By Giuseppe Reale, MD, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
In daily clinical practice, it is common experience to be asked to formulate a stroke prognosis soon after the index event. This task requires caution and experience. The strongest stroke outcome predictor is clinical severity, while other additional predictors of outcome are age, infarct volume and location, aetiology, revascularization treatment and comorbidities1-6. Nevertheless, even if all the mentioned stroke outcome predictors are taken into account, it is easy to make mistakes.
In this view, Clinical Neurophysiology and its commitment to obtain objective, quantitative and reproducible measurements might offer new perspectives.
Non-invasive brain stimulation is a fascinating branch of Clinical Neurophysiology. Motor evoked potential (MEP) are electric signals recorded from the descending motor pathways or from muscles following a stimulation of the motor pathways within the brain. In a recently published study, Greve and Colleagues described that upper limb MEPs recovery during mechanical thrombectomy can predict three-months clinical outcomes7. In particular, during mechanical thrombectomy, they applied a transcranial electrical stimulation (TES) on the primary motor cortex and recorded MEPs from abductor pollicis brevis muscle bilaterally. The Authors of this study found that the return of a stable MEP (if absent at the baseline) or a >50% amplitude increase (if reduced at the baseline) was an outcome predictor stronger than successful recanalization (defined as TICI >2b). Moreover, MEP recovery was associated to small infarcts sparing the motor pathways descending from primary motor cortex.
Moving from single pathways to widespread brain networks, electroencephalography (EEG) analysis can describe with high temporal resolution the interdependent relationship between cortical areas in terms of synchronization (functional connectivity). Moreover, EEG can describe brain networks’ behavior analyzing each frequency band that lies under the electrical storm registered at the scalp. Each frequency band (alpha, beta, delta, theta and gamma) has its own generator and its own function, from “physiological” rhythms such as alpha, beta and gamma, to more “pathological” rhythms such as delta and theta. It has been demonstrated that acute ischemic stroke changes the “network signature” for each band and these modifications can predict functional outcome8,9.
From electrical waves to limb movement, a new frontier is sensors. Movement sensors can detect linear accelerations (acceleromters) or angular accelerations (gyroscopes). Actigraphy is a well-known technique usually used in sleep medicine, where it is possible to track sleep behavior using wristwatch-like accelerometers. However, several studies have provided evidence that spontaneous movements monitoring of paretic limb using actigraphy can be used to assess and monitor stroke clinical severity in both the acute and the chronic phase10,11 . From these data it is possible to obtain precise indices that describe the spontaneous movements of paretic limbs over a period of hours or days, giving precise information on the motor recovery trend.
This is just an overview on how an old-fashioned but ever extremely creative branch of the Neurosciences can provide a new insight on the difficult task of formulating a reliable stroke prognosis.
References
1. Adams HP, Davis PH, Leira EC, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: A report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology. 1999;53:126-131.
2. Weimar C, König IR, Kraywinkel K, Ziegler A, Diener HC, German Stroke Study Collaboration. Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models. Stroke. 2004;35:158-162.
3. Petty GW, Brown RD, Whisnant JP, Sicks JD, O’Fallon WM, Wiebers DO. Ischemic stroke subtypes : a population-based study of functional outcome, survival, and recurrence. Stroke. 2000;31:1062-1068.
4. Paciaroni M, Caso V, Venti M, et al. Outcome in patients with stroke associated with internal carotid artery occlusion. Cerebrovasc Dis. 2005;20:108-113.
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6. Sommer P, Posekany A, Serles W, et al. Is Functional Outcome Different in Posterior and Anterior Circulation Stroke? Stroke. 2018;49:2728-2732.
7. Greve T, Wagner A, Ille S, et al. Motor evoked potentials during revascularization in ischemic stroke predict motor pathway ischemia and clinical outcome. Clin Neurophysiol. 2020;131:2307-2314.
8. Caliandro P, Vecchio F, Miraglia F, et al. Small-World Characteristics of Cortical Connectivity Changes in Acute Stroke. Neurorehabil Neural Repair. 2017;31:81-94.
9. Vecchio F, Tomino C, Miraglia F, et al. Cortical connectivity from EEG data in acute stroke: A study via graph theory as a potential biomarker for functional recovery. Int J Psychophysiol. 2019;146:133-138.
10. Iacovelli C, Caliandro P, Rabuffetti M, et al. Actigraphic measurement of the upper limbs movements in acute stroke patients. J Neuroeng Rehabil. 2019;16(1):153
11. Urbin MA, Waddel KJ, Lang CE. Acceleration metrics are responsive to change in upper extremity function of stroke survivors. Arch Phys Med Rehabil. 2015;96:854–61.
