Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Abstract

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) methods have long been used for exploring brain electrical activity and functional connectivity modifications after stroke. However, results obtained so far are not univocal. Here, we used basic and advanced EEG methods to characterize how brain activity and functional connectivity change after stroke. Thirty-three unilateral post stroke patients in the sub-acute phase and ten neurologically intact age-matched right-handed subjects were enrolled. Patients were subdivided into two groups based on lesion location: cortico-subcortical (CS, n = 18) and subcortical (S, n = 15), respectively. Stroke patients were evaluated in the period ranging from 45 days since the acute event (T0) up to 3 months after stroke (T1) with both neurophysiological (resting state EEG) and clinical assessment (Barthel Index, BI) measures, while healthy subjects were evaluated once. Brain power at T0 was similar between the two groups of patients in all frequency bands considered (δ, θ, α, and β). However, evolution of θ-band power over time was different, with a normalization only in the CS group. Instead, average connectivity and specific network measures (Integration, Segregation, and Small-worldness) in the β-band at T0 were significantly different between the two groups. The connectivity and network measures at T0 also appear to have a predictive role in functional recovery (BI T1-T0), again group-dependent. The results obtained in this study showed that connectivity measures and correlations between EEG features and recovery depend on lesion location. These data, if confirmed in further studies, on the one hand could explain the heterogeneity of results so far observed in previous studies, on the other hand they could be used by researchers as biomarkers predicting spontaneous recovery, to select homogenous groups of patients for the inclusion in clinical trials.

The Effect of dry Needling in Chronic Stroke with a complex Network Approach: A Case Study

“…[36][37][38][39] The results on the delta, theta, and alpha bands showed that the small-worldness variations in the patient with stroke were greater than those in the healthy control, which is consistent with the previous a reports for the theta band. 40 Accordingly, Caliandro et al 32 found an increased segregation and a decreased integration in θ-band network consistent with a previous fMRI study. 41 This study has strength and limitations.…”

Spectral exponent assessment and neurofilament light chain: a comprehensive approach to describe recovery patterns in stroke

“…Regarding quantitative EEG measures, we confirmed our previous findings ( 34 ), showing significant hemispheric SE asymmetry at T1 and a reduction of AH SE values from T1 to T2. Likewise, SE showed a marginally significant reduction in hemispheric asymmetry of from T1 to T2.…”

Resting State EEG Directed Functional Connectivity Unveils Changes in Motor Network Organization in Subacute Stroke Patients After Rehabilitation

“…Moreover, the high inter-individual variability in lesion location can represent a relevant factor in the significant founding of our population results. In fact, previous studies already reported that the size and location of the lesion, especially if this appears in cortical or sub-cortical brain regions, may influence the reorganization of resting-state networks in stroke recovery ( Fanciullacci et al, 2021 ). Moreover, we want to remark that the signal pre-processing steps and the choice of analysis parameters, e.g., de-noising filters, time-series window length, MVAR model order, electrode or source space, can affect the reproducibility of connectivity results.…”

“…Moreover, the majority of studies exploited fMRI to assess functional, non-directed connectivity ( Desowska and Turner, 2019 ). Considering EEG analysis, up to now, the majority of studies explored functional connectivity in post-stroke populations in both acute ( Caliandro et al, 2017 ; Vecchio et al, 2019a , 2019b ; Fanciullacci et al, 2021 ; Hoshino et al, 2021 ) and chronic stages ( Gerloff et al, 2006 ; De Vico Fallani et al, 2009 , 2017 ; Wu et al, 2015 ; Hordacre et al, 2020 ; Molteni et al, 2020 ; Waterstone et al, 2020 ; Romeo et al, 2021 ); only fewer studies investigated the resting-state causal connectivity after stroke ( Guo et al, 2014 ; Pichiorri et al, 2015 , 2018 ; Calabrò et al, 2018 ; Nicolo et al, 2018 ; Lin et al, 2021 ; Yuan et al, 2021 ). The few studies cited above employing EEG rarely focused on the investigation of the motor network, preferring a large-scale connectivity analysis of the whole brain with synthetic descriptors.…”