Q-ball Imaging

Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown 02129, USA.
Magnetic Resonance in Medicine (Impact Factor: 3.57). 12/2004; 52(6):1358-72. DOI: 10.1002/mrm.20279
Source: PubMed


Magnetic resonance diffusion tensor imaging (DTI) provides a powerful tool for mapping neural histoarchitecture in vivo. However, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, bending, or twisting within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sampling. It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.
Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomographic inversion called the Funk–Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity. The present paper reviews the theory of q-ball imaging and describes a simple linear matrix formulation for the q-ball reconstruction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are discussed. Magn Reson Med 52:1358–1372, 2004.

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    • "The intravoxel fiber orientations were determined from the decomposed ODFs to provide the fiber direction field for tractography (Yeh and Tseng, 2013). Generalized fractional anisotropy (GFA) was quantified from the ODF by calculating: (standard deviation of the ODF)/ (root mean square of the ODF) (Tuch, 2004). GFA indicates the directionality of the ODF, ranging from zero (when the diffusion is isotropic) to one (when the diffusion is restricted to one direction). "
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    ABSTRACT: The mirror neuron system (MNS) may be implicated in schizophrenia. This study investigated MNS structures, including the pars opercularis (Pop), the supramarginal gyrus (SMg), the third branch of the superior longitudinal fasciculus, and callosal fibers interconnecting bilateral Pop (CC-Pop) and SMg (CC-SMg), and clarified their relationships with positive and negative symptoms of schizophrenia. Participants comprised 32 schizophrenia patients and 32 matched controls who received T1-weighted structural magnetic resonance imaging (MRI, T1WI) and diffusion spectrum imaging (DSI). The cortical measures were computed from the T1WI data. Tract integrity was assessed using a tractography-based analysis of the generalized fractional anisotropy (GFA) derived from the DSI data. Pearson׳s correlations and multiple linear regression analysis were used to investigate the associations between MNS structures and positive and negative symptom scores of schizophrenia. Cortical thickness in bilateral Pop and SMg were significantly thinner and mean GFA of CC-Pop was significantly decreased in patients. Negative symptoms were significantly correlated with left SMg volume, and positive symptoms were significantly correlated with right SMg thickness. Multiple linear regression analysis showed left SMg volume to be the strongest contributor to the negative symptoms. The association between left SMg volume and negative symptoms may reflect the degree of social cognition impairment in schizophrenia. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Psychiatry Research: Neuroimaging 01/2015; 231(3). DOI:10.1016/j.pscychresns.2015.01.010 · 2.42 Impact Factor
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    • "An ODF (Orientation Distribution Function) was evaluated for a set of vectors representing the vertices of a regular polyhedron, the 362 vertex 6-fold geodesated icosahedron, of mean nearest-neighbor separation = 0.16, rad = 9°. Next, the GFA (Tuch, 2004) that is defined as an analog for q-ball imaging of the FA in DTI was computed from the ODFs. The GFA is expressed as: "
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    ABSTRACT: Focal epilepsy is increasingly recognized as the result of an altered brain network, both on the structural and functional levels and the characterization of these widespread brain alterations is crucial for our understanding of the clinical manifestation of seizure and cognitive deficits as well as for the management of candidates to epilepsy surgery. Tractography based on Diffusion Tensor Imaging allows non-invasive mapping of white matter tracts in vivo. Recently, diffusion spectrum imaging (DSI), based on an increased number of diffusion directions and intensities, has improved the sensitivity of tractography, notably with respect to the problem of fiber crossing and recent developments allow acquisition times compatible with clinical application. We used DSI and parcellation of the gray matter in regions of interest to build whole-brain connectivity matrices describing the mutual connections between cortical and subcortical regions in patients with focal epilepsy and healthy controls. In addition, the high angular and radial resolution of DSI allowed us to evaluate also some of the biophysical compartment models, to better understand the cause of the changes in diffusion anisotropy. Global connectivity, hub architecture and regional connectivity patterns were altered in TLE patients and showed different characteristics in RTLE vs LTLE with stronger abnormalities in RTLE. The microstructural analysis suggested that disturbed axonal density contributed more than fiber orientation to the connectivity changes affecting the temporal lobes whereas fiber orientation changes were more involved in extratemporal lobe changes. Our study provides further structural evidence that RTLE and LTLE are not symmetrical entities and DSI-based imaging could help investigate the microstructural correlate of these imaging abnormalities.
    Clinical neuroimaging 12/2014; 5. DOI:10.1016/j.nicl.2014.07.013 · 2.53 Impact Factor
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    • ", 2001 ) . GFA presents an extension of FA to high - angular resolution diffusion - weighted image that is capable of measuring anisotropy across multiple diffusion directions ( Tuch , 2004 ) . GFA is computed by dividing the standard deviation by the root mean square of the SDF . "
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    ABSTRACT: The neural mechanisms that mediate metacognitive ability (the capacity to accurately reflect on one's own cognition and experience) remain poorly understood. An important question is whether metacognitive capacity is a domain-general skill supported by a core neuroanatomical substrate or whether regionally specific neural structures underlie accurate reflection in different cognitive domains. Providing preliminary support for the latter possibility, recent findings have shown that individual differences in metacognitive ability in the domains of memory and perception are related to variation in distinct gray matter volume and resting-state functional connectivity. The current investigation sought to build on these findings by evaluating how metacognitive ability in these domains is related to variation in white matter microstructure. We quantified metacognitive ability across memory and perception domains and used diffusion spectrum imaging to examine the relation between high-resolution measurements of white matter microstructure and individual differences in metacognitive accuracy in each domain. We found that metacognitive accuracy for perceptual decisions and memory were uncorrelated across individuals and that metacognitive accuracy in each domain was related to variation in white matter microstructure in distinct brain areas. Metacognitive accuracy for perceptual decisions was associated with increased diffusion anisotropy in white matter underlying the ACC, whereas metacognitive accuracy for memory retrieval was associated with increased diffusion anisotropy in the white matter extending into the inferior parietal lobule. Together, these results extend previous findings linking metacognitive ability in the domains of perception and memory to variation in distinct brain structures and connections.
    Journal of Cognitive Neuroscience 10/2014; 27(3):1-13. DOI:10.1162/jocn_a_00741 · 4.09 Impact Factor
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