Topographic localization of brain activation in diffuse optical imaging using spherical wavelets

Abstract

Diffuse optical imaging is a non-invasive technique that uses near-infrared light to measure changes in brain activity through an array of sensors placed on the surface of the head. Compared to functional MRI, optical imaging has the advantage of being portable while offering the ability to record functional changes in both oxy-and deoxy-hemoglobin within the brain at a high temporal resolution. However, the reconstruction of accurate spatial images of brain activity from optical measurements represents an ill-posed and underdetermined problem that requires regularization. These reconstructions benefit from incorporating prior information about the underlying spatial structure and function of the brain. In this work, we describe a novel image reconstruction approach which uses surface-based wavelets derived from structural MRI to incorporate high-resolution anatomical and structural prior information about the brain. This surface-based approach is used to approximate brain activation patterns through the reconstruction and presentation of topographical (twodimensional) maps of brain activation directly onto the folded surface of the cortex. The set of wavelet coefficients is directly estimated by a truncated singular-value decomposition based pseudoinversion of the wavelet projection of the optical forward model. We use a reconstruction metric based on Shannon entropy which quantifies the sparse loading of the wavelet coefficients and is used to determine the optimal truncation and regularization of this inverse model. In this work, examples of the performance of this model are illustrated for several cases of numerical simulation and experimental data with comparison to functional magnetic resonance imaging.

Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation

“…10), it is unclear how well spatial agreements can be achieved in practice given the inherent difficulties and ill-posed nature of the reconstruction of low-density fNIRS measurements into spatial images. Previously, we have described and numerically validated several advanced methods for fNIRS image reconstruction that incorporate anatomical information from structural MRI, 14 group-level random effects models, 12 and hierarchical regularization models. 13 In addition, we have recently detailed several methods for improved time-series analysis and generalizations of the linear model to deal with fNIRS specific noise structures.…”

“…In previous work, we have estimated the registration error of this approach to be about 5 mm. 14 The timing of the stimulus paradigm was triggered from the MRI scanner.…”

“…Of note, this same cortical surface is also used in the MEG and fMRI models via AFNI-SUMA. The registered "spherical" inflated surface for each hemisphere from the Freesurfer model is reparameterized using spherical wavelet basis set described in Abdelnour et al 14 Spherical wavelets are used to describe the image (e.g., brain activity map) on the surface of the cortex at different spatial resolutions similar to more conventional wavelet representations with the distinction that these spherical wavelets are specifically designed to model a two-dimensional topography on a closed three-dimensional surface. The spherical wavelets are derived from a recursive subdivision of an icosahedral mesh, which can easily be extracted from the existing Freesurfer cortical surfaces from the icosahedral levels 1 to 4 (2562 nodes per hemisphere with about 6.2-mm vertex spacing and 39-mm 2 face areas).…”

“…In recent years, our group has developed a number of image reconstruction methods based on cortical surface topology for improving image reconstruction of fNIRS data. [11][12][13][14][15] The purpose of this study was to evaluate the performance of these recent image reconstruction methods applied to low-density fNIRS measurements and compare these results to fMRI and MEG source localization. Healthy volunteer participants completed two neuroimaging sessions involving concurrent fNIRS/MEG and concurrent fNIRS/fMRI during a somatosensory stimulation task.…”

A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems

“…Many of them have limitations on types of meshes that can be utilized. For example, Spherical wavelets rely on recursive subdivision property of a geometry object, e.g., icosahedrons [29], and Lounsbery wavelets can only be used in quaternary subdivided meshes [30]. These restrictions limit their applications in EEG/MEG inverse problems since CCD models in practice are irregular meshes.…”