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Found 12 papers, 3 papers with code
Gradients of brain organization: Smooth sailing from methods development to user community
Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain.
Transformer-based normative modelling for anomaly detection of early schizophrenia
Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0. 82 when assessing the difference between controls and individuals with early-stage schizophrenia.
Neuroadaptive electroencephalography: a proof-of-principle study in infants
Our approach is optimal when theoretical frameworks or previous empirical data are impoverished.
Fine-tuning neural excitation/inhibition for tailored ketamine use in treatment-resistant depression
We demonstrate that the Poincar\'e diagram offers classification capability for TRD patients, in that the further the patients' coordinates were shifted (by virtue of ketamine) toward the stable (top-left) quadrant of the Poincar\'e diagram, the more their depressive symptoms improved.
Causal Autoregressive Flows
We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.
Bayesian optimization for automatic design of face stimuli
Formally, we employ Bayesian optimization to efficiently search the latent space of state-of-the-art GAN models, with the aim to automatically generate novel faces, to maximize an individual subject's response.
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age.
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function.
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings.
Streaming regularization parameter selection via stochastic gradient descent
We propose a framework to perform streaming covariance selection.
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Such studies have recently gained momentum and have been applied in a wide variety of settings; ranging from training of healthy subjects to self-regulate neuronal activity to being suggested as potential treatments for clinical populations.
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series
We apply the SINGLE algorithm to functional MRI data from 24 healthy patients performing a choice-response task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task.
