Functional networks, language impairment and recovery after treatment in aphasia
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1
Boston University, United States
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2
Boston University, Cognitive Imaging Center, United States
This abstract is part of the symposium “Neuronal networks in language and aphasia”. Neuroimaging studies have typically focused on characterizing regions of activation involved in the left or right hemisphere as evidence for language recovery in aphasia. Indeed, language recovery is more nuanced than simple hemispheric differences between the damaged left hemisphere, homologous right hemisphere regions or findings of bilateral activation. One approach to addressing the complex nature of aphasia recovery is to examine functional networks of recovery during language processing or at rest. In this presentation, we focus on three sets of complementary data examining language impairment and recovery (after treatment) in aphasia. First, we discuss studies examining effective connectivity (using dynamic causal modeling) in patients with aphasia and healthy controls while they perform a semantic processing task. A constrained set of bilateral frontal and temporal regions were selected as regions of interest and various combinations of connections between these regions were specified and divided into families of models. Results show that relative to healthy controls who demonstrate preference for a left-lateralized model, patients’ fMRI task data are best explained by either left-lateralized or bilateral posterior connectivity models. These results suggest that in aphasia, left frontal and bilateral temporal regions function as a network to subserve language recovery.
In the second set of studies, we discuss functional connectivity (FC) (computed using the CONN toolbox) within a broad set of ROIs while patients are performing a semantic feature or naming task. Results show that healthy controls show significantly more strong positive connections than patients with aphasia, particularly in bilateral frontal regions, indicating that task-based functional connectivity is reduced in patients with aphasia. Further, once patients receive and improve after therapy, the differences in FC in patients reduce relative to healthy controls indicating some form of normalization of function after intervention.
In the third set of studies, we discuss graph measures of network connectivity and efficiency using rsFMRI. This approach uses a workflow implemented in MATLAB that includes clustering spatially contiguous voxels to form data-driven regions-of-interest or nodes in the network. The averaged cluster time series is converted into a correlation matrix, which is then analyzed using a set of network measures that include global and local efficiency, node degree, and betweenness centrality. Differences between healthy controls and patients indicate altered measures of betweenness centrality, local efficiency, and node degree, which show common spatial localization. Further, after patients with aphasia receive therapy, changes in these metrics are observed as a function of intervention.
The results of these studies are discussed in the context of proposed theories of language recovery in the brain and the effect of treatment on this process of recovery. Importantly, these results highlight the importance of examining functional networks in the brain that are dynamically engaged during language processing and after intervention.
Keywords:
networks,
Aphasia,
Neuroimaging (anatomic and functional),
Rehabilitation,
graph theory
Conference:
Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018.
Presentation Type:
symposium
Topic:
not eligible for a student prize
Citation:
Kiran
S,
Meier
EL,
Johnson
JP,
Pan
Y,
El Guindy
S and
Bohland
JW
(2019). Functional networks, language impairment and recovery after treatment in aphasia.
Conference Abstract:
Academy of Aphasia 56th Annual Meeting.
doi: 10.3389/conf.fnhum.2018.228.00038
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Received:
30 Apr 2018;
Published Online:
22 Jan 2019.
*
Correspondence:
Prof. Swathi Kiran, Boston University, Boston, United States, kirans@bu.edu