A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping

Abstract

Functional neuroimaging commands a dominant role in current neuroscience research. However its use in bedside clinical and certain neuro-scientific studies has been limited because the current tools lack the combination of being non-invasive, non-ionizing and portable while maintaining moderate resolution and localization accuracy. Optical neuroimaging satisfies many of these requirements, but, until recent advances in high-density diffuse optical tomography (HD-DOT), has been hampered by limited resolution. While early results of HD-DOT have been promising, a quantitative voxel-wise comparison and validation of HD-DOT against the gold standard of functional magnetic resonance imaging (fMRI) has been lacking. Herein, we provide such an analysis within the visual cortex using matched visual stimulation protocols in a single group of subjects (n=5) during separate HD-DOT and fMRI scanning sessions. To attain the needed voxelto-voxel co-registration between HD-DOT and fMRI image spaces, we implemented subjectspecific head modeling that incorporated MRI anatomy, detailed segmentation, and alignment of source and detector positions. Comparisons of the visual responses found an average localization error between HD-DOT and fMRI of 4.4 +/− 1 mm, significantly less than the average distance between cortical gyri. This specificity demonstrates that HD-DOT has sufficient image quality to be useful as a surrogate for fMRI.

The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience

“…Several studies [48][49][50][51][52] have been conducted to validate and to compare the metabolic correlates of neural activity as measured by fNIRS (i.e., increase in HbO 2 and decrease in HbR) with the gold standard measured by fMRI (i.e., the blood oxygenation level-dependent (BOLD) response). Positive correlations between the BOLD signal and HbO 2 were found as well as anticorrelations with HbR.…”

Atlas‐guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision‐making responses in young adults

“…Anatomically guided DOT reconstruction has been used in brain research within the fNIRS community to overcome the major drawback of DOT, namely, lack of structural information. Two major approaches, atlas anatomical-guided [Custo et al, 2010] and subject-specific anatomical-guided 3D DOT Eggebrecht et al, 2012], have been intensely presented in the field recently. Although subject-specific anatomical-guided 3D DOT demonstrated accurate localization of brain activation compared with fMRI , the expected localization errors when using atlas-guided DOT could result from: (1) differences between subject specific anatomy and atlas anatomy, and (2) registration errors between subject space and atlas space, including measurement variations of optode locations in subject space by a 3D digitizer.…”

“…The purpose of using a band pass filter from 0.03 to 0.2 Hz on raw data was to remove (1) high-frequency physiological interference due to respiration (>0.2 Hz) and cardiac (>1 Hz) signals and (2) low-frequency (<0.03 Hz) system drift. Utilization of a band-pass filter has been a common image pre-processing procedure for reconstructing DOT by numerous research groups [Boas et al, 2004;Eggebrecht et al, 2012;Fekete et al, 2013;Ferradal et al, 2014;Folley and Park, 2005;Gagnon et al, 2012;Tian et al, 2012;Zhang et al, 2011]. It might be true that when GLM is applied by deconvolving HbO (and HbR) signals with a given HRF, a band-pass filter may not be necessary as the temporal HbO/HbR profiles are fitted in a least squares sense with the model.…”

Global Motion Detection and Censoring in High-Density Diffuse Optical Tomography

“…HD-DOT light measurement data were converted to log-ratio (using the temporal mean of a given SD-pair measurement as the relative baseline for that measurement). Noisy measurements were empirically defined as those with greater than 7.5% temporal standard deviation in the least noisy (lowest mean GVTD) 60 seconds of each run [19], and were excluded from further processing. Then the data were high-pass filtered (0.02 Hz cut-off for task-based datasets, 0.009 Hz for resting state datasets) to remove low-frequency drift.…”

“…Sparse fNIRS imaging arrays yield poor resolution and low image quality. HD-DOT provides improved image resolution and depth profiling, particularly when used with anatomical head models [18][19][20]. However, as in both fMRI and fNIRS, detection, classification, and removal of motion-induced artifacts remains a challenge for HD-DOT.…”