Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models

Neuroscience studies of human decision-making abilities commonly involve subjects completing a decision-making task while BOLD signals are recorded using fMRI. Hypotheses are tested about which brain regions mediate the effect of past experience, such as rewards, on future actions. One standard approach to this is model-based fMRI data analysis, in which a model is fitted to the behavioral data, i.e., a subject's choices, and then the neural data are parsed to find brain regions whose BOLD signals are related to the model's internal signals. However, the internal mechanics of such purely behavioral models are not constrained by the neural data, and therefore might miss or mischaracterize aspects of the brain. To address this limitation, we introduce a new method using recurrent neural network models that are flexible enough to be jointly fitted to the behavioral and neural data. We trained a model so that its internal states were suitably related to neural activity during the task, while at the same time its output predicted the next action a subject would execute. We then used the fitted model to create a novel visualization of the relationship between the activity in brain regions at different times following a reward and the choices the subject subsequently made. Finally, we validated our method using a previously published dataset. We found that the model was able to recover the underlying neural substrates that were discovered by explicit model engineering in the previous work, and also derived new results regarding the temporal pattern of brain activity

Computational Neuroscience with Deep Learning for Brain Imaging Analysis and Behaviour Classification

Recent advances of artificial neural networks and deep learning model have produced significant results in problems related to neuroscience. For example, deep learning models have demonstrated superior performance in non-linear, multivariate pattern classification problems such as Alzheimer’s disease classification, brain lesion segmentation, skull stripping and brain age prediction. Deep learning provides unique advantages for high-dimensional data such as MRI data, since it does not require extensive feature engineering. The thesis investigates three problems related to neuroscience and discuss solutions to those scenarios. MRI has been used to analyse the structure of the brain and its pathology. However, for ex- ample, due to the heterogeneity of these scanners, MRI protocol, variation in site thermal and power stability can introduce scanning differences and artefacts for the same individual under- going different scans. Therefore combining images from different sites or even different days can introduce biases that obscure the signal of interest or can produce results that could be driven by these differences. An algorithm, the CycleGAN, will be presented and analysed which uses generative adversarial networks to transform a set of images from a given MRI site into images with characteristics of a different MRI site. Secondly, the MRI scans of the brain can come in the form of different modalities such as T1- weighted and FLAIR which have been used to investigate a wide range of neurological disorders. The acquisition of all of these modalities are expensive, time-consuming, inconvenient and the required modalities are often not available. As a result, these datasets contain large amounts of unpaired data, where examples in the dataset do not contain all modalities. On the other hand, there is a smaller fraction of examples that contain all modalities (paired data). This thesis presents a method to address the issue of translating between two neuroimaging modalities with a dataset of unpaired and paired, in semi-supervised learning framework. Lastly, behavioural modelling will be considered, where it is associated with an impressive range of decision-making tasks that are designed to index sub-components of psychological and neural computations that are distinct across groups of people, including people with an underlying disease. The thesis proposes a method that learns prototypical behaviours of each population in the form of readily interpretable, subsequences of choices, and classifies subjects by finding signatures of these prototypes in their behaviour