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Published on: October 13, 2016
Energy-Landscape Analysis of Brain Network Dynamics in a Multicenter Alzheimer's Disease and Mild Cognitive
Rixing Jing1, Peng Li2, Kun Zhao3
1School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China.
Background:
Convergent dynamic functional connectivity studies have demonstrated their potential as a hallmark for capturing the impairments in brain function associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, our understanding of whole-brain dynamic patterns remains limited, which hampers understanding of cognitive impairment and symptomatology in AD and MCI.
Methods:
An energy-landscape analysis was conducted to investigate brain dynamics across 7 large-scale networks in 516 normal control participants (NCs), 404 patients with AD, and 441 participants with MCI from a multicenter cohort.
Results:
This method identified major brain states and quantified their size, duration, and transitions. In AD and MCI, transitions between these major states were excessively frequent, state durations were abnormal, and brain state sizes were enlarged. Furthermore, direct transitions between major states were significantly negatively correlated with cognitive ability and structural characteristics.
Conclusions:
This study has revealed aberrant brain dynamics in large-scale networks among patients compared with NCs, suggesting that patients experience less stable states and more frequent transitions. The brain dynamic-cognition and dynamic-structure associations indicate that the dynamics of brain states could serve as a critical biological endophenotype of AD. These findings provide new insights into understanding and addressing brain network dynamics in AD and MCI.
