Perturbation of intrinsic oscillatory modes by ischemic stroke in rats
Poster No:
1728
Submission Type:
Abstract Submission
Authors:
Rita Alves1, Joana Cabral1,2, Noam Shemesh1
Institutions:
1Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal, 2Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
First Author:
Co-Author(s):
Joana Cabral
Champalimaud Research, Champalimaud Foundation|Life and Health Sciences Research Institute, University of Minho
Lisbon, Portugal|Braga, Portugal
Champalimaud Research, Champalimaud Foundation|Life and Health Sciences Research Institute, University of Minho
Lisbon, Portugal|Braga, Portugal
Introduction:
Disruption of functional networks is a hallmark of numerous brain disorders (Fornito, 2015). In stroke, loss of interhemispheric functional connectivity (FC) has been reported at the acute stages, possibly followed by renormalization (van Meer, 2010). However, the biophysical mechanisms underlying these changes in FC remain to be elucidated. Recently, a repertoire of intrinsic oscillatory modes exhibiting stationary wave pattern features were discovered in healthy animal brains (Cabral, 2023) and humans (Pang, 2023), and provided insight into the organizing principles underpinning spontaneous long-range FC. Here, we investigated how disrupting a specific and well-localized cortical area modulates these intrinsic modes.
Methods:
All experiments received proper ethical approvement.
Stroke induction: A photothrombotic stroke (Watson, 1985) was induced unilaterally in Long-Evan rats. N = 10 stroked animals were imaged 1w poststroke along with N = 10 healthy controls (under medetomidine).
MRI: Ultra-fast rs-fMRI data were acquired with a GE-EPI sequence in a 9.4T scanner: TR/TE = 90/16ms, FOV = 21x21mm2, resolution = 250x250µm2, slice = 1.2mm, tacq = 24min, flip angle = 20°, Nreps = 16000, 2 scans per animal, BW = 277kHz axial and coronal.
Data analysis: The first 2000 frames were removed due to significant gradient temperature drift during this time.
Correlation analyses: fMRI signals within the brain mask were bandpass filtered between 0.1-0.3Hz, and ROI seed-based correlation maps were performed (Pearson's).
Spectral analysis of data: Power spectra were computed voxelwise, after aligning (Guizar-Sicairos, 2008) and detrending. Spectral power was taken as the integral under the peaks in each bin.
Extracting intrinsic modes from Principle Component Analysis: For each scan, the fMRI signals in N = 1263 (coronal) and N = 2007 (axial) brain voxels were band-pass filtered (0.01-0.3Hz) and the N×N covariance matrix was computed and averaged across the 20 scans in each group. The first 10 eigen vectors were extracted and mapped.
Stroke induction: A photothrombotic stroke (Watson, 1985) was induced unilaterally in Long-Evan rats. N = 10 stroked animals were imaged 1w poststroke along with N = 10 healthy controls (under medetomidine).
MRI: Ultra-fast rs-fMRI data were acquired with a GE-EPI sequence in a 9.4T scanner: TR/TE = 90/16ms, FOV = 21x21mm2, resolution = 250x250µm2, slice = 1.2mm, tacq = 24min, flip angle = 20°, Nreps = 16000, 2 scans per animal, BW = 277kHz axial and coronal.
Data analysis: The first 2000 frames were removed due to significant gradient temperature drift during this time.
Correlation analyses: fMRI signals within the brain mask were bandpass filtered between 0.1-0.3Hz, and ROI seed-based correlation maps were performed (Pearson's).
Spectral analysis of data: Power spectra were computed voxelwise, after aligning (Guizar-Sicairos, 2008) and detrending. Spectral power was taken as the integral under the peaks in each bin.
Extracting intrinsic modes from Principle Component Analysis: For each scan, the fMRI signals in N = 1263 (coronal) and N = 2007 (axial) brain voxels were band-pass filtered (0.01-0.3Hz) and the N×N covariance matrix was computed and averaged across the 20 scans in each group. The first 10 eigen vectors were extracted and mapped.
Results:
Fig.1C-D shows a conventional seed-based functional connectivity analysis. The healthy group seeds show higher degree of correlation compared to the stroke group. Particularly, when the contralesional cortex served as a seed, the stroked area showed nearly no connectivity (ROI 1). A more distant contralateral cortical area exhibited low correlation values (ROI 2). Finally, the contralesional striatum showed relatively stronger interhemispheric correlation to ROI 3 in stroke compared to the healthy group. In Fig 1D, the axial slices revealed similar trends, with stronger cortico-cortical correlations in healthy controls, and weaker correlations near the lesion.
Fig.2A-D show the averaged spatial maps of spectral power in 7 frequency bands (0.05-0.4Hz). Higher power was observed in the cortex in the healthy group only until 0.2 Hz. Figure 2E shows the first 10 intrinsic modes detected for the coronal slice. Modes were similar between animals in every group but their spatial features were clearly different between groups. For instance, ψ1 and ψ2 show strong cortical oscillations in the healthy controls, while in stroke the oscillations strongly involve striatal areas.
Figure 2F shows the first 10 intrinsic modes for the axial slice. Multiple spatial wave patterns are disturbed due to the lesion.
Fig.2A-D show the averaged spatial maps of spectral power in 7 frequency bands (0.05-0.4Hz). Higher power was observed in the cortex in the healthy group only until 0.2 Hz. Figure 2E shows the first 10 intrinsic modes detected for the coronal slice. Modes were similar between animals in every group but their spatial features were clearly different between groups. For instance, ψ1 and ψ2 show strong cortical oscillations in the healthy controls, while in stroke the oscillations strongly involve striatal areas.
Figure 2F shows the first 10 intrinsic modes for the axial slice. Multiple spatial wave patterns are disturbed due to the lesion.
Conclusions:
Our findings likely reflect reorganization of activity in networks in the stroked brain. In ischemia, weaker FC is observed in the cortico-cortical network. Interestingly, there is an increase in inter-hemispheric striatal connectivity, in line with the stronger oscillatory patterns observed in the striatal areas. The inter-hemispheric asymmetry observed in both axial and coronal slices in the stroke group's intrinsic modes may indicate that the contralesional hemisphere is more involved in compensatory mechanisms for the connection breakdown driven by the lesion.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Cerebrovascular Disease
Cortex
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
HIGH FIELD MR
MRI
1|2Indicates the priority used for review
Provide references using author date format
Fornito, A. (2015), 'The connectomics of brain disorders', Nature Reviews Neuroscience, vol. 16, no.3, pp.159–172
Guizar-Sicairos, M. (2008), ‘Efficient subpixel registration’, Optics Letters, vol. 33, pp. 156–158
Pang, J. (2023), ‘Geometric constraints on human brain function’, Nature, vol. 618, pp. 566–574
van Meer, M. P. (2010), ‘Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity’, The Journal of Neuroscience, vol. 30, pp. 3964–3972
Watson, B.D. (1985), ‘Induction of reproducible brain infarction by photochemically initiated thrombosis’, Annals of Neurology., vol. 17, no. 5, pp. 497–504