A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices

Abstract

Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.

Keywords: brain function; brain-machine interface; genome-wide association studies; neuropsychiatric disorders; translational research.

Figure 1:

Temporal trend of scientific publications in PubMed using as keywords “Genome-Wide Association Study” and [“Deep-Brain Stimulation” OR “Brain-Computer Interface”].

Figure 2:

Integrating findings from large-scale genome-wide association studies (GWAS) with RNA sequencing approaches and brain imaging modalities to identify potential targets for deep-brain stimulation (DBS) and brain-computer interface (BCI) devices.