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Neuroimage Clin. 2020 Jan 17;25:102186. doi: 10.1016/j.nicl.2020.102186. [Epub ahead of print]

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

Author information

1
School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
2
School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore. Electronic address: asjagath@ntu.edu.sg.
3
MIT Center for Statistics and Data Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.

Abstract

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.

KEYWORDS:

Alzheimer’s disease; Ambivert degree; Brain modules; Functional MRI; Functional connectivity; Gateway coefficient; Hubs; Participation coefficient

PMID:
32000101
DOI:
10.1016/j.nicl.2020.102186
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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no competing interests.

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