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Quantitative Assessment following Stroke
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Quantitative Assessment of Neurophysiological Signals and Motor Function following Stroke

Lead Investigator: Dr Andrei Agius Anastasi
Supervisor: Dr Owen Falzon, Centre for Biomedical Cybernetics
Co-Supervisor: Dr Malcolm Vella, Faculty of Medicine and Surgery

Abstract 

Stroke is a leading cause of disability in adulthood worldwide. One of the commonest types of disability that follow stroke is hemiplegia, that is weakness or paralysis of one’s arm and leg. Rehabilitation remains the key strategy for recovery of function yet the journey each person’s makes is often challenging, and always unique. From the exact areas of the brain affected to the motor learning skills of each individual human being, physical therapy post-stroke is never a one-size-fits-all solution. Similar to what is now being recognised in other fields of medicine, patient-tailored therapy has always been a crucial dogma in rehabilitation medicine. Nevertheless, new technologies have now opened up new windows into ways we can achieve recovery more efficiently and effectively.

The aim of this project was to investigate the role of multi-modal neurophysiological signals, like electroencephalography (EEG) and electromyography (EMG), in the assessment and prognosis of patients in the early phases post-stroke. In particular, the relationship between upper limb motor function and quantitative EEG parameters was explored. Ten male stroke survivors and ten healthy matched controls were invited to be a part of this project whereby bedside clinical examination was first done (scored using Medical Research Council (MRC) scale, motricity index (MI) and Fugl-Meyer (FM) assessment score). Subsequently, a multi-modal neurophysiological signal acquisition setup consisting of EEG, EMG and muscle forces measurement was used during both upper limb movement and at rest. EEG was further analysed to obtain quantitative parameters including brain symmetry index (BSI) and event related desynchronisation (ERD). Subsequently, two novel parameters were introduced: a long-reference event related desynchronisation (LERD) and an ERD-derived working-hemisphere symmetry index (WHSI). 

 

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Event related desynchronization in Case A: a healthy subject during right fist movement, showing ERD at C3 and C4 (i) and topoplots of average ERDs over central electrodes from 0-0.5s (ii), 0.5s-1s (iii), 1s-1.5s (iv) and 1.5s-2s (v). Similar graphs show data from Case B: a right subcortical stroke subject during paretic hand movement and Case C: a left cortical stroke subject  during paretic hand movement.

Comparative and correlation analyses performed on all variables involved found a significantly higher BSI in stroke survivors when compared to healthy subjects, indicating a higher asymmetry following stroke, especially within the cortical stroke subgroup. More importantly, a statistically significant negative correlation was found between the functional motor score (Fugl-Meyer) in follow up sessions and the initial BSI recorded a few days post-stroke. This suggests that BSI may be predictive of the motor function and recovery in the first few months that follow stroke. With such predictive tools in hands clinicians may be more able to tailor therapy plans according to extent and areas of brain involved and allow for accurate monitoring of neuroplasticity and other adaptive changes within the brain.  

Publications

A. Agius Anastasi, O. Falzon, K. P. Camilleri, M. Vella and R. Muscat, “Brain Symmetry Index in Healthy and Stroke Patients for Assessment and Prognosis”, Journal of Stroke Research, 2017.

A. Agius Anastasi, O. Falzon, K. Camilleri, M. Vella and R. Muscat, "Quantitative Scoring and Neurophysiological Signal Indices for Poststroke Motor Function Assessment and Prognostication", 20th European Congress of Physical and Rehabilitation Medicine, Estoril - Lisbon, April 2016. 

M. Molinari, A. Esquenazi, A. Agius Anastasi, R. Kragh Nielsen, O. Stoller, A. D’Andrea and M. Bayon Calatayud, “Rehabilitation Technologies Application in Stroke and Traumatic Brain Injury Patients”, in Emerging Therapies in Neurorehabilitation II, Ed. Springer International Publishing, 2016, pp.29-64. 

Calendar
Notices
Funding Award for BrainApp

CBC awarded research funding under the FUSION Technology Development Programme (TDP) 2017

New peer-reviewed journal article

CBC publishes in Biomedical Physics & Engineering Express

CBC attends EMBC 2017

Three Papers Accepted for Presentation at the Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)

Research at UoM: Brain Computer Interface

Communicating using brain signals

 
 
Last Updated: 29 May 2017

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