Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity.

Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization.

In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset.

Event time analysis of longitudinal neuroimage data.

This paper presents a method for the statistical analysis of the associations between longitudinal neuroimaging measurements, e.g., of cortical thickness, and the timing of a clinical event of interest, e.

Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data.

We present an extension of the Linear Mixed Effects (LME) modeling approach to be applied to the mass-univariate analysis of longitudinal neuroimaging (LNI) data. The proposed method, called spatiotemporal LME or ST-LME, builds on the flexible LME framework and exploits the spatial structure in image data. We instantiated ST-LME for the analysis of cortical surface measurements (e.

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data.

Determining the optimal level of smoothing in cortical thickness analysis: a hierarchical approach based on sequential statistical thresholding.

The extent of smoothing applied to cortical thickness maps critically influences sensitivity, anatomical precision and resolution of statistical change detection. Theoretically, it could be optimized by increasing the trade-off between vertex-wise sensitivity and specificity across several levels of smoothing. But to date neither parametric nor nonparametric methods are able to control the error at the vertex level if the null hypothesis is rejected after smoothing of cortical thickness maps.

Detection of focal changes in human cortical thickness: spherical wavelets versus Gaussian smoothing.

Subtle but progressive variations in human cortical thickness have been associated with the initial phases of prevalent neurological and psychiatric conditions. But slight changes in cortical thickness at preclinical stages are typically masked by effects of the Gaussian kernel smoothing on the cortical surface shape descriptors. Here we present the first study aimed at detecting changes in human cortical thickness maps by applying soft-thresholding to multiresolution spherical wavelet coefficients.