Gray Matter Probability Research Articles

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08). Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.

Subcallosal brain structure: Correlation with hearing threshold at supra-clinical frequencies (>8 kHz), but not with tinnitus

This study tested for differences in brain structure between tinnitus and control subjects, focusing on a subcallosal brain region where striking differences have been inconsistently found previously. Voxel-based morphometry (VBM) was used to compare structural MRIs of tinnitus subjects and non-tinnitus controls. Audiograms of all subjects were normal or near-normal at standard clinical frequencies (≤8 kHz). Mean threshold through 14 kHz, age, sex and handedness were matched between groups. There were no definitive differences between tinnitus and control groups in modulated or unmodulated maps of gray matter (GM) probability (i.e., GM volume and concentration, respectively). However, when the image data were tested for correlations with parameters that were either not measured or not matched between the tinnitus and control groups of previous studies, a notable correlation was found: Threshold at supra-clinical frequencies (above 8 kHz) was negatively correlated with modulated GM probability in ventral posterior cingulate cortex, dorsomedial prefrontal cortex, and a subcallosal region that included ventromedial prefrontal cortex and coincided with previously-reported differences between tinnitus and control subjects. The results suggest an explanation for the discrepant findings in subcallosal brain: threshold at supra-clinical frequencies may have differed systematically between tinnitus and control groups in some studies but not others. The observed correlation between (1) brain structure in regions engaged in cognitive and attentional processes and (2) hearing sensitivity at frequencies generally considered unnecessary for normal human auditory behavior is surprising and worthy of follow up.

Correlating 3T MRI and histopathology in patients undergoing epilepsy surgery

PurposeTo investigate whether specific semi-quantitative 3T MRI parameters are associated with particular histological features in temporal lobe specimens in epilepsy surgery patients whose conventional MRI scan appeared normal. These MRI techniques have the potential to visualise subtle structural abnormalities currently undetected on conventional MRI; but correlation between pre-operative in vivo MRI and histopathology is needed to understand the basis of these MRI abnormalities. Predicting subtle histopathology with semi-quantitative MRI techniques could contribute to pre-surgical evaluation of epilepsy patients. Materials and methodsMRI techniques: normalised FLAIR signal intensity (nFSI), grey matter probability and diffusion tensor imaging (DTI) were correlated with quantitative histopathological measures: NeuN (neuronal nuclear antigen); GFAP (glial fibrillary acidic protein) and MBP (myelin basic protein) field fractions and stereological neuronal densities obtained in grey and white matter regions in twenty-four patients who underwent anterior temporal lobe resections. ResultsThere were no significant correlations between the histopathological measurements and MRI values in grey or white matter in macroscopically normal appearing tissue. ConclusionFindings suggest that in macroscopically normal appearing tissue, the studied semiquantitative MRI measurements are not significantly related to the measures of gliosis, neuronal loss/gain and myelin used in the current study. Studies of macroscopically abnormal tissue as well as improvements to the MRI techniques may increase the sensitivity of future correlative studies to improve our understanding of the histopathological basis of MRI signal characteristics.

Predicting Language Lateralization from Gray Matter

It has long been predicted that the degree to which language is lateralized to the left or right hemisphere might be reflected in the underlying brain anatomy. We investigated this relationship on a voxel-by-voxel basis across the whole brain using structural and functional magnetic resonance images from 86 healthy participants. Structural images were converted to gray matter probability images, and language activation was assessed during naming and semantic decision. All images were spatially normalized to the same symmetrical template, and lateralization images were generated by subtracting right from left hemisphere signal at each voxel. We show that the degree to which language was left or right lateralized was positively correlated with the degree to which gray matter density was lateralized. Post hoc analyses revealed a general relationship between gray matter probability and blood oxygenation level-dependent signal. This is the first demonstration that structural brain scans can be used to predict language lateralization on a voxel-by-voxel basis in the normal healthy brain.

Serotonin transporter binding and genotype in the nonhuman primate brain using [C-11]DASB PET

The length polymorphism of the serotonin (5-HT) transporter gene promoter region has been implicated in altered 5-HT function and, in turn, neuropsychiatric illnesses, such as anxiety and depression. The nonhuman primate has been used as a model to study anxiety-related mechanisms in humans based upon similarities in behavior and the presence of a similar 5-HT transporter gene polymorphism. Stressful and threatening contexts in the nonhuman primate model have revealed 5-HT transporter genotype dependent differences in regional glucose metabolism. Using the rhesus monkey, we examined the extent to which serotonin transporter genotype is associated with 5-HT transporter binding in brain regions implicated in emotion-related pathology. MethodsGenotype data and high resolution PET scans were acquired in 29 rhesus (Macaca mulatta) monkeys. [C-11]DASB dynamic PET scans were acquired for 90 min in the anesthetized animals and images of distribution volume ratio (DVR) were created to serve as a metric of 5-HT transporter binding for group comparison based on a reference region method of analysis. Regional and voxelwise statistical analysis were performed with corrections for anatomical differences in gray matter probability, sex, age and radioligand mass. ResultsThere were no significant differences when comparing l/l homozygotes with s-carriers in the regions of the brain implicated in anxiety and mood related illnesses (amygdala, striatum, thalamus, raphe nuclei, temporal and prefrontal cortex). There was a significant sex difference in 5-HT transporter binding in all regions with females having 18%–28% higher DVR than males. ConclusionsBecause these findings are consistent with similar genotype findings in humans, this further strengthens the use of the rhesus model for studying anxiety-related neuropathologies.

Quantitative grey matter histological measures do not correlate with grey matter probability values from in vivo MRI in the temporal lobe

Voxel-based morphometry (VBM) is commonly used to study systematic differences in brain morphology from patients with various disorders, usually by comparisons with control subjects. It has often been suggested, however, that VBM is also sensitive to variations in composition in grey matter. The nature of the grey matter changes identified with VBM is still poorly understood. The aim of the current study was to determine whether grey matter histopathological measurements of neuronal tissue or gliosis influenced grey matter probability values that are used for VBM analyses.Grey matter probability values (obtained using both SPM5 and FSL-FAST) were correlated with neuronal density, and field fraction of NeuN and GFAP immunopositivity in a grey matter region of interest in the middle temporal gyrus, in 19 patients undergoing temporal lobe resection for refractory epilepsy.There were no significant correlations between any quantitative neuropathological measure and grey matter probability values in normal-appearing grey matter using either segmentation program.The lack of correlation between grey matter probability values and the cortical neuropathological measures in normal-appearing grey matter suggests that intrinsic cortical changes of the type we have measured do not influence grey matter probability maps used for VBM analyses.

Brain volumetry counterparts of cognitive impairment in patients with multiple sclerosis

Background Cognitive impairment is frequent in multiple sclerosis (MS). Tissue-specific atrophy measures have been shown to correlate with cognitive performance in several studies. Voxel-based morphometry (VBM) aims to identify regional differences in the local composition of brain tissue and makes possible to correlate these findings with cognitive impairment patterns. Aim To investigate the associations between cognitive impairment in MS and tissue-specific atrophy and regional distribution of grey matter. Method 15 patients with MS and cognitive impairment were included. Demographic (age and years of schooling) and clinical (Multiple Sclerosis Functional Composite—MSFC and subtests, Expanded Disability Status Scale—EDSS, disease duration) variables were recorded and neuropsychological assessments performed (Trail Making Test A and B—TMTA and B, Symbol Digit Modalities Test—SDMT, Digit Span Test—DST and Rey's Auditory Verbal Learning Test Delayed Recall—RAVLT-DR). Magnetic resonance (MR) 3D sequences (MPRAGE) were performed on all subjects and tissue-specific volumes (SIENAx and SPM2 software) and VBM grey matter probability maps (SPM2) were obtained. Results Moderate correlations were obtained between tissue volumes obtained with SPM2 and SIENAx. Using SIENAx moderate correlations were obtained between normalised brain volume (NBV) and disease duration (rho = − 0.575, p = 0.025) and RAVLT-DR (rho = 0.518, p = 0.048). Using SPM2 moderate correlations were obtained between white matter and brain parenchymal fractions (WMF and BPF) and RAVLT-DR (rho = 0.572 and 0.539, p = 0.026 and 0.038), between grey matter fraction (GMF) and Z scores on the Paced Auditory Serial Addition Test (PASAT) (rho = 0.570, p = 0.026), and between BPF and disease duration (rho = − 0.6, p = 0.018). Significant correlations were observed only between regional grey matter probability maps and grey matter (and to a much lesser extent white matter) volumes from SPM2. Conclusion Quantitative tissue-specific atrophy measures may display better correlations with patients' variables than regional grey matter atrophy distribution obtained using VBM methodology. These results should be confirmed in larger samples.

IC-P2-125: Functional and structural MRI measures of cognitive progression in Alzheimer's disease

Neuronal dysfunction and structural changes in the earliest stages of Alzheimer's disease (AD) may provide independent information compared to commonly used metrics of cognitive decline, such as the mini-mental status examination (MMSE). Structural and functional MRI (fMRI) studies have demonstrated distinct changes among AD, mild cognitive impairment (MCI) and control subjects. In particular, deactivation in the posteromedial cortex (PMC) and volumetric decline in the medial temporal lobe (MTL) have been identified as consistent indicators of current or future impaired cognitive status. However, few studies have combined structural and functional measures to determine their contribution to standard neuropsychological diagnostics in characterizing disease progression. To determine the contribution of structural and functional MRI measures to group classification, 75 subjects (28 controls, 34 MCI, 13 AD) underwent structural and functional MRI scanning at 4T. Task-related signal change in the PMC region and voxel-based morphometry measures of grey matter (GM) probability in the MTL were obtained. Neuropsychological testing was performed at baseline for all subjects, and during follow-up between 1–3.5 years in MCI patients. Conversion to AD was defined based upon clinical and neuropsychological evaluation and change in Clinical Dementia Rating (CDR) scale score from 0.5 to 1.0. Subjects were reclassified to four cognitive status levels: 1= control, 2= MCI-Nonconverter, 3= MCI-Converter, and 4= AD. An ordinal regression model was created with MTL structural and PMC fMRI measures, age and MMSE as the independent variables. Both imaging and follow-up data were available in 32 MCI subjects, 10 of whom converted to AD (total sample = 28 controls, 22 MCI-Nonconverters, 10 MCI-Converters, and 13 AD). In our regression model, the main effects of all four factors were statistically significant (p < 0.05), independent of one another. Contributions to model variance (highest to lowest) were as follows: MTL GM probability, MMSE, Age, and PMC signal change. PMC deactivation and MTL GM probability measures contribute independently to diagnostic classification, including prognosis in MCI patients. Combined use of structural and functional MRI information may serve as adjuncts to neuropsychological testing in early identification and monitoring of disease progression.

IC-P2-126: MRI-based cortical thickness measurement improves the prediction of MCI to AD conversion

grey matter (GM) probability in the MTL were obtained. Neuropsychological testing was performed at baseline for all subjects, and during follow-up between 1-3.5 years in MCI patients. Conversion to AD was defined based upon clinical and neuropsychological evaluation and change in Clinical Dementia Rating (CDR) scale score from 0.5 to 1.0. Subjects were reclassified to four cognitive status levels: 1 control, 2 MCI-Nonconverter, 3 MCI-Converter, and 4 AD. An ordinal regression model was created with MTL structural and PMC fMRI measures, age and MMSE as the independent variables. Results: Both imaging and follow-up data were available in 32 MCI subjects, 10 of whom converted to AD (total sample 28 controls, 22 MCI-Nonconverters, 10 MCI-Converters, and 13 AD). In our regression model, the main effects of all four factors were statistically significant (p 0.05), independent of one another. Contributions to model variance (highest to lowest) were as follows: MTL GM probability, MMSE, Age, and PMC signal change. Conclusions: PMC deactivation and MTL GM probability measures contribute independently to diagnostic classification, including prognosis in MCI patients. Combined use of structural and functional MRI information may serve as adjuncts to neuropsychological testing in early identification and monitoring of disease progression.

Longitudinal changes in patients with traumatic brain injury assessed with diffusion-tensor and volumetric imaging

Traumatic brain injury (TBI) is associated with brain volume loss, but there is little information on the regional gray matter (GM) and white matter (WM) changes that contribute to overall loss. Since axonal injury is a common occurrence in TBI, imaging methods that are sensitive to WM damage such as diffusion-tensor imaging (DTI) may be useful for characterizing microstructural brain injury contributing to regional WM loss in TBI. High-resolution T1-weighted imaging and DTI were used to evaluate regional changes in TBI patients compared to matched controls. Patients received neuropsychological testing and were imaged approximately 2 months and 12.7 months post-injury. Paradoxically, neuropsychological function improved from Visit 1 to Visit 2, while voxel-based analyses of fractional anisotropy (FA), and mean diffusivity (MD) from the DTI images, and voxel-based analyses of the GM and WM probability maps from the T1-weighted images, mainly revealed significantly greater deleterious GM and WM change over time in patients compared to controls. Cross-sectional comparisons of the DTI measures indicated that patients have decreased FA and increased MD compared to controls over large regions of the brain. TBI affected virtually all of the major fiber bundles in the brain including the corpus callosum, cingulum, the superior and inferior longitudinal fascicules, the uncinate fasciculus, and brain stem fiber tracts. The results indicate that both GM and WM degeneration are significant contributors to brain volume loss in the months following brain injury, and also suggest that DTI measures may be more useful than high-resolution anatomical images in assessment of group differences.

The Relationship Between Gray Matter Morphometry and Neuropsychological Performance in a Large Sample of Cognitively Healthy Adults

Voxel-based morphometry (VBM) was used to examine the relationship between gray matter (GM) volume and performance on two commonly used clinical neuropsychological measures of frontal lobe or executive function, the Trail Making Test part B (TrailsB) and the Controlled Oral Word Association Test (COWAT) in 221 cognitively healthy adults between the ages of 18 and 84. We hypothesized that these measures would be associated with GM volume in the dorsolateral frontal lobes. Voxel-based multiple regression was used to correlate cognitive function with modulated GM probability maps while controlling for age, education, gender, and total intracranial volume. A relationship with TrailsB was found in bilateral lateral inferior frontal gyri and left basal ganglia. A relationship with COWAT was found in the left lateral inferior and middle frontal gyri. Lesion studies have long implicated the importance of these regions for executive function. The present results confirm and extend those prior findings to healthy adults.

Relationships between gray matter metabolic abnormalities and white matter inflammation in patients at the very early stage of MS

Proton magnetic resonance spectroscopic imaging ((1)H-MRSI) was used to study metabolic abnormalities inside the gray matter (GM) during or distant to white matter (WM) inflammatory processes reflected by T(1) gadolinium-enhancing lesions in patients at the very early stage of multiple sclerosis (MS). The spectroscopic examination was performed in the axial plane using a home-designed acquisition-weighted, hamming shape, 2D-SE pulse sequence (TE = 135 ms; TR = 1,600 ms). Bilateral thalami and the medial occipital cortex were explored in 35 patients (15 with and 20 without T(1)-Gd enhancing lesions) with clinically isolated syndrome suggestive of MS and in 30 controls. The mean duration since the first presenting symptom was 9.1 (+/-6.7) months. The two groups of patients (with or without T(1) Gd-enhancing lesions) did not differ in terms of time elapsed since the first clinical onset and T(2) lesion load. The spatial contamination of surrounding WM tissues was obtained in each GM region by determining the tissue component in the ROI from GM and WM probability maps smoothed with the point spread function of the MRSI acquisition. Contribution of WM signal was important (60%) inside thalami while the region centered on the medial occipital cortex was well representative of GM metabolism (>70%). Comparisons of relative metabolite levels (ratios of each metabolite over the sum of all metabolites) between all patients and controls showed significant decrease in relative N-acetyl aspartate (NAA) levels, increase in relative choline-containing compounds (Cho) levels and no change in relative creatine/phosphocreatine levels inside the three ROIs. Decrease in relative NAA levels and increase in relative Cho levels were found in patients with inflammatory activity, while no metabolic alterations were present in patients without T(1) Gd-enhancing lesions. These results suggest that abnormalities in GM metabolism observed in patients at the very early stage of MS are mainly related to neuronal dysfunction occurring during acute inflammatory processes.

Whole brain analysis of T2* weighted baseline FMRI signal in dementia

Brain activation in studies using blood oxygenation level dependent (BOLD) FMRI is associated with an increase in T2* weighted signal between baseline and an active condition. This BOLD technique is often applied to study differences in brain activation between patients and healthy controls. However, the baseline T2* signal itself may also be different between groups, as shown in the hippocampus in Alzheimer's disease using the resting oxygen or ROXY approach (Small et al. [2002]: Ann Neurol 51:290-295). In the current study, we analyzed whole brain, voxel-wise T2* weighted signal of averaged baseline scans of a BOLD FMRI experiment in 41 healthy elderly controls and 46 patients with mild cognitive impairment or Alzheimer's disease. In each subject, T2* weighted images were normalized to the CSF signal of the same image. Additionally, gray matter probability maps of high-resolution structural scans were also compared between groups to assess atrophy. T2* signal was decreased in dementia in the hippocampus, insula/putamen, posterior and middle cingulate cortex, and parietal cortex. Most of these regions also showed decreased gray matter, except insula/putamen. Hippocampal and posterior cingulate gray matter differences were significantly larger than T2* differences. Therefore, decreased T2* signal in most regions are likely to be caused by gray matter atrophy, although decreased metabolism or perhaps iron deposition are also factors that may contribute. We conclude that in FMRI studies of dementia, not only the dynamic BOLD signal (activation and deactivation) but also the average baseline signal is diminished in certain regions. The method we applied may also be used in task-rel

Non-normality and transformations of random fields, with an application to voxel-based morphometry

Parametric tests of linear models for images modeled as random fields are based like ordinary univariate tests on distributional assumptions. It is here shown that the effect of departures from assumptions in random field tests is more pronounced than in the univariate condition. Simulations are presented investigating in detail the influence of smoothing, unbalancedness and leverages on empirical thresholds. In certain conditions, significance tests may become invalid. As a case study, the existence and effect of departures from normality of gray matter probability maps, commonly used in voxel-based morphometry, is investigated, as well as the effect of different transformation strategies involving estimating the degree of transformation from the data by maximum likelihood. The best results are achieved with a voxel-by-voxel transformation, suggesting heterogeneity of distributional form across the volume for this kind of data.

Comparison of two methods for correction of partial volume effects in PET

The low spatial resolution of PET scanners results in partial volume (PV) effects which limit the accurate quantification in anatomical structures such as the cerebral cortex and subcortical regions. Several correction methods have been proposed to deal with these effects. In a first study 1, using simulated data, we compared the PV correction of some cortical regions, obtained with i) the correction algorithm implemented at the Research Center Juelich (PVC-J) and ii) the one implemented at the Brain Imaging Centre of Montreal (PVC-M). Here, still using simulated data, the impact of mis-registration between the PET and the anatomical data on accuracy of the correction, as well as the correction of real dynamic PET data are presented. The PVC-J algorithm - based on the work of Muller-Gartner - has a fully 3D implementation. The resulting corrected grey matter (GM) activity images are obtained by dividing voxel-wise the uncorrected GM images (without white matter contribution) by the GM probability map, derived from the convolution of the corresponding MR segmented image by a 3D spatially variant gaussian function, which reproduces the actual PET image resolution. The PVC-M algorithm - based on the work of Rousset - accounts for the mutual PV effects between any possible tissue structure. After tissue classification of the high resolution MR data, cerebral structures of interest are chosen. Each structure is convolved with a spatially invariant 3D gaussian kernel yielding a set of 3D probability maps, from which recovery and cross-contamination factors are computed. Measured time-activity curves are finally corrected using the correction factor set. A PET dynamic acquisition of an adenosin receptor study was first corrected by both methods and furthermore used to build simulated dynamic data 1 which were translated and rotated by 1 & 2 mm and 1 & 2 degrees, respectively, with respect to the simulated MR image. In case of real data, the mean recovery factor over the dynamic data (6–90 min) of GM is 1.360.05 for PVC-M and 1.410.01 for PVC-J, while in the thalamus region it is 1.170.03 for PVC-M and 1.220.02 for PVC-J. For the mismatched data, the relative difference between the corrected activity values, obtained with the two correction algorithms, and the references was compared. In all but the rotation about the z-axis case, the results are highly consistent. For the rotations about the z axis, the values given by PVC-M and PVC-J show a difference up to about 2.0%. Furthermore, some curves show an instability in the values during the last 30 minutes of the dynamic series, independent from region, mismatch, and method. In this time period the receptor data are affected by lower statistics and low signal-to-contrast ratio. As in the first comparison study, the presented data show an overall high consistency of the results obtained from the two different methods concerning the adenosine receptor. Some of the observed discrepancies could result from the ways the time activity curves are computed with both correction algorithms.