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Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia.
Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures.
Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score.
Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.
Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.
BACKGROUND AND PURPOSE: We evaluated cerebral white and gray matter changes in patients with iRLS in order to shed light on the pathophysiology of this disease.
METHODS: Twelve patients with iRLS were compared to 12 age- and sex-matched controls using whole-head diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) techniques. Evaluation of the DTI scans included the voxelwise analysis of the fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD).
RESULTS: Diffusion tensor imaging revealed areas of altered FA in subcortical white matter bilaterally, mainly in temporal regions as well as in the right internal capsule, the pons, and the right cerebellum. These changes overlapped with changes in RD. Voxel-based morphometry did not reveal any gray matter alterations.
CONCLUSIONS: We showed altered diffusion properties in several white matter regions in patients with iRLS. White matter changes could mainly be attributed to changes in RD, a parameter thought to reflect altered myelination. Areas with altered white matter microstructure included areas in the internal capsule which include the corticospinal tract to the lower limbs, thereby supporting studies that suggest changes in sensorimotor pathways associated with RLS.
Purpose: Diffuse cortical damage in relapsing–remitting multiple sclerosis (RRMS) is clinically relevant but cannot be directly assessed with conventional MRI. In this study, it was aimed to use diffusion tensor imaging (DTI) techniques with optimized intrinsic eddy current compensation to quantify and characterize cortical mean diffusivity (MD) and fractional anisotropy (FA) changes in RRMS and to analyze the distribution of these changes across the cortex.
Materials and Methods: Three-Tesla MRI acquisition, mapping of the MD providing information about the integrity of microstructural barriers and of the FA reflecting axonal density and surface-based analysis with Freesurfer were performed for 24 RRMS patients and 25 control subjects.
Results: Across the whole cortex, MD was increased in patients (p < 0.001), while surface-based analysis revealed focal cortical FA decreases. MD and FA changes were distributed inhomogeneously across the cortex, the MD increase being more widespread than the FA decrease. Cortical MD correlated with the Expanded Disability Status Scale (EDSS, r = 0.38, p = 0.03).
Conclusion: Damage of microstructural barriers occurs inhomogeneously across the cortex in RRMS and might be spatially more widespread than axonal degeneration. The results and, in particular, the correlation with the clinical status indicate that DTI might be a promising technique for the monitoring of cortical damage under treatment in larger clinical studies.
In binocular rivalry, presentation of different images to the separate eyes leads to conscious perception alternating between the two possible interpretations every few seconds. During perceptual transitions, a stimulus emerging into dominance can spread in a wave-like manner across the visual field. These traveling waves of rivalry dominance have been successfully related to the cortical magnification properties and functional activity of early visual areas, including the primary visual cortex (V1). Curiously however, these traveling waves undergo a delay when passing from one hemifield to another. In the current study, we used diffusion tensor imaging (DTI) to investigate whether the strength of interhemispheric connections between the left and right visual cortex might be related to the delay of traveling waves across hemifields. We measured the delay in traveling wave times (ΔTWT) in 19 participants and repeated this test 6 weeks later to evaluate the reliability of our behavioral measures. We found large interindividual variability but also good test–retest reliability for individual measures of ΔTWT. Using DTI in connection with fiber tractography, we identified parts of the corpus callosum connecting functionally defined visual areas V1–V3. We found that individual differences in ΔTWT was reliably predicted by the diffusion properties of transcallosal fibers connecting left and right V1, but observed no such effect for neighboring transcallosal visual fibers connecting V2 and V3. Our results demonstrate that the anatomical characteristics of topographically specific transcallosal connections predict the individual delay of interhemispheric traveling waves, providing further evidence that V1 is an important site for neural processes underlying binocular rivalry.
The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal–frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.
The MR signal is sensitive to diffusion. This effect can be increased by the use of large, balanced bipolar gradients. The gradient systems of MR scanners are calibrated at installation and during regular servicing visits. Because the measured apparent diffusion constant (ADC) depends on the square of the amplitude of the diffusion sensitizing gradients, errors in the gradient calibration are exaggerated. If the error is varying among the different gradient axes, it will affect the estimated degree of anisotropy. To assess the gradient calibration accuracy in a whole-body MRI scanner, ADC values were calculated for a uniform water phantom along each gradient direction while monitoring the temperature. Knowledge of the temperature allows the expected diffusion constant of water to be calculated independent of the MRI measurement. It was found that the gradient axes (±x, ±y, ±z) were calibrated differently, resulting in offset ADC values. A method is presented to rescale the amplitude of each of the six principal gradient axes within the MR pulse sequence. The scaling factor is the square root of the ratio of the expected and observed diffusion constants. In addition, fiber tracking results in the human brain were noticeably affected by improving the gradient system calibration. Magn Reson Med 58:763–768, 2007. © 2007 Wiley-Liss, Inc. Keywords: diffusion tensor imaging, apparent diffusion constant, magnetic field gradient, fibre tracking, anisotropy