Jorge Samper-González

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.

Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer’s Disease

In recent years, the number of papers on Alzheimers disease classification has increased dramatically, generating interesting methodological ideas on the use machine learning and feature extraction methods. However, practical impact is much more limited and, eventually, one could not tell which of these approaches are the most efficient. While over 90\% of these works make use of ADNI an objective comparison between approaches is impossible due to variations in the subjects included, image pre-processing, performance metrics and cross-validation procedures. In this paper, we propose a framework for reproducible classification experiments using multimodal MRI and PET data from ADNI. The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features. This provides a flexible framework for benchmarking different feature extraction and classification tools in a reproducible manner. We demonstrate its use on all (1519) baseline T1 MR images and all (1102) baseline FDG PET images from ADNI 1, GO and 2 with SPM-based feature extraction pipelines and three different classification techniques (linear SVM, anatomically regularized SVM and multiple kernel learning SVM). The highest accuracies achieved were: 91% for AD vs CN, 83% for MCIc vs CN, 75% for MCIc vs MCInc, 94% for AD-A

Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data

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Using diffusion MRI for classification and prediction of Alzheimer's Disease: a reproducible study

+ vs CN-A

Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality Patterns

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Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET Data

- and 72% for MCIc-A

Diagnosis of Alzheimer’s Disease Through Identification of Abnormality Patterns in FDG PET Data

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Clinica: an open source software platform for reproducible clinical neuroscience studies

+ vs MCInc-A

Comparison of DTI Features for the Classification of Alzheimer's Disease: A Reproducible Study

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Reproducible evaluation of Alzheimer's Disease classification from MRI and PET data

+. The code is publicly available at this https URL (depends on the Clinica software platform, publicly available at this http URL).