Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Predicting stroke recovery right now is complete fucking stupidity because  no one is looking at actual damage in the brain to make predictions, they are looking at external effects of that damage. For example, how do you predict recovery if you don't know which of these nine causes is the real reason for your deficit?
1. Penumbra damage to the motor cortex.
2. Dead brain in the motor cortex.
3. Penumbra damage in the pre-motor cortex.
4. Dead brain in the pre-motor cortex.
5. Penumbra damage in the executive control area.
6. Dead brain in the executive control area.
7. Penumbra damage in the white matter underlying any of these three.
8. Dead brain in the white matter underlying any of these three.
9. Spasticity preventing movement from occurring.
https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0148-3?

• David J. ReinkensmeyerEmail author,
• Etienne Burdet,
• Maura Casadio,
• John W. Krakauer,
• Gert Kwakkel,
• Catherine E. Lang,
• Stephan P. Swinnen,
• Nick S. Ward and
• Nicolas Schweighofer

Journal of NeuroEngineering and Rehabilitation201613:42

DOI: 10.1186/s12984-016-0148-3

©  Reinkensmeyer et al. 2016

Received: 19 November 2015

Accepted: 13 April 2016

Published: 30 April 2016

Abstract

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Keywords

Neurorehabilitation Computational modeling Motor control Plasticity Motor learning Stroke recovery

Background

Nature of the problem and definition of computational neurorehabilitation

Mobility-related disability arising from neurologic injury is a worldwide problem of pressing concern. For example, 16.9 million people suffer a first stroke each year, resulting in about 33 million survivors of stroke who are currently alive, making stroke one of the main causes of acquired adult disability [1]. Up to 74 % of stroke survivors worldwide require some assistance from caregivers for their basic activities of daily living (ADL) [2]. Disabling disorders such as stroke can be classified within the World Health Organization’s International Classification of Functioning, Disability, and Health (ICF) framework, which highlights the multi-tiered effect of stroke on the individual in terms of pathology (disease or diagnosis), impairment (symptoms and signs), activity limitations (disability), and participation restriction (handicap) (see Fig. 1 in refs [3, 4]). The present paper argues that mechanism-based, computational modeling of neurorehabilitation (Fig. 1) will be a valuable tool for improving rehabilitation strategies and furthering the recovery of individuals with neurologic injury at all of these levels.

The diagram from there is useless.

More at link.