Computational Methods and Optimization for Naturalistic Functional Brain Mapping Using Diffuse Optical Tomography

Naturalistic neuroimaging has significantly advanced our understanding of the brain in everyday situations. Stimulus designs that previously targeted individual brain regions are now being replaced by activities such as watching movies, engaging in conversations, and interacting with virtual reality environments. However, traditional functional neuroimaging techniques like functional magnetic resonance imaging (fMRI) are inherently unnaturalistic, as participants must lay down and remain still within the magnet's bore. Consequently, these naturalistic paradigms require an equally naturalistic neuroimaging modality. High-density diffuse optical tomography (HD-DOT) offers a wearable alternative to fMRI by utilizing overlapping optical measurements to densely sample cortical brain activity. HD-DOT has proven effective in mapping brain responses to features from audiovisual movies, indicating that this method is appropriate for naturalistic neuroimaging. Here, we aim to optimize computational methods to advance naturalistic neuroimaging using HD-DOT. With movie viewing as an accessible naturalistic task, we map (or encode) audiovisual features, including concurrent features such as speech and faces from animated clips. We further demonstrate the feasibility of multi-sensory decoding by predicting which movie clip a participant viewed based on their DOT data. To improve our DOT data, we constructed a very high-density DOT imaging system for whole-head, high-resolution optical neuroimaging. This was validated in healthy adults completing functional localizer and movie-viewing tasks and was directly compared to fMRI. Decoding tasks underscore the repeatability of our signal, which is essential for naturalistic neuroimaging studies. Finally, a simulation approach was introduced to estimate the performance of optical neuroimaging systems using large-scale fMRI datasets. This provides a data-driven method for decision-making regarding new imaging systems and stimulus designs. Overall, this dissertation establishes and optimizes computational methods for naturalistic neuroimaging using DOT as a wearable surrogate for fMRI.