Deans' stroke musings: Assessment of functional decline in stroke patients using 3D deep learning and dynamic functional connectivity based on resting-state fMRI

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 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:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. 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 lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Wednesday, November 26, 2025

Assessment of functional decline in stroke patients using 3D deep learning and dynamic functional connectivity based on resting-state fMRI

'Assessments' DO NOTHING TOWARDS RECOVERY! You're all fired! Does no one in stroke have any thinking skills whatsoever?

Assessment of functional decline in stroke patients using 3D deep learning and dynamic functional connectivity based on resting-state fMRI

Hewei Wang³^*

Zhiyong Zhao²^*

¹The First Hospital of Xinjiang Production and Construction Group, Aksu, Xinjiang, China
²Department of Biomedical Engineering, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou, China
³Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China

Introduction: This study aimed to develop an automated approach for assessing upper limb (UL) motor impairment severity in stroke patients using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI).

Methods: Dynamic functional connectivity (dFC) was computed with the ipsilesional primary motor cortex (M1) as a seed and extracted from rs-fMRI data of 69 stroke patients. These dFC features were used to train a three-dimensional convolutional neural network (3D-CNN) for automatic classification of UL motor impairment severity. Patients were divided into two groups according to UL Fugl-Meyer Assessment (UL-FMA) scores: mild-to-moderate impairment (UL-FMA > 20; n = 29, maximum = 66) and severe impairment (0 ≤ UL-FMA ≤ 20; n = 40). UL-FMA scores served as labels for supervised learning.

Results: The model achieved a balanced accuracy of 99.8% ± 0.2%, with a specificity of 99.9% ± 0.2% and a sensitivity of 99.7% ± 0.3%. Several brain regions—including the angular gyrus, medial orbitofrontal cortex, dorsolateral superior frontal gyrus, superior parietal lobule, supplementary motor area, thalamus, cerebellum, and middle temporal gyrus—were linked to UL motor impairment severity.

Discussion: These findings demonstrate that a 3D deep learning framework based on dFC features from rs-fMRI enables highly accurate and objective classification of UL motor impairment in stroke patients. This approach may provide a valuable alternative to manual UL-FMA scoring, particularly in clinical settings with limited access to experienced evaluators.