Democratization of HPC Part 2: HPC in Personalized Non-invasive Clinical Treatment of Schizophrenia and Parkinson’s

By Wolfgang Gentzsch

October 4, 2018

This is the second in a series of articles demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas. In this article we present UberCloud use case #200 on how India’s National Institute of Health was able for schizophrenia – and potentially Parkinson’s disease, depression, and other brain disorders – to replace the current highly risky procedure of brain-invasive operations with an innovative technique of non-invasive low-risk treatment based on HPC that is also significantly more affordable.

This UberCloud Experiment #200 is based on computer simulations of non-invasive transcranial electro-stimulation of the human brain in schizophrenia, a serious mental illness characterized by illogical thoughts, bizarre behavior and speech, and delusions or hallucinations. This work represents an initial effort to demonstrate the high value of computational modeling and simulation in improving the clinical application of non-invasive electro-stimulation of the human brain in schizophrenia and other neuropsychiatric disorders. With the addition of HPC, clinicians can now precisely and non-invasively target regions of the brain without affecting major parts of the healthy brain. The HPC simulations have been collaboratively performed by NIMHANS National Institute of Mental Health & Neuro Sciences in India, Dassault SIMULIA, Advania Data Centers, and UberCloud, with sponsorship from Hewlett Packard Enterprise and Intel.

Figure 1: Schematic of electrical stimulation modalities.

The Problem

Neuromodulation refers to neural activity via an artificial stimulus such as an electrical current or a chemical agent. It may involve (highly risky) invasive approaches such as spinal cord stimulation or deep brain stimulation wherein electrodes are implanted directly on the nerves to be stimulated. It may also be performed non-invasively using methods such as electrical stimulation wherein external electrodes induce the required neural activity changes without the need for surgical implantation, but in which low intensity (mA) electrical currents are applied to the head via scalp-mounted electrodes, as shown in Figure 1 [Yavari 2017]. Stimulation with the negative pole (cathode) placed over a selected cortical region will decrease neuronal activity under the electrode, whereas stimulation with the positive pole (anode) will increase neuronal activity under the electrode. Therefore, this method may be used to increase cortical brain activity in specific brain areas that are under aroused, or alternatively decrease activity in areas that are overexcited. This procedure is simple, affordable, and portable, and the human is fully conscious and experiences minimal discomfort.

HPC Brain Simulation in the Advania Cloud

The power of multi-physics technology on the Advania Data Centers Cloud Platform allowed us to simulate deep brain stimulation by placing two sets of electrodes on the scalp to generate temporal interference deep inside the grey matter of the brain. However, a basic level of customization in post processing was required in making this methodology available to the clinician in real time and also reduce overall computational effort, where doctors can choose two pre-computed electrical fields of an electrode pair to generate temporal interference at specific regions of the grey matter of the brain.

After a satisfactory 3D head/brain model was developed, electrode placement was performed with Synopsys Simpleware ScanIP and CAD modules using the 10/10 international convention with the anode at AF3 and the cathode at CP5 (Figure 2). Finally, a high-resolution tetrahedral FE mesh (element size = 1mm3) was generated using the ScanIP and ScanFE modules.

Figure 2: International 10/10 convention (left) showing anode AF3 (red) and cathode CP5 (blue); subject-specific model (right).

A high-fidelity finite element human head model was considered including skin, skull, CSF, sinus grey & white matter, which demanded high computing resources to try various electrode configurations. Access to the HPE cluster at Advania and SIMULIA’s Abaqus 2017 code in an UberCloud HPC container empowered us to simulate numerous configurations of electrode placements and sizes. This also allowed us to study the sensitivity of electrode placements and sizes which was not possible before on our inhouse workstations and HPC systems.

During the final production phase, we have run 26 different SIMULIA Abaqus jobs – each representing a different electrode configuration – on the Advania/UberCloud HPC cluster of HPE ProLiant servers XL230 Gen9 with 2x Intel Broadwell E5-2683 v4 and Intel OmniPath interconnect. Each job contained 1.8M finite elements. On our own cluster with 16 cores, a single run took about 75 minutes, whereas on the UberCloud cluster a single run took about 28 minutes on 24 cores. Thus, we got a significant speedup running on UberCloud/Advania.

Figure 3 results are for two sets of electrical fields superimposed to produce temporal interference. Left: Electrical fields generated from electrodes placed on the left and right side of pre-temporal region of the scalp. Right: Electrical fields generated from electrodes placed on the left of the pre-temporal and rear region of the scalp.

Figure 3: The results show the sensitivity of the temporal interference region deep inside the brain based on two different electrode placements on the scalp.


The HPC application discussed in this case study demonstrates a breakthrough for deep brain stimulation in a non-invasive way which has the potential to replace the more painful/high risk brain surgeries such as in schizophrenia and Parkinson’s. The huge benefits of these HPC simulations are that (i) they predict the electrical current distribution with high resolution; (ii) allow for personalized and quantifiable treatment; (iii) facilitate electrode montage variations; and (iv) clinicians can devise the most effective treatment for a specific patient. HPCwire readers can download UberCloud Case Study #200 by Ganesh Venkatasubramanian from NIMHANS, Umashankar Gunasheker and Karl D’Souza from Dassault Systemes, and Wolfgang Gentzsch from UberCloud.

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