Significance of multiple neurophysiological measures in patients with chronic disorders of consciousness

Sebastiano, Davide Rossi; Panzica, Ferruccio; Visani, Elisa; Rotondi, Fabio; Scaioli, V.; Leonardi, Matilde; Sattin, Davide; D’Incerti, Ludovico; Parati, Eugenio; Ferini‐Strambi, Luigi; Franceschetti, Silvana

doi:10.1016/j.clinph.2014.07.004

Cited by 72 publications

(72 citation statements)

References 36 publications

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“…They usually hallmark states like deep sleep or pathological states with associated absence of conscious awareness. Previous research suggested UWS and MCS patients to differ in terms of spectral power, with more pronounced lower or higher frequencies in UWS and MCS patients, respectively 8,19,21–25 . Our results add to these findings by indicating that the reactivity of these EEG frequencies to a circadian Zeitgeber, namely light, is also different across clinical entities.…”

Section: Discussionmentioning

confidence: 95%

Night and day variations of sleep in patients with disorders of consciousness

Wisłowska

Giudice

Lechinger

et al. 2017

Sci Rep

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Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded ~24 h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.

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Section: Discussionmentioning

confidence: 95%

Night and day variations of sleep in patients with disorders of consciousness

Wisłowska

Giudice

Lechinger

et al. 2017

Sci Rep

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“…The assessment included (i) clinical evaluation with the CRS-R 24 , (ii) a multiple neurophysiological evaluation including a long-lasting EEG, and (iii) neuroradiological assessment including a structural T1-weighted MRI. The patient cohort was also described in previous works of our group [25][26][27][28] . The acquisition of both EEG and structural MRI (T1-weighted) datasets, constituted the inclusion criteria for each patient.…”

Section: Participantsmentioning

confidence: 99%

“…Then, the selected EEG activity was analyzed by dividing it into 90 non-overlapping 2 s segments. Absolute total power and relative power were evaluated in the delta (1-4 Hz), theta (4-8 Hz), alpha(8)(9)(10)(11)(12)(13), beta(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (>30 Hz) bands, and averaged within each EEG channel.MRI data acquisition and processingNeuroimaging data were obtained with a 3T MR scanner (Achieva, Philips Healthcare BV, Best, NL) equipped with a 32-channel head coil. The MRI protocol comprised a high resolution 3D-TFE T1-weighted sequence (185 sagittal slices, TR = 9.781 ms, TE = 4.6 ms, FOV = 240 × 240 mm 2 , voxel size = 1 × 1 × 1 mm 3 , flip angle = 8 • ).…”

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confidence: 99%

EEG power spectra and subcortical pathology in chronic disorders of consciousness

Lutkenhoff

Nigri

Sebastiano

et al. 2019

Preprint

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Objective:To determine (i) the association between long-term impairment of consciousness after severe brain injury, spontaneous brain oscillations, and underlying subcortical damage, and (ii) whether such data can be used to aid patient diagnosis, a process known to be susceptible to high rates of error.Methods: Cross-sectional observational sample of 116 patients with an acquired disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018.Results: Systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in brainstem. Furthermore, we showed that the combination of behavioral, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis.Interpretation: These results associate, for the first time, electroencephalographic presentation, as detected with routine clinical techniquesthus grounding them in the underlying brain pathology of disorders of consciousnessand demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of patients in this cohort.

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“…Linking these behavioral markers with specific injury attributes remains a persistent clinical challenge (McGee et al, 2016). Electroencephalographic recordings offer the potential to help to disassociate underlying causes of DOC by revealing systematic electrophysiological correlates of injury and behavior (Sitt et al, 2014; Sebastiano et al, 2015). However, patients in coma exhibit substantial heterogeneity in etiology, including injury type and location.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, approaches involving direct (e.g., electromagnetic) stimulation of the cortex (Bagnato et al, 2012; Rosanova et al, 2012; Casali et al, 2013; Zhang et al, 2017), while conceptually more favorable, raise the instrumentation burden substantially and are thus not ideal for routine clinical assessment. More comprehensive reviews of the previous findings in detecting signs of consciousness using electrophysiological methods can be found in (Sitt et al, 2014; Rapp et al, 2015; Sebastiano et al, 2015; Cruse et al, 2016; Estraneo et al, 2016; Ragazzoni et al, 2017). …”

Section: Introductionmentioning

confidence: 99%