Deep learning to detect anterior cruciate ligament tear on knee MRI: multi-continental external validation.

Objectives: To develop a deep-learning algorithm for anterior cruciate ligament (ACL) tear detection and to compare its accuracy using two external datasets.

Methods: A database of 19,765 knee MRI scans (17,738 patients) issued from different manufacturers and magnetic fields was used to build a deep learning-based ACL tear detector. Fifteen percent showed partial or complete ACL rupture.

Neuroimaging evidence for structural correlates in adolescents resilient to polysubstance use: A five-year follow-up study.

Early initiation of polysubstance use (PSU) is a strong predictor of subsequent addiction, however scarce individuals present resilience capacity. This neuroimaging study aimed to investigate structural correlates associated with cessation or reduction of PSU and determine the extent to which brain structural features accounted for this resilient outcome. Participants from a European community-based cohort self-reported their alcohol, tobacco and cannabis use frequency at ages 14, 16 and 19 and had neuroimaging sessions at ages 14 and 19.

Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data.

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms.

Biomarker Identification Through Integrating fMRI and Epigenetics.

Objective: Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases.

Refined measure of functional connectomes for improved identifiability and prediction.

Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns.

FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics.

Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimaging and genomics, where data sets are typically high-dimensional, which means that the number of explanatory variables exceeds the sample size. The false discovery rate (FDR) is a criterion that can be employed to address that issue.

Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation.

Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data.

Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret.

Fused Estimation of Sparse Connectivity Patterns From Rest fMRI-Application to Comparison of Children and Adult Brains.

In this paper, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools.

Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework.

Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this paper, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA).

ENFORCING CO-EXPRESSION IN MULTIMODAL REGRESSION FRAMEWORK.

We consider the problem of multimodal data integration for the study of complex neurological diseases (e.g. schizophrenia).

Observation model based on scale interactions for optical flow estimation.

In this paper, an original observation model for multiresolution optical flow estimation is introduced. Multiresolution frameworks, often based on coarse-to-fine warping strategies, are widely used by state-of-the-art optical flow methods. They allow the recovery of large motions by successive estimations of the flow field at several resolution levels.