It’s about time: Linking dynamical systems with human neuroimaging to understand the brain

Abstract

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain’s time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective; by focusing on dynamics instead of static snapshots of neural activity; and by embracing modeling approaches that map neural dynamics using “forward” models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Learning Multiple Movements in Parallel—Accurately and in Random Order, or Each with Added Noise?

“…These brain states correspond to those described by the hypo-frontality hypothesis [ 88 ], which are often achieved immediately after endurance sports training and lead to effects on executive functions with prolonged use. While endurance exercises normally require more than 15 min of relatively high intensity to achieve these brain states and can rarely be applied multiple times a day, DL training appears to achieve similar brain states after only 3 min [ 89 ] and can be applied multiple times a day. In contrast to CI theory, the overloading of the working memory as an explanation for the interference phenomenon after the acquisition phase in CI learning by even more various exercises in DL did not lead to an interfered performance in the post-test but rather led to a qualitative switch in information processing during the processing of multiple gross motor tasks by getting rid of the controlling and limiting activities of the frontal lobe to be able to use additional neuronal resources [ 63 , 90 ].…”

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

“…Coupled NMMs are rapidly moving from mathematical curiosities to real applications in healthy and diseased brains (Honey and Sporns, 2008 ; Alstott et al, 2009 ; de Haan et al, 2012 ; Yang et al, 2016 ; Zimmermann et al, 2018 ; Singh et al, 2020 ) and can potentially be used to investigate biological processes such as neuromodulation (Shine et al, 2018 , 2021 ; John et al, 2022 ). Such NMMs have also been simulated via the Virtual Brain platform—an open source neuroinformatics tool that provides access to brain network simulation tools (Ritter et al, 2013 ; Sanz-Leon et al, 2015 ).…”

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

“…FCD is an indicator of the brain's capacity to explore flexibly a large range of brain states, which reduces with cognitive decline in both healthy aging (Battaglia et al, 2020) and Alzheimer's disease (Córdova-Palomera et al, 2017). Additionally, there is a growing body of literature that observed evidence of a nonlinear mapping between SC and FC (John et al, 2022;Shine et al, 2019) and especially between interhemispheric SC and homotopic FC (Mollink et al, 2019;Roland et al, 2017), between SC and FCD (John et al, 2022;Naik et al, 2017;Shen et al, 2015) as well as respective age-related findings (Esfahlani et al, 2021;Naik et al, 2017;Roland et al, 2017), which might benefit from a more holistic approach based on modeling to explain the link between structure and function. To deepen the understanding of the complex link between structural changes, functional changes and cognitive decline, the current challenge for aging models is then twofold: on the one hand, the comprehension of the cause-effect of SC and its functional phenotype and on the other, the link to cognitive decline as suggested by various aging theories (Goh, 2011;Naik et al, 2017;Reuter-Lorenz & Park, 2014).…”