Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 493 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It's quite disgusting that this information is not available from every stroke association and doctors group.
My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html

Saturday, May 12, 2018

Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation using Functional Connectivity

No fucking clue what this means or usefulness for stroke survivors. I bet your doctor can't figure it out either, or anyone at our fucking failures of stroke associations .
https://www.frontiersin.org/articles/10.3389/fnins.2018.00353/abstract

 Rosaleena Mohanty1*,  Anita M. Sinha1,  Alexander B. Remsik1,  Keith C. Dodd1,  Brittany M. Young1,  Tyler Jacobson1, Matthew Mcmillan1,  Jaclyn Thoma1, Hemali Advani1,  Veena A. Nair1, Theresa Kang1,  Kristin Caldera1, Dorothy F. Edwards1, Justin C. Williams1 and  Vivek Prabhakaran1
  • 1University of Wisconsin-Madison, United States
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post- and one month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was followed by a principal component analysis-based feature transformation to reduce the number of features which were used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.

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