Characterizing white matter connectivity in major depressive disorder: Automated fiber quantification and maximum density paths

Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be used to assess aberrations associated with disease. Several new methods permit the automated assessment of important white matter characteristics. In the current study we used Automated Fiber Quantification (AFQ) to assess differences between depressed and nondepressed individuals in 18 major white matter tracts. We then used the Maximum Density Path (MDP) method to further characterize group differences identified with AFQ. The results of the AFQ analyses indicated that fractional anisotropy (FA; an index of white matter integrity) along bilateral corticospinal tracts (CST) was higher in depressed than in nondepressed individuals. MDP analyses revealed that white matter anomalies were restricted to four subregions that included the corona radiata and the internal and external capsules. These results provide further evidence that MDD is associated with abnormalities in cortical-to-subcortical connectivity.

[1]  Karl J. Friston,et al.  Generative and recognition models for neuroanatomy , 2004, NeuroImage.

[2]  Essa Yacoub,et al.  A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography , 2011, Medical Image Anal..

[3]  Christophe Lenglet,et al.  Atlas-based fiber clustering for multi-subject analysis of high angular resolution diffusion imaging tractography , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  Babak A. Ardekani,et al.  Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans , 2005, Journal of Neuroscience Methods.

[5]  Anuj Srivastava,et al.  A Novel Representation for Riemannian Analysis of Elastic Curves in Rn , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Bruce R. Rosen,et al.  Microstructural Abnormalities in Subcortical Reward Circuitry of Subjects with Major Depressive Disorder , 2010, PloS one.

[7]  Paul M. Thompson,et al.  Tractography density and network measures in Alzheimer'S disease , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[8]  Derek K. Jones,et al.  RESTORE: Robust estimation of tensors by outlier rejection , 2005, Magnetic resonance in medicine.

[9]  Anuj Srivastava,et al.  Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves , 2007, EMMCVPR.

[10]  Daniella J. Furman,et al.  Frontostriatal functional connectivity in major depressive disorder , 2011, Biology of Mood & Anxiety Disorders.

[11]  S. Lui,et al.  Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. , 2013, Journal of psychiatry & neuroscience : JPN.

[12]  B. Wandell,et al.  Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification , 2012, PloS one.

[13]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[14]  Shantanu H. Joshi,et al.  White matter tract analysis in 454 adults using maximum density paths , 2011 .

[15]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[16]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .