Morphometric analysis of brain structures for improved discrimination

Abstract. We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95 % (whole set) and 90 % (right-handed males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.

Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study

The goal of this work is to develop an approach to shape representation and classification that will allow us to detect and quantify differences in shape of anatomical structures due to various disorders. We used a robust version of skeletons for feature extraction and linear discriminant analysis (the Fisher linear discriminant and the linear Support Vectors method) for classification. We propose a way to map the classification results back into the image domain, interpreting shape differences as a deformation required to bring a shape from one class to the other. An example of analyzing corpus callosum shape in schizophrenia is reported, as well as the results of the study of the statistical properties of the classifier using cross validation techniques

Parallel transport in diffeomorphisms distinguishes the time-dependent pattern of hippocampal surface deformation due to healthy aging and the dementia of the Alzheimer's type

10.1016/j.neuroimage.2007.11.041NeuroImage40168-76NEIM

Early Life Stress as a Risk Factor for Substance use Disorders: Clinical and Neurobiological Substrates

BACKGROUND: Early Life Stress (ELS) can profoundly influence an individual's genotype and phenotype. Effects of ELS can manifest in the short-term, late life and even in subsequent generations. ELS activate corticotrophin releasing factor (CRF); CRF influences drug seeking and addiction. The aim of this study was to examine the effects of endogenous elevated levels of CRF on addiction. MATERIALS AND METHODS: Inducible forebrain over-expression of CRF mice (tetop-CRH x CaMKII-tTA) was used for this study. Morphine (10 mg/kg) was administered every other day for 10 days or with increasing doses of morphine: 20, 40, 60, 80, 100, and 100 mg/kg. The behavioral trials including morphine sensitization, Somatic Opiate Withdrawal Symptoms (SOWS) were conducted in a single, open field, activity. After behavioral trial, animals were perfused for immunohistochemistry analysis. RESULTS: CRF-over expressed (CRF-OE) mice showed increase in morphine sensitization and withdrawal symptoms after morphine administration compared to wild type (WT) mice. The two-way ANOVA in the morphine sensitization study showed a significant effect of treatment (P<0.05) and genotype for distance traveled (P<0.01). In the SOWS study, opiate withdrawal symptoms such as rearings, circling behavior, grooming, and jump in CRF-OE were amplified in parallel to WT mice. In the immunohistochemistry study, pro-dynorphine (PDYN) expression was increased after morphine administration in both amygdala and nucleus accumbens (NAcc). CONCLUSIONS: CRF-OE in the forebrain increases the sensitization and withdrawal symptoms in morphine treated mice. On exposure to morphine, in CRF-OE mice the PDYN protein expression was increased as compared to WT mice in the amygdala and NAcc

Boundary and Medial Shape Analysis of the Hippocampus in Schizophrenia

Statistical shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes and thus potentially discriminate between healthy and pathological structures. This paper describes a combined boundary and medial shape analysis based on two di#erent shape descriptions applied to a study of the hippocampus shape abnormalities in schizophrenia. The first shape description is the sampled boundary implied by the spherical harmonic SPHARM description. The second one is the medial shape description called M-rep. Both descriptions are sampled descriptions with inherent point correspondence. Their shape analysis is based on computing di#erences from an average template structure analyzed using standard group mean di#erence tests. The results of the global and local shape analysis in the presented hippocampus study exhibit the same patterns for the boundary and the medial analysis. The results strongly suggest that the normalized hippocampal shape of the schizophrenic group is di#erent from the control group, most significantly as a deformation di#erence in the tail region

Mid-term and long-term efficacy and effectiveness of antipsychotic medications for schizophrenia : a data-driven, personalized clinical approach

Objective: Our aim in this article is 2-fold: first, to examine the mid-term to long-term data on efficacy, from controlled and naturalistic and other studies, in order to determine if they are consistent with the quantitative meta-analyses of mostly short-term, randomized controlled trials Our second (and most important) aim is to use these and other data to provide guidance about the potential relationship of these differences among antipsychotics to the individual patient's own experience with antipsychotic drugs in the process of shared decision-making with the patients and their significant others. Data Sources: A search of PubMed, Embase, and PsychINFO was conducted for articles published in English between January 1, 1999, and April 2011, using the search terms double-blind AND randomized AND olanzapine AND (ziprasidone OR risperidone OR quetiapine OR haloperidol OR fluphenazine OR perphenazine OR aripiprazole). Study Selection: Studies with a duration 3 months or longer, including patients with schizophrenia or schizoaffective disorder, reporting survival analysis for all-cause discontinuation and relapse or dropout due to poor efficacy were selected. Data Extraction: We extracted the number of patients relapsed due to poor efficacy and hazard rates for relapses. Data Synthesis: Overall, the efficacy patterns of both controlled effectiveness and observational long-term studies closely parallel the efficacy observed in the short-term, controlled studies. The results of Phase 1 Clinical Antipsychotic Trials of Intervention Effectiveness are very similar to, but not identical with, the controlled short-term efficacy studies, the European First-Episode Schizophrenia Trial, and naturalistic studies, The mid-term and long-term data suggest that olanzapine is more effective than risperidone and that both of these are better than the other first- and second-generation antipsychotics except for clozapine, which is the most efficacious of all. Further large differences emerged regarding the specific mid-term and long-term safety profiles of individual antipsychotics. Conclusions: Despite intraclass differences and the complexities of antipsychotic choice, the second-generation antipsychotics are important contributions not only to the acute phase but, more importantly, to the maintenance treatment of schizophrenia

Simultaneous Segmentation and Grading of Hippocampus for Patient Classification with Alzheimer’s Disease

Abstract. Purpose: To propose an innovative approach to better detect Alzheimer’s Disease (AD) based on a finer detection of hippocampus (HC) atrophy patterns. Method: In this paper, we propose a new approach to simultaneously perform segmentation and grading of the HC to better capture the patterns of pathology occurring during AD. Based on a patch-based framework, the novel proposed grading measure estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. The training library used during our experiments was composed by 2 populations, 50 Cognitively Normal subjects (CN) and 50 patients with AD. Tests were completed in a leave-one-out framework. Results: First, the evaluation of HC segmentation accuracy yielded a Dice’s Kappa of 0.88 for CN and 0.84 for AD. Second, the proposed HC grading enables detection of AD with a success rate of 89%. Finally, a comparison of several biomarkers was investigated using a linear discriminant analysis. Conclusion: Using the volume and the grade of the HC at the same time resulted in an efficient patient classification with a success rate of 90%

Small Sample Size Learning for Shape Analysis of Anatomical Structures

We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medical image analysis, combined with a typically small number of training examples, places the problem outside the realm of classical statistics. This difficulty is traditionally overcome by first reducing dimensionality of the shape representation (e.g., using PCA) and then performing training and classification in the reduced space defined by a few principal components. We propose to learn the shape differences between the classes in the original high dimensional parameter space, while controlling the capacity (generalization error) of the classifier. This approach makes significantly fewer assumptions on the properties and the distribution of the underlying data, which can be advantageous in anatomical shape analysis where little is known about the true nature of the input data. Support Vector Mach..