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. 2009 Aug 1;47(1):148-56.
doi: 10.1016/j.neuroimage.2009.03.058. Epub 2009 Apr 1.

Resting-state functional connectivity in the human brain revealed with diffuse optical tomography

Brian R White  1 , Abraham Z Snyder, Alexander L Cohen, Steven E Petersen, Marcus E Raichle, Bradley L Schlaggar, Joseph P Culver
Affiliations

Resting-state functional connectivity in the human brain revealed with diffuse optical tomography

Brian R White et al. Neuroimage. .

Abstract

Mapping resting-state networks allows insight into the brain's functional architecture and physiology and has rapidly become important in contemporary neuroscience research. Diffuse optical tomography (DOT) is an emerging functional neuroimaging technique with the advantages, relative to functional magnetic resonance imaging (fMRI), of portability and the ability to simultaneously measure both oxy- and deoxyhemoglobin. Previous optical studies have evaluated the temporal features of spontaneous resting brain signals. Herein, we develop techniques for spatially mapping functional connectivity with DOT (fc-DOT). Simultaneous imaging over the motor and visual cortices yielded robust correlation maps reproducing the expected functional neural architecture. The localization of the maps was confirmed with task-response studies and with subject-matched fc-MRI. These fc-DOT methods provide a task-less approach to mapping brain function in populations that were previously difficult to research. Our advances may permit new studies of early childhood development and of unconscious patients. In addition, the comprehensive hemoglobin contrasts of fc-DOT enable innovative studies of the biophysical origin of the functional connectivity signal.

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Figures

Fig. 1
Fig. 1
Our DOT imaging system with functional responses. (a) Schematic of the visual cortex imaging pad (24 sources, red, and 28 detectors, blue). (b) Schematic of the motor cortex imaging pad (24 sources, red, and 18 detectors, blue). (c) A left visual cortex response (ΔHbO2), posterior coronal projection of a cortical shell. (d) A motor cortex response (ΔHbO2), superior axial projection of a cortical shell.
Fig. 2
Fig. 2
Power spectra of resting-state DOT signals, ΔHbO2. (a) Spectral power of a single 2nd-nearest-neighbor resting-state time-trace, sampling both brain and superficial tissues, before the application of any filters (5 minutes, subject 2). The low frequency components follow a 1/f curve (red), and there are peaks at the respiratory (0.16 Hz) and cardiac rates (0.95 Hz). (b) Spectral power of the superficial regressor derived from all first-nearest neighbor measurements in the visual pad. These systemic low frequency fluctuations are removed from the data prior to performing functional connectivity mapping. (c) Spectral power of a filtered imaged signal (5 min from a single voxel under the measurement in (a)). This remaining spectral power within the desired frequency range is used for perform fc-DOT. All traces have been smoothed with a moving average filter, width 5 points.
Fig. 3
Fig. 3
fc-DOT using correlation analysis (ΔHbR, subject 1, day 1). (a) A functional response in the left visual cortex. There is a decrease in HbR with high contrast-to-noise. The response is scaled to its maximum contrast (scale reversed so decreases in HbR are positive contrast). The left visual cortex seed is defined by the gray box. (b) Correlation map in the visual cortex using the left visual cortex seed. There is correlation with both hemispheres of the visual cortex, but not with the lower region of the pad. Boxes for both right and left seed regions are shown. All correlation images scale from r=-1 to 1. (c) Correlation map in the motor cortex using the left visual cortex seed. The correlation throughout the field-of-view is low. Both right and left motor seed boxes are shown for reference. (d)-(f) fc-DOT using the right visual cortex seed. Note the similar pattern to the left visual seed. (g)-(i) fc-DOT using the left motor cortex seed. Note the high inter-hemispheric correlation in the motor cortex, but lack of any high correlations with the visual cortex. (j)-(l) fc-DOT using the right motor cortex seed. Note the similar pattern to the left motor seed.
Fig. 4
Fig. 4
Repeatability of fc-DOT over multiple imaging sessions (subject 1, ΔHbR). Seed boxes are shown in gray. Images from different days are not co-registered. (a-c) Correlation maps within the visual cortex from the left visual cortex seed. (d-f) Correlation maps within the motor cortex from the right motor cortex seed. Note the similarity in the patterns in each session (with slight linear translations between days).
Fig. 5
Fig. 5
Robustness of fc-DOT mapping in multiple subjects (ΔHbR). Seed boxes are shown in gray. For subject 1, day 1 is shown. (a-c) Correlation maps within the visual cortex from the left visual cortex seed. (d-f) Correlation maps within the motor cortex from the right motor cortex seed. All subjects have of high inter-hemispheric connectivity in both networks.
Fig. 6
Fig. 6
Multi-session of average of all fc-DOT correlation maps (ΔHbR). (a) Correlation map in the visual cortex using the left visual cortex seed. There is correlation with both hemispheres of the visual cortex, but not with the lower region of the pad. Boxes for both right and left seed regions are shown. All correlation images scale from r=-1 to 1. (b) Correlation map in the motor cortex using the left visual cortex seed. The correlation throughout the field-of-view is low. Both right and left motor seed boxes are shown for reference. (c-d) fc-DOT using the right visual cortex seed. Note the similar pattern to the left visual seed. (e-h) fc-DOT using the motor cortex seeds. Note the high inter-hemispheric correlation in the motor cortex, but lack of any high correlations with the visual cortex.
Fig. 7
Fig. 7
fc-DOT analysis with all three hemoglobin contrasts (ΔHbO2, ΔHbR, and ΔHbT). (a-c) Visual correlation maps from the right visual cortex seed for each of the three contrasts (subject 1, day 1). Seed regions are shown in gray. The maps for ΔHbO2 and ΔHbR are very similar. The map for ΔHbT is less localized, has more regions of negative correlation, and is more variable from subject-to-subject. (d) Correlation coefficients across multiple subjects and days for all three contrasts (mean and standard deviation). The p-value comparing each inter-hemispheric correlation to the visual-to-motor correlation within each contrast is shown. Visual and motor networks are significantly correlated with all three contrasts, while there is little correlation between the visual and motor cortices.
Fig. 8
Fig. 8
fc-DOT with and without our regression and signal-to-noise reduction techniques (ΔHbR, subject 1, day 1). (a-b) Correlation maps in the visual and motor cortices using the left visual cortex seed with regression and noise removal. (The same images as Fig. 3b-c.) (c-d) Correlation maps using the left visual cortex seed without regression and noise removal. While this is the same raw data as (a) and (b), we now see global high correlations due to systemic confounds and artifact structure (possibly from coupling to optode motion) that obscures any underlying structure. Note the lack of any local correlation structure and the high correlations with the motor cortex. (e-f) Correlation maps using the right visual cortex seed with regression and noise removal. (The same images as Fig. 3e-f.) (g-h) Correlation maps using the right visual cortex seed without regression and noise removal. While this is the same raw data as (e) and (f), we now lack of the local correlation pattern seen in (e).
Fig. 9
Fig. 9
Similarity of correlation maps from fc-DOT (subject 1, day 1) and fc-MRI (subject 1). (a) Sagittal slice (5 mm left of midline) from subject's anatomical MRI with schematic of the visual cortex DOT pad superimposed (yellow), showing its position over the visual cortex. (b) Sagittal slice (18 mm left of midline) from an anatomical MRI with schematic of the motor cortex DOT pad superimposed (yellow), showing its position over the central sulcus (red). (c) Cross-correlation matrix for all four seeds from fc-DOT imaging (LM: left motor cortex, RM: right motor cortex, LV: left visual cortex, RV: right visual cortex). Note the high inter-hemispheric correlations and low correlations between the motor and visual networks. (d) Cross-correlation matrix for all four seeds from fc-fMRI imaging. Note the similarity to the fc-DOT correlation matrix. (e) fc-DOT correlation map using the left motor cortex seed. (f) fc-MRI correlation map, dorsal view, using the left motor cortex seed. The DOT motor imaging pad's position is shown in cyan. (g-h) fc-DOT and fc-MRI (dorsal view) using the right motor cortex seed. (i-j) fc-DOT and fc-MRI (posterior view) using the left visual cortex seed. (k-l) fc-DOT and fc-MRI (posterior view) using the right visual cortex seed. Note the similarity of the fc-DOT and fc-MRI connectivity maps for all four seeds. The color scale has a threshold at r=0.25.
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