Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

By Susumu Mori, K. Oishi, Hangyi Jiang, Li Jiang, Xin Li, K. Akhter, Kegang Hua, A. Faria, Asif Mahmood, R. Woods and 9 more. NeuroImage, 2008

1,831 citations recorded by the source corpus.

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2bb7838d8a9d4acfefc0de30de0fdfa851ebce31

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “, the ICBM-DTI-81 WM labels atlas and the JHU WM tractography atlas) have been proposed (Mori et al., 2008).”
Human Brain White Matter Atlas: Identification and Assignment of Common Anatomical Structures in Superficial White Matter (2008, methodology): “63 (18 − 59 years old), right-handed) as described before (Mori et al., 2008a).”
The SRI24 Multi-Channel Atlas of Normal Adult Human Brain Structure (2009, background): “…where they can then serve in such diverse applications as stereotactic localization [Kikinis et al., 1996] or as standardized seed and target regions in population studies of fiber tracking [Mori and van Zijl, 2002; Mori et al., 2008] in diffusion tensor images (DTI) [Basser and Pierpaoli, 1996].”
Multi-Contrast Human Neonatal Brain Atlas: Application to Normal Neonate Development Analysis (2011, positive): “The deep white matter structures identified in our previous studies of adult brains (Mori et al., 2008; Mori et al., 2005; Oishi et al., 2009)were readily identified in the neonate brains using DTI contrasts.”
Networks of anatomical covariance (2013, neutral): “…in acquisition, e.g. HARDI (Tuch et al., 2002) and analysis (Behrens et al., 2007; Canales-Rodríguez et al., 2010a, 2010b; Jbabdi et al., 2007; Lazar et al., 2003; Mori et al., 1999, 2008; Ozarslan et al., 2006; Savadjiev et al., 2006; Tuch, 2004; Wedeen et al., 2005, 2008; Weinstein et al., 1999).”
Dorsolateral prefrontal contributions to human working memory. (2013, methodology): “…white matter pathways in the focal dlPFC samples, identifying that each patient group entailed damage within or adjacent to the (1) superior longitudinal fasciculus (primarily branch 1), (2) frontal aslant tract, and (3) fronto-striatal tracts (Thiebaut de Schotten et al., 2012; Mori et al., 2008).”
Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group (2013, methodology): “We use two different approaches to controlling false positives, the false discovery rate (FDR) (Genovese et al., 2002) and familywise error rate (FWE) (Nichols and Hayasaka, 2003).”
NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction (2011, methodology): “To satisfy these purposes, studies have been conducted using affine transform (Jones et al., 2002; Mori et al., 2008; Muller et al., 2007) or nonlinear transformation (Ardekani and Sinha, 2006;Goodlett et al.”
Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis (2015, methodology): “In this study, we used a parcellation based on classical anatomical de ﬁ nitions (Mori, 2008; Oishi, 2009).”