Mahsa Dadar

Network structure of brain atrophy in de novo Parkinson's disease

We mapped the distribution of atrophy in Parkinsons disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinsons Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation. DOI: http://dx.doi.org/10.7554/eLife.08440.001

Structural Brain Alterations Associated with Rapid Eye Movement Sleep Behavior Disorder in Parkinson’s Disease

Characterized by dream-enactment motor manifestations arising from rapid eye movement (REM) sleep, REM sleep behavior disorder (RBD) is frequently encountered in Parkinson’s disease (PD). Yet the specific neurostructural changes associated with RBD in PD patients remain to be revealed by neuroimaging. Here we identified such neurostructural alterations by comparing large samples of magnetic resonance imaging (MRI) scans in 69 PD patients with probable RBD, 240 patients without RBD and 138 healthy controls, using deformation-based morphometry (p < 0.05 corrected for multiple comparisons). All data were extracted from the Parkinson’s Progression Markers Initiative. PD patients with probable RBD showed smaller volumes than patients without RBD and than healthy controls in the pontomesencephalic tegmentum, medullary reticular formation, hypothalamus, thalamus, putamen, amygdala and anterior cingulate cortex. These results demonstrate that RBD is associated with a prominent loss of volume in the pontomesencephalic tegmentum, where cholinergic, GABAergic and glutamatergic neurons are located and implicated in the promotion of REM sleep and muscle atonia. It is additionally associated with more widespread atrophy in other subcortical and cortical regions whose loss also likely contributes to the altered regulation of sleep-wake states and motor activity underlying RBD in PD patients.

Network connectivity determines cortical thinning in early Parkinson’s disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Introduction: White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimers disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter‐rater and intra‐rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. Methods: We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co‐registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi‐site datasets, including images acquired with different scanners and different scan‐parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k‐nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel‐wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra‐Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open‐source science, we also make available a fully automated tool for segmentation of WMHs with pre‐trained classifiers for all these techniques. Results: Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95). Conclusion: Our investigations showed that with appropriate features, most off‐the‐shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Association between apolipoprotein a-i levels and white matter hyperintensities depends on CSF tau levels in a high-risk cohort of aging cognitively normal persons: The prevent-alzheimer's disease study

Values Nelly Joseph-Mathurin, Yi Su, Andrei Vlassenko, Lars Couture, Tyler Blazey, Karl A. Friedrichsen, Christopher J. Owen, Russ C. Hornbeck, Lisa Cash, Trish A. Stevenson, Beau Ances, Chengjie Xiong, Virginia Buckles, Krista L. Moulder, John C. Morris, Randall Bateman, Marcus E. Raichle, Tammie L. S. Benzinger,Washington University School ofMedicine, St Louis, MO, USA. Contact e-mail: mathurinn@npg.wustl.edu

Neurobehavioral correlates of obesity are largely heritable

Significance Obesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in body mass index (BMI) is explained by brain and behavioral factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in BMI had genetic correlations 0.25–0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, BMI was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioral factors. In summary, the genetic vulnerability to BMI is expressed in the brain. This may inform intervention strategies. Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.

White matter hyperintensities are linked to future cognitive decline in de novo Parkinson's disease patients

White Matter Hyperintensities (WMHs) are associated with cognitive decline in aging and Alzheimers disease. However, the pathogenesis of cognitive decline in Parkinsons disease (PD) is not as clearly related to vascular causes, and therefore the role of WMHs as a marker of small-vessel disease (SVD) in PD is less clear. Currently, SVD in PD is assessed and treated independently of the disease. However, if WMH as the major MRI sign of SVD has a higher impact on cognitive decline in PD patients than in healthy controls, vascular pathology needs to be assessed and treated with a higher priority in this population. Here we investigate whether the presence of WMHs leads to increased cognitive decline in de novo PD, and if these effects relate to cortical atrophy. WMHs and cortical thickness were measured in de novo PD patients and age-matched controls (NPD = 365, NControl = 174) from Parkinsons Progression Markers Initiative (PPMI) to study the relationship between baseline WMHs, future cognitive decline (follow-up: 4.09 ± 1.14 years) and cortical atrophy (follow-up: 1.05 ± 0.10 years). PD subjects with high baseline WMH loads had significantly greater cognitive decline than i) PD subjects with low WMH load, and ii) control subjects with high WMH load. Furthermore, in PD subjects, high WMH load resulted in more cortical thinning in the right frontal lobe. Theses results show that the presence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical atrophy than in normal aging.

Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging

Fluid‐attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimers disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.

Deep learning of quality control for stereotaxic registration of human brain MRI

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the following image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none of them has a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. We propose a completely automatic quality control method based on deep learning that replaces human rater and accurately performs quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9693 MRI scans from several publically available datasets and applied five linear registration tools. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. Our method was able to achieve 88% accuracy and 11% false positive rate in detecting scans that should pass quality control, better than a manual QC rater.

Neural and behavioral endophenotypes of obesity

Background Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, BMI, and longitudinal weight changes. Additionally, we analyzed the associations between general impulsivity domains and brain cortical thickness to elucidate brain vulnerability factors related to weight gain. Methods Students in their first year of university - a risky period for weight gain - completed questionnaire measures of impulsivity and eating behavior at the beginning (N = 2318) of the school year. We also collected their weight at the end of the term (N = 1197). Impulsivity was divided into factors stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested the plausibility of a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. 71 participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness. Results Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus. Conclusion The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.Background: Impulsivity is a risk factor for obesity. It has different underlying facets that can be assessed using questionnaires. Impulsivity can be further refined by the use of food-specific questionnaires, which measure a tendency to uncontrolled eating. We examined how these impulsivity measures relate to each other, to obesity, and to brain anatomy. Methods: We assessed students in their first year of university - a risky period for weight gain - at the beginning (N = 2214) and at the end of the school year (N = 1145) using questionnaire measures of impulsivity, personality, stress reactivity and eating-specific traits. A subset of participants (N = 72) underwent T1-weighted MRI to investigate the brain correlates of impulsivity. Results: Using factor analysis, we show that impulsivity can be stratified into three domains, which we label stress reactivity, reward sensitivity and self-control, while eating questionnaires resolve into a single latent factor - uncontrolled eating. A watershed model shows that uncontrolled eating mediates the effect of impulsivity traits on BMI. Self-control and stress reactivity scores are associated with a thinner lateral orbitofrontal cortex. In addition, stress reactivity correlates positively with amygdala and negatively with hippocampal volume. Longitudinally, lack of self-control, not uncontrolled eating, correlates with weight gain, while stress reactivity correlates with weight loss in male students. Conclusions: The brain-impulsivity-obesity relationship is hierarchical. Structural brain differences relate to differences in impulsivity domains which affect BMI via uncontrolled eating. However, longitudinally, low self-control, not uncontrolled eating, is a predictor of weight gain in this sample.