A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus From 7 T MRI.

OBJECTIVE: 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon. METHODS: We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non-closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects. RESULTS: We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge. CONCLUSION: This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients. SIGNIFICANCE: As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).

Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database.

Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. However, these methods were assessed on different populations, making it difficult to compare their performance. In this paper, we evaluated the performance of ten approaches (five voxel-based methods, three methods based on cortical thickness and two methods based on the hippocampus) using 509 subjects from the ADNI database. Three classification experiments were performed: CN vs AD, CN vs MCIc (MCI who had converted to AD within 18 months, MCI converters - MCIc) and MCIc vs MCInc (MCI who had not converted to AD within 18 months, MCI non-converters - MCInc). Data from 81 CN, 67 MCInc, 39 MCIc and 69 AD were used for training and hyperparameters optimization. The remaining independent samples of 81 CN, 67 MCInc, 37 MCIc and 68 AD were used to obtain an unbiased estimate of the performance of the methods. For AD vs CN, whole-brain methods (voxel-based or cortical thickness-based) achieved high accuracies (up to 81% sensitivity and 95% specificity). For the detection of prodromal AD (CN vs MCIc), the sensitivity was substantially lower. For the prediction of conversion, no classifier obtained significantly better results than chance. We also compared the results obtained using the DARTEL registration to that using SPM5 unified segmentation. DARTEL significantly improved six out of 20 classification experiments and led to lower results in only two cases. Overall, the use of feature selection did not improve the performance but substantially increased the computation times.

Distinct structural changes underpin clinical phenotypes in patients with Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome is a childhood-onset neurodevelopmental disorder characterized by tics that are often associated with psychiatric co-morbidities. The clinical heterogeneity of Gilles de la Tourette syndrome has been attributed to the disturbance of functionally distinct cortico-striato-thalamo-cortical circuits, but this remains to be demonstrated. The aim of this study was to determine the structural correlates of the diversity of symptoms observed in Gilles de la Tourette syndrome. We examined 60 adult patients and 30 age- and gender-matched control subjects using cortical thickness measurement and 3 T high-resolution T(1)-weighted images. Patients were divided into three clinical subgroups: (i) simple tics; (ii) simple and complex tics and (iii) tics with associated obsessive-compulsive disorders. Patients with Gilles de la Tourette syndrome had reduced cortical thickness in motor, premotor, prefrontal and lateral orbito-frontal cortical areas. The severity of tics was assessed using the Yale Global Tic Severity Scale and correlated negatively with cortical thinning in these regions, as well as in parietal and temporal cortices. The pattern of cortical thinning differed among the clinical subgroups of patients. In patients with simple tics, cortical thinning was mostly found in primary motor regions. In patients with simple and complex tics, thinning extended into larger premotor, prefrontal and parietal regions. In patients with associated obsessive-compulsive disorders, there was a trend for reduced cortical thickness in the anterior cingulate cortex and hippocampal morphology was altered. In this clinical subgroup, scores on the Yale-Brown Obsessive-Compulsive Scale correlated negatively with cortical thickness in the anterior cingulate cortex and positively in medial premotor regions. These data support the hypothesis that different symptom dimensions in Gilles de la Tourette syndrome are associated with dysfunction of distinct cortical areas and have clear implications for the current neuroanatomical model of this syndrome.

The amnestic syndrome of hippocampal type in Alzheimer's disease: an MRI study.

The Free and Cued Selective Reminding Test (FCSRT) is a verbal episodic memory test used to identify patients with mild Alzheimer's disease (AD). The present study investigates the relationships between performance on FCSRT and grey matter atrophy assessed with structural MRI in patients with AD. Three complementary MRI-based analyses (VBM analysis, ROI-based analysis, and three-dimensional hippocampal surface-based shape analysis) were performed in 35 patients with AD to analyze correlations between regional atrophy and their scores for episodic memory using the FCSRT. With VBM analysis, the total score on the FCSRT was correlated with left medial temporal lobe atrophy including the left hippocampus but also the thalami. In addition, using ROI-based analysis, the total recall score on the FCSRT was correlated with the left hippocampal volume. With three-dimensional hippocampal surface-based shape analysis, both free recall and total recall scores were correlated with regions corresponding approximately to the CA1 field. No correlation was found with short term memory scores using any of these methods of analysis. In AD, the FCSRT may be considered as a useful clinical marker of memory disorders due to medial temporal damage, specially the CA1 field of the hippocampus.

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging.

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI.

The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume.