The identification of reliable biomarkers predicting outcomes in ultra-high risk (UHR) psychosis remains a vital challenge in preventive psychiatry. While genetic factors are implicated in psychosis risk, specific markers predicting social and cognitive trajectories have remained elusive. In this 24-month longitudinal study of UHR and Healthy Control (HC) participants, we investigated for the first time the predictive relationship between key immune-related genes, inflammatory response genes, stress response genes, and neurological-related genes with social and cognitive outcomes. Participants underwent comprehensive social and cognitive assessments at baseline and three 6-month intervals. We employed a brain-inspired Spiking Neural Network (SNN) model to map gene-behavior interactions and to identify key genetic predictors that influence both social and cognitive functioning. Additionally, we explore subgroup differences within the UHR population to further understand psychosis risk.

In this paper, we present a novel Liquid State Machine (LSM) based approach for modelling of multimodal longitudinal data: the Mosaic LSM. Our model harnesses the strengths of multiple LSMs, each designed to capture the temporal patterns of a specific data modality. This temporal information is then added to the raw data to create a composite representation that encompasses both the multimodal and the longitudinal aspects of the data. We demonstrate the performance of our approach on a real-world dataset that contains clinical, cognitive, and genetic modalities with the aim of predicting the Ultra-High Risk (UHR) status in individuals, six months in advance. Our results show that the Mosaic LSM outperforms traditional machine learning models, achieving an outstanding Matthew's Correlation Coefficient of 0.84 and prediction accuracy of 92.4%. Overall, our work highlights the potential of Mosaic LSM as a powerful tool for disease prognosis, and its ability to leverage both the multimodality and temporality of the data to improve performance.

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

The article demonstrates for the first time that a brain-inspired spiking neural network (SNN) architecture can be used not only to learn spatio-temporal data, but also to extract fuzzy spatio-temporal rules from such data and to update these rules incrementally in a transfer learning mode. We propose a method, where a SNN model learns incrementally new time-space data related to new classes/tasks/categories, always utilizing some previously learned knowledge, and presents the evolved knowledge as fuzzy spatio-temporal rules. Similarly, to how the brain manifests transfer learning, these SNN models do not need to be restricted in number of layers and neurons in each layer as they adopt self-organizing learning principles. The continuously evolved fuzzy rules from spatio-temporal data are interpretable for a better understanding of the processes that generate the data. The proposed method is based on a brain-inspired SNN architecture NeuCube, which is structured according to a brain three-dimensional structural template. It is illustrated on tasks of incremental and transfer learning and knowledge transfer using spatio-temporal data measuring brain activity, when subjects are performing tasks in space and time. The method is a general one and opens the field to create new types of adaptable and explainable spatio-temporal learning systems across domain areas.

This paper is in the scope of emotion recognition by employing a recurrent spiking neural network (BI-SNN) architecture for modelling, mapping, learning, classifying, visualising, and understanding of spatio-temporal Electroencephalogram (EEG) data related to different emotional states. It further explores, develops, and applies a methodology based on the NeuCube BI-SNN, that includes methods for EEG data encoding, data mapping into a 3-dimensional BI-SNN model, unsupervised learning using spike-timing dependent plasticity (STDP) rule, spike-driven supervised learning, output classification, network analysis, and model visualisation and interpretation. The research conducted to model different emotional subtypes through mapping both space (brain regions) and time (brain dynamics) components of EEG brain data into SNN architecture. Here, a benchmark EEG dataset was used to design an empirical study that consisted of different experiments for classification of emotions. The obtained accuracy of 94.83% for EEG classification of four types of emotions was superior when compared with traditional machine learning techniques. The BI-SNN models not only detected the brain activity patterns related to positive and negative emotions with a high accuracy, but also revealed new knowledge about the brain areas activated in relation to different emotions. The research confirmed that neural activation increased in the frontal sites of brain (F7, F3, AF4) associated with positive emotions, while in the case of the negative emotions, connectivity strength was concentrated in the frontal (F4, AF3, F7, F8) and parietal sites of the brain (P7, P8).

This paper studies sleep stage classification using NeuCube, a Spiking Neural Network (SNN) architecture, simulated on SpiNNaker, a neuromorphic computer. The sleep electroencephalogram (EEG) time series is converted to spikes and provided as an input to NeuCube. Relevant feature vectors are extracted at different stages of training. We used six standard machine learning classifiers on different combinations of these feature vectors and calculated 5-fold cross-validation accuracy. We observed that the gradient boosted decision trees classifier performed the best by achieving 81.25% accuracy on a combination of two feature vectors. An evaluation of the results using confusion matrices and classification reports showed that the Awake, N2, SWS and REM sleep stages can be classified with ≥ 80% F1-score using the gradient boosted decision trees algorithm. Overall, our proof-of-concept work towards autonomous sleep-stage classification using NeuCube shows promise and will form the base for continued research in this direction.

This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.

This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology-fMRI data collected from subjects when reading affirmative or negative sentences and another one-on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real-time applications.

This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data. For every individual, based on selected subset of similar to this individual clinical data, a subset of STBD is used for training a personalised Spiking Neural Network (PSNN) model using the recently proposed NeuCube SNN architecture. The proposed method is illustrated on a case study of personalised modelling using clinical and EEG data of two groups of subjects - drug addicts and addicts under medication. The PSNN models help to achieve a better classification accuracy compared to global SNN models or when using traditional AI methods. A PSNN model visualisation enables discovery of new knowledge about individual persons and to distinguish complex STBD across subjects.