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Outcome prediction in acute monohemispheric stroke via magnetoencephalography

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Abstract

Background

Following an ischemic stroke a highly variable clinical outcome is commonly evident despite similar onset symptoms as well as lesion characteristics. The aim of this study was to identify indexes providing early prediction of functional recovery, in addition to clinical severity and lesion dimension at onset of stroke.

Methods

In 32 patients, magnetoencephalographic (MEG) parameters collected in the acute phase (< 10 days from symptoms onset, T0) from affected (AH) and unaffected (UH) hemispheres at rest and evoked by sensory stimuli were evaluated in association with the clinical outcome in a stabilized phase (T1, median 7.8 months) classified with three levels: worsening, partial and full recovery.

Results

Multiple multinomial logistic regression indicated AH gamma and UH delta band powers able to prognosticate clinical outcome at T1. After inclusion in this analysis, lesion volume had the strongest predictive ability, and UH delta band power remained as a predictive factor with a measurable cut-off, maximizing both sensitivity and specificity of the prediction: a patient with UH delta below cut-off would recover to some extent; a patient with UH delta above cut-off would have a probability of about 70% to worsen.

Conclusions

MEG UH delta and AH gamma band powers were found to provide useful information about long-term outcome prognosis. Only the increase of delta band activity in the unaffected hemisphere contains information about the outcome in addition to the lesion volume.

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Acknowledgements

This work has been partially supported by the 2003 - protocol 2003060892 of the Italian Department of University and Research (MIUR) and by the RBNE01AZ92_003, PNR 2001–2003, Fondi di Investimento per la Ricerca di Base (FIRB) and by the IST/FET Integrated Project NEUROBOTICS - The fusion of NEUROscience and roBOTICS, Project no. 001917 under the 6th Framework Programme. The Authors thank Professors Vittorio Pizzella and GianLuca Romani for their continued support, Doctors Claudia Altamura, Maria Filippi, Antonio Oliviero, Francesco Tibuzzi, Giancarlo Zito for patient testing and TNFP Matilde Ercolani for her excellent technical support.

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Authors and Affiliations

  1. Istituto di Scienze e Tecnologie della Cognizione (ISTC), CNR, Rome, Italy

    Franca Tecchio & Filippo Zappasodi

  2. Dipartimenti di Neuroscienze e di, Diagnostica per immagini, Ospedale “Fatebenefratelli”, AFaR, Isola Tiberina, Rome, Italy

    Patrizio Pasqualetti, Domenico Lupoi, Fabrizio Vernieri & Paolo Maria Rossini

  3. IRCCS “Centro S. Giovanni di Dio-Fatebenefratelli”, Brescia

    Patrizio Pasqualetti & Paolo Maria Rossini

  4. Clinica Neurologica, Università Campus Biomedico, Rome, Italy

    Mario Tombini, Fabrizio Vernieri & Paolo Maria Rossini

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  1. Franca Tecchio
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Received in revised form: 6 July 2006

Appendix 1

Appendix 1

In all the text, the term “absolute” is used to indicate the absolute value of a variable with no reference to the value of the same variable in the other hemisphere; for the inter-hemispheric difference the term “asymmetry” is employed.

Evoked activity recording and analysis

Subjects underwent separate electrical stimulation of their left and right median nerves at the wrist. Stimuli consisted of 0.2 ms electric pulses (cathode proximal), with an inter-stimulus interval of 641 ms, and delivered through surface disks. Stimulus intensities were adjusted just above the threshold inducing a painless thumb twitch. Data were collected from the contralateral hemisphere, focusing on two early waves (M20, M30, i.e., respectively around 20 and 30 ms from the stimulus). These components are selectively generated in the brain’s sensorimotor areas contralateral to the stimulated hand, are quite stable, repeatable and independent of the subject’s attention.

Averaged M20 and M30 latencies, strengths and locations of their equivalent current dipoles (ECD), and their inter-hemispheric differences from stroke patients sample were compared with a normative data set recorded from a population of sex-age matched healthy subjects.

The SEF wave morphologies in the two hemispheres were also compared via a correlation based technique (see [66]). This morphologic analysis provides an evaluation of the inter-hemispheric correlations between sensory processes activated within primary sensorimotor areas, i.e. in the time interval starting at the M20 onset and lasting 30 ms; due to strong inter-hemispheric similarity in the same subject, a normative set was defined (sim_SI, 0.76 [66]) and the wave shape analysis was carried out taking the unaffected hemisphere’s wave shape as a template. Since a measurable onset of the M20 component is needed to define the two epochs, patients with missing SEF are not included in the inter-hemispheric morphology evaluation, unless being considered out of normative ranges.

Spontaneous brain activity recording and analysis

Spontaneous activity was recorded for three minutes. After a visual inspection data and the application of an artefact rejection procedures [5], the Power Spectral Density (PSD) was estimated for each MEG channel via the Welch procedure (2048 ms duration, Hanning window, 60% overlap, about 180 artefact free trials used). The total PSD was calculated as the mean of the PSDs obtained by the 16 inner gradiometer channels which covered a circular area of about 12 cm diameter. Total signal power was obtained by integrating the PSD value in the 2–44 Hz frequency interval. Spectral properties were investigated in the classical frequency bands [36], instead of being settled on the basis of individual spectral characteristics [33], as spectral properties are known to be affected by stroke. The investigated frequency bands were: 2–3.5 Hz (Delta), 4–7.5 Hz (Theta), 8–12.5 Hz (Alpha), 13–23 Hz (Beta1), 23.5–33 Hz (Beta2), 33.5–44 Hz (Gamma). Relative power spectral density (rPSD) was obtained as the ratio of PSD to total power in the 2–44 Hz frequency range. The relative power within each frequency band was calculated in the same way. All power values, absolute and relative, were log transformed in order to better fit normal distribution for statistical analysis.

Spectral entropy quantifies the complexity of the frequency content, i.e. the spectral shape. It gives a measure of how much a rPSD is fragmented (minimal entropy) or flat (maximal entropy), independently of the total power. For example, a sinusoid is characterized by only one spectral component and has minimal entropy; on the opposite extreme white noise, whose PSD contains all the frequencies with the same weight, has maximal entropy. Entropy is calculated in the 2–44 Hz frequency interval:

$$ -{\sum \limits_{f = 2}^{44}} {\rm rPSD}(f) \log_{2} {\rm rPSD}(f) $$

Since our frequency resolution was 0.49 Hz in a total range 2–44 Hz, the total number of points is 86 (= 42/0.49), resulting in a maximal entropy value of 6.476 (flat spectrum); for the lower limit, we considered one peak whose amplitude was 99% above the basal level, resulting in entropy value of 4.037 (peaked spectrum).

We also defined a hemispheric individual alpha frequency (IAF) as the frequency with maximal PSD in the 6–13 Hz band in each hemisphere [33].

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Tecchio, F., Pasqualetti, P., Zappasodi, F. et al. Outcome prediction in acute monohemispheric stroke via magnetoencephalography. J Neurol 254, 296–305 (2007). https://doi.org/10.1007/s00415-006-0355-0

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