Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

• Published in: NeuroImage (Orlando, Fla.) Vol. 39; no. 4; pp. 1693 - 1705
• Main Authors: Chung, SungWon, Pelletier, Daniel, Sdika, Michael, Lu, Ying, Berman, Jeffrey I., Henry, Roland G.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2008; Elsevier Limited; Elsevier
• Subjects: Adult; Algorithms; Brain - anatomy & histology; Brain - pathology; Central Nervous System Diseases - diagnosis; Central Nervous System Diseases - pathology; Cerebral Cortex - anatomy & histology; Cerebral Cortex - pathology; Cluster; Cluster Analysis; Computer Science; Corpus Callosum - pathology; Diffusion Magnetic Resonance Imaging - statistics & numerical data; Diffusion tensor MRI; Engineering Sciences; Fiber tracking; Humans; Image Processing; Image Processing, Computer-Assisted - methods; Longitudinal; Medical Imaging; Monte Carlo Method; Multiple sclerosis; Multiple Sclerosis - pathology; NMR; Nonlinear Dynamics; Nuclear magnetic resonance; Permutation; Pyramidal Tracts - pathology; Reference Values; Serial; Signal and Image Processing; Statistical analysis; Statistics; Studies; Voxel; White matter; Fiber tracking; Serial; Multiple sclerosis; Longitudinal; Permutation; Voxel; Diffusion tensor MRI; Cluster; White matter; Brain; USA; Corpus Callosum; Humans; Cerebral Cortex; Reference Values; reseau_international; Central Nervous System Diseases; Computer-Assisted; Multiple Sclerosis; Pyramidal Tracts; Algorithms; categₘixte; Image Processing; Diffusion Magnetic Resonance Imaging; Adult; Images et Modèles; Nonlinear Dynamics; Monte Carlo Method; Cluster Analysis
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2007.10.039

Diffusion tensor MRI (DTI) has been widely used to investigate brain microstructural changes in pathological conditions as well as for normal development and aging. In particular, longitudinal changes are vital to the understanding of progression but these studies are typically designed for specific regions of interest. To analyze changes in these regions traditional statistical methods are often employed to elucidate group differences which are measured against the variability found in a control cohort. However, in some cases, rather than collecting multiple subjects into two groups, it is necessary and more informative to analyze the data for individual subjects. There is also a need for understanding changes in a single subject without prior information regarding the spatial distribution of the pathology, but no formal statistical framework exists for these voxel-wise analyses of DTI. In this study, we present PERVADE (permutation voxel-wise analysis of diffusion estimates), a whole brain analysis method for detecting localized FA changes between two separate points in time of any given subject, without any prior hypothesis about where changes might occur. Exploiting the nature of DTI that it is calculated from multiple diffusion-weighted images of each region, permutation testing, a non-parametric hypothesis testing technique, was modified for the analysis of serial DTI data and implemented for voxel-wise hypothesis tests of diffusion metric changes, as well as for suprathreshold cluster analysis to correct for multiple comparisons. We describe PERVADE in detail and present results from Monte Carlo simulation supporting the validity of the technique as well as illustrative examples from a healthy subject and patients in the early stages of multiple sclerosis.