Functional Imaging of the Developing Brain at the Bedside Using Diffuse Optical Tomography

Abstract

While histological studies and conventional magnetic resonance imaging (MRI) investigations have elucidated the trajectory of structural changes in the developing brain, less is known regarding early functional cerebral development. Recent investigations have demonstrated that resting-state functional connectivity MRI (fcMRI) can identify networks of functional cerebral connections in infants. However, technical and logistical challenges frequently limit the ability to perform MRI scans early or repeatedly in neonates, particularly in those at greatest risk for adverse neurodevelopmental outcomes. High-density diffuse optical tomography (HD-DOT), a portable imaging modality, potentially enables early continuous and quantitative monitoring of brain function in infants. We introduce an HD-DOT imaging system that combines advancements in cap design, ergonomics, and data analysis methods to allow bedside mapping of functional brain development in infants. In a cohort of healthy, full-term neonates scanned within the first days of life, HD-DOT results demonstrate strong congruence with those obtained using co-registered, subject-matched fcMRI and reflect patterns of typical brain development. These findings represent a transformative advance in functional neuroimaging in infants, and introduce HD-DOT as a powerful and practical method for quantitative mapping of early functional brain development in normal and high-risk neonates.

Keywords: developmental neuroimaging; diffuse optical tomography; functional magnetic resonance imaging; infant; neurodevelopment.

© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1.

DOT data acquisition. Neonatal imaging cap: The neonatal imaging cap was designed to provide comfort while maximizing coupling between the optodes and the infant's scalp. (a) During a scanning session, the cap was placed on the back of the head, above the inion, and extended laterally above the infant's ears. (b) To improve ergonomics, the cap was divided into 5 patches of flexible plastic molded to fit the infant's head curvature. All patches were attached to a neoprene headband that is wrapped around the infant's head using Velcro straps. (c) Silicone nubs (6 mm diameter) were attached to the fiberoptic tips through rubber rings to avoid scratches and increase comfort. Data quality assessment: (d) Average light levels for each optode position were displayed on an unwrapped and flattened view of the cap. (e) Measurements that passed the noise threshold were shown as green lines between optodes. (f) The temporal mean of measurements across the cap had a light level fall-off that was logarithmically related to the SD pair separation distance. (g) After cropping the noisy segments, most measurements exhibited a variance lower than the noise threshold (7.5%). (h) Power spectra for second nearest-neighbor measurements at both wavelengths showed a clear peak at the pulse frequency (∼2.2 Hz) as well as strong power in the functional connectivity frequency band (0.009–0.08 Hz).

Figure 2.

Neonatal head modeling. (a) Anatomical MR images (T1-weighted and T2-weighted) obtained for each individual infant were used to create a subject-specific head model. (b) The MR images were first segmented into 4 tissue types, namely, gray matter (GM), white matter (WM), CSF, and extracerebral tissue (scalp and skull). (c) A FEM was created using the Nirview modeling software and then optical properties were assigned to each node according to the tissue type determined by the segmentation. (d) The optode grid was placed over the head model based on the fiducial points measured during the DOT scanning session. (e) A subject-specific sensitivity matrix was calculated using the NIRFAST light modeling software.

Figure 3.

Single subject fcDOT and fcMRI comparisons. Resting-state functional connectivity maps obtained for a single infant. (a) ROI locations used in present analyses overlaid on subject-specific DOT field of view (blue). (b) Individual fcDOT correlation maps illustrating identified networks. (c) fcMRI results from comparable analysis. Note the high degree of spatial agreement between the fcDOT and fcMRI results. All results are overlaid onto sagittal, coronal, and axial slices of the infant's T2-weighted MRI volume centered at each seed location. Color threshold r > 0.2 (Vis, visual; MT, middle temporal; A1, auditory).

Figure 4.

Group comparisons. (a) Average correlation maps for a group of 9 healthy, full-term infants. All maps are displayed on an average surface-based atlas of the neonatal cortex. (b) ROI-to-ROI correlation matrices for fcDOT (left) and fcMRI (right) group maps. Seeds are organized from left (l) to right (r). (c) Average interhemispheric correlations for each pair of seeds and contrasts (i.e., oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin, and BOLD). Error bars denote standard error across subjects.

Figure 5.

Voxelwise comparisons. Spatial correlations (Pearson's r) of correlation maps (ΔHbO2 vs. BOLD) calculated for each possible seed located within the DOT field of view. The DOT field of view was defined as the intersection of the individual field of view (defined in the atlas space) corresponding to each subject.

Figure 6.

Seed-based versus ICA fcDOT group maps. Mean maps projected on the average surface-based neonatal atlas obtained using (a) seed-based correlation analysis and (b) ICA exhibit remarkably similar spatial patterns for 3 investigated resting-state networks.