Functional MRI has decoded complex information about naturalistic stimuli using brain responses, but other non-invasive technologies have not achieved similar decoding capabilities. To evaluate feasibility of naturalistic visual decoding with Diffuse Optical Tomography (DOT), a 6.5-mm-spaced optode grid was employed to decode which of four 90-second movies was viewed by human subjects. >90% and >80% average decoding accuracy were achieved using a template-matching decoder within and between sessions, respectively. Average accuracy remained >60% and above chance using a model-based decoder to identify four and 40 clips outside the decoder's training set, respectively. DOT therefore has potential for more-complex neural decoding.
High-Density Diffuse Optical Tomography (HD-DOT) is an emerging modality that uses a dense array of overlapping measurements that provides image quality validated against fMRI. Here, we doubled the optode density of our previously reported HD-DOT system, expanded the field of view and achieve higher resolution and image quality. Task-based and movie viewing activation maps reveal strong contrast to noise of cortical function across these tasks. Additionally, we show the expanded field of view covers functional networks not available with our previously reported HD-DOT system. This system is promising for future studies using resting state functional connectivity, decoding, and naturalistic paradigms.
Functional magnetic resonance imaging has decoded complex information about naturalistic stimuli using brain responses, but other non-invasive technologies have not achieved similar decoding capabilities. To evaluate feasibility of naturalistic visual decoding with diffuse optical tomography (DOT), a 6.5-mm-spaced optode grid was employed to decode which of four naturalistic, 90-second movie clips was viewed by human subjects. Over 85% average decoding accuracy was achieved using a template-matching decoder. Average accuracy remained above 60% and above chance using a model-based decoder to identify 4 and 40 clips outside the decoder's training set, respectively. DOT therefore has potential for more-complex neural decoding tasks.
Studying brain development requires child-friendly imaging modalities and stimulus paradigms. High density diffuse optical tomography provides enhanced image quality over fNIRS and is validated extensively against fMRI in adults. Movie viewing reduces head motion and increases task engagement. Movie features are tracked and correlated with brain activity to map multiple processing pathways in parallel. We propose machine learning methods to extract high-level audiovisual features to avoid the time-consuming, subjective task of manual coding these feature regressors. Using a Faster Region-based Convolutional Neural Network, we achieve high correlation values between manually and automatically generated face regressors and regression coefficient maps.
Functional magnetic resonance imaging has decoded complex information about naturalistic stimuli using brain responses, but other non-invasive technologies have not achieved similar decoding capabilities. To evaluate feasibility of naturalistic visual decoding with diffuse optical tomography (DOT), a 6.5-mm-spaced optode grid was employed to decode which of four naturalistic, 90-second, audio-free movie clips was viewed by human subjects. Over 85% average decoding accuracy was achieved using a simple template-matching decoder, and this exceeded the accuracy from a sparser optode grid with 13-mm spacing. High-density DOT is therefore promising for more-complex neural decoding tasks in the future.
High density diffuse optical tomography (HD-DOT) combines logistical advantages of fNIRS with enhanced image quality, validated extensively against fMRI in adults and neonates. However, HD-DOT is yet to be evaluated in preschool-age children. Here we present an HD-DOT system optimized for preschoolers, including a 128-source by 125-detector console, light-weight fiber optics, and an expanded field-of-view. We validated the system by mapping cortical activations during visual, auditory, and motor tasks in adults. We then imaged children while they watched movies, finding reproducible patterns of brain activity and showing that feature regressors can map functionally specific regions from movie-viewing data in preschoolers.
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