Synopsis
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.manuscript
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.
Acknowledgements
noneReferences
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