Poster No:
1346
Submission Type:
Abstract Submission
Authors:
Ilaria Mazzonetto1, Miriam Celli2, Lorenzo Pini2, Antonio Luigi Bisogno3,2, Silvia Facchini2, Andrea Zangrossi4, Ester Fusaro3, Gianluigi De Nardi2, Giorgia Adamo2, Camillo Porcaro2, Claudio Baracchini5, Anna Maria Basile6, Renzo Manara2, Maurizio Corbetta2,1,3
Institutions:
1Veneto Institute of Molecular Medicine (VIMM), Padova, Italy, 2Department of Neuroscience, University of Padova, Padova, Italy, 3Padova Neuroscience Center, University of Padova, Padova, Italy, 4Department of General Psychology, University of Padova, Padova, Italy, 5Clinica Neurologica Azienda Ospedaliera Università di Padova, Padova, Italy, 6Azienda Ospedaliera Università di Padova, Padova, Italy
First Author:
Co-Author(s):
Miriam Celli
Department of Neuroscience, University of Padova
Padova, Italy
Lorenzo Pini
Department of Neuroscience, University of Padova
Padova, Italy
Antonio Luigi Bisogno
Padova Neuroscience Center, University of Padova|Department of Neuroscience, University of Padova
Padova, Italy|Padova, Italy
Silvia Facchini
Department of Neuroscience, University of Padova
Padova, Italy
Andrea Zangrossi
Department of General Psychology, University of Padova
Padova, Italy
Ester Fusaro
Padova Neuroscience Center, University of Padova
Padova, Italy
Giorgia Adamo
Department of Neuroscience, University of Padova
Padova, Italy
Claudio Baracchini
Clinica Neurologica Azienda Ospedaliera Università di Padova
Padova, Italy
Renzo Manara
Department of Neuroscience, University of Padova
Padova, Italy
Maurizio Corbetta
Department of Neuroscience, University of Padova|Veneto Institute of Molecular Medicine (VIMM)|Padova Neuroscience Center, University of Padova
Padova, Italy|Padova, Italy|Padova, Italy
Introduction:
Based on studies in both animal and human models, it is well established that stroke lesions induce a slowing of electrophysiological signals, with the effect being most pronounced near the lesion site (Gloor et al.1977, Butz et al.2014). Recent findings have further demonstrated that, in healthy sleep-deprived individuals, slow waves in specific cortical regions are associated with region-specific cognitive deficits. This study aimed to characterize the distribution of activity across different frequency bands during the acute stage and to explore whether these changes could serve as a neurophysiological marker of acute post-stroke impairments.
Methods:
We collected high-density EEG data from 54 stroke patients (mean age ± SD: 63 ± 14 years; 20 females) during the acute stage. EEG recordings were performed under resting-state conditions, with 10 minutes of eyes-closed and 10 minutes of eyes-open data acquisition. Structural anatomical images were obtained for each patient to enable lesion segmentation. Behavioral assessments included the NIHSS and OCS tests. EEG data preprocessing followed state-of-the-art methodologies, including band-pass filtering (1–45 Hz), bad channel interpolation, artifact removal using Independent Component Analysis, and average re-referencing. EEG source activity was reconstructed using subject-specific head models that accounted for lesion geometry (Vorwerk et al.2018) and eLORETA algorithm (Pascual-Marqui et al.2011). The mean absolute power in the delta (1–4 Hz), theta (5–8 Hz), alpha (9–12 Hz), and beta (13–30 Hz) bands was calculated for each region of interest (ROI) defined by the Brainnetome atlas (cortical regions) and the SUIT atlas (cerebellar regions) (Fan et al.2016, Diedrichsen 2006). For each patient, we also derived a structural indirect connectivity map. Regions were classified into three categories: (1) "perilesional" regions, intersecting a mask extending 0–2 cm from the lesion border; (2) "structurally disconnected" regions, intersecting the thresholded indirect connectivity map but not the perilesional space; and (3) "unaffected" regions. Differences in EEG power across the frequency bands among these three groups of regions were assessed using a linear mixed-effects model, with subjects included as a random effect. Finally, the relationship between EEG power in the perilesional regions across different frequency bands and behavioral performance was examined using linear regression models. To reduce the number of variables, principal component analysis was applied. Demographic variables, lesion characteristics, and treatment type were included as covariates in the analysis.
Results:
The linear mixed-effects model revealed that absolute power in the delta and theta bands during the eyes-open condition was significantly higher (p<0.05) in both "perilesional" and "structurally disconnected" regions compared to "unaffected" regions. These results remained stable when varying the threshold of the indirect connectivity map between 0.5 and 0.7. No significant differences were observed during the eyes-closed condition. Additionally, we identified a significant relationship (p<0.05) between the third principal component, primarily associated with motor deficits, and the mean power in the delta and theta bands within the perilesional space in both eyes-open and eyes-closed conditions. Specifically, higher delta and theta power were associated with more severe neurological deficits.
Conclusions:
This study effectively measured changes in slow activity within cortical perilesional and structurally disconnected regions using high-density EEG and advanced inverse problem-solving techniques. Furthermore, by establishing a connection between slow activity patterns and cognitive as well as neurological outcomes, this research offers important insights into the mechanisms driving stroke-related deficits and identifies potential biomarkers for evaluating stroke severity and informing rehabilitation strategies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis
Keywords:
Electroencephaolography (EEG)
Source Localization
Other - Stroke
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Structural MRI
Diffusion MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
Gloor et al (1977). Brain lesions that produce delta waves in the EEG. Neurology 27, 326–333.
Butz et al (2014). Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke. Frontiers in Neuroanatomy. 8.
Vorwerk et al (2018). The FieldTrip‐SimBio pipeline for EEG forward solutions. BioMedical Engineering OnLine.
Pascual-Marqui et al (2011). Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 369, 3768–3784. 10.1098/rsta.2011.0081.
Fan et al (2016). The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 26 (8): 3508-3526.
Diedrichsen (2006). A spatially unbiased atlas template of the human cerebellum. Neuroimage, 33, 1, p.127-138
No