Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However, visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper, we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing interpretable anatomical information regarding the morphological aspect of a given hippocampus. We relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and can be used with different machine learning models. In this paper, we explored two different backbone models for the prediction: a linear method (ridge regression) and a deep convolutional neural network. We demonstrated that the interpretable, anatomical based prediction was at least as good as when predicting directly the presence of IHI, while providing interpretable information to the clinician or neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized radiologically by several anatomical features.
Dajiang Zhu, Qingyang Li, Brandalyn Riedel, Neda Jahanshad, Derrek Hibar, Ilya Veer, Henrik Walter, Lianne Schmaal, Dick Veltman, Dominik Grotegerd, Udo Dannlowski, Matthew Sacchet, Ian Gotlib, Jieping Ye, Paul Thompson
KEYWORDS: Data centers, Brain, Magnetic resonance imaging, Feature selection, Data analysis, Control systems, Psychiatry, Diagnostics, Neuroimaging, Data modeling
Compared to many neurological disorders, for which imaging biomarkers are often available, there are no accepted imaging biomarkers to assist in the diagnosis of major depressive disorder (MDD). One major barrier to understanding MDD has been the lack of a practical and efficient platform for collaborative efforts across multiple data centers; integrating the knowledge from different centers should make it easier to identify characteristic measures that are consistently associated with the illness. Here we applied our newly developed “distributed Lasso” method to brain MRI data from multiple centers to perform feature selection and classification. Over 1,000 participants were involved in the study; our results indicate the potential of the proposed framework to enable large-scale collaborative data analysis in the future.
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