Decoding visual information from high-density diffuse optical tomography neuroimaging data

Abstract

Background: Neural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated. Objective: To assess the feasibility and performance of decoding with HD-DOT in visual cortex. Methods and Results: To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs > 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing. Conclusions: Visual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations.

Optical imaging and spectroscopy for the study of the human brain: status report

“… 4 A diverse set of functional neuroimaging paradigms has also been illustrated with HD-DOT, including traditional tasks, 2 resting-state functional connectivity, 6 , 7 naturalistic movie mapping, 8 and most recently decoding studies. 9 …”

“…4 A diverse set of functional neuroimaging paradigms has also been illustrated with HD-DOT, including traditional tasks, 2 resting-state functional connectivity, 6,7 naturalistic movie mapping, 8 and most recently decoding studies. 9 One of the main limitations of HD-DOT has been the mass of the fibers used in fiber-based HD-DOT systems. While fiber-based systems have thus far set the standard regarding specifications 2,6,10 (including detectivity, dynamic range, crosstalk, frame rate, optode-scalp coupling, and modulation/demodulation strategies for encoding source illuminations 4 ), several research groups have been demonstrating fiberless implementations of HD-DOT.…”

Anatomical Modeling and Optimization of Speckle Contrast Optical Tomography

“…Indeed, HD-DOT imaging has been demonstrated extensively in functional neuroimaging paradigms including visual retinotopy, [15][16][17] functional connectivity, 18 language mapping, 11,19 and decoding of neural activity. 20 More recently, fiberless HD-DOT systems have retained the same optode design but with active electro-optics. 17,[21][22][23][24][25][26] These HD-DOT designs can be leveraged for designing SCOT systems.…”

Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography

“…[13][14][15][16][17] We decode the identities of novel, previously unseen movie clips among 4-40 options with a motion-energy encoding model, 2 expanding on previous optical studies that used template-based decoding or simpler methods and thus were not able to decode outside of the training stimuli. [9][10][11][12]18 Neural decoding infers partial information about a stimulus, state, or imagined action from measurements of brain activity evoked by that stimulus, state, or action. 8 This decoding enables key scientific and clinical applications, such as testing whether particular brain regions carry information about specific stimulus features and forming brain-computer interfaces that enable people to perform augmented communication or control a device with thought instead of explicit movement.…”