Longitudinal Change of Clinical and Biological Measures in Early Parkinson's Disease: Parkinson's Progression Markers Initiative Cohort.

OBJECTIVE: The objective of this study was to assess longitudinal change in clinical and dopamine transporter imaging outcomes in early, untreated PD. METHODS: We describe 5-year longitudinal change of the MDS-UPDRS and other clinical measures using results from the Parkinson's Progression Markers Initiative, a longitudinal cohort study of early Parkinson's disease (PD) participants untreated at baseline. We also provide data on the longitudinal change in dopamine transporter 123-I Ioflupane striatal binding and correlation between the 2 measures. RESULTS: A total of 423 PD participants were recruited, and 358 remain in the study at year 5. Baseline MDS-UPDRS total score was 32.4 (standard deviation 13.1), and the average annual change (assessed medications OFF for the treated participants) was 7.45 (11.6), 3.11 (11.7), 4(11.9), 4.7 (11.1), and 1.74(11.9) for years 1, 2, 3, 4, and 5, respectively (P < .0001 for the change over time), with a steeper change in year 1. Dopaminergic therapy had a significant effect on the change of MDS-UPDRS. There was a significant longitudinal change in dopamine transporter binding in all striatal regions (P < .001). There was a significant but weak correlation between MDS-UPDRS and dopamine transporter binding at baseline and years 1, 2, and 4, but no correlation between the rate of change of the 2 variables. CONCLUSIONS: We present 5-year longitudinal data on the change of the MDS-UPDRS and other clinical and dopamine transporter imaging outcome measures in early PD. These data can be used for sample size estimates for interventional studies in the de novo PD population. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints.

PURPOSE: To accelerate denoising of magnitude diffusion-weighted images subject to joint rank and edge constraints. METHODS: We extend a previously proposed majorize-minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme. RESULTS: The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi-Newton-based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion-encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high-resolution Q-ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm. CONCLUSION: The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm.

Quantitative framework for prospective motion correction evaluation.

PURPOSE: Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans. METHODS: A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC. RESULTS: Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance. CONCLUSION: Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.

Amyloid in dementia associated with familial FTLD: not an innocent bystander.

Patients with frontotemporal lobar degeneration (FTLD) can show superimposed amyloid pathology, though the impact of amyloid on the clinical presentation of FTLD is not well characterized. This cross-sectional case-control study compared clinical features, fluorodeoxyglucose-positron emission tomography metabolism and gray matter volume loss in 30 patients with familial FTLD in whom amyloid status was confirmed with autopsy or Pittsburgh compound B-PET. Compared to the amyloid-negative patients, the amyloid-positive patients performed significantly worse on several cognitive tests and showed hypometabolism and volume loss in more temporoparietal regions. Our results suggest that in FTLD amyloid positivity is associated with a more Alzheimer's disease-like pattern of neurodegeneration.

Discriminative Power of Arterial Spin Labeling Magnetic Resonance Imaging and 18F-Fluorodeoxyglucose Positron Emission Tomography Changes for Amyloid-ß-Positive Subjects in the Alzheimer's Disease Continuum.

BACKGROUND: Recent studies have demonstrated that arterial spin labeling magnetic resonance imaging (ASL-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) identify similar regional abnormalities and have comparable diagnostic accuracy in Alzheimer's disease (AD). The agreement between these modalities in the AD continuum, which is an important concept for early detection and disease monitoring, is yet unclear. OBJECTIVE: We aimed to assess the ability of the cerebral blood flow (CBF) measures from ASL-MRI and cerebral metabolic rate for glucose (CMRgl) measures from FDG-PET to distinguish amyloid-ß-positive (Aß+) subjects in the AD continuum from healthy controls. METHODS: The study included asymptomatic, cognitively normal (CN) controls and patients with early mild cognitive impairment (MCI), late MCI, and AD, all with significant levels of cortical Aß based on their florbetapir PET scans to restrict the study to patients truly in the AD continuum. The discrimination power of each modality was based on the whole-brain patterns of CBF and CMRgl changes identified by partial least squares logistic regression, a multivariate analysis technique. RESULTS: While CBF changes in the posterior inferior aspects of the brain and a pattern of CMRgl changes in the superior aspects of the brain including frontal and parietal regions best discriminated the Aß+ subjects in the early disease stages from the Aß- CN subjects, there was a greater agreement in the whole-brain patterns of CBF and CMRgl changes that best discriminated the Aß+ subjects from the Aß- CN subjects in the later disease stages. Despite the differences in the whole-brain patterns of CBF and CMRgl changes, the discriminative powers of both modalities were similar with statistically nonsignificant performance differences in sensitivity and specificity. CONCLUSION: The results comparing measurements of CBF to CMRgl add to previous reports that MRI-measured CBF has a similar diagnostic ability to detect AD as has FDG-PET. Our findings that CBF and CMRgl changes occur in different brain regions in Aß+ subjects across the AD continuum compared with Aß- CN subjects may be the result of methodological differences. Alternatively, these findings may signal alterations in neurovascular coupling which alter relationships between brain perfusion and glucose metabolism in the AD continuum.

Bayesian segmentation of brainstem structures in MRI.

In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.

Diffusion tensor imaging of the nigrostriatal fibers in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is histopathologically characterized by the loss of dopamine neurons in the substantia nigra pars compacta. The depletion of these neurons is thought to reduce the dopaminergic function of the nigrostriatal pathway, as well as the neural fibers that link the substantia nigra to the striatum (putamen and caudate), causing a dysregulation in striatal activity that ultimately leads to lack of movement control. Based on diffusion tensor imaging, visualizing this pathway and measuring alterations of the fiber integrity remain challenging. The objectives were to 1) develop a diffusion tensor tractography protocol for reliably tracking the nigrostriatal fibers on multicenter data; 2) test whether the integrities measured by diffusion tensor imaging of the nigrostriatal fibers are abnormal in PD; and 3) test whether abnormal integrities of the nigrostriatal fibers in PD patients are associated with the severity of motor disability and putaminal dopamine binding ratios. METHODS: Diffusion tensor tractography was performed on 50 drug-naïve PD patients and 27 healthy control subjects from the international multicenter Parkinson's Progression Marker Initiative. RESULTS: Tractography consistently detected the nigrostriatal fibers, yielding reliable diffusion measures. Fractional anisotropy, along with radial and axial diffusivity of the nigrostriatal tract, showed systematic abnormalities in patients. In addition, variations in fractional anisotropy and radial diffusivity of the nigrostriatal tract were associated with the degree of motor deficits in PD patients. CONCLUSION: Taken together, the findings imply that the diffusion tensor imaging characteristic of the nigrostriatal tract is potentially an index for detecting and staging of early PD.

Diffusion imaging of nigral alterations in early Parkinson's disease with dopaminergic deficits.

BACKGROUND: This study reports the baseline characteristics of diffusion tensor imaging data in Parkinson's disease (PD) patients and healthy control subjects from the Parkinson's Progression Markers Initiative. The main goals were to replicate previous findings of abnormal diffusion imaging values from the substantia nigra. in a large multicenter cohort and determine whether nigral diffusion alterations are associated with dopamine deficits. METHODS: Two hundred twenty subjects (PD = 153; control = 67) from 10 imaging sites were included. All subjects had a full neurological exam, a ((123) I)ioflupane dopamine transporter (DAT) single-photon emission computer tomography scan, and diffusion tensor imaging. Fractional anisotropy as well as radial and axial diffusivity was computed within multiple regions across the substantia nigra. RESULTS: A repeated-measures analysis of variance found a marginally nonsignificant interaction between regional fractional anisotropy of the substantia nigra and disease status (P = 0.08), conflicting with an earlier study. However, a linear mixed model that included control regions in addition to the nigral regions revealed a significant interaction between regions and disease status (P = 0.002), implying a characteristic distribution of reduced fractional anisotropy across the substantia nigra in PD. Reduced fractional anisotropy in PD was also associated with diminished DAT binding ratios. Both axial and radial diffusivity were also abnormal in PD. CONCLUSIONS: Although routine nigral measurements of fractional anisotropy are clinically not helpful, the findings in this study suggest that more-sophisticated diffusion imaging protocols should be used when exploring the clinical utility of this imaging modality.

Association of brain amyloid-ß with cerebral perfusion and structure in Alzheimer's disease and mild cognitive impairment.

Patients with Alzheimer's disease have reduced cerebral blood flow measured by arterial spin labelling magnetic resonance imaging, but it is unclear how this is related to amyloid-ß pathology. Using 182 subjects from the Alzheimer's Disease Neuroimaging Initiative we tested associations of amyloid-ß with regional cerebral blood flow in healthy controls (n = 51), early (n = 66) and late (n = 41) mild cognitive impairment, and Alzheimer's disease with dementia (n = 24). Based on the theory that Alzheimer's disease starts with amyloid-ß accumulation and progresses with symptoms and secondary pathologies in different trajectories, we tested if cerebral blood flow differed between amyloid-ß-negative controls and -positive subjects in different diagnostic groups, and if amyloid-ß had different associations with cerebral blood flow and grey matter volume. Global amyloid-ß load was measured by florbetapir positron emission tomography, and regional blood flow and volume were measured in eight a priori defined regions of interest. Cerebral blood flow was reduced in patients with dementia in most brain regions. Higher amyloid-ß load was related to lower cerebral blood flow in several regions, independent of diagnostic group. When comparing amyloid-ß-positive subjects with -negative controls, we found reductions of cerebral blood flow in several diagnostic groups, including in precuneus, entorhinal cortex and hippocampus (dementia), inferior parietal cortex (late mild cognitive impairment and dementia), and inferior temporal cortex (early and late mild cognitive impairment and dementia). The associations of amyloid-ß with cerebral blood flow and volume differed across the disease spectrum, with high amyloid-ß being associated with greater cerebral blood flow reduction in controls and greater volume reduction in late mild cognitive impairment and dementia. In addition to disease stage, amyloid-ß pathology affects cerebral blood flow across the span from controls to dementia patients. Amyloid-ß pathology has different associations with cerebral blood flow and volume, and may cause more loss of blood flow in early stages, whereas volume loss dominates in late disease stages.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2.

INTRODUCTION: Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS: We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS: Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION: Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

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 remain 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.

Numerical modeling of susceptibility-related MR signal dephasing with vessel size measurement: phantom validation at 3T.

PURPOSE: MRI is used to obtain quantitative oxygenation and blood volume information from the susceptibility-related MR signal dephasing induced by blood vessels. However, analytical models that fit the MR signal are usually not accurate over the range of small blood vessels. Moreover, recent studies have demonstrated limitations in the simultaneous assessment of oxygenation and blood volume. In this study, a multiparametric MRI framework that aims to measure vessel radii in addition to magnetic susceptibility and volume fraction was introduced. METHODS: The protocol consisted of gradient-echo sampling of the spin-echo, diffusion, T2, and B0 acquisitions. After correction steps, the data were postprocessed with a versatile numerical model of the MR signal. An important analytical model was implemented for comparison. The approach was validated in phantoms with coiling strings as proxy for blood vessels. RESULTS: The feasibility of the vessel radius measurement is demonstrated. The numerical model shows an improved accuracy compared with the analytical approach. However, both methods overestimate the radius. The simultaneous measurement of the magnetic susceptibility and the volume fraction remains challenging. CONCLUSION: The results suggest that this approach could be interesting in vivo to better characterize the microvasculature without contrast agent.

Denoising diffusion-weighted magnitude MR images using rank and edge constraints.

PURPOSE: To improve signal-to-noise ratio for diffusion-weighted magnetic resonance images. METHODS: 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. RESULTS: 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 model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in single-to-noise ratio and diffusion tensor estimation 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 improvement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art nonlocal means-based denoising algorithms, both qualitatively and quantitatively. CONCLUSION: The single-to-noise ratio of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy.

Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.

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 local 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 the 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 that 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.

Improved diffusion imaging through SNR-enhancing joint reconstruction.

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article 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 signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio 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 signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments.

T2-based arterial spin labeling measurements of blood to tissue water transfer in human brain.

PURPOSE: 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 (gradient and spin echo) pulsed ASL sequence with multiecho 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 were collected from healthy volunteers on a 3 T system. The mean transfer time parameter T(bl → ex) (blood to extravascular compartment transfer time) was derived voxelwise by nonlinear least-squares fitting. RESULTS: Whole-brain maps of T(bl → ex) show stable results in cortical regions, yielding different values depending on the brain region. The mean value across subjects and regions of interest (ROIs) in gray matter was 440 ± 30 msec. CONCLUSION: 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 cerebrovascular diseases.

The effect of subsyndromal symptoms of depression and white matter lesions on disability for individuals with mild cognitive impairment.

OBJECTIVE: To assess the effect of subsyndromal symptoms of depression (SSD) on ratings of disability for individuals with mild cognitive impairment (MCI). METHODS: Data from 405 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were analyzed. Participants were evaluated at baseline and at 6-month intervals over 2 years. Severity of depressive symptoms was rated utilizing the Geriatric Depression Scale. Disability was assessed utilizing the Functional Assessment Questionnaire (FAQ). Other clinical variables included white matter lesion (WML) and intracranial brain (ICV) volumes derived from magnetic resonance imaging, ratings of overall cognitive function (Alzheimer's Disease Assessment Scale, ADAS), and apolipoprotein E (ApoE) status. Demographic variables included age, education, and gender. RESULTS: SSD individuals had a lower volume of WML and higher frequency of ApoE ε4 alleles than nondepressed participants but the two groups did not differ with respect to other clinical or demographic variables. At baseline, SSD individuals were 1.77 times more likely to have poorer FAQ scores than individuals with no symptoms of depression after controlling for the effect of cognitive functioning, ICV, WML, and ApoE status. The presence of SSD at baseline was not associated with a poorer course of disability outcomes, cognitive functioning, or conversion to dementia over 24 months. CONCLUSIONS: SSD demonstrated a significant impact on disability for MCI individuals, who are also at high risk for functional limitations related to neurodegenerative disease. Therefore, the treatment of SSD may represent a significant avenue to reduce the burden of disability in this vulnerable patient population.

Patterns of reduced cortical thickness in late-life depression and relationship to psychotherapeutic response.

OBJECTIVE: 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. Furthermore, 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. METHODS: 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 underwent magnetic resonance imaging of the brain, and cortical mapping of gray matter tissue thickness was calculated. RESULTS: 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 nonresponders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbitofrontal and lateral occipital cortices relative to treatment responders. CONCLUSIONS: Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. In addition, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy.

CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects.

Beta-amyloid (Aß) is a histopathological hallmark of Alzheimer's disease dementia, but high levels of Aß 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 Aß levels, as assessed with cerebrospinal fluid and molecular positron emission tomography (PET)-based biomarkers of Aß. In subjects with mild cognitive impairment, increased brain Aß 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 Aß and cognitive decline. In contrast, elderly cognitively healthy controls (HC) with high Aß levels showed only a faster medial temporal lobe and precuneus volume decline compared with HC with low Aß. In conclusion, the current results suggest not only that both functional and volumetric brain changes are associated with high Aß years before the onset of dementia but also that HC with substantial Aß levels show higher Aß pathology resistance, lack other pathologies that condition neurotoxic effects of Aß, or accumulated Aß for a shorter time period.

Standardization of analysis sets for reporting results from ADNI MRI data.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) three-dimensional T1-weighted magnetic resonance imaging (MRI) acquisitions provide a rich data set 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), 2-year annual completers (screening, 1-year and 2-year scans), 2-year completers (screening, 6-months, 1-year, 18-months [mild cognitive impaired (MCI) only], and 2-year scans), and complete visits (screening, 6-month, 1-year, 18-month [MCI only], 2-year, and 3-year [normal and MCI only] scans). As the ADNI-GO/ADNI-2 data become available, updated standard analysis sets will be posted regularly.