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Characterization of Cognitive Function in Survivors of Diffuse Gliomas Using Morphometric Correlation Networks.

Authors :
Wang C
Cho NS
Dyk KV
Islam S
Raymond C
Choi J
Salamon N
Pope WB
Lai A
Cloughesy TF
Nghiemphu PL
Ellingson BM
Source :
Tomography (Ann Arbor, Mich.) [Tomography] 2022 May 26; Vol. 8 (3), pp. 1437-1452. Date of Electronic Publication: 2022 May 26.
Publication Year :
2022

Abstract

This pilot study investigates structural alterations and their relationships with cognitive function in survivors of diffuse gliomas. Twenty-four survivors of diffuse gliomas (mean age 44.5 ± 11.5), from whom high-resolution T1-weighted images, neuropsychological tests, and self-report questionnaires were obtained, were analyzed. Patients were grouped by degree of cognitive impairment, and interregional correlations of cortical thickness were computed to generate morphometric correlation networks (MCNs). The results show that the cortical thickness of the right insula ( R <superscript>2</superscript> = 0.3025, p = 0.0054) was negatively associated with time since the last treatment, and the cortical thickness of the left superior temporal gyrus ( R <superscript>2</superscript> = 0.2839, p = 0.0107) was positively associated with cognitive performance. Multiple cortical regions in the default mode, salience, and language networks were identified as predominant nodes in the MCNs of survivors of diffuse gliomas. Compared to cognitively impaired patients, cognitively non-impaired patients tended to have higher network stability in network nodes removal analysis, especially when the fraction of removed nodes (among 66 nodes in total) exceeded 55%. These findings suggest that structural networks are altered in survivors of diffuse gliomas and that their cortical structures may also be adapting to support cognitive function during survivorship.

Details

Language :
English
ISSN :
2379-139X
Volume :
8
Issue :
3
Database :
MEDLINE
Journal :
Tomography (Ann Arbor, Mich.)
Publication Type :
Academic Journal
Accession number :
35736864
Full Text :
https://doi.org/10.3390/tomography8030116