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author:("Doshi, limit")
1.  Multi-Atlas Skull-Stripping 
Academic radiology  2013;20(12):10.1016/j.acra.2013.09.010.
Rationale and Objectives
We present a new method for automatic brain extraction on structural magnetic resonance images, based on a multi-atlas registration framework.
Materials and Methods
Our method addresses fundamental challenges of multi-atlas approaches. To overcome the difficulties arising from the variability of imaging characteristics between studies, we propose a study-specific template selection strategy, by which we select a set of templates that best represent the anatomical variations within the data set. Against the difficulties of registering brain images with skull, we use a particularly adapted registration algorithm that is more robust to large variations between images, as it adaptively aligns different regions of the two images based not only on their similarity but also on the reliability of the matching between images. Finally, a spatially adaptive weighted voting strategy, which uses the ranking of Jacobian determinant values to measure the local similarity between the template and the target images, is applied for combining coregistered template masks.
The method is validated on three different public data sets and obtained a higher accuracy than recent state-of-the-art brain extraction methods. Also, the proposed method is successfully applied on several recent imaging studies, each containing thousands of magnetic resonance images, thus reducing the manual correction time significantly.
The new method, available as a stand-alone software package for public use, provides a robust and accurate brain extraction tool applicable for both clinical use and large population studies.
PMCID: PMC3880117  PMID: 24200484
Brain extraction; registration; multi-atlas; label fusion; Jacobian determinant
2.  Memory decline shows stronger associations with estimated spatial patterns of amyloid deposition progression than total amyloid burden 
Neurobiology of aging  2013;34(12):2835-2842.
The development of amyloid imaging compounds has allowed in vivo imaging of amyloid deposition. In this study, we examine the spatial patterns of amyloid deposition throughout the brain using Pittsburgh Compound Blue (11C-PiB) PET data from the Baltimore Longitudinal Study of Aging. We used a new methodology that allows us to approximate spatial patterns of the temporal progression of amyloid plaque deposition from cross-sectional data. Our results are consistent with patterns of progression known from autopsy studies, with frontal and precuneus regions affected early and occipital and sensorimotor cortices affected later in disease progression – here, disease progression means lower-to-higher total amyloid burden. Furthermore, we divided participants into subgroups based on longitudinal change in memory performance and demonstrated significantly different spatial patterns of the estimated progression of amyloid deposition between these subgroups. Our results indicate that the spatial pattern of amyloid deposition is related to cognitive performance and may be more informative than a biomarker reflecting total amyloid burden, which is the current practice. This finding has broad implications for our understanding of the relationship between cognitive decline/resilience and amyloid deposition, as well as for the use of amyloid imaging as a biomarker in research and clinical applications.
PMCID: PMC3893024  PMID: 23859610
Amyloid; PiB; PET; CVLT; cognition
3.  Multimodal Magnetic Resonance Imaging Study of Treatment-Naïve Adults with Attention-Deficit/Hyperactivity Disorder 
PLoS ONE  2014;9(10):e110199.
Attention-Deficit/Hiperactivity Disorder (ADHD) is a prevalent disorder, but its neuroanatomical circuitry is still relatively understudied, especially in the adult population. The few morphometric magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) studies available to date have found heterogeneous results. This may be at least partly attributable to some well-known technical limitations of the conventional voxel-based methods usually employed to analyze such neuroimaging data. Moreover, there is a great paucity of imaging studies of adult ADHD to date that have excluded patients with history of use of stimulant medication.
A newly validated method named optimally-discriminative voxel-based analysis (ODVBA) was applied to multimodal (structural and DTI) MRI data acquired from 22 treatment-naïve ADHD adults and 19 age- and gender-matched healthy controls (HC).
Regarding DTI data, we found higher fractional anisotropy in ADHD relative to HC encompassing the white matter (WM) of the bilateral superior frontal gyrus, right middle frontal left gyrus, left postcentral gyrus, bilateral cingulate gyrus, bilateral middle temporal gyrus and right superior temporal gyrus; reductions in trace (a measure of diffusivity) in ADHD relative to HC were also found in fronto-striatal-parieto-occipital circuits, including the right superior frontal gyrus and bilateral middle frontal gyrus, right precentral gyrus, left middle occipital gyrus and bilateral cingulate gyrus, as well as the left body and right splenium of the corpus callosum, right superior corona radiata, and right superior longitudinal and fronto-occipital fasciculi. Volumetric abnormalities in ADHD subjects were found only at a trend level of significance, including reduced gray matter (GM) in the right angular gyrus, and increased GM in the right supplementary motor area and superior frontal gyrus.
Our results suggest that adult ADHD is associated with neuroanatomical abnormalities mainly affecting the WM microstructure in fronto-parieto-temporal circuits that have been implicated in cognitive, emotional and visuomotor processes.
PMCID: PMC4195718  PMID: 25310815
4.  Longitudinal imaging pattern analysis (SPARE-CD index) detects early structural and functional changes before cognitive decline in healthy older adults 
Neurobiology of aging  2012;33(12):2733-2745.
This article investigates longitudinal imaging characteristics of early cognitive decline during normal aging, leveraging on high-dimensional imaging pattern classification methods for the development of early biomarkers of cognitive decline. By combining magnetic resonance imaging (MRI) and resting positron emission tomography (PET) cerebral blood flow (CBF) images, an individualized score is generated using high-dimensional pattern classification, which predicts subsequent cognitive decline in cognitively normal older adults of the Baltimore Longitudinal Study of Aging. The resulting score, termed SPARE-CD (Spatial Pattern of Abnormality for Recognition of Early Cognitive Decline), analyzed longitudinally for 143 cognitively normal subjects over 8 years, shows functional and structural changes well before (2.3–2.9 years) changes in neurocognitive testing (California Verbal Learning Test [CVLT] scores) can be measured. Additionally, this score is found to be correlated to the [11C] Pittsburgh compound B (PiB) PET mean distribution volume ratio at a later time. This work indicates that MRI and PET images, combined with advanced pattern recognition methods, may be useful for very early detection of cognitive decline.
PMCID: PMC4023476  PMID: 22365049
Cognitive impairment; Magnetic resonance imaging; Positron emission tomography; Support vector machines; Classification
5.  Neuroanatomical Classification in a Population-Based Sample of Psychotic Major Depression and Bipolar I Disorder with 1 Year of Diagnostic Stability 
BioMed Research International  2014;2014:706157.
The presence of psychotic features in the course of a depressive disorder is known to increase the risk for bipolarity, but the early identification of such cases remains challenging in clinical practice. In the present study, we evaluated the diagnostic performance of a neuroanatomical pattern classification method in the discrimination between psychotic major depressive disorder (MDD), bipolar I disorder (BD-I), and healthy controls (HC) using a homogenous sample of patients at an early course of their illness. Twenty-three cases of first-episode psychotic mania (BD-I) and 19 individuals with a first episode of psychotic MDD whose diagnosis remained stable during 1 year of followup underwent 1.5 T MRI at baseline. A previously validated multivariate classifier based on support vector machine (SVM) was employed and measures of diagnostic performance were obtained for the discrimination between each diagnostic group and subsamples of age- and gender-matched controls recruited in the same neighborhood of the patients. Based on T1-weighted images only, the SVM-classifier afforded poor discrimination in all 3 pairwise comparisons: BD-I versus HC; MDD versus HC; and BD-I versus MDD. Thus, at the population level and using structural MRI only, we failed to achieve good discrimination between BD-I, psychotic MDD, and HC in this proof of concept study.
PMCID: PMC3915628  PMID: 24575411
6.  Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's disease 
Brain  2011;135(1):170-180.
Research suggests overlap in brain regions undergoing neurodegeneration in Parkinson's and Alzheimer's disease. To assess the clinical significance of this, we applied a validated Alzheimer's disease-spatial pattern of brain atrophy to patients with Parkinson's disease with a range of cognitive abilities to determine its association with cognitive performance and decline. At baseline, 84 subjects received structural magnetic resonance imaging brain scans and completed the Dementia Rating Scale-2, and new robust and expanded Dementia Rating Scale-2 norms were applied to cognitively classify participants. Fifty-nine non-demented subjects were assessed annually with the Dementia Rating Scale-2 for two additional years. Magnetic resonance imaging scans were quantified using both a region of interest approach and voxel-based morphometry analysis, and a method for quantifying the presence of an Alzheimer's disease spatial pattern of brain atrophy was applied to each scan. In multivariate models, higher Alzheimer's disease pattern of atrophy score was associated with worse global cognitive performance (β = −0.31, P = 0.007), including in non-demented patients (β = −0.28, P = 0.05). In linear mixed model analyses, higher baseline Alzheimer's disease pattern of atrophy score predicted long-term global cognitive decline in non-demented patients [F(1, 110) = 9.72, P = 0.002], remarkably even in those with normal cognition at baseline [F(1, 80) = 4.71, P = 0.03]. In contrast, in cross-sectional and longitudinal analyses there was no association between region of interest brain volumes and cognitive performance in patients with Parkinson's disease with normal cognition. These findings support involvement of the hippocampus and parietal–temporal cortex with cognitive impairment and long-term decline in Parkinson's disease. In addition, an Alzheimer's disease pattern of brain atrophy may be a preclinical biomarker of cognitive decline in Parkinson's disease.
PMCID: PMC3316476  PMID: 22108576
Alzheimer's disease; dementia; mild cognitive impairment; Parkinson's disease; neurodegeneration
7.  Neurodegeneration Across Stages of Cognitive Decline in Parkinson Disease 
Archives of Neurology  2011;68(12):1562-1568.
To assess regions and patterns of brain atrophy in patients with Parkinson disease (PD) with normal cognition (PD-NC), mild cognitive impairment (PD-MCI), and dementia-level cognitive deficits (PDD).
Images were quantified using a region-of-interest approach and voxel-based morphometry analysis. We used a high-dimensional pattern classification approach to delineate brain regions that collectively formed the Spatial Pattern of Abnormalities for Recognition of PDD.
The Parkinson’s Disease and Movement Disorders Center at the University of Pennsylvania.
Eighty-four PD patients (61 PD-NC, 12 PD-MCI, and 11 PDD) and 23 healthy control subjects (HCs) underwent magnetic resonance imaging of the brain.
The PD-NC patients did not demonstrate significant brain atrophy compared with HCs. Compared with PD-NC patients, PD-MCI patients had hippocampal atrophy (β=−0.37; P=.001), and PDD patients demonstrated hippocampal (β=−0.32; P=.004) and additional medial temporal lobe atrophy (β=−0.36; P=.003). The PD-MCI patients had a different pattern of atrophy compared with PD-NC patients (P=.04) and a similar pattern to that of PDD patients (P=.81), characterized by hippocampal, prefrontal cortex gray and white matter, occipital lobe gray and white matter, and parietal lobe white matter atrophy. In nondemented PD patients, there was a correlation between memory-encoding performance and hippocampal volume.
Hippocampal atrophy is a biomarker of initial cognitive decline in PD, including impaired memory encoding and storage, suggesting heterogeneity in the neural substrate of memory impairment. Use of a pattern classification approach may allow identification of diffuse regions of cortical gray and white matter atrophy early in the course of cognitive decline.
PMCID: PMC3290902  PMID: 22159053

Results 1-7 (7)