Search Results for author: Robert Leech

Found 12 papers, 3 papers with code

Transformer-based normative modelling for anomaly detection of early schizophrenia

no code implementations8 Dec 2022 Pedro F Da Costa, Jessica Dafflon, Sergio Leonardo Mendes, João Ricardo Sato, M. Jorge Cardoso, Robert Leech, Emily JH Jones, Walter H. L. Pinaya

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.

Anomaly Detection

Fine-tuning neural excitation/inhibition for tailored ketamine use in treatment-resistant depression

no code implementations4 Feb 2021 Erik D. Fagerholm, Robert Leech, Steven Williams, Carlos A. Zarate Jr., Rosalyn J. Moran, Jessica R. Gilbert

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

2 code implementations4 Nov 2020 Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen

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.

Causal Discovery Causal Inference +1

Bayesian optimization for automatic design of face stimuli

1 code implementation20 Jul 2020 Pedro F. da Costa, Romy Lorenz, Ricardo Pio Monti, Emily Jones, Robert Leech

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.

Bayesian Optimization

Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures

no code implementations8 Oct 2019 Jessica Dafflon, Walter H. L Pinaya, Federico Turkheimer, James H. Cole, Robert Leech, Mathew A. Harris, Simon R. Cox, Heather C. Whalley, Andrew M. McIntosh, Peter J. Hellyer

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.

AutoML BIG-bench Machine Learning

Text-mining the NeuroSynth corpus using Deep Boltzmann Machines

no code implementations1 May 2016 Ricardo Pio Monti, Romy Lorenz, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana

Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function.

Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces

no code implementations8 Feb 2015 Ricardo Pio Monti, Romy Lorenz, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana

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.

Brain Computer Interface

Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series

no code implementations14 Oct 2013 Ricardo Pio Monti, Peter Hellyer, David Sharp, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana

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.

Time Series Time Series Analysis

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