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) has been shown to be a promising alternative to fMRI for mapping cortical hemodynamics in young healthy adults. HD-DOT imaging can be more precise when coupled with subject specific head models rather than generic atlas-based head models. While MRI-derived head models are commonly used, in some patient groups including subjects with metal and/or electrical implants, only CT images can be obtained. In this study, we developed a CT-based head modeling pipeline and demonstrated the feasibility of improved mapping of brain responses to tasks compared to a generic atlas-based head-model.
Deep-brain stimulation (DBS) of the ventro-intermediate nucleus of the thalamus (VIM) can provide substantial clinical motor benefit to Essential Tremor (ET) patients. However, the DBS impact on the functional connectivity (FC) of networks is difficult to study using standard neuroimaging modalities either due to limited temporal resolution (PET) or safety concerns from contraindications (fMRI). In this study, we tested the feasibility and sensitivity of High-Density Diffuse Optical Tomography (HD-DOT), which avoids these concerns, for mapping cortical blood flow responses to sensory stimuli and measuring resting state cortical FC in ET patients with VIM DBS OFF vs ON.
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.
Current gold standard neuroimaging tools lack either necessary temporal resolution (PET) or optimal safety due to contraindications (fMRI) for measuring the neural mechanisms underlying the effects of deep brain stimulation of the subthalamic nucleus (STN DBS) in Parkinson disease (PD). In this study, we validate the feasibility of High-Density Diffuse Optical Tomography (HD-DOT) for mapping the cortical activity of the PD patients with their STN DBS ON and OFF during auditory and visual tasks and during resting state.
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.
Using naturalistic language generation tasks (e.g. overt speech) to capture the neural correlates of speech production is important, as less naturalistic (but often used) tasks such as covert language generation are not a reliable substitute for accurately assessing cortical activation associated with naturalistic speech. fMRI poses challenges to implementing naturalistic language tasks, especially in clinical populations, because it is noisy, physically constraining, and contraindicated in populations with metal implants. High-density diffuse optical tomography (HD-DOT) is particularly well-suited for naturalistic language tasks because it is silent, wearable, portable, and metal-compatible. This study investigates cortical activity underlying naturalistic language generation using HD-DOT. Six adult subjects aged 20-26 years completed two scans on two separate days consisting of three different tasks: covert word reading (RW), covert verb generation (CV), and overt verb generation (OV). Cortical responses were apparent in expected anatomical areas for all tasks and RW, CV, and OV evoked responses of increasing strength (peak ΔHbO (μMol) = 7.58, 10.3, and 11.0, respectively). Notably, OV recruits additional activation in Broca’s area and right-lateralized primary motor cortex as compared to CV. These findings are consistent with those obtained using fMRI9,10,11 and underscore the need to use naturalistic language tasks when assessing the neural representations of natural speech. These results motivate extension to further investigations of naturalistic language processing of increased complexity, both receptive and productive, such as within-room conversation.
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