TSSG Brain Initiative

Specialist Area Leader: Sasitharan Balasubramaniam

The TSSG Brain Initiative (TBI) research area was established in the TSSG in 2017.

The initiative is multidisciplinary and crosses between multiple Research Units. The decision to start this new initiative and research direction is due to the importance of this new research field globally, where we are witnessing large amount of investments both in Europe (EU FET Flagship “Human Brain Project”) and the US (Obama “BRAIN” initiative). While the field of Brain research has predominantly been driven by Neuroscience, ICT has started to play a role in developing new approaches for understanding the operations of neural systems, as well as diagnosing diseases. TSSG has traditionally been an ICT research centre that focuses on research in communication networks and services, and it is the intention of this initiative to bring theories from traditional “communication and networking” to understand the brain’s communication process. The latter is mainly focused on new solutions to help patients suffering from neurodegenerative diseases. The other motivation for the creation of the initiative is the linkages we are now starting to witness between the brain and machines.

This includes development of new brain-inspired algorithms for Artificial Intelligence (e.g. Deep Learning), as well as Brain-Machine Interfaces (BMI).

There are five strands in TBI, and they include: 

  • Strand 1: Modeling Multi-scale Brain Communications for Diagnosing and Treating Neurodegenerative Diseases 
  • Strand 2: Nanodevice Arrays for Peripheral Nerve Fascicle Activation
  • Strand 3: Wireless Optogenetic Nanonetwork for Brain Stimulation 
  • Strand 4: NeuroVR – Modeling Brain response to Virtual Reality and its application for Neurorehabilitation 
  • Strand 5: Brain-inspired Machine Learning and Artificial Intelligence Algorithms and Applications 
strand 1

Modeling Multi-scale Brain Communications for Diagnosing and Treating Neurodegenerative Diseases

  • Dr. Michael Barros
  • Dr. Sasitharan Balasubramaniam
  • Denise Manton

Neuronal communications in the brain are separated over many scales and layers. This creates a heterogeneous environment whereby chemical, electrical and molecular signals are propagated in different ways, while synchronously ensuring that the transmission, storage and processing of information possible. The action potentials and synapses in different layers of the brain contributes to the Cognitive, psyche and motor functions. Any failure in this communication process is linked to brain diseases and neurodegeneration.

These types of pathologies and diseases have increasingly affected people’s lives in many different forms due to the increase in life expectancy in the ageing society. For example, Alzheimer’s disease currently affects 15 million people in the US with a death rate of 29.5% among people at the age of 65+ years, and cost around $200 billion per annum.

Research Objectives
  • Create a framework for multi-scale brain communication models based on curated bio-physical models as well as experimental data.
  • Utilise information and communication theory, control theory and systems biology for analysing molecular communications between pre and post-synaptic neurons, and analysing its role in large-scale brain communication networks.
  • Investigate neurodegenerative diseases (Alzheimer’s and Parkinson’s) that are initiated from communication failures in microglial cell.
Selected Publications
  • Michael T. Barros. Capacity of the Hierarchical Cortical Microcircuit Communication Channel,submitted for journal publication, 2017.
  • Michael T. Barros, Subhrakanti. Dey. Set Point Regulation of Astrocyte Intracellular Ca2+ Signaling, in Proc. of 17th IEEE International Conference on Nanotechnology (IEEE NANO 2017), Pittsburg, USA. 2017.
  • Michael T. Barros, Sasitharan Balasubramaniam, Brendan Jennings. Comparative End-to-end Analysis of Ca2+ Signaling-based Molecular Communication in Biological Tissues, IEEE Transactions on Communications, vol. 63, no. 12, 2015.

Source: www.siliconrepublic.com/innovation/alzheimers-research-tssg

strand 2

Nanodevice Arrays for Peripheral Nerve Fascicle Activation

  • Michael Donoghue
  • Dr. Brendan Jennings
  • Dr. Sasitharan Balasubramaniam
  • Prof. Josep Miquel Jornet, University at Buffalo, State University of New York

Neural activation relies on the use of electrical current to stimulate specific parts of the nervous system in order to treat neurological conditions (e.g., Parkinson’s Disease), nerve breakages resulting from accidents, or neural connectivity for prosthetics. Stimulation of motor nerves at present is carried out by externally powered electrodes placed on the skin surface (transcutaneous) or under the skin (subcutaneous) in close proximity to muscles or nerves, which does not allow patients freedom of movement.

TBI are aiming to investigate how miniature devices constructed from nanoscale components could be embedded and interfaced to the nervous system to stimulate peripheral nerve neuron bundles (fascicles). The challenges in this research includes using wireless charging to power the embedded nanoscale devices, as well as synchronising the stimulation to target different nerve bundles.

Research Objectives
  • Modelling the use of wireless ultrasound energy harvesting (maximum 720 mW/cm2) for implanted nanodevice arrays with electrodes for selective stimulation of peripheral nerve fascicles in the human body.
  • Modeling the input ultrasound energy (maximum 720 mW/cm2) and harvested power for single fixed-size nanowire-based nanodevices (1000µm2 with 20 nanowires per µm2) at different tissue depths and comparing these with the current and voltage levels required for peripheral neural stimulation.
  • Modelling the dimensions of nanodevice arrays, embedded in biocompatible tissue patches, to meet neural stimulation requirements. This also considers modeling the harvested energy due to tilts of the nanowire unit.
Selected Publications
  • Michael Donoghue, Sasitharan Balasubramaniam, Brendan Jennings, Josep Miquel Jornet, Nanodevice Arrays for Peripheral Nerve Fascicle Activation Using Ultrasound Energy-harvesting, to appear in IEEE Transactions on Nanotechnology, 2017.
  • Michael Donoghue, Sasitharan Balasubramaniam, Brendan Jennings, Josep Miquel Jornet, Powering In-body Nanosensors With UltrasoundsIEEE Transactions on Nanotechnology, vol. 15, no. 2, 2016.

In the future, such stimulation will have a greater role in treating debilitating neural conditions, compensating for nerve damage and enhancing prosthetic control. This would entail the deployment of such nanodevice arrays not only in the peripheral nervous system but also in the central nervous system and possibly on the surface of the brain. The wireless nanodevice patch could also be utilised to communicate through the nervous system itself by generating action potentials to send coded data messages to distant receivers for bioelectronics medicine applications.

strand 3

Wireless Optogenetic Nanonetwork for Brain Stimulation

  • Dr. Michael Barros
  • Dr. Sasitharan Balasubramaniam
  • Prof. Josep Miquel Jornet, University at Buffalo, State University of New York;
  • Stefanus Wirdatmadja, Prof. Yevgeni Koucheryavy, Tampere University of Technology, Finland.

Optogenetics is a method of artificially stimulating neural communication using light. This requires neurons to be engineered so that they are sensitive to light at specific wavelengths in order to have either excitatory or inhibitory effects. The research in this strand aims to develop miniature devices using nanoscale components, which we term Wireless Optogenetic Nanoscale Device (WiOptND), and embedding them into the cortex to enable single-neuron level stimulation. The devices will be charged through ultrasound signals, which in turn will vibrate a nanowire unit that will generate energy to power an LED. The ultrasound unit will be placed under the dura (sub-dura transceiver), which in turn will communicate with an external transceiver that is placed outside the head.

TBI will investigate how a network of WiOptNDs that are placed in the cortex, can be used to synchronously stimulate multiple neurons, for people suffering from neurodegenerative diseases.

Research Objectives
  • Modeling light propagation in the brain tissue to determine the required intensity needed for successful stimulation.
  • Developing wireless charging protocols to enable parallel charging, which could maximize the firing ratio of neurons, while minimizing energy dissipation from the sub-dura transceiver.
  • Incorporating neural spike prediction schemes to minimise knowledge required for charging and triggering the WiOptNDs for neural stimulation.
Selected Publications
  • Stefanus A. Wirdatmadja, Michael Taynnan Barros, Yevgeni Koucheryavy, Josep Miquel Jornet, Sasitharan Balasubramaniam, Wireless Optogenetic Nanonetworks: Device Model and Charging Protocols, submitted for journal publication. 2017.
  • Wirdatmaja, S., Balasubramaniam, S., Koucheryavy, Y., Jornet, J.M., Wireless Optogenetic Neural Dust for Deep Brain Stimulation, in Proc. of IEEE Healthcom, Munich, Germany, September 2016.

The WiOptND nanonetwork, which will be placed in different layers of the cortical column, can provide new opportunities for future Brain-Machine Interface. This could lead to long-term deployments of WiOptNDs in the brain.

strand 4

NeuroVR – Modeling Brain response to Virtual Reality and its application for Neurorehabilitation

  • Ian Mills
  • Dr. Michael Barros
  • Dr. Sasitharan Balasubramaniam

Virtual reality (VR) is the immersion of a person into a simulated virtual environment using either a head mounted display or a projection based system. It allows for the person to experience and interact, using either peripherals or tracked body parts, with the simulated virtual environment.

TBI will investigate the impact of VR on the brain, and how it interprets virtual environments. The research is aiming to understand how the brain functional network, which is modeled using graph theory, can be used to understand the emotions of VR users. The brain shares a number of key topological properties with modern networks. As such we can study the activity of the brain and carry out analysis using graph theory. By linking both VR and the brain, we can study the network patterns of a functional brain network and identify common topological properties associated with correct brain function (Small worldness, Modularity, Fat Tailed degree distribution). This in turn could lead to new forms of virtual-neurorehabilitation, which could be applied to patients suffereing from Alzheimers, schizophrenia, depression and Parkinsons. Several of these neurological disorders and condition affect the brain network properties and exhibit different patterns and properties. Therefore, through virtual-neurorehabilitation, we may be able to correct the network properties as patients are immersed into virtual environments.

Research Objectives
  • Composing the brain functional networks based on virtual reality stimuli and determining the core network topological structures and properties, and linking this to different emotional states based on the fluctuations in brain signal activity.
  • Defining the predictive brain functional network state using learning algorithms. The aim is to predict the next possible neural state for a defined segment of VR content.
  • VR adaptive content derived from emotional/predictive network state models.

This research will allow for VR adaptive content to be applied not only to entertainment industry for Brain Machine Interface inputs and dynamically changing content, but also to medical studies for monitoring and providing neurofeedback for neurological disorders.

Source: www.siliconrepublic.com/machines/vr-brain-disease

strand 5

Brain-inspired Machine Learning and Artificial Intelligence Algorithms and Applications

  • Eric Robson
  • Jerry Horgan
  • Kevin Doolin

Artificial intelligence (AI) is the development of intelligent machines and systems that mimicking the human brain. Over the years, numerous research initiatives have led to sophisticated AI algorithms that are capable of performing tasks to the level of humans. This TBI research strands aims to develop AI and machine learning algorithms that mimic the human brain, incorporating communication and connectivity of neuronal networks, all the way to cognition processes at the brain level. Since AI algorithms are far from the capabilities of the human brain, especially in terms of resources and energy consumption of computing systems, these algorithms will be deployed over Hyper Scale Systems (HSS). HSS will be used to provide a dynamic software layer that will seamlessly interact with all the associated hardware elements of TBI. It is specifically designed to provide the large-scale, plasticity, and low-latency (high-speed) processing and storage (cognitive) resources that are required to augment (or emulate) brain functions. HSS will be used to hide or abstract the high level of system complexity that is created by dense interconnectedness at scale.

Research Objectives
  • Development of neuronal network simulation models that can represent AI algorithms that also incorporates learning functions.
  • Development of AI platforms that can be executed on Hyper Scale System, providing remote intelligence to external devices, systems, as well as supporting new applications for Brain Machine Interfaces.
Dr. Sasitharan Balasubramaniam

Director of Research, TSSG
Lead PI

Kevin Doolin

Director of Innovation, TSSG

Prof Willie Donnelly

President of WIT

Eric Robson

AIML Research Unit Manager

Jerry Horgan

Infrastructure Manager

Dr. Michael Barros

Post Doc Researcher

Denise Manton

TSSG Technology Gateway Manager

Dr. Brendan Jennings

Head of Graduate Studies, WIT

Gary McManus

Research Project Manager

Philip O’Brien

Senior Software Research Engineer

Ian Mills

AR / VR Tech Lead

Michael Donohoe

PhD Student

Geoflly Adonias

PhD Student

Daniel Martins

PhD Student

Prof. Josep Miquel Jornet

University at Buffalo,
State University of New York.

Stefanus Wirdatmadja

Tampere University of Technology, Finland.

Prof. Yevgeni Koucheryavy

Tampere University of Technology, Finland.