A modelling study of beta-amyloid induced change in hippocampal theta rhythm

Many dementia cases, such as Alzheimer’s disease (AD), are characterized by an increase in low frequency field potential oscillations. However, a definitive understanding of the effects of the beta-Amyloid peptide, which is a main marker of AD, on the low frequency theta rhythm (4-7Hz) is still unavailable. In this work, we investigate the neural mechanisms associated with beta-Amyloid toxicity using a conductance-based neuronal network model of the hippocampus CA1 region. We simulate the effects of beta-Amyloid on the A-type fast inactivating K+ channel by modulating the maximum conductance of the current in pyramidal cells, denoted by gA. Our simulation results demonstrate that as gA decreases (through A[beta]
blockage), the theta band power first increases then decreases. Thus there exists a value of gA that maximizes the theta band power. The neuronal and network mechanism underlying the change in theta rhythm is systematically analyzed. We show that the increase in theta power is due to the improved synchronization of pyramidal neurons, and the theta decrease is induced by the faster depolarisation of pyramidal neurons

Knowledge Extraction using Capsule Deep Learning Approaches

Limited training data, high dimensionality, image (generated from spatiotemporal signals in BCI) complexity and similarity between classes are the main challenges confronting deep learning (DL) methods and can result in suboptimal classification performance. Most DL methods employ Convolutional Neural networks (CNN), which contain pooling in their architecture. Pooling loses valuable information and exact spatial correlations between different entity parts. More importantly, the new viewpoint of an object in the image cannot be preserved by pooling. The Capsule Neural Network (CapsNet) has been introduced to address these shortcomings by preserving the hierarchy between different entity parts in an image, even when using limited training samples [1], [2]. The potential for advancements in CapsNets methods has been demonstrated in the multidisciplinary field, including hyperspectral imaging, image classification, segmentation, video detection, and human movement recognition. Motivated by CapsNet, we have recently developed an end-to-end DL architecture, the Hybrid Capsule Network (HCapsNet), with the state of the art result for hyperspectral image classification while using extremely fewer training samples [3]. Also in another study, the proposed CapsNet architecture yielded encouraging results for the investigation of infant intrinsic movement at various phases of the new experiment in collaboration with the Human Brain and Behavior Lab at Florida Atlantic University (Prof Kelso and colleagues) [4]. The result showed the performance of 2D CapsNets in assessing the spatial relationships between different body parts using 2D histogram features.The non-invasive electroencephalography (EEG) provided by wearable neurotechnology is a massive challenge for AI. My study also focused on developing novel AI algorithms to address the issues involved with decoding EEG signals into control signals for neurotechnology based on brain-computer interfaces (BCIs). These methods are expected to be well-suited for BCI applications, particularly when learning various EEG properties with limited training data (typically the case for BCIs). Upon recent work for decoding imagined speech using CNNs we also applied CapsNet to direct speech BCIs [5]. In this research, the CapsNet architecture is modified using multi-level feature maps and multiple capsule layers. In addition, the new Tier 2 Northern Ireland High-Performance Computing facility enabled us to train models in deep approaches with enormous processing power. Therefore, Massively parallel computing using Asynchronous Successive Halving Algorithm (ASHA) is used for hyperparameter optimisation.Since CapsNet is still in its early stages of development and has demonstrated promising results on several challenging datasets, this method has the potential to develop relationships with colleagues in other disciplines, which could result in new research applicationsReferences[1]S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” in Advances in Neural Information Processing Systems, 2017, pp. 3857–3867. Accessed: Apr. 09, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/2cad8fa47bbef282badbb8de5374b894-Abstract.html[2]G. E. Hinton, S. Sabour, and N. Frosst, “Matrix capsules with {EM} routing,” International Conference on Learning Representations (ICLR), pp. 1–15, 2018, [Online]. Available: https://openreview.net/pdf?id=HJWLfGWRb[3]M. Khodadadzadeh, X. Ding, P. Chaurasia, and D. Coyle, “A Hybrid Capsule Network for Hyperspectral Image Classification,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 14, pp. 11824–11839, 2021, doi: 10.1109/JSTARS.2021.3126427.[4]Massoud Khodadadzadeh, Aliza Sloan, Scott Kelso, and Damien Coyle, “2D Capsule Networks Detect Perceived Changes in Infant~Environment Relationship Reflected in 3D Infant Movement Dynamics. Manuscript submitted for publication in Scientific Reports, Nature,” 2023.[5]M. Khodadadzadeh and D. Coyle, “Imagined Speech Classification from Electroencephalography with a Features-Guided Capsule Neural Network.” Dec. 18, 2022. Accessed: Mar. 03, 2023. [Online]. Available: https://pure.ulster.ac.uk/en/publications/imagined-speech-classification-from-electroencephalography-with-a <br/

Assessing Alpha Band Event-related Synchronisation/Desynchronisation Using a Bio-Inspired Computational Model

This paper describes a study of the effects of variation of synaptic connectivity in a thalamo-cortical circuitry using a neural mass model. The oscillatory behaviour of the model output is assessed within the alpha frequency band. The model presented here is a modification of an existing model involving the introduction of biologically plausible synaptic connectivities as well as synaptic structure. Our goal is to study altered event related desynchronisation/synchronisation (ERD/ERS) patterns within the alpha band in Alzheimers disease as observed in experimental studies. ERD is an amplitude attenuation of certain EEG rhythms when an event is initiated or while a certain event is taking place in the brain. ERS is an amplitude enhancement of a certain EEG rhythm when cortical areas are not specifically engaged in a given mode of activity at a certain instant of time. EEG desynchronisation normally blocks alpha rhythms in the EEG due to sensory processing or behaviour. The results show that a decrease in synaptic connectivity induces a time lag in both ERD and ERS peaks in the model output. Furthermore, a deficiency induced in the inhibitory cholinergic pathway results in a distinct effect on time to peak in the ERD/ERS response. These observations are consistent with experimental findings in AD. Variation of the level of interconnectivity has a pronounced effect on the ERS behaviour of the model while the excitatory connectivity in the retino-geniculate pathway during the resting state is more influential on the ERD behaviour