Phase-encoded retinotopy as an evaluation of diffuse optical neuroimaging

Department of Physics, Washington University in St. Louis, San Luis, Missouri, United States
NeuroImage (Impact Factor: 6.36). 08/2009; 49(1):568-77. DOI: 10.1016/j.neuroimage.2009.07.023
Source: PubMed


Optical techniques enable portable, non-invasive functional neuroimaging. However, low lateral resolution and poor discrimination between brain hemodynamics and systemic contaminants have hampered the translation of near infrared spectroscopy from research instrument to widespread neuroscience tool. In this paper, we demonstrate that improvements in spatial resolution and signal-to-noise, afforded by recently developed high-density diffuse optical tomography approaches, now permit detailed phase-encoded mapping of the visual cortex's retinotopic organization. Due to its highly organized structure, the visual cortex has long served as a benchmark for judging neuroimaging techniques, including the original development of functional magnetic resonance imaging (fMRI) and positron emission tomography. Using phase-encoded visual stimuli that create traveling waves of cortical activations, we are able to discriminate the representations of multiple visual angles and eccentricities within an individual hemisphere, reproducing classic fMRI results. High contrast-to-noise and repeatable imaging allow the detection of inter-subject differences. These results represent a significant advancement in the level of detail that can be obtained from non-invasive optical imaging of functional brain responses. In addition, these phase-encoded paradigms and the maps they generate form a standardized model with which to judge new developments in optical algorithms and systems, such as new image reconstruction techniques and registration with anatomic imaging. With these advances in techniques and validation paradigms, optical neuroimaging can be extended into studies of higher-order brain function and of clinical utility with greater performance and confidence.

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Available from: Joseph P Culver, Mar 13, 2014
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    • "This is why GLM-based analysis has been developed and popularly used for topographic image analysis [Cui et al., 2011; Tak et al., 2010, 2011; Ye et al., 2009]. To address the second limitation in DOT, several studies have reconstructed DOT images using MRI-based 3D human head structure templates [Boas and Dale, 2005; Cooper et al., 2012; Custo et al., 2010; Zhan et al., 2012] to greatly improve 3D visualization and spatial localization/identification of activated cortical regions under respective stimulations [Eggebrecht et al., 2012; White and Culver, 2010a]. Regarding the third limitation, several DOT reconstruction algorithms including hard-prior usage [Boas and Dale, 2005], spatial variant regularization (SVR) [Culver et al., 2003; Pogue et al., 1999], and depth-compensation algorithm (DCA) [Niu et al., 2010a, b] have been developed to compensate or counter-balance the sensitivity of DOT that exponentially attenuates with the increase of penetration depth to reduce the localization error of brain activation with respect to the depth. "
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    ABSTRACT: Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Full-text · Article · Aug 2014 · Human Brain Mapping
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    • "With recent improvements in spatial resolution and brain specificity , along with the advantages of non-ionizing portable and wearable technology, high density diffuse optical tomography (HD-DOT) has become a promising neuroimaging modality for translation to clinical settings and cognitive studies in child brain development (Bluestone et al., 2001; Boas et al., 2004a, 2004b; Eggebrecht et al., 2012; Gibson et al., 2005, 2006; Habermehl et al., 2012; Joseph et al., 2006; White and Culver, 2010a, 2010b; Zeff et al., 2007). However, thus far HD-DOT reports have lacked event-related designs and accurate statistical tools that are common to fMRI and crucial for imaging complex cognitive processes. "
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    ABSTRACT: High density diffuse optical tomography (HD-DOT) is a noninvasive neuroimaging modality with moderate spatial resolution and localization accuracy. Due to portability and wear-ability advantages, HD-DOT has the potential to be used in populations that are not amenable to functional magnetic resonance imaging (fMRI), such as hospitalized patients and young children. However, whereas the use of event-related stimuli designs, general linear model (GLM) analysis, and imaging statistics are standardized and routine with fMRI, such tools are not yet common practice in HD-DOT. In this paper we adapt and optimize fundamental elements of fMRI analysis for application to HD-DOT. We show the use of event-related protocols and GLM de-convolution analysis in un-mixing multi-stimuli event-related HD-DOT data. Statistical parametric mapping (SPM) in the framework of a general linear model is developed considering the temporal and spatial characteristics of HD-DOT data. The statistical analysis utilizes a random field noise model that incorporates estimates of the local temporal and spatial correlations of the GLM residuals. The multiple-comparison problem is addressed using a cluster analysis based on non-stationary Gaussian random field theory. These analysis tools provide access to a wide range of experimental designs necessary for the study of the complex brain functions. In addition, they provide a foundation for understanding and interpreting HD-DOT results with quantitative estimates for the statistical significance of detected activation foci.
    Full-text · Article · May 2013 · NeuroImage
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    • "Multiple strategies have been used for building models of the human head for DOT reconstruction. An early simple approach used layered hemispherical models in which each layer has homogeneous optical properties associated with a particular tissue type, e.g., skull, scalp, and brain (White and Culver, 2010b; Zeff et al., 2007). Concentric shell models are attractively simple but inaccurate (Dehaes et al., 2011; Heiskala et al., 2009). "
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    ABSTRACT: Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available.
    Full-text · Article · Apr 2013 · NeuroImage
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