Automated determination of axonal orientation in the deep white matter of the human brain.
Ontology highlight
ABSTRACT:
SUBMITTER: Bartsch H
PROVIDER: S-EPMC3621296 | biostudies-other | 2012
REPOSITORIES: biostudies-other
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Project description:PurposeThe multi-exponential T2 decay of the MRI signal from cerebral white matter can be separated into short T2 components related to myelin water and long T2 components related to intracellular and extracellular water. In this study, we investigated to what degree the apparent myelin water fraction (MWF) depends on the angle between white matter fibers and the main magnetic field.MethodsMaps of the apparent MWF were acquired using multi-echo Carr-Purcell-Meiboom-Gill and gradient-echo spin-echo sequences. The Carr-Purcell-Meiboom-Gill sequence was acquired with a TR of 1073 ms, 1500 ms, and 2000 ms. The fiber orientation was mapped with DTI. By angle-wise pooling the voxels across the brain's white matter, orientation-dependent apparent MWF curves were generated.ResultsWe found that the apparent MWF varied between 25% and 35% across different fiber orientations. Furthermore, the selection of the TR influences the apparent MWF.ConclusionWhite matter fiber orientation induces a strong systematic bias on the estimation of the apparent MWF. This finding has implications for future research and the interpretation of MWI results in previously published studies.
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Project description:Diffusion tensor imaging (DTI) studies of human brain development have consistently shown widespread, but nonlinear increases in white matter anisotropy through childhood, adolescence, and into adulthood. However, despite its sensitivity to changes in tissue microstructure, DTI lacks the specificity to disentangle distinct microstructural features of white and gray matter. Neurite orientation dispersion and density imaging (NODDI) is a recently proposed multi-compartment biophysical model of brain microstructure that can estimate non-collinear properties of white matter, such as neurite orientation dispersion index (ODI) and neurite density index (NDI). In this study, we apply NODDI to 66 healthy controls aged 7-63 years to investigate changes of ODI and NDI with brain maturation, with comparison to standard DTI metrics. Using both region-of-interest and voxel-wise analyses, we find that NDI exhibits striking increases over the studied age range following a logarithmic growth pattern, while ODI rises following an exponential growth pattern. This novel finding is consistent with well-established age-related changes of FA over the lifespan that show growth during childhood and adolescence, plateau during early adulthood, and accelerating decay after the fourth decade of life. Our results suggest that the rise of FA during the first two decades of life is dominated by increasing NDI, while the fall in FA after the fourth decade is driven by the exponential rise of ODI that overcomes the slower increases of NDI. Using partial least squares regression, we further demonstrate that NODDI better predicts chronological age than DTI. Finally, we show excellent test-retest reliability of NODDI metrics, with coefficients of variation below 5% in all measured regions of interest. Our results support the conclusion that NODDI reveals biologically specific characteristics of brain development that are more closely linked to the microstructural features of white matter than are the empirical metrics provided by DTI.
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Project description:The brains of large mammals have lower rates of metabolism than those of small mammals, but the functional consequences of this scaling are not well understood. An attractive target for analysis is axons, whose size, speed and energy consumption are straightforwardly related. Here we show that from shrews to whales, the composition of white matter shifts from compact, slow-conducting, and energetically expensive unmyelinated axons to large, fast-conducting, and energetically inexpensive myelinated axons. The fastest axons have conduction times of 1-5 ms across the neocortex and <1 ms from the eye to the brain, suggesting that in select sets of communicating fibers, large brains reduce transmission delays and metabolic firing costs at the expense of increased volume. Delays and potential imprecision in cross-brain conduction times are especially great in unmyelinated axons, which may transmit information via firing rate rather than precise spike timing. In neocortex, axon size distributions can account for the scaling of per-volume metabolic rate and suggest a maximum supportable firing rate, averaged across all axons, of 7 +/- 2 Hz. Axon size distributions also account for the scaling of white matter volume with respect to brain size. The heterogeneous white matter composition found in large brains thus reflects a metabolically constrained trade-off that reduces both volume and conduction time.
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Project description:Diffusion tensor imaging (DTI) has been useful in showing compromise after traumatic axonal injury (TAI) at the chronic stage; however, white matter (WM) compromise from acute stage of TAI to chronic stage is not yet well understood. This study aims to examine changes in WM integrity following TAI by obtaining DTI, on average, 1?d post injury and again approximately seven months post-injury. Sixteen patients with complicated mild to severe brain injuries consistent with TAI were recruited in the intensive care unit of a Level I trauma center. Thirteen of these patients were studied longitudinally over the course of the first seven months post-injury. The first scan occurred, on average, 1?d after injury and the second an average of seven months post-injury. Ten healthy individuals, similar to the cohort of patients, were recruited as controls. Whole brain WM and voxel-based analyses of DTI data were conducted. DTI metrics of interest included: fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), and radial diffusivity (RD). tract-based spatial statistics were used to examine DTI metrics spatially. Acutely, AD and RD increased and RD positively correlated with injury severity. Longitudinal analysis showed reduction in FA and AD (p<0.01), but no change in RD. Possible explanations for the microstructural changes observed over time are discussed.
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Project description:AimsThe effects of subthalamic nucleus (STN)-deep brain stimulation (DBS) on brain topological metrics, functional connectivity (FC), and white matter integrity were studied in levodopa-treated Parkinson's disease (PD) patients before and after DBS.MethodsClinical assessment, resting-state functional MRI (rs-fMRI), and diffusion tensor imaging (DTI) were performed pre- and post-DBS in 15 PD patients, using a within-subject design. The rs-fMRI identified brain network topological metric and FC changes using graph-theory- and seed-based methods. White matter integrity was determined by DTI and tract-based spatial statistics.ResultsUnified Parkinson's Disease Rating Scale III (UPDRS- III) scores were significantly improved by 35.3% (p < 0.01) after DBS in PD patients, compared with pre-DBS patients without medication. Post-DBS PD patients showed a significant decrease in the graph-theory-based degree and cost in the middle temporal gyrus and temporo-occipital part-Right. Changes in FC were seen in four brain regions, and a decrease in white matter integrity was seen in the left anterior corona radiata. The topological metrics changes were correlated with Beck Depression Inventory II (BDI-II) and the FC changes with UPDRS-III scores.ConclusionSTN-DBS modulated graph-theoretical metrics, FC, and white matter integrity. Brain connectivity changes observed with multi-modal imaging were also associated with postoperative clinical improvement. These findings suggest that the effects of STN-DBS are caused by brain network alterations.
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