In biological systems, the diffusion of water is impeded by tissue structures such as cell membranes, myelin sheaths, intracellular microtubules, and associated proteins. An important consequence of the interaction between water molecules and sub-cellular structures is “diffusion anisotropy”; this term describes the fact that diffusion occurs most freely in a direction parallel, rather than perpendicular, to an axon or myelin sheath. It is no doubt brain shrinks and brain diffusion changes with age. Although measured brain volumes may reflect the macrostructural change at a late stage and be insensitive to degeneration of the neural tissues at an early stage, especially in white matter, the measurement of anisotropy is a promising method for noninvasively detecting the degree of fiber damage in demyelinating diseases, neurodegenerative disorders such as amyotrophic lateral sclerosis and Alzheimer’s disease, psychiatric disorders such as depression, posttraumatic stress disorder, and schizophrenia, maturation processes, and normal aging. However, when accurately measuring minute motion in the brain tissue, ultrafast acquisition techniques must be employed. In most clinical settings, diffusion magnetic resonance imaging (MRI) is acquired with echo planar imaging (EPI). EPI suffers from signal dropout or pileup, and spatial distortion originating from local susceptibility-induced field gradient, especially in higher magnetic fields. Furthermore, in diffusion MRI, eddy current resulting from the strong motion-probing gradients is another source of geometric distortion. The Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) is a comprehensive software library to post-process images and test statistical inference for functional, diffusional, and structural brain MRI (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki) [1]. Recently, FSL tools, ‘‘TOPUP’’ and ‘‘EDDY’’, have been developed to estimate susceptibility and eddy current induced distortions, respectively, and correct them simultaneously [2,3].

Recent neuroimaging has provided important insights into the neurobiological basis for normal development, aging, and various disease processes in the central nervous system (CNS). Regional patterns for volume and diffusion changes are different from area to area as well as from age to age. Also, brain volume and diffusion change are observed in various neurodegenerative disorders at specific locations in an accelerated manner. In order to diagnose neurodegenerative disorders, we have to know about normal aging pattern and extent for diffusion changes in the CNS. However, we can hardly detect diffusion abnormalities on parametric diffusion maps, such as mean diffusivity (MD) or fractional anisotropy (FA) by visual inspection because signal changes on these maps are too subtle to perceive in normal aging or neurodegenerative disorders. A number of unbiased techniques to analyze the entire brain are now emerging due to the improved spatial and temporal resolutions of structural, diffusion, and functional MRI scans as well as the development of sophisticated image-processing tools. For example, the voxel-based approach has advantages over manual region-of-interest (ROI) analysis when searching for abnormalities throughout the brain. Statistical parametric mapping (http://www.fil.ion.ucl.ac.uk/spm/) is a standard tool for voxel-based morphometry [4] and sometimes used for diffusion MRI analysis [5]. Voxel-based analyses has provided objective and reliable results in several studies, eliminating the effects of operator bias whilst it could also give some controversy as to registration or spatial normalization accuracy. In recent years, to overcome these problems, Tract-Based Spatial Statistics (TBSS) has been developed and widely adopted as the recent standard technique for parametric analyses of diffusion data.

Diffusion MRI has not only parametric but also directional information. Using this directional information, we can non-invasively reconstruct white matter pathways, assuming that trajectories with coherent orientation of maximal diffusion are likely to represent real white matter fiber tracts. It is called diffusion tractography or fiber tracking, which provides a newer information to investigate how distant areas in the brain are interconnected and interact or not, as does functional MRI. In the graph theoretical analysis, the predefine areas in the cortex or subcortical cortex represent nodes. All pairs of connectivity between two different nodes can be quantified and a comprehensive map of different neural connections of nodes in the brain is created and referred to as connectivity matrix. A connectivity between a pair of two nodes is called an edge. When this matrix is estimated from diffusion MRI data, it is called structural connectivity [6]. Once connectivity matrix is obtained, graph theoretical analysis can provide measures of graph (i.e., clustering coefficient, characteristic path length, degree, betweenness centrality, etc) [7].

Diffusion MRI can provide not only parametric and but also directional information, which quantify white matter fiber integrity and cellular density as well as neural connectivity between nodes in the brain. In this talk, we will give the audience how to preprocess imaging data, for example, distortion and bias correction. Then, we will show several results for parametric analyses and structural connectivity measured by diffusion MRI in normal aging and neurodegenerative disorders.