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PURPOSE: The new diffusional magnetic resonance imaging (dMRI) techniques, diffusional kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) have been developed to clarify the microstructural changes. To our knowledge, however, there is little information on the similarities and differences of these metrics evaluated by the image-by-image paired t test. MATERIALS AND METHODS: Twenty-three healthy subjects underwent dMRI. We estimated the relationships of these metrics evaluated by the image-by-image paired t-test and compared aging effects on each metric. RESULTS: We found that fractional anisotropy (FA), mean kurtosis (MK) derived from DKI and neurite density index (NDI) values derived from NODDI correlated with each other positively, and mean diffusivity (MD) and orientation dispersion index (ODI) values from NODDI correlated negatively with the FA value. There were no significant relationships of age with FA or MD values, while MK, ODI and NDI values showed significant correlations with age. CONCLUSION: These results may indicate not only the similar tendency among the metrics, but also the higher sensitivity of NODDI and DKI to the changes in microstructural tissue organization with advancing age. These techniques could shed light on both normal and degenerated brain changes.

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INTRODUCTION: Biomarkers are urgently needed for the critical yet understudied preclinical stage of Alzheimer's disease (AD). METHODS: CSF collection, [C-11]PiB amyloid imaging, and MRI were acquired in n=104 cognitively healthy adults enriched with risk for sporadic AD. Image-derived cerebral ß-amyloid (Aß) burden, measured concurrently and longitudinally, was regressed on CSF measures of Aß, neural injury, and inflammation, as well as ratios with Aß42. Linear mixed effects regression was used to model the effect of the CSF measures that predicted longitudinal brain amyloid accumulation on longitudinal cognitive decline, measured by memory test scores. RESULTS: At baseline, Aß42/Aß40 and all CSF ratios to Aß42 were associated with PiB binding in AD-vulnerable regions. Longitudinally, Aß42/Aß40 and ratios of total tau, phosphorylated-tau, neurofilament light protein, and monocyte chemoattractant protein-1 to Aß42 were associated with increased ß-amyloid deposition over two years, predominantly in lateral parietal and temporal cortex. However, these CSF ratios were not significantly associated with cognitive decline, and the effect seems to be largely driven by Aß42 in the denominator. DISCUSSION: These results corroborate previous findings that t-tau/Aß42 and p-tau/Aß42 are the strongest candidate biomarkers during the preclinical timeframe. They support a framework in which neural injury and amyloid deposition are likely occurring simultaneously. It may be that neurodegenerative processes influence progressive amyloid accumulation, even in the preclinical time frame. CSF biomarkers for non-specific axonal injury and inflammation may provide more information at more advanced stages of the preclinical time course.

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BACKGROUND: Most previous studies that examined regional cerebral blood flow (rCBF) abnormalities in major depressive disorder (MDD) required the injection of radioisotopes into subjects. Here by using magnetic resonance imaging (MRI) with the pseudo-continuous arterial spin labeling (pCASL) method which does not require radioisotopes, we examined rCBF in patients with MDD in comparison with that in patients with schizophrenia and healthy subjects, taking the regional cerebral gray matter volume into account. METHODS: Subjects were 27 patients with MDD, 42 with schizophrenia and 43 healthy volunteers who underwent 3-T MRI with pCASL. Obtained pCASL imaging data were subject to the voxel-by-voxel statistical analysis. RESULTS: There were significant reductions of rCBF in the right inferior prefrontal cortex and anterior cingulate cortices (ACCs) in the MDD patients compared with the healthy controls. When compared with the schizophrenic patients, the MDD patients showed lower rCBF in the subgenual ACC and higher rCBF in left occipital region. LIMITATION: The abnormalities of rCBF in MDD were known to reverse during symptom remission. Further study with follow-up period would bring the perception about the treatment response. CONCLUSION: The rCBF reduction in the subgenual region may be a specific functional abnormality to MDD patients, which may provide a biological marker for MDD. The MRI with pCASL method is a promising tool to detect rCBF abnormalities controlling for gray matter volume in psychiatric disorders.

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UNLABELLED: Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a novel noninvasive technique that can measure regional cerebral blood flow (rCBF). To our knowledge, few studies have examined rCBF in patients with schizophrenia by ASL-MRI. Here we used pseudo-continuous ASL (pCASL) to examine the structural and functional imaging data in schizophrenic patients, taking the regional cerebral gray matter volume into account. The subjects were 36 patients with schizophrenia and 42 healthy volunteers who underwent 3-tesla MRI, diffusion tensor imaging (DTI), and pCASL. We evaluated the gray matter volume imaging, DTI, and pCASL imaging data in a voxel-by-voxel statistical analysis. The schizophrenia patients showed reduced rCBF in the left prefrontal and bilateral occipital cortices compared to the healthy volunteers. There was a significant reduction of gray matter volume in the left inferior frontal cortex in the schizophrenia patients. With respect to the fractional anisotropy (FA) values in the DTI, there were significant FA reductions in the left superior temporal, left external capsule, and left inferior prefrontal regions in the patients compared to the controls. CONCLUSION: Our pCASL study with partial volume effect correction together with volumetry and DTI data demonstrated hypoactivity in the left prefrontal area beyond structural abnormalities in schizophrenia patients. There were also hypofunction areas in bilateral occipital cortices, although structural abnormalities were not apparent.

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Structure and function are closely related in the healthy human brain. In patients with chronic heroin exposure, brain imaging studies have identified long-lasting changes in gray matter (GM) volume. More recently, we showed that acute application of heroin in dependent patients results in hypoperfusion of fronto-temporal areas compared with the placebo condition. However, the relationship between structural and cerebral blood flow (CBF) changes in heroin addiction has not yet been investigated. Moreover, it is not known whether there is any interaction between the chronic structural changes and the short and long-term effects on perfusion caused by heroin. Using a double-blind, within-subject design, heroin or placebo (saline) was administered to 14 heroin-dependent patients from a stable heroin-assisted treatment program, in order to observe acute short-term effects. Arterial spin labeling (ASL) was used to calculate perfusion quantification maps in both treatment conditions, while Voxel-Based Morphometry (VBM) was conducted to calculate regional GM density. VBM and ASL data were used to calculate homologous correlation fields by Biological Parametric Mapping (BPM) and a whole-brain Pearson r correlation. We correlated each perfusion condition (heroin and placebo) separately with a VBM sample that was identical for the two treatment conditions. It was assumed that heroin-associated perfusion is manifested in short-term effects, while placebo-associated perfusion is more related to long-term effects. In order to restrict our analyses to fronto-temporal regions, we used an explicit mask for our analyses. Correlation analyses revealed a significant positive correlation in frontal areas between GM and both perfusion conditions (heroin and placebo). Heroin-associated perfusion was also negatively correlated with GM in the inferior temporal gyrus on both hemispheres. These findings indicate that, in heroin-dependent patients, low GM volume is positively associated with low perfusion within frontal regions.

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Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.