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 six strands in TBI, and they include: 

  • Strand 1: Modeling Multi-scale Brain Communications for Diagnosing and Treating Neurodegenerative Diseases 
  • Strand 2Miniature Devices for Neuron and Peripheral Nerve Activation
  • Strand 3: Design of Bio-nanomachine brain organoids implants
  • Strand 4: VR Neurorehabilitation – Modelling adaptive virtual content based on attentive brain networks
  • Strand 5: Molecular Computing Systems from Engineered Neurons
  • Strand 6: Modelling Multi-scale Brain Communications for Diagnosing and Treating Glioblastoma 
strand 1

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

  • Dr. Sasitharan Balasubramaniam
  • Dr. Michael Barros
  • Dr. Hamdan Awan 
  • Geoflly Adonias 
  • Dr. Nicola Marchetti (Trinity College Dublin)
  • Dr. Harun Siljak (Trinity College Dublin) 
  • Dr. Mark White (Waterford Institute of Technology) 

Neuronal communications in the brain over many scales and cortical layers. This creates a heterogeneous environment whereby electro-chemical signals are exchanged through the movement of molecules propagated in different ways, while synchronously ensuring that the transmission, storage and processing of neuronal information remains possible. The action potentials and synapses in different layers of the brain contributes to the cognitive, sensory and motor functions. Any failure in this communication may be linked to brain diseases and the degeneration of neural tissue. 

These types of pathologies and diseases have been increasingly affecting people’s lifestyles in many ways especially considering older people are more likely to develop some kind of neurodegeneration.  For example, epilepsy affects around 50 million people around the world, and it is estimated 10% of world population will have at least one seizure in their lifetime.

Research Objectives
  • Create a framework for multi-scale brain communication models based on curated biophysical 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, such as Alzheimer’s, Parkinson’s and Epilepsy, that are initiated from communication failures involving neuronal and non-neuronal cells. 
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.
strand 2

Miniature Devices for Neuron and Peripheral Nerve Activation

  • Michael Donoghue
  • Dr. Brendan Jennings
  • Dr. Sasitharan Balasubramaniam
  • Prof. Josep Miquel Jornet, Northeastern University, Boston, US

Artificial neural stimulation uses electrical current to stimulate specific parts of the human nervous system. At present it is used to treat neurological conditions (e.g. Parkinson’s Disease), neural impairments or to enhance neural connectivity for prosthetics. Stimulation may be delivered by externally powered electrodes placed on the skin surface (transcutaneous) or under the skin (subcutaneous) in closer proximity to muscles or nerves. 

This research models the use of ultrasound as a method of wirelessly activating an implanted neural-stimulation device at a shallow depth of tissue. The medical use of ultrasound for imaging is widespread, well understood and has recommended safety levels. Arrays of devices containing piezoelectric nanowires can convert incident ultrasound energy into electrical pulses. These pulses can stimulate nerve bundles (fascicles) to generate a stream of modulated signals along the nerve and deliver data packets to a more deeply embedded receiver. The maximum bit rate is 200 bit/s, limited by the rate at which nerves can generate electrical signals. The modulation is simple on-off keying (OOK) to create a stream of logic “ones” and “zeroes”. The send and receive timing is asynchronous and the direction of transmission is one-way, so no re-sending of faulty packets can be supported.  

We modelled a specific scenario of a stimulus system on the vagus nerve in the neck sending modulated data pulses to an embedded, multi-reservoir drug-delivery system in the brain. The drug-delivery system could use cerebrospinal glucose as a source for energy harvesting. Forward error correction (FEC) is analysed as a potential method to improve transmission performance. The overall energy-harvesting and communications system is simple, biocompatible and safe and could be used to power and communicate with devices in other locations in the body. 

Research Objectives
  • Modelling 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. The effect of degrees of tilt of the nanowire unit is also calculated. 
  • Using transmission theory to calculate the data capacity and transmission range of a stimulated nerve for different modulation techniques, subject to an overall limit of 200 bits/s. 
  • Developing a scenario of vagus nerve stimulation in the neck to deliver commands to a drug-delivery system implanted at the brainstem.      
  • Exploring the wider use of neural communications to communicate with distributed embedded prosthetic devices in the brain.  
Selected Publications
[1] Michael Donohoe, Sasitharan Balasubramaniam, Brendan Jennings, and Josep Miquel Jornet.  Powering In-body Nanosensors with Ultrasounds. IEEE Transactions on Nanotechnology 15(2):151-154, March 2016. 


[2] Michael Donohoe, Brendan Jennings, Josep Miquel Jornet, and Sasitharan Balasubramaniam. Nanodevice Arrays for Peripheral Nerve Fascicle Activation Using Ultrasound Energy-harvesting. IEEE Transactions on Nanotechnology 16(6):919-930, Nov 2017.  


[3] Michael Donohoe, Brendan Jennings, and Sasitharan Balasubramaniam. Capacity Analysis of a Peripheral Nerve using Modulated Compound Action Potential Pulses.  IEEE Transactions on Communications 67(1):154-164, Jan 2019. 


[4] Michael Donohoe, Brendan Jennings, and Sasitharan Balasubramaniam. Deep Brain Drug-delivery Control using Vagus Nerve Communications. Elsevier Computer Networks 171:107137, April 2020. 


  • SFI CONNECT – The Nano network 
strand 3

Design of Bio-nanomachine brain organoids implants

  • Caio Queiroz da Fonseca (lead)
  • Michael Barros
  • Sasitharan Balasubramaniam
  • Dr. Ina Meiser (IBMT – Fraunhofer)

Glioblastoma multiform is an aggressive form of cancer in the brain and many groups have been doing research on how and when it originates and how to treat it. One of the proposed solutions by the Gladiator Project, is to implant externally controllable bio-nanodevices that will target the tumor and deliver therapeutic molecules in order to treat it. These bio-nanodevices are made of organoids, which are aggregate of cells with many amazing features, one of which is that they can be designed to play specific roles in various applications, such as a communication bio-nanodevice that will receive a signal and deliver therapeutic molecules to a specific target.

Brain organoids are a fundamental topic in Neuroscience research nowadays, because they allow researchers to investigate the origin and nature of specific or neurodegenerative diseases in the brain. These organoids can be developed by various protocols, where the first step consists in taking some cells from a patient, such as skin (epithelial) cells, and reprogram them into pluripotent stem cells (hiPSC), which are cells that have the potential to differentiate into any specific cell in the human body. These hiPSCs then can be differentiated into neural stem cells, with the potential to become neurons, astrocytes, oligodendrocytes, or brain organoids. One big advantage of the brain organoids is that as described before, the process to produce organoids depends on the cells of the patient, and therefore has their genetic code, which can reproduce certain diseases from a patient.

Sometimes called “mini-brains”, these brain organoids allow us to study the development of the brain as well as where does some diseases originate in this development. The structures of the organoids are similar to the structures of the real brain, enabling us to learn much more about the brain without the need of a real brain. Organoids can also be used as bio-nanodevices to be optimized to execute certain functions, such as to work as a communication device, or a drug-delivery system.

Research Objectives
  • Create a model for the development and formation of brain organoids as bio-nanodevices, based on small molecules, signalling pathways and gene regulatory networks.
  • Utilise evolutionary algorithms as a optimization tool, for the programming and design of brain organoids as bio-nanodevices.
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.
  • This work is linked to the FET- EU – H2020 – Gladiator Project. https://fet-gladiator.eu
strand 4

VR Neurorehabilitation – Modelling adaptive virtual content based on attentive brain networks.

  • Ian Mills, Technical Lead AR/VR

Electroencephalography (EEG) is a non-invasive technique for acquiring brain activity data where electrodes attached to the scalp record electrical currents produced by the brain with millisecond temporal resolution. Machine learning techniques such as LDAS (for ERP analysis), support vector machines (SVMs for derived graphs) enable real-time classification of complex patterns distributed across numerous EEG traces simultaneously. Using brain stimulation methods encoded in the AR/VR content to identify ERPs such as P300 responses. Using these auditory and visual stimulations we can identify key brain reactions to this content. These stimulations in turn affect the current brain activity and allow us to determine normal and abnormal reactions to the stimulations.

By focusing initially on the visual ERP reaction to visual stimulus we can determine how the brain itself perceives the virtual world as opposed to real world stimuli. Using these events as a means of performing neurofeedback to the user through dynamically changing content we seek to alter over a series of sessions the overall emotional reaction through neurorehabilitation.

We investigate both the event and the resulting signals to determine its effects on overall brain activity. A person’s brain functional network can then be modelled with graph theory and artificial intelligence methods such as graph convolutional networks and spectral convolutional graph networks. The activity of the brain is studied by characterizing these EEG patterns and associating common network topologies with correct brain function. This in turn is used to train classifiers which improves the overall prediction model. One application is identification of neurological disorders which exhibit distorted patterns and network topologies. This prediction model can be further trained based on the neurofeedback system supplying new stimulations to achieve the desired brain activity reaction and thus the approximated network topology. These stimulations devised by deep learning model are encoded as parts of dynamic content in the neurotherapy feedback to allow the users activity to directly affect the experience. As more subjective data is gathered these predictions gain accuracy. Based on the functionally healthy brain topologies, weightings can be determined based on the subjective reactions to the stimulations that allow a dynamic rehabilitation system to be developed.

Emotional analysis using deep learning through colour-depth imagery and HEG of VR participants through partial occulted facial types.

Validation of the emotional pattern recognition alogithm through use of facial coding methods and HEG. This research focus on creating a validation mechanism for emotional reocginition algorithm by testing FER(Facial emotiona recognition) and HEG (hemoencelopgraphy) to determine current state while the face is partially occulded by a VR headset of other head worn device.

Research Objectives

  • Understanding how the brain interrupts and processes virtualised is fundamental to crate more immersive and engaging content. From results so far we see a heightened attentive allocation in VR while performing a key attentive visual task. By demonstrating this heightened sense of attentiveness it can be utilised to create more immersive VR content, adapt this content based on the evoked responses and help to reduce the overall time needed for neurorehabilitation.


  • Those undergoing neurorehabilitation are often frustrated and fatigued by long months of visual and motor control tasks to improve their conditions. By introducing new methods of interaction that show a heightened sensor of attentiveness in the participant we hope to allow the rehabilitation program to impact these users more effectively than traditional methods.
  • By providing a means of immersive neurofeedback to the user we can adapt the content of the experience dynamically in response to fluctuation in their emotional valence and arousal.
Title: Evaluation of evoked EEG differences in P300 ERP response in 2D and VR mediums.

Short abstract:We used a neural activity marker of attention, the Event-Related Potential (ERP) P300 effect, to show that the attentive spike in neural activity for the event is higher when human participants observe contents in a virtual reality environment compared to when the digital content is viewed on a 2D real world medium.

Targeted submission: Nature Scientific Reports


Title: Emotional analysis using deep learning through colour-depth imagery and HEG of VR participants through partial occulted facial types.

Short abstract: Using depth and colour (RGB/RGB-D) imagery coupled with HEG we will examine the current emotional state of a VR user through a semi occluded face. Using partial facial data as occluded through a headset we coupled this with an off the shelf FER (Facial emotion recognition) system.

Targeted submission: IEEEVR(Sep)

  • VisionaryCF: Utilising virtual reality to gauge visual perception. Expanding on this is how the visual cortex perceives the VR world as well as differences between them. This allows for the mapping of locked state visual stimulus allowing mapping of visual inputs to neurological outputs in the occipital lobe.
strand 5

Molecular Computing Systems from Engineered Neurons

  • Sasitharan Balasubramaniam
  • Michael Taynnan Barros
  • Geoflly Adonias
  • Nicola Marchetti (Trinity College Dublin)
  • Harun Siljak (Trinity College Dublin)
  • Mark White (Waterford Institute of Technology)

With the advancement of synthetic biology and cellular reprogramming techniques we should be able to push the limits of the brain and augment its functions and may be placed at the centre of regenerative approaches towards the correction of abnormalities at a cellular level. Although biological computing is pervasive in living systems, having the capability to engineer new molecular computing systems have the power to pave the way to levels of control over biological components never seen before that can be applied to all areas of bioengineering and biomedicine. Several theoretical concepts on computation and control system engineering serve as inspiration for the construction of molecular computing systems such as digital logic gates and circuits, and filters.

Research Objectives
  • Create a framework to evaluate the impact of synthetic computing units on natural cells for potential long-term use of such components.
  • Utilise theoretical concepts from information and communication engineering as well as control systems engineering to analytically approach the molecular communication performed by the pre- and post-synaptic neurons and analyse its role on cortical networks.
  • Investigate potential applications such as the precise treatment of neurodegenerative disorders and possible enhancement of cognitive functions.
Partially funded with the financial support of Science Foundation Ireland CONNECT project grant no. 13/RC/2077
Strand 6

Modelling Multi-scale Brain Communications for Diagnosing and Treating Glioblastoma 

  • Hamdan Awan
  • Sasitharan Balasubramaniam 

Glioblastoma Multiforme is the most prevalent and devastating brain disease whose treatment have the lowest success rates compared to other therapeutic cancer technologies. The development of brain drug delivery systems for this type of cancer is very challenging because of side effects, the complexity of the structures of the brain, and the stringent Blood-Brain Barrier (BBB) that protects the brain from damage and potentially toxic blood-borne molecules. In addition, the lack of efficient technologies to deliver drugs in the deep located and functional brain regions, such as the brain parenchyma, and across the BBB hinders treatment of brain pathologies. Hence, novel technologies for Glioblastoma cancer therapy must emerge to overcome the BBB blockage while efficiently reaching the brain parenchyma within safety guidelines. An externally controllable molecular communication platform that consists of stem cells acting as therapeutic, reporting and diagnostic bio-nanomachines has been proposed in the recently granted EU project GLADIATOR: Next-generation Theranostics of Brain Pathologies with Autonomous Externally Controllable Nanonet- works: a Trans-disciplinary Approach with Bio-nanodevice Interfaces (EU-H2020-FET-Open #828837).  

Our role in this project is to work on the FET open Gladiator project where the main focus of our research is to understand the underlying mechanisms involved in the proliferation of Glioblastoma cells in brain. We use the concepts from molecular communication and information theory to present a theoretical model for intercellular molecular communication in Glial cells. The theoretical results will be compared with corresponding experimental results obtained by the partners in FET consortium. We aim to present a genearlized simulation platform for the theoretical model which will work with a variety of different scenarios regarding the proliferation of tumour cells.  The next step of this project is to focus on the therapeutic component of the model i.e. by using molecular communication we aim to present different techniques to reduce the proliferation of Glioblastoma brain tumour.  

Research Objectives
GLADIATOR establishes a baseline of feasibility and innovation potential and envisions taking a radical step towards a drastic transformation in the way we investigate and manage complex malignancies and potentially other major pathologies with long term radical advances:  For Pre-clinical oncology research: an emerging supra-discipline of “bio-nanomachine diagnostics” which lays the grounds for future autonomous treatments of diseases that require micro-scale devices, monitored and controlled externally, advancing ICT, engineering and synthetic biology research.  For Clinical Oncology: a radical shift towards “nanonetwork therapeutics” which includes fully functional autonomous closed loop disease management systems; (ii) drastic renewal of ideas in tumour theranostic, by MC sub-disciplines, affecting pharmacology, biotechnology, therapy selection and disease monitoring. 

Impact on society and economy: Patients’ prognosis: minimal recurrences, reduced drug toxicity, prolonged survival. Caregivers’ work load: drastically improved patient response, reduced return visits. Health Care Systems: improved health, life expectancy and productivity, reduced sick-leaves, shorter hospitalizations, less personnel involved. Policy Makers: technology-driven novel strategies for autonomous therapeutics, transformation of healthcare concepts. Patient support groups: paradigm shift in the advocacy for adoption and endorsement of the new technology by regulatory bodies and insurances.  

Impact on market creation: The project aims to deliver innovative biological and nanotechnology-based materials and theoretical underpinnings, development methods, computational and analytical tools to create a new transformational market in the fields of “bio-nanomachine diagnostics and nanonetwork therapeutics. 

Riaz, Muhammad Usman, Hamdan Awan, and Chun Tung Chou. “Using spatial partitioning to reduce the bit error rate of diffusion-based molecular communications.” IEEE Transactions on Communications 68.4 (2020): 2204-2220.


M Veletić, MT Barros, H Arjmandi, S Balasubramaniam, I Balasingham “Modeling of Modulated Exosome Release from Differentiated Induced Neural Stem Cells for Targeted Drug Delivery” IEEE Transactions on NanoBioscience 2020.

Hamdan Awan., Zeid, K., Adve, R. S., Wallbridge, N., Plummer, C., & Eckford, A. W. (2019). Communication in Plants: Comparison of Multiple Action Potential and Mechanosensitive Signals with Experiments. IEEE transactions on Nanobioscience 2019.

M Veletić, MT Barros, H Arjmandi, S Balasubramaniam, I Balasingham A Molecular Communication Model of Exosome-mediated Brain Drug Delivery. In  Proceedings of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication (pp. 1-7).

Partially funded with the financial support of Science Foundation Ireland CONNECT project grant no. 13/RC/207
Dr. Sasitharan Balasubramaniam

Director of Research, TSSG
Lead PI

Dr. Hamdan Awan

Postdoctoral Research Fellow, TSSG

Michael Donohoe

PhD Researcher

Dr. Brendan Jennings

Interim Director of Connect

Ian Mills

AR / VR Tech Lead

Caio Queiroz de Fonseca

PhD Student

Geoflly Adonias

PhD Student

Prof. Josep Miquel Jornet

Northeastern University, Boston, US

Dr. Nicola Marchetti

Trinity College Dublin

Dr. Harun Siljak

Trinity College Dublin 

Dr. Mark White

Waterford Institute of Technology

Dr. Ina Meiser

IBMT – Fraunhofer

Chun Tung Chou

University of New South Wales, Sydney, Australia

Andrew Eckford

York University, Canada

Maurizio Magarini

Politecnico Di Milano Italy

Dr. Michael Barros

The University of Essex, UK.