Mapping brain function during naturalistic viewing using high-density diffuse optical tomography

Abstract

Naturalistic stimuli, such as movies, more closely recapitulate “real life” sensory processing and behavioral demands relative to paradigms that rely on highly distilled and repetitive stimulus presentations. The rich complexity inherent in naturalistic stimuli demands an imaging system capable of measuring spatially distributed brain responses, and analysis tools optimized for unmixing responses to concurrently presented features. In this work, the combination of passive movie viewing with high-density diffuse optical tomography (HD-DOT) is developed as a platform for naturalistic brain mapping. We imaged healthy young adults during free viewing of a feature film using HD-DOT and observed reproducible, synchronized cortical responses across a majority of the field-of-view, most prominently in hierarchical cortical areas related to visual and auditory processing, both within and between individuals. In order to more precisely interpret broad patterns of cortical synchronization, we extracted visual and auditory features from the movie stimulus and mapped the cortical responses to the features. The results demonstrate the sensitivity of HD-DOT to evoked responses during naturalistic viewing, and that feature-based decomposition strategies enable functional mapping of naturalistic stimulus processing, including human-generated speech.

Keep it real: rethinking the primacy of experimental control in cognitive neuroscience

“…These datasets will eventually become exhausted as competing models improve and reach ceiling performance; data generators will never be out of work and there will always be a market for innovations in data acquisition. Developing technologies, such as continuous intracranial electroencephalography (iEEG; e.g., Wang et al, 2016 ), functional near-infrared spectroscopy (fNIRS; e.g., Liu et al, 2017 ), high-density diffuse optical tomography (HD-DOT; e.g., Fishell et al, 2019 ), and wearable magnetoencephalography (MEG; Boto et al, 2018 ) promise higher-fidelity and more ergonomic neuroimaging. Even the workhorse fMRI is beginning to see increased adoption of immersive virtual reality paradigms ( Mathiak and Weber, 2006 ; Spiers and Maguire, 2006 , 2007 ; Maguire, 2012 ).…”

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

“…The resulting sensitivity matrix A is the linear transformation for each time point between x , the vector of absorption coefficients at 750nm and 850nm within the brain volume, and y , the vector of relative changes in measured light levels at each wavelength detected at the head surface, as per the linear Rytov approximation: This sensitivity matrix was inverted using Tikhonov regularization (λ 1 =0.01) and spatially variant regularization (λ 2 = 0.1), following Eggebrecht et al (2014) . The wavelength-dependent absorption and scattering coefficients (units mm −1 ) for the five non-uniform tissue compartments were as follows: scalp ( μ a , 750 = 0.017; μ a , 850 = 0.019; μ S , 750’ = 0.74; μ S ,850’ = 0.64), skull ( μ a , 750 = 0.012; μ a ,850 = 0.014; μ S ,750’ = 0.94; μ S ,850’ = 0.84), cerebrospinal fluid ( μ a ,750 = 0.004; μ a ,850 = 0.004; μ S ,750’ = 0.3; μ S ,850’ = 0.3), grey matter ( μ a ,750 = 0.018; μ a ,850 = 0.019; μ S ,750’ = 0.84; μ S ,850’ = 0.67), and white matter ( μ a ,750 = 0.017; μ a ,850 = 0.021; μ S ,750’ = 1.19; μ S ,850’ = 1.01) ( Bevilacqua et al, 1999 ; Custo et al, 2006 ; Eggebrecht et al, 2012 ; Fishell et al, 2019 ; Strangman et al, 2002 ).…”

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

“…The most common methods are linear filtering of the signal, such as the application of band pass filter [40, 80, 81], low [82] or high pass filter [83]. Other less used methods are Kalman filter [81, 83], adaptive filter [84], principal component analysis [85] or envelope [86] depending on the tasks or study area. A problem during the processing of DOT data which generally occurs during signal filtering is that the judgment of the researcher may lead to an error in the cutoff frequencies selected.…”