Closed-loop rehabilitation of age-related cognitive disorders

Abstract

Cognitive deficits are common in older adults, as a result of both the natural aging process and neurodegenerative disease. Although medical advancements have successfully prolonged the human lifespan, the challenge of remediating cognitive aging remains. The authors discuss the current state of cognitive therapeutic interventions and then present the need for development and validation of more powerful neurocognitive therapeutics. They propose that the next generation of interventions be implemented as closed-loop systems that target specific neural processing deficits, incorporate quantitative feedback to the individual and clinician, and are personalized to the individual's neurocognitive capacities using real-time performance-adaptive algorithms. This approach should be multimodal and seamlessly integrate other treatment approaches, including neurofeedback and transcranial electrical stimulation. This novel approach will involve the generation of software that engages the individual in an immersive and enjoyable game-based interface, integrated with advanced biosensing hardware, to maximally harness plasticity and assure adherence. Introducing such next-generation closed-loop neurocognitive therapeutics into the mainstream of our mental health care system will require the combined efforts of clinicians, neuroscientists, bioengineers, software game developers, and industry and policy makers working together to meet the challenges and opportunities of translational neuroscience in the 21st century.

Fig. 1

A snapshot of the “Glass Brain,” an anatomically realistic three-dimensional (3D) brain visualization rendered in the Unity game engine, depicting real-time source-localized activity (power and effective connectivity) from electroencephalographic signals. Each color represents source power and connectivity in a different frequency band (θ, α, β, γ) and the golden lines are white matter anatomical fiber tracts. Estimated information transfer between brain regions is visualized as pulses of light flowing along the fiber tracts connecting the regions. The modeling pipeline includes brain magnetic resonance imaging to generate a high-resolution 3D model of an individual’s brain, skull, and scalp tissue; DTI (diffusion tensor imaging) for reconstructing white matter tracts; and BCILAB/SIFT to remove artifacts and statistically reconstruct the locations and dynamics (amplitude and multivariate Granger-causal interactions) of multiple sources of activity inside the brain from signals measured at electrodes on the scalp.