Linganisha mbinu

	Takwimu za Kianzio-Njia za Spesho (TBSS)	NODDI	Mofometria Inayotegemea Vokseli
Nyanja	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2006	2012	2000
Mwanzilishi	Stephen M. Smith	Hui Zhang	John Ashburner
Aina	Diffusion MRI white matter analysis pipeline	Microstructural white matter mapping	Structural MRI gray matter analysis pipeline
Chanzo asilia	Smith, S. M., Jenkinson, M., Johansen-Berg, H., et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505.	Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.	Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11(6), 805–821.
Majina mbadala	TBSS, white matter skeleton analysis	NODDI, neurite density mapping	VBM, grey matter morphometry
Zinazohusiana	3	3	2
Muhtasari	Tract-Based Spatial Statistics (TBSS) is a voxel-wise analysis method for detecting group differences in white matter microstructure from diffusion MRI data. Published by Stephen M. Smith and colleagues in 2006, TBSS addresses registration and multiple comparison problems inherent in voxel-wise analysis by projecting individual FA maps onto a white matter skeleton derived from a population template.	Neurite Orientation Dispersion and Density Imaging (NODDI) is a biophysical diffusion MRI model that quantifies microstructural properties of white matter: neurite density (axonal density), orientation dispersion (fiber coherence), and isotropic diffusion (free water or cerebrospinal fluid). Introduced by Zhang and colleagues in 2012, NODDI provides biologically interpretable metrics directly linking diffusion MRI signals to tissue microstructure.	Voxel-Based Morphometry (VBM) is a whole-brain statistical technique for detecting local differences in gray matter volume or concentration from structural MRI. Introduced by John Ashburner and Karl Friston in 2000, VBM enables researchers to identify regional brain volume changes associated with disease, aging, learning, and other factors without requiring a priori region-of-interest definitions.