APOE
            ‐
            ε4
            shapes temporo‐parietal network properties in middle‐aged, cognitively unimpaired individuals: A graph theory analysis

Background: Neuronal loss in Parkinson’s Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored.Objective/Hypothesis: To investigate the distributed network properties associated with STN-DBS in patients with advanced PD.Methods: Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states.Results: Dynamic analysis identified two unique brain states: a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state – STN-DBS was negatively correlated with network assortativity.Conclusion: Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS.

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Brain networks in eating disorders: a systematic review of graph theory studies

Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity ◽

10.1007/s40519-021-01172-x ◽

2021 ◽

Author(s):

Enrico Collantoni ◽

Francesco Alberti ◽

Valentina Meregalli ◽

Paolo Meneguzzo ◽

Elena Tenconi ◽

...

Keyword(s):

Systematic Review ◽

Eating Disorders ◽

Graph Theory ◽

Brain Networks ◽

Neural Systems ◽

Theory Analysis ◽

Level Of Evidence ◽

Network Properties ◽

Level I ◽

Graph Theory Analysis

Abstract Purpose Recent evidence from neuroimaging research has shown that eating disorders (EDs) are characterized by alterations in interconnected neural systems, whose characteristics can be usefully described by connectomics tools. The present paper aimed to review the neuroimaging literature in EDs employing connectomic tools, and, specifically, graph theory analysis. Methods A systematic review of the literature was conducted to identify studies employing graph theory analysis on patients with eating disorders published before the 22nd of June 2020. Results Twelve studies were included in the systematic review. Ten of them address anorexia nervosa (AN) (AN = 199; acute AN = 85, weight recovered AN with acute diagnosis = 24; fully recovered AN = 90). The remaining two articles address patients with bulimia nervosa (BN) (BN = 48). Global and regional unbalance in segregation and integration properties were described in both disorders. Discussion The literature concerning the use of connectomics tools in EDs evidenced the presence of alterations in the topological characteristics of brain networks at a global and at a regional level. Changes in local characteristics involve areas that have been demonstrated to be crucial in the neurobiology and pathophysiology of EDs. Regional imbalances in network properties seem to reflect on global patterns. Level of evidence Level I, systematic review.

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