Journal article
Tracking longitudinal language network reorganisation using functional MRI connectivity fingerprints
- Abstract:
- Large individual differences in how brain networks respond to treatment hinder efforts to personalise treatment in neurological conditions. We used a brain network fingerprinting approach to longitudinally track re-organisation of complementary phonological and semantic language networks in 19 patients before and after brain-tumour surgery. Patient task fingerprints were individually compared to normal networks established in 17 healthy controls. Additionally, pre- and post-operative patient fingerprints were directly compared to assess longitudinal network adaptations. We found that task networks remained stable over time in healthy controls, whereas treatment induced reorganisation in 47.4% of patient fluency networks and 15.8% of semantic networks. How networks adapted after surgery was highly unique; a subset of patients (10%) showed ‘normalisation’ while others (21%) developed newly atypical networks after treatment. The strongest predictor of adaptation of the fluency network was the presence of clinically reported language symptoms. Our findings indicate a tight coupling between processes disrupting performance and neural network adaptation, the patterns of which appear to be both task- and individually-unique. We propose that connectivity fingerprinting offers potential as a clinical marker to track adaptation of specific functional networks across treatment interventions over time.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 2.7MB, Terms of use)
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- Publisher copy:
- 10.1016/j.nicl.2021.102689
Authors
- Publisher:
- Elsevier
- Journal:
- NeuroImage: Clinical More from this journal
- Volume:
- 30
- Article number:
- 102689
- Publication date:
- 2021-04-30
- Acceptance date:
- 2021-04-27
- DOI:
- EISSN:
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2213-1582
- Language:
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English
- Keywords:
- Pubs id:
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1175380
- Local pid:
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pubs:1175380
- Deposit date:
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2021-05-10
Terms of use
- Copyright holder:
- NL Voets et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
- Licence:
- CC Attribution (CC BY)
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