Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.
multivariate analysis; multiple comparisons; multimodality imaging; diffusion tensor imaging; structural magnetic resonance imaging; perfusion weighted magnetic resonance imaging; Alzheimer's disease
Diffusion spectrum imaging (DSI) is a generalization of diffusion tensor imaging to map fibrous structure of white matter and potentially very sensitive to alterations of the cingulum bundles in dementia. In this in-vivo 4T study, DSI parameters especially spatial resolution and diffusion encoding bandwidth were optimized on humans to segment the cingulum bundles for tract level measurements of diffusion. The careful tailoring of the DSI acquisitions in conjunction with fiber tracking provided an optimal DSI setting for a reliable quantification of the cingulum bundle tracts. The optimization of tracking the cingulum bundle was verified using fiber tract quantifications, including coefficients of variability of DSI measurements along the fibers between and within healthy subjects in back-to-back studies and variogram analysis of spatial correlations between diffusion orientation distribution functions (ODF) along the cingulum bundle tracts. The results demonstrate identification of the cingulum bundle in human brain is reproducible using an optimized DSI parameter for maximum b-value and high spatial resolution of the DSI acquisition with a feasible acquisition time of whole brain in clinical practice. This optimized DSI setting should be useful for detecting alterations along the cingulum bundle in Alzheimer disease and related neurodegenerative disorders.
MR diffusion; Diffusion spectrum imaging; optimization; cingulum bundle; Alzheimer
Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer’s disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of locally linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with following diagnoses: Cognitive normal (CN; n= 137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found classifications using embedded MRI features generally outperformed (p < 0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p = 0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: = 0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: = 0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
Alzheimer’s disease; locally linear embedding; statistical learning; classification of AD; MRI
To improve signal-to-noise ratio (SNR) for diffusion-weighted MR images.
A new method is proposed for denoising diffusion-weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low-rank model. The resulting optimization problem is solved efficiently using a half-quadratic method with an alternating minimization scheme.
The performance of the proposed method has been validated using simulated and experimental data. Diffusion-weighted images and noisy data were simulated based on the diffusion tensor imaging (DTI) model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in SNR and diffusion tensor es-timation after denoising. In-vivo diffusion imaging data at different b-values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative im-provement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art non-local means based denoising algorithms, both qualitatively and quantitatively.
The SNR of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy.
Diffusion-weighted imaging; diffusion tensor imaging; Rician distribution; noncentral χ distribution; low-rank approximation; edge constraints
Cortical atrophy has been associated with late life depression (LLD) and recent findings suggest that reduced right hemisphere cortical thickness is associated with familial risk for major depressive disorder but cortical thickness abnormalities in LLD have not been explored. Further, cortical atrophy has been posited as a contributor to poor antidepressant treatment response in LLD but the impact of cortical thickness on psychotherapy response is unknown. This study was conducted to evaluate patterns of cortical thickness in LLD and in relation to psychotherapy treatment outcomes.
Participants included 22 individuals with LLD and 12 age matched comparison subjects. LLD participants completed 12 weeks of psychotherapy and treatment response was defined as a 50% reduction in depressive symptoms. All participants participated in Magnetic Resonance Imaging (MRI) of the brain and cortical mapping of grey matter tissue thickness was calculated.
LLD individuals demonstrated thinner cortex than controls prominently in the right frontal, parietal, and temporal brain regions. Eleven participants (50%) exhibited positive psychotherapy response after 12 weeks of treatment. Psychotherapy non-responders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbito-frontal and lateral occipital cortices relative to treatment responders.
Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. Additionally, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy.
The ADNI 3D T1-weighted MRI acquisitions provide a rich dataset for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI-1 have been created, comprising: screening visits, 1 year completers (subjects who all have screening, 6 and 12 month scans), two year annual completers (screening, 1, and 2 year scans), two year completers (screening, 6 months, 1 year, 18 months (MCI only) and 2 years) and complete visits (screening, 6 months, 1 year, 18 months (MCI only), 2, and 3 year (normal and MCI only) scans). As the ADNI-GO/ADNI-2 data becomes available, updated standard analysis sets will be posted regularly.
To investigate blood to tissue water transfer in human brain, in vivo and spatially resolved using a T2-based arterial spin labeling (ASL) method with 3D readout.
Materials and Methods
A T2-ASL method is introduced to measure the water transfer processes between arterial blood and brain tissue based on a 3D-GRASE pulsed ASL sequence with multi-echo readout. An analytical mathematical model is derived based on the General Kinetic Model, including blood- and tissue compartment, T1 and T2 relaxation and a blood-to-tissue transfer term. Data has been collected from healthy volunteers on a 3 Tesla system. The mean transfer time parameter Tbl→ex (blood- to extravascular compartment transfer time) has been derived voxel-wise by non-linear least-squares fitting.
Whole-brain maps of Tbl→ex show stable results in cortical regions, yielding different values depending on brain region. The mean value across subjects and ROIs in grey matter is 440±30 ms.
A novel method to derive whole-brain maps of blood to tissue water transfer dynamics is demonstrated. It is promising for the investigation of underlying physiological mechanisms and development of diagnostic applications in cerebro-vascular diseases.
Arterial Spin Labeling; two-compartment model; water transfer; transverse relaxation; T2; 3D-GRASE
Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio (SNR) represent significant hurdles for many in vivo applications. This paper presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for Gaussian noise modeling and theoretical characterizations of the resolution and SNR of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling, q-space modeling, etc.). The joint reconstruction method can provide significant improvements in SNR relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this SNR-enhancing joint reconstruction approach for a range of different diffusion imaging experiments.
Diffusion imaging; denoising; statistical reconstruction; feature preservation
We propose a linear-elastic registration method to register diffusion-weighted MRI (DW-MRI) data sets by mapping their diffusion orientation distribution functions (ODFs). The ODFs were reconstructed using a q-ball imaging (QBI) technique to resolve intravoxel fiber crossing. The registration method is based on mapping the ODF maps represented by spherical harmonics which yield analytic solutions and reduce the computational complexity. ODF reorientation is required to maintain the consistency with transformed local fiber directions. The reorientation matrices are extracted from the local Jacobian and directly applied to the coefficients of spherical harmonics. The similarity cost of the registration is defined by the ODF shape distance calculated from the spherical harmonic coefficients. The transformation fields are regularized by linear elastic constraints. The proposed method was validated using both synthetic and real data sets. Experimental results show that the elastic registration improved the affine alignment by further reducing the ODF shape difference; reorientation during the registration produced registered ODF maps with more consistent principle directions compared to registrations without reorientation or simultaneous reorientation.
Non-rigid registration of diffusion MRI is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.
Diffusion MRI; orientation distribution function (ODF); spherical harmonics; ODF reorientation; registration; diffeomorphisms
Brain changes reminiscent of Alzheimer disease (AD) have been previously reported in a substantial portion of elderly cognitive healthy (HC) subjects. The major aim was to evaluate the accuracy of MRI assessed regional gray matter (GM) volume, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), and neuropsychological test scores to identify those HC subjects who subsequently convert to mild cognitive impairment (MCI) or AD dementia. We obtained in 54 healthy control (HC) subjects a priori defined region of interest (ROI) values of medial temporal and parietal FDG-PET and medial temporal GM volume. In logistic regression analyses, these ROI values were tested together with neuropsychological test scores (free recall, trail making test B (TMT-B)) as predictors of HC conversion during a clinical follow-up between 3 and 4 years. In voxel-based analyses, FDG-PET and MRI GM maps were compared between HC converters and HC non-converters. Out of the 54 HC subjects, 11 subjects converted to MCI or AD dementia. Lower FDG-PET ROI values were associated with higher likelihood of conversion (p = 0.004), with the area under the curve (AUC) yielding 82.0% (95% CI = (95.5%, 68.5%)). The GM volume ROI was not a significant predictor (p = 0.07). TMT-B but not the free recall tests were a significant predictor (AUC = 71% (95% CI = 50.4%, 91.7%)). For the combination of FDG-PET and TMT-B, the AUC was 93.4% (sensitivity = 82%, specificity = 93%). Voxel-based group comparison showed reduced FDG-PET metabolism within the temporo-parietal and prefrontal cortex in HC converters. In conclusion, medial temporal and-parietal FDG-PET and executive function show a clinically acceptable accuracy for predicting clinical progression in elderly HC subjects.
•To predict conversion from cognitively normal to MCI & dementia within 4 years•FDG-PET and the TMT-B test were significant predictors of conversion•FDG-PET showed stronger decrease than gray matter volume in converters.
Preclinical AD; Conversion; Diagnosis; FDG-PET; Gray matter volume; Executive function
Whether longitudinal diffusion tensor MRI imaging (DTI) can capture disease progression in patients with amyotrophic lateral sclerosis (ALS) is unclear. The primary goal of this study was to determine if DTI detects progression of the corticospinal tracts (CST) degeneration in ALS. Seventeen ALS patients and 19 age- and gender-matched healthy controls were scanned with DTI at baseline for cross-sectional analyses. For longitudinal analyses, the ALS patients had repeat DTI scans after eight months. Tractography of the CST was used to guide regions-of-interest (ROI) analysis and complemented by a voxelwise analysis. Cross-sectional study found that baseline FA of the right superior CST was markedly reduced in ALS patients compared to controls. The FA reductions in this region correlated with the disease severity in ALS patients. Longitudinal study found that FA change rate of the right superior CST significantly declined over time. In conclusion, longitudinal DTI study captures progression of upper motor fiber degeneration in ALS. DTI can be useful for monitoring ALS progression and efficacy of treatment interventions.
Amyotrophic lateral sclerosis; diffusion tensor imaging; longitudinal study; corticospinal tracts; brain MRI
This supplement to the Journal of Alzheimer's Disease contains more than half of the chapters from The Handbook of Imaging the Alzheimer Brain, which was first presented at the International Conference on Alzheimer's Disease in Paris, in July, 2011.
While the Handbook contains 27 chapters that are modified articles from 2009, 2010, and 2011 issues of the Journal of Alzheimer's Disease, this supplement contains the 31 new chapters of that book and an introductory article drawn from the introductions to each section of the book.
The Handbook was designed to provide a multilevel overview of the full field of brain imaging related to Alzheimer's disease (AD). The Handbook, as well as this supplement, contains both reviews of the basic concepts of imaging, the latest developments in imaging, and various discussions and perspectives of the problems of the field and promising directions.
The Handbook was designed to be useful for students and clinicians interested in AD as well as scientists studying the brain and pathology related to AD.
Beta-amyloid (Aβ) is a histopathological hallmark of Alzheimer’s disease dementia, but high levels of Aβ in the brain can also be found in a substantial proportion of nondemented subjects. Here we investigated which 2-year rate of brain and cognitive changes are present in nondemented subjects with high and low Aβ levels, as assessed with cerebrospinal fluid and molecular positron emission tomography (PET)–based biomarkers of Aβ. In subjects with mild cognitive impairment, increased brain Aβ levels were associated with significantly faster cognitive decline, progression of gray matter atrophy within temporal and parietal brain regions, and a trend for a faster decline in parietal Fludeoxyglucose (FDG)-PET metabolism. Changes in gray matter and FDG-PET mediated the association between Aβ and cognitive decline. In contrast, elderly cognitively healthy controls (HC) with high Aβ levels showed only a faster medial temporal lobe and precuneus volume decline compared with HC with low Aβ. In conclusion, the current results suggest not only that both functional and volumetric brain changes are associated with high Aβ years before the onset of dementia but also that HC with substantial Aβ levels show higher Aβ pathology resistance, lack other pathologies that condition neurotoxic effects of Aβ, or accumulated Aβ for a shorter time period.
Aβ; FDG-PET; MCI; PIB-PET
To characterize and compare measurements of the posterior cingulate glucose metabolism, the hippocampal glucose metabolism, and hippocampal volume so as to distinguish cognitively normal, late-middle-aged persons with 2, 1, or 0 copies of the apolipoprotein E (APOE) ε4 allele, reflecting 3 levels of risk for late-onset Alzheimer disease.
Cross-sectional comparison of measurements of cerebral glucose metabolism using 18F-fluorodeoxy-glucose positron emission tomography and measurements of brain volume using magnetic resonance imaging in cognitively normal ε4 homozygotes, ε4 heterozygotes, and noncarriers.
Academic medical center.
A total of 31 ε4 homozygotes, 42 ε4 heterozygotes, and 76 noncarriers, 49 to 67 years old, matched for sex, age, and educational level.
Main Outcome Measures
The measurements of posterior cingulate and hippocampal glucose metabolism were characterized using automated region-of-interest algorithms and normalized for whole-brain measurements. The hippocampal volume measurements were characterized using a semiautomated algorithm and normalized for total intracranial volume.
Although there were no significant differences among the 3 groups of participants in their clinical ratings, neuropsychological test scores, hippocampal volumes (P=.60), or hippocampal glucose metabolism measurements (P = .12), there were significant group differences in their posterior cingulate glucose metabolism measurements (P=.001). The APOE ε4 gene dose was significantly associated with posterior cingulate glucose metabolism (r=0.29, P=.0003), and this association was significantly greater than those with hippocampal volume or hippocampal glucose metabolism (P<.05, determined by use of pairwise Fisher z tests).
Although our findings may depend in part on the analysis algorithms used, they suggest that a reduction in posterior cingulate glucose metabolism precedes a reduction in hippocampal volume or metabolism in cognitively normal persons at increased genetic risk for Alzheimer disease.
Brain magnetic resonance imaging (MRI) studies have demonstrated regional patterns of brain macrostructural atrophy and white matter microstructural alterations separately in the three major subtypes of frontotemporal lobar degeneration (FTLD), which includes behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA). This study was to investigate to what extent the pattern of white matter microstructural alterations in FTLD subtypes mirrors the pattern of brain atrophy, and to compare the ability of various diffusion tensor imaging (DTI) indices in characterizing FTLD patients, as well as to determine whether DTI measures provide greater classification power for FTLD than measuring brain atrophy. Twenty-five patients with FTLD (13 with bvFTD, 6 with SD, and 6 with PNFA) and 19 healthy age-matched control subjects underwent both structural MRI and DTI scans. Measurements of regional brain atrophy were based on T1-weighted MRI data and voxel-based morphometry. Measurements of regional white matter degradation were based on voxelwise as well as regions-of-interest tests of DTI variations, expressed as fractional anisotropy, axial diffusivity, and radial diffusivity. Compared to controls, bvFTD, SD, and PNFA patients each exhibited characteristic regional patterns of brain atrophy and white matter damage. DTI overall provided significantly greater accuracy for FTLD classification than brain atrophy. Moreover, radial diffusivity was more sensitive in assessing white matter damage in FTLD than other DTI indices. The findings suggest that DTI in general and radial diffusivity in particular are more powerful measures for the classification of FTLD patients from controls than brain atrophy.
Behavioral variant frontotemporal dementia; diffusion tensor imaging; frontotemporal lobar degeneration; multimodality MRI; progressive nonfluent aphasia; semantic dementia
The aim of the study was to evaluate the value of assessing white matter integrity using diffusion tensor imaging (DTI) for classification of mild cognitive impairment (MCI) and prediction of cognitive impairments in comparison to brain atrophy measurements using structural MRI. Fifty-one patients with MCI and 66 cognitive normal controls (CN) underwent DTI and T1-weighted structural MRI. DTI measures included fractional anisotropy (FA) and radial diffusivity (DR) from 20 predetermined regions-of-interest (ROIs) in the commissural, limbic and association tracts, which are thought to be involved in Alzheimer's disease; measures of regional gray matter (GM) volume included 21 ROIs in medial temporal lobe, parietal cortex, and subcortical regions. Significant group differences between MCI and CN were detected by each MRI modality: In particular, reduced FA was found in splenium, left isthmus cingulum and fornix; increased DR was found in splenium, left isthmus cingulum and bilateral uncinate fasciculi; reduced GM volume was found in bilateral hippocampi, left entorhinal cortex, right amygdala and bilateral thalamus; and thinner cortex was found in the left entorhinal cortex. Group classifications based on FA or DR was significant and better than classifications based on GM volume. Using either DR or FA together with GM volume improved classification accuracy. Furthermore, all three measures, FA, DR and GM volume were similarly accurate in predicting cognitive performance in MCI patients. Taken together, the results imply that DTI measures are as accurate as measures of GM volume in detecting brain alterations that are associated with cognitive impairment. Furthermore, a combination of DTI and structural MRI measurements improves classification accuracy.
The goal was to elucidate the time course of regional brain atrophy rates relative to age in cognitively normal (CN) aging, mild cognitively impairment (MCI) and Alzheimer’s disease (AD), without a-priori models for atrophy progression. Regional brain volumes from 147 CN subjects, 164 stable MCI, 93 MCI-to-AD converters and 111 AD patients, between 51 to 91 years old and who had repeated 1.5T magnetic resonance imaging (MRI) scans over 30 months, were analyzed. Relations between regional brain volume change and age were determined using generalized additive models, an established non-parametric concept for approximating nonlinear relations. Brain atrophy rates varied nonlinearly with age, predominantly in regions of the temporal lobe. Moreover, the atrophy rates of some regions leveled off with increasing age in control and stable MCI subjects whereas those rates progressed further in MCI-to-AD converters and AD patients. The approach has potential uses for early detection of AD and differentiation between stable and progressing MCI.
Alzheimer’s disease; mild cognitive impairment; aging; brain atrophy; hippocampus; magnetic resonance imaging; generalized additive models
To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.
CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.
The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD.
Magnetic Resonance Imaging (MRI) provides various imaging modes to study the brain. We tested the benefits of a joint analysis of multimodality MRI data in combination with a large-scale analysis that involved simultaneously all image voxels using joint independent components analysis (jICA) and compared the outcome to results using conventional voxel-by-voxel unimodality tests. Specifically, we designed a jICA to decompose multimodality MRI data into independent components that explain joint variations between the image modalities as well as variations across brain regions. We tested the jICA design on structural and perfusion-weighted MRI data from 12 patients diagnosed with behavioral variant frontotemporal dementia (bvFTD) and 12 cognitively normal elderly individuals. While unimodality analyses showed widespread brain atrophy and hypoperfusion in the patients, jICA further revealed two significant joint components of variations between atrophy and hypoperfusion across brain regions. The 1st joint component revealed associated brain atrophy and hypoperfusion predominantly in the right brain hemisphere in behavioral variant frontotemporal dementia, and the 2nd joint component revealed greater atrophy relative to hypoperfusion affecting predominantly the left hemisphere in behavioral variant frontotemporal dementia. The patterns are consistent with the clinical symptoms of behavioral variant frontotemporal dementia that relate to asymmetric compromises of the left and right brain hemispheres. The joint components also revealed that that structural alterations can be associated with physiological alterations in spatially separated but potentially connected brain regions. Finally, jICA outperformed voxel-by-voxel unimodal tests significantly in terms of an effect size, separating the behavioral variant frontotemporal dementia patients from the controls. Taken together, the results demonstrate the benefit of multimodality MRI in conjunction with jICA for mapping neurodegeneration, which may lead ultimately to an improved diagnosis of behavioral variant frontotemporal dementia and other forms of neurodegenerative diseases.
Brain atrophy; Brain hypoperfusion; Dementia; Neurodegenerative diseases; Joint ICA; Multimodality MRI
The goal of this study was to determine whether PTSD was associated with an increase in time-related decline in macrostructural brain volume and whether these changes were associated with accelerated cognitive decline. To quantify brain structure, 3 dimensional T1-weighted MRI scans were performed at baseline and again after a minimum of 24 months in 25 patients with PTSD and 22 controls. Longitudinal changes in brain volume were measured using deformation morphometry. For the group as a whole PTSD+ patients did not show significant ongoing brain atrophy compared to PTSD-. PTSD+ patients were then subgrouped into those with decreasing or increasing symptoms. We found little evidence for brain markers of accelerated atrophy in PTSD+ veterans whose symptoms improved over time, with only a small left parietal region showing greater ongoing tissue loss than PTSD-. PTSD patients whose symptoms increased over time showed accelerated atrophy throughout the brain, particularly brainstem and frontal and temporal lobes. Lastly, for the sample as a whole greater rates of brain atrophy were associated with greater rates of decline in verbal memory and delayed facial recognition.
deformation morphometry; longitudinal; MRI; neuropsychological testing; PTSD; Vietnam veterans
To test the hypothesis that white matter lesions (WML) are primarily associated with regional frontal cortical volumes, and to determine the mediating effects of these regional frontal cortices on the associations of WML with depressive symptoms and cognitive dysfunction.
Structural brains MRIs were performed on 161 participants: cognitively normal, cognitive impaired but not demented, and demented participants. Lobar WML volumes, regional frontal cortical volumes, depressive symptom severity, and cognitive abilities were measured. Multiple linear regression analyses were used to identify WML volume effects on frontal cortical volume. Structural equation modeling was used to determine the MRI-depression and the MRI-cognition path relationships.
WML predicted frontal cortical volume, particularly in medial orbirtofrontal cortex, irrespective of age, gender, education, and group status. WML directly predicted depressive score, and this relationship was not mediated by regional frontal cortices. In contrast, the association between WML and cognitive function was indirect and mediated by regional frontal cortices.
These findings suggest that the neurobiological mechanisms underpinning depressive symptoms and cognitive dysfunction in older adults may differ.
Leukoaraiosis; Depression; Cognition; Frontal lobe; Mediation
This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6 months, for 2 years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0-6 months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1.4% to 0.28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1.6 to 2.4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.
Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer’s disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by 11C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer’s disease pathology, encouraging for the use of 11C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer’s disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer’s disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer’s disease.
MRI; 11C-PiB PET; Alzheimer’s disease; mild cognitive impairment; amyloid-β; amyloid; brain atrophy rate; multimodal brain imaging
Decreased hippocampal volume is described in posttraumatic stress disorder (PTSD) and depression. However, it is not known whether it is a risk factor for the development of PTSD or a consequence of PTSD. We sought to determine the effects of PTSD and depressive symptoms on hippocampal volume.
Clinical and magnetic resonance imaging data were collected in a cross sectional study of 244 Gulf War veterans. Measures included lifetime and current Clinician Administered PTSD Scale, Hamilton Depression Scale, Life Stressor Checklist, and Lifetime Drinking History. Magnetic resonance imaging data were acquired with a 1.5-T scanner and analyzed with automated and semiautomated image processing techniques.
Eighty-two veterans had lifetime PTSD, 44 had current PTSD, and 38 had current depression. In the linear regression analysis, current PTSD symptoms (standardized coefficient β = −.25, p = .03) but neither lifetime PTSD symptoms nor current depression were associated with smaller hippocampal volume. Gender, age, history of early life trauma, education, lifetime and current alcohol use, current marijuana use, and treatment with antidepressants did not have independent effects. Participants with chronic PTSD had, on average, a smaller hippocampus compared with those with remitted PTSD.
The finding that current but not lifetime PTSD symptom severity explains hippocampal size raises two possibilities: either a small hippocampus is a risk factor for lack of recovery from PTSD (trait) or PTSD effects on hippocampal volume are reversible once PTSD symptoms remit and the patient recovers (state).
Depression; Gulf War veterans; hippocampus; magnetic resonance imaging; posttraumatic stress disorder