Karteek Popuri

Morphological alterations in the caudate, putamen, pallidum, and thalamus in Parkinson's disease

Like many neurodegenerative diseases, the clinical symptoms of Parkinsons disease (PD) do not manifest until significant progression of the disease has already taken place, motivating the need for sensitive biomarkers of the disease. While structural imaging is a potentially attractive method due to its widespread availability and non-invasive nature, global morphometric measures (e.g., volume) have proven insensitive to subtle disease change. Here we use individual surface displacements from deformations of an average surface model to capture disease related changes in shape of the subcortical structures in PD. Data were obtained from both the University of British Columbia (UBC) [n = 54 healthy controls (HC) and n = 55 Parkinsons disease (PD) patients] and the publicly available Parkinsons Progression Markers Initiative (PPMI) [n = 137 (HC) and n = 189 (PD)] database. A high dimensional non-rigid registration algorithm was used to register target segmentation labels (caudate, putamen, pallidum, and thalamus) to a set of segmentation labels defined on the average-template. The vertex-wise surface displacements were significantly different between PD and HC in thalamic and caudate structures. However, overall displacements did not correlate with disease severity, as assessed by the Unified Parkinsons Disease Rating Scale (UPDRS). The results from this study suggest disease-relevant shape abnormalities can be robustly detected in subcortical structures in PD. Future studies will be required to determine if shape changes in subcortical structures are seen in the prodromal phases of the disease.

Body Composition as a Predictor of Toxicity in Patients Receiving Anthracycline and Taxane Based Chemotherapy for Early Stage Breast Cancer.

Purpose: Poor body composition metrics (BCM) are associated with inferior cancer outcomes; however, in early breast cancer (EBC), there is a paucity of evidence regarding the impact of BCM on toxicities. This study investigates associations between BCM and treatment-related toxicity in patients with EBC receiving anthracyclines and taxane–based chemotherapy. Experimental Design: Pretreatment computerized tomographic (CT) images were evaluated for skeletal muscle area (SMA), skeletal muscle density (SMD), and fat tissue at the third lumbar vertebrae. Skeletal muscle index (SMI = SMA/height2) and skeletal muscle gauge (SMG = SMI × SMD) were also calculated. Relative risks (RR) are reported for associations between body composition measures and toxicity outcomes, after adjustment for age and body surface area (BSA). Results: BCM were calculated for 151 patients with EBC (median age, 49 years; range, 23–75 years). Fifty patients (33%) developed grade 3/4 toxicity, which was significantly higher in those with low SMI (RR, 1.29; P = 0.002), low SMG (RR, 1.09; P = 0.01), and low lean body mass (RR, 1.48; P = 0.002). Receiver operating characteristic analysis showed the SMG measure to be the best predictor of grade 3/4 toxicity. Dividing SMG into tertiles showed toxicity rates of 46% and 22% for lowest versus highest tertile, respectively (P = 0.005). After adjusting for age and BSA, low SMG (<1,475 units) was significantly associated with hematologic (RR, 2.12; P = 0.02), gastrointestinal grade 3/4 toxicities (RR, 6.49; P = 0.02), and hospitalizations (RR, 1.91; P = 0.05). Conclusions: Poor BCMs are significantly associated with increased treatment-related toxicities. Further studies are needed to investigate how these metrics can be used to more precisely dose chemotherapy to reduce treatment-related toxicity while maintaining efficacy. Clin Cancer Res; 23(14); 3537–43. ©2017 AACR.

Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework

&NA; We propose a novel approach for quantitative shape variability analysis in retinal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with functional measures, such as retinal layer thickness defined on the layer surface, for registration across anatomical shapes. This is used to generate a population mean template of the geometry‐function measures from each individual. Shape variability across multiple retinas can be measured by the geometrical deformation and functional residual between the template and each of the observations. To demonstrate the clinical relevance and application of the framework, we generated atlases of the inner layer surface and layer thickness of the Retinal Nerve Fiber Layer (RNFL) of glaucomatous and normal subjects, visualizing detailed spatial pattern of RNFL loss in glaucoma. Additionally, a regularized linear discriminant analysis classifier was used to automatically classify glaucoma, glaucoma‐suspect, and control cases based on RNFL fshape metrics.

Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures.

Abstract. Manual segmentation of anatomy in brain MRI data taken to be the closest to the “gold standard” in quality is often used in automated registration-based segmentation paradigms for transfer of template labels onto the unlabeled MRI images. This study presents a library of template data with 16 subcortical structures in the central brain area which were manually labeled for MRI data from 22 children (8 male, mean age=8±0.6  years). The lateral ventricle, thalamus, caudate, putamen, hippocampus, cerebellum, third vevntricle, fourth ventricle, brainstem, and corpuscallosum were segmented by two expert raters. Cross-validation experiments with randomized template subset selection were conducted to test for their ability to accurately segment MRI data under an automated segmentation pipeline. A high value of the dice similarity coefficient (0.86±0.06, min=0.74, max=0.96) and small Hausdorff distance (3.33±4.24, min=0.63, max=25.24) of the automated segmentation against the manual labels was obtained on this template library data. Additionally, comparison with segmentation obtained from adult templates showed significant improvement in accuracy with the use of an age-matched library in this cohort. A manually delineated pediatric template library such as the one described here could provide a useful benchmark for testing segmentation algorithms.

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Gray matter changes in asymptomatic C9orf72 and GRN mutation carriers

Frontotemporal dementia (FTD) is a neurodegenerative disease with a strong genetic basis. Understanding the structural brain changes during pre-symptomatic stages may allow for earlier diagnosis of patients suffering from FTD; therefore, we investigated asymptomatic members of FTD families with mutations in C9orf72 and granulin (GRN) genes. Clinically asymptomatic subjects from families with C9orf72 mutation (15 mutation carriers, C9orf72+; and 23 non-carriers, C9orf72−) and GRN mutations (9 mutation carriers, GRN+; and 15 non-carriers, GRN−) underwent structural neuroimaging (MRI). Cortical thickness and subcortical gray matter volumes were calculated using FreeSurfer. Group differences were evaluated, correcting for age, sex and years to mean age of disease onset within the subjects family. Mean age of C9orf72+ and C9orf72− were 42.6 ± 11.3 and 49.7 ± 15.5 years, respectively; while GRN+ and GRN− groups were 50.1 ± 8.7 and 53.2 ± 11.2 years respectively. The C9orf72+ group exhibited cortical thinning in the temporal, parietal and frontal regions, as well as reduced volumes of bilateral thalamus and left caudate compared to the entire group of mutation non-carriers (NC: C9orf72− and GRN− combined). In contrast, the GRN+ group did not show any significant differences compared to NC. C9orf72 mutation carriers demonstrate a pattern of reduced gray matter on MRI prior to symptom onset compared to GRN mutation carriers. These findings suggest that the preclinical course of FTD differs depending on the genetic basis and that the choice of neuroimaging biomarkers for FTD may need to take into account the specific genes involved in causing the disease.

Development and validation of a novel dementia of Alzheimer’s type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimers type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

HIGHLIGHTSProposed a novel multiscale deep neural network to learn the patterns of metabolism changes due to AD pathology as discriminative from the patterns of metabolism in normal controls (NC).Showed that by transferring samples from NC and AD individuals, the deep architecture can obtain better discriminative ability in the early diagnosis task.Demonstrated that ensemble multiple classifiers with different validation settings can make the proposed method more stable and robust, and improve its classification performance.We present a comprehensive validation of our method analyzing metabolism measures taken from 1051 subjects that were processed with stringent quality control requirements including expert manual editing of all segmentations for ensuring accuracy. To‐date, our study is perhaps the first study to utilize such a large number of FDG‐PET images, and hence, these results indicate good potential for generalizability. ABSTRACT Alzheimers disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self‐care. However, not all individuals clinically diagnosed with MCI progress to AD. A fraction of subjects with MCI either progress to non‐AD dementia or remain stable at the MCI stage without progressing to dementia. Although a curative treatment of AD is currently unavailable, it is extremely important to correctly identify the individuals in the MCI phase that will go on to develop AD so that they may benefit from a curative treatment when one becomes available in the near future. At the same time, it would be highly desirable to also correctly identify those in the MCI phase that do not have AD pathology so they may be spared from unnecessary pharmocologic interventions that, at best, may provide them no benefit, and at worse, could further harm them with adverse side‐effects. Additionally, it may be easier and simpler to identify the cause of the cognitive impairment in these non‐AD cases, and hence proper identification of prodromal AD will be of benefit to these individuals as well. Fluorodeoxy glucose positron emission tomography (FDG‐PET) captures the metabolic activity of the brain, and this imaging modality has been reported to identify changes related to AD prior to the onset of structural changes. Prior work on designing classifier using FDG‐PET imaging has been promising. Since deep‐learning has recently emerged as a powerful tool to mine features and use them for accurate labeling of the group membership of given images, we propose a novel deep‐learning framework using FDG‐PET metabolism imaging to identify subjects at the MCI stage with presymptomatic AD and discriminate them from other subjects with MCI (non‐AD / non‐progressive). Our multiscale deep neural network obtained 82.51% accuracy of classification just using measures from a single modality (FDG‐PET metabolism data) outperforming other comparable FDG‐PET classifiers published in the recent literature.

Surface displacement based shape analysis of central brain structures in preterm-born children

Many studies using T1 magnetic resonance imaging (MRI) data have found associations between changes in global metrics (e.g. volume) of brain structures and preterm birth. In this work, we use the surface displacement feature extracted from the deformations of the surface models of the third ventricle, fourth ventricle and brainstem to capture the variation in shape in these structures at 8 years of age that may be due to differences in the trajectory of brain development as a result of very preterm birth (24-32 weeks gestation). Understanding the spatial patterns of shape alterations in these structures in children who were born very preterm as compared to those who were born at full term may lead to better insights into mechanisms of differing brain development between these two groups. The T1 MRI data for the brain was acquired from children born full term (FT, n=14, 8 males) and preterm (PT, n=51, 22 males) at age 8-years. Accurate segmentation labels for these structures were obtained via a multi-template fusion based segmentation method. A high dimensional non-rigid registration algorithm was utilized to register the target segmentation labels to a set of segmentation labels defined on an average-template. The surface displacement data for the brainstem and the third ventricle were found to be significantly different (p < 0.05) between the PT and FT groups. Further, spatially localized clusters with inward and outward deformation were found to be associated with lower gestational age. The results from this study present a shape analysis method for pediatric MRI data and reveal shape changes that may be due to preterm birth.

Beyond sarcopenia: Characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population

Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent and prognostic of adverse outcomes in oncology. However, there is limited information on adults with early breast cancer and examination of other skeletal muscle indices, despite the potential prognostic importance. This study characterizes and examines age‐related changes in body composition of adults with early breast cancer and describes the creation of a novel integrated muscle measure. Female patients diagnosed with stage I‐III breast cancer with abdominal computerized tomography (CT) scans within 12 weeks from diagnosis were identified from local tumor registry (N = 241). Skeletal muscle index (muscle area per height [cm2/m2]), skeletal muscle density, and subcutaneous and visceral adipose tissue areas, were determined from CT L3 lumbar segments. We calculated a novel integrated skeletal measure, skeletal muscle gauge, which combines skeletal muscle index and density (SMI × SMD). 241 patients were identified with available CT imaging. Median age 52 years and range of 23‐87. Skeletal muscle index and density significantly decreased with age. Using literature based cut‐points, older adults (≥65 years) had significantly higher proportions of sarcopenia (63 vs 28%) and myosteatosis (90 vs 11%) compared to younger adults (<50 years). Body mass index was positively correlated with skeletal muscle index and negatively correlated with muscle density. Skeletal muscle gauge correlated better with increasing age (ρ = 0.52) than with either skeletal muscle index (ρ = 0.20) or density (ρ = 0.46). Wide variations and age‐related changes in body composition metrics were found using routinely obtained abdominal CT imaging. Skeletal muscle index and density provide independent, complementary information, and the product of the two metrics, skeletal muscle gauge, requires further research to explore its impact on outcomes in women with curable breast cancer.