Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia.

Stavros I Dimitriadis,Jack Μ Fletcher,Andrew C Papanicolaou,Panagiotis G Simos

doi:10.3390/brainsci9120380

Abstract

Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8–60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity.

Highlights

The study of human brain development is rapidly becoming a central research area for understanding the nature of neuropsychiatric diseases and various developmental disorders [1,2].A major breakthrough in this line of research was the demonstration of coherent patterns of brain activity at rest [3,4,5], advancing the notion that the human brain is a self-organizing system constantly displaying coherent patterns of activity, both locally and globally, rather than a passive device solely driven by bottom-up processes [6,7,8]
Previous resting-state functional magnetic resonance imaging (fMRI) studies based on static brain connectivity and multivariate pattern analysis tools (MVPA, support vector machine, and support vector regressor) attempted to predict individual age [9,13] and classify individual participants according to their actual age [13,14]
A dynamic functional connectivity study based on fMRI resting-state further demonstrated that the temporal variability in the strength of specific connections afforded more accurate modelling of spontaneous fluctuations related to maturation age [11]

Summary

Introduction

The study of human brain development is rapidly becoming a central research area for understanding the nature of neuropsychiatric diseases and various developmental disorders [1,2].A major breakthrough in this line of research was the demonstration of coherent patterns of brain activity at rest [3,4,5], advancing the notion that the human brain is a self-organizing system constantly displaying coherent patterns of activity, both locally and globally, rather than a passive device solely driven by bottom-up processes [6,7,8]. The study of human brain development is rapidly becoming a central research area for understanding the nature of neuropsychiatric diseases and various developmental disorders [1,2]. A key development in this line of research was the demonstration of intrinsic connectivity networks such as the salience, prefrontal, sensorimotor, and default mode networks, derived from functional connectivity analyses of resting-state functional magnetic resonance imaging (fMRI) data. Several developmental trends have since been described including changes in the strength of shortand long-range connections [9], the expansion of cortical hubs outside sensorimotor regions [10], and increasing variability of connections between the default mode, visual, and cerebellar networks [11]. A dynamic functional connectivity study based on fMRI resting-state further demonstrated that the temporal variability in the strength of specific connections afforded more accurate modelling of spontaneous fluctuations related to maturation age [11]

Methods

Results

Discussion

Conclusion