Abstract
This paper describes a group-level analysis of 14 subjects with prefrontal cortex (pFC) lesions and 20 healthy controls performing multiple lateralized visuospatial working memory (WM) trials. Using effective brain connectivity measures inferred from directed information (DI) during memory encoding, we first show that DI features can correctly classify 18 control subjects and 11 subjects with pFC lesions, providing an overall accuracy of 85.3%. Second, we show that differential DI, the change in DI during the encoding phase from pretrial, can successfully overcome inter-subject variability and correctly identify the class of all 34 subjects (100% accuracy). These accuracy results are based on two-thirds majority thresholding among all trials. Finally, we use Welch's t-test to identify the crucial differences in the two classes' sub-networks responsible for memory encoding. While the inflow of information to the prefrontal region is significant among subjects with pFC lesions, the outflow from the prefrontal to the frontal and central regions is diminished compared to the control subjects. We further identify specific neural pathways that are exclusively activated for each group during the encoding phase.
Original language | English (US) |
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Title of host publication | 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781665473583 |
DOIs | |
State | Published - 2023 |
Event | 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia Duration: Apr 18 2023 → Apr 21 2023 |
Publication series
Name | 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) |
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Conference
Conference | 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 |
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Country/Territory | Colombia |
City | Cartagena |
Period | 4/18/23 → 4/21/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Directed information (DI)
- Effective connectivity
- Prefrontal cortex (pFC) lesion
- Working memory
- differential DI