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

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum

“…Figure 2 displays the temporal parameters assessing the transience and stability of each state. Overall, MLT across the 8 states varied between 100 and 225 ms, which is in line with previous MEG envelope HMM studies 33 , 34 , 36 , 37 . The only significant group effects emerged for MLT and FO of the posterior DMN activation state (State 6).…”

“…The 8 transient recurrent HMM states disclosed in our participants exhibited spatial and temporal patterns rather similar to those previously described 33 , 34 , 36 , 37 , 41 . Importantly, we also found one state (State 6) characterized by activated bilateral TPJs and precunei when visited, corresponding to an activated posterior DMN state.…”

“… 35 ). From MEG data, about 6–8 transient recurring states lasting 50–200 ms are typically disclosed with a spatial topography quite similar to that of the main RSNs 33 , 34 , 36 , 37 . Results obtained in AD patients demonstrated that a state of increased power in the DMN (including bilateral inferior parietal lobes, medial prefrontal cortex and lateral temporal cortices) was visited less often and for shorter periods of time in participants with AD than in matched healthy subjects 34 .…”

The “neural shift” of sleep quality and cognitive ageing: A resting-state MEG study of transient neural dynamics

“…In addition to MEG scanning, these individuals also completed a self-report sleep questionnaire (PSQI: Pittsburgh Sleep Quality Index; Buysse et al, 1989) and a wide range of cognitive tasks. We utilized the characteristics of the transient neural dynamics using Hidden Markov Models (HMM; Baker et al, 2014;Brookes et al, 2018;Hawkins et al, 2020;Vidaurre et al, 2016Vidaurre et al, , 2017Vidaurre et al, , 2018 as inferred in our previous study (Tibon et al, 2021). HMM is a datadriven 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 NEURAL DYNAMICS, SLEEP & COGNITIVE AGEING 6 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 characterization of its temporal dynamics. This technique had been used to identify neural dynamics in resting-state or task MEG datasets (Baker et al, 2014;Hawkins et al, 2020;Vidaurre et al, 2016), and was recently linked to age and cognition in a largescale cohort (Tibon et al, 2021). The size of the Cam-CAN cohort allowed us to then take a multivariate approach, namely, to use PLS analysis to relate the temporal properties of the data-driven HMM states to profiles of sleep quality and cognitive performance.…”