Detecting Cognitive Fatigue in Subjects with Traumatic Brain Injury from FMRI Scans Using Self-Supervised Learning
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Date
2023-07Author
Jaiswal, Ashish
Ashwin, Ramesh Babu
Mohammad Zaki, Zadeh
Wylie, Glenn
Makedon, Fillia
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Show full item recordAbstract
Understanding cognitive states from fMRI data have yet to be investigated to its full extent due to its complex nature. In this work, the
problem of understanding cognitive fatigue among TBI patients has
been formulated as a multi-class classification problem. We built a
Spatio-temporal encoder model using convolutions and LSTMs as
the building blocks to extract spatial features and to model the 4D
nature of fMRI scans. To learn a better representation of the data
and the condition, we used a self-supervised learning technique
called "Contrastive Learning" to pretrain our encoder with a public
dataset BOLD5000 and further fine-tuned our labeled dataset to
predict cognitive fatigue. Furthermore, we present an fMRI dataset
that contains scans from a mix of Traumatic Brain Injury (TBI)
patients and healthy controls (HCs) while performing a series of
standardized N-back cognitive tasks. This method establishes a
state-of-the-art technique to analyze cognitive fatigue from fMRI
data and beats previous approaches to solve this problem with
different modalities. Besides, the ability of our models to take in
raw fMRI scans (noisy images with artifacts output directly from
the scanner) eliminates the need to implement a manual signal
processing pipeline that varies based on the scanner used. Finally,
we study the impact of different brain regions contributing to CF.
The proposed technique outperforms the state-of-the-art method
by over 13 percent on this dataset.