Evolution of brain network dynamics in neurodevelopment

Abstract

Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8–11 and 19–22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to young adulthood. Collectively, these results suggest that healthy development is accompanied by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks.

The energy landscape underpinning module dynamics in the human brain connectome

“…Several studies have proposed statistical approaches to test for the presence of non-stationarity, including the assessment of the variance of the FC time series (Sakoğlu et al, 2010), test statistics based on the FC time series’ Fourier-transform (Handwerker et al, 2012), linear (e.g., variance of correlation series (Hindriks et al, 2016)) and nonlinear test statistics (Zalesky et al, 2014), among others (Chang and Glover, 2010; Keilholz et al, 2013; Laumann et al, 2016). The bulk of the evidence points to the non-stationary nature of BOLD FC, a conclusion that is consistent with recent work suggesting that non-stationarity in BOLD functional connectivity reflects changes in ongoing cognitive processes supporting learning, working memory function, linguistic processing, and executive function (Bassett et al, 2011; Braun et al, 2015; Chai et al, 2016, 2017; Hutchison et al, 2013a). Nonetheless, some of these reports are inconclusive, mainly because test statistics are commonly compared against that of null (stationary) time series and creating such time series with matching covariance structure, spectral properties, and stationary FC to this day remains a challenge (Hindriks et al, 2016).…”

“…A natural question following the observation of these modules was “What do they do? And how are they recruited as we go through life performing a variety of functions?” To address these questions, dynamic community detection methods were developed and applied to neuroimaging data, revealing the fact that modules reconfigure in support of working memory (Braun et al, 2015, 2016), reinforcement learning (Gerraty et al, 2016), visuo-motor learning (Bassett et al, 2011, 2013b, 2015), and linguistic processing (Chai et al, 2017; Doron et al, 2012a). Module reconfiguration at rest has also been reported as a marker of aging and development (Betzel et al, 2015).…”

Network neuroscience

“…Although dynamics on networks and dynamics of networks are both important areas of research, emerging tools from machine learning are beginning to bring the two areas together to better understand the time-varying expression of sets of networks, where all networks in the set exist at every time point, but each network is expressed to differing amounts 93 . In addition, developing methods for understanding the dynamics of networks-of-networks that are interconnected with one another across contexts and scales remains challenging 94 .…”

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

“…In recent applications to brain networks, these models have been exercised in the context of static network representations in both health [111] and disease [112], and in both humans and non-human animals [113]. Extending these tools into the temporal domain is a particularly exciting prospect which could offer fundamental insights into the mechanisms of network reconfiguration, and alterations in those mechanisms that may accompany normative neurodevelopment [114], healthy aging [115], or aberrant dynamics in neurological disease [116-118] or psychiatric disorders [107,119,120] that impact on learning. Classical network models are summarized in Box 3.…”

“…While we have focused this review on the application of network neuroscience tools to non-invasive neuroimaging data from adult humans, particularly collected while individuals are learning a new visuomotor skill, it will also be of interest in future to extend these ideas to non-adult cohorts where patterns of network reconfiguration may display distinct trajectories [114]. In addition, it will be important to understand the degree to which the reconfiguration of modules is an important biomarker of other types of learning, and whether these same biomarkers are characteristic of neural networks constructed at much finer spatial scales [142], such as where neurons are treated as network nodes, and synapses or similarities in spiking or calcium transients are treated as network edges.…”