximum correlation across all states. To refer to the states, we use the same naming scheme applied by Hawkins et al 23 .…”

“…In addition to the rsMEG scan, these individuals also completed a wide range of cognitive tasks. We characterised transient network dynamics using the recent application of Hidden Markov Models (HMMs) in order to explore the temporal dynamics of rsMEG networks [21][22][23][24][25][26] . An HMM is a data-driven method that identifies a sequence of "states", where each state corresponds to a unique pattern of brain covariance that reoccurs at different points in time.…”

“…By quantifying the time-series of MEG data as a sequence of transient states, the HMM provides information about the periods of time at which each state is active, enabling the characterisation of its temporal dynamics. While this technique has identified network dynamics in small resting-state or task MEG datasets 21,23,24 , these dynamics have not yet been linked to age and cognition. In particular, the size of the Cam-CAN cohort allowed us to take a multivariate approach, namely to use canonical correlation analysis (CCA) to relate the temporal properties of the data-driven HMM states to profiles of cognitive performance, and to test whether these relations differ with age.…”