Claire Cury

Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts

BACKGROUND Despite known relationship between hippocampal volumes and major depressive episodes (MDE) and the increased suicidality in MDE, the links between hippocampal volumes and suicidality remain unclear in major depressive disorders (MDD). If the hippocampus could be a biomarker of suicide attempts in depression, it could be useful for prevention matters. This study assessed the association between hippocampal volumes and suicide attempts in MDD. METHODS Hippocampal volumes assessed with automatic segmentation were compared in 63 patients with MDD, with (n = 24) or without (n = 39) suicide attempts. Acute (one month) suicide attempts were studied. RESULTS Although not different in terms of socio-demographic, MDD and MDE clinical features, suicide attempters had lower total hippocampus volumes than non-attempters (4.61 (± 1.15) cm(3) vs 5.22 (± 0.99) cm(3); w = 625.5; p = 0.03), especially for acute suicide attempts (4.19 (± 0.81) cm(3) vs 5.22 (± 0.99) cm(3); w = 334; p = 0.005), even after adjustment on brain volumes, sex, age, Hamilton Depression Rating Scale (HDRS) scores and MDD duration. A ROC analysis showed that a total hippocampal volume threshold of 5.00 cm(3) had a 98.2% negative predictive value for acute suicide attempts. CONCLUSION Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts, independently from socio-demographics and MDD characteristics. This difference is related to acute suicide attempts but neither to past suicide attempts nor to duration since the first suicide attempt, suggesting that hippocampal volume could be a suicidal state marker in MDE. Further studies are required to better understand this association.

Template Estimation for Large Database: A Diffeomorphic Iterative Centroid Method Using Currents

Computing a template in the Large Deformation Diffeomorphic Metric Mapping framework is a key step for the shape analysis of anatomical structures, but can lead to very computationally expensive algorithms in the case of large databases. We present an iterative method which quickly provides a centroid of the population in shape space. This centroid can be used as a rough template estimate or as initialization for template estimation methods.

Incomplete Hippocampal Inversion: A Comprehensive MRI Study of Over 2000 Subjects.

The incomplete-hippocampal-inversion (IHI), also known as malrotation, is an atypical anatomical pattern of the hippocampus, which has been reported in healthy subjects in different studies. However, extensive characterization of IHI in a large sample has not yet been performed. Furthermore, it is unclear whether IHI are restricted to the medial-temporal lobe or are associated with more extensive anatomical changes. Here, we studied the characteristics of IHI in a community-based sample of 2008 subjects of the IMAGEN database and their association with extra-hippocampal anatomical variations. The presence of IHI was assessed on T1-weighted anatomical magnetic resonance imaging (MRI) using visual criteria. We assessed the association of IHI with other anatomical changes throughout the brain using automatic morphometry of cortical sulci. We found that IHI were much more frequent in the left hippocampus (left: 17%, right: 6%, χ2−test, p < 10−28). Compared to subjects without IHI, subjects with IHI displayed morphological changes in several sulci located mainly in the limbic lobe. Our results demonstrate that IHI are a common left-sided phenomenon in normal subjects and that they are associated with morphological changes outside the medial temporal lobe.

Hippocampal volume predicts antidepressant efficacy in depressed patients without incomplete hippocampal inversion

Background Incomplete hippocampal inversion (IHI), also called malrotation, is a frequent atypical anatomical pattern of the hippocampus. Because of the crucial implication of the hippocampus in Major Depressive Disorder (MDD) and the neurodevelopmental hypothesis of MDD, we aimed to assess the prevalence of IHI in patients with MDD, the link of IHI with hippocampal volume (HV) and the impact of IHI on the predictive value of HV for response and remission after antidepressant treatment. Methods IHI (right and left, partial and total and IHI scores) and HV were assessed in 60 patients with a current Major Depressive Episode (MDE) in a context of MDD and 60 matched controls. Patients were prospectively assessed at baseline and after one, three and six months of antidepressant treatment for response and remission. Results The prevalence of IHI did not significantly differ between MDD patients (right = 23.3%; left = 38.3%) and controls (right = 16.7%; left = 33.3%). IHI was not significantly associated with MDD clinical characteristics. IHI alone did not predict response and remission after antidepressant treatment. However, an interaction between left HV and left IHI predicted six-month response (p = 0.04), HDRS score decrease (p = 0.02) and both three-month (p = 0.04) and six-month (p = 0.03) remission. A case-control design in 30 matched patients with or without left IHI confirmed that interaction. In patients without left IHI, left HV at baseline were smaller in six-month non-remitters as compared to remitters (2.2(± 0.43) cm3 vs 2.97(± 0.5) cm3 p = 0.02), and in six-month non-responders as compared to responders (2.18(± 0.42) cm3 vs 2.86(± 0.54) cm3, p = 0.03). In patients with left IHI, no association was found between left HV at baseline and antidepressant response and remission. Conclusion IHI is not more frequent in MDD patients than in controls, is not associated with HV, but is a confounder that decreases the predictive value of hippocampal volume to predict response or remission after antidepressant treatment. IHI should be systematically assessed in future research studies assessing hippocampal volume in MDD.

Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease

This article presents a new approach for template-based analysis of anatomical variability in populations, in the framework of Large Deformation Diffeomorphic Metric Mappings and mathematical currents. We propose a fast approach in which the template is computed using a diffeomorphic iterative centroid method. Statistical analysis is then performed on the initial momenta that define the deformations between the centroid and each individual subject. We applied the approach to study the variability of the hippocampus in 134 patients with Alzheimers disease (AD) and 160 elderly control subjects. We show that this approach can describe the main modes of variability of the two populations and can predict the performance to a memory test in AD patients.

Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

A common approach for analysis of anatomical variability relies on the estimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This chapter presents an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach is evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results show that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation.

Statistical shape analysis of large datasets using diffeomorphic iterative centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1000 surfaces, and we are able to analyse this dataset in 26 hours using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus in healthy subjects.