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Found 16 papers, 3 papers with code
Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses
For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls.
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter.
Tackling the dimensions in imaging genetics with CLUB-PLS
The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes.
Linking Symptom Inventories using Semantic Textual Similarity
An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues.
A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs
A quality control algorithm is also built-in, trained on the midCC shape features.
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders.
Partial Identification of Dose Responses with Hidden Confounders
Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden.
3D Grid-Attention Networks for Interpretable Age and Alzheimer's Disease Prediction from Structural MRI
Building on a 3D convolutional neural network, we added two attention modules at different layers of abstraction, so that features learned are spatially related to the global features for the task.
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on connectome data.
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
There is no consensus on how to construct structural brain networks from diffusion MRI.
Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.
Structural Connectome Validation Using Pairwise Classification
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains.
An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.
