Alterations of Cerebello‑cerebral Functional Connectivity Networks in Autism Spectrum Disorder
Poster No:
332
Submission Type:
Abstract Submission
Authors:
Sheeba Rani Arnold Anteraper1, samuel Joseph2, Elizabeth Valles-Capetillo3, Haley Holm4, Despina Stavrinos3, Rajesh Kana3
Institutions:
1UTSW, Dallas, TX, 2Austin Preparatory School, Reading, MA, 3The University of Alabama at Birmingham, Birmingham, AL, 4Children’s Healthcare of Atlanta, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Evidence for the involvement of cerebellum in autism spectrum disorder (ASD) literature is accumulating [1]. The objective of this study was to investigate the cerebellar networks in ASD using resting-state functional connectivity (RsFc).
Methods:
This study was approved by the University of Alabama at Birmingham IRB.
We used the data from 27 participants (14 ASD, 8 M; 6 F, 19.55±2.28 years, 13 healthy controls [HC], 7 M; 6 F, 21.73±3.66 years) who underwent 3T MRI (Siemens Prisma). Resting state data (2mm voxels) had TR/TE/flip angle of 0.8ms/37ms/52°, multi-band factor 8, and 840 time-points. Anatomical data (0.8 mm voxels) had TR/TE/TI/flip angle of 2400ms/2.22ms/1s/8°.
Selection of cerebellar region of interest (ROI): ROI was selected based on the gradual organization of cerebellar functional regions from motor to task-unfocused regions (gradient 1) [2]. Binary masks of voxels from gradient 1 (top 5% values) as corresponding to default mode network (DMN) was used as the ROI for seed-based RsFc analysis.
Data Analyses: CONN 22.a[3] and SPM12[4].
Preprocessing: A flexible pipeline [5] was used for realignment with correction of susceptibility distortion interactions, outlier detection, direct segmentation and MNI-space normalization, and smoothing (5 mm Gaussian kernel). Outlier scans were identified using ART [6] as acquisitions with framewise displacement above 0.5 mm or global BOLD signal changes above 3 standard deviations [7]. Functional and anatomical data were normalized into standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to 2 mm isotropic voxels following a direct normalization procedure using SPM unified segmentation and normalization [8]. Potential confounding effects characterized by white matter and CSF timeseries, motion parameters and their first order derivatives, outlier scans, session effects and their first order derivatives, and linear trends within each functional run were regressed out, followed by bandpass filtering (0.008-0.09 Hz) of the BOLD timeseries. CompCor [9] noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject's eroded segmentation masks. Pearson's correlation coefficients were generated by computing the correlations between the time series of the ROI and time series of the rest of the voxels in the brain volume. Whole-brain correlation maps from the seed ROI were then computed to generate first-level connectivity measures.
Group-level analyses: Whole-brain seed-to-voxel r-maps were transformed to z-maps (Fisher's r-to-z transform) and voxel wise GLM analysis was conducted on connectivity values for within-group and between-group comparisons. Results for the between group analyses (ASD vs. HC) were thresholded using a cluster-forming p < 0.001 voxel-level threshold, and a false discovery rate corrected p < 0.05 cluster-size threshold.
We used the data from 27 participants (14 ASD, 8 M; 6 F, 19.55±2.28 years, 13 healthy controls [HC], 7 M; 6 F, 21.73±3.66 years) who underwent 3T MRI (Siemens Prisma). Resting state data (2mm voxels) had TR/TE/flip angle of 0.8ms/37ms/52°, multi-band factor 8, and 840 time-points. Anatomical data (0.8 mm voxels) had TR/TE/TI/flip angle of 2400ms/2.22ms/1s/8°.
Selection of cerebellar region of interest (ROI): ROI was selected based on the gradual organization of cerebellar functional regions from motor to task-unfocused regions (gradient 1) [2]. Binary masks of voxels from gradient 1 (top 5% values) as corresponding to default mode network (DMN) was used as the ROI for seed-based RsFc analysis.
Data Analyses: CONN 22.a[3] and SPM12[4].
Preprocessing: A flexible pipeline [5] was used for realignment with correction of susceptibility distortion interactions, outlier detection, direct segmentation and MNI-space normalization, and smoothing (5 mm Gaussian kernel). Outlier scans were identified using ART [6] as acquisitions with framewise displacement above 0.5 mm or global BOLD signal changes above 3 standard deviations [7]. Functional and anatomical data were normalized into standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to 2 mm isotropic voxels following a direct normalization procedure using SPM unified segmentation and normalization [8]. Potential confounding effects characterized by white matter and CSF timeseries, motion parameters and their first order derivatives, outlier scans, session effects and their first order derivatives, and linear trends within each functional run were regressed out, followed by bandpass filtering (0.008-0.09 Hz) of the BOLD timeseries. CompCor [9] noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject's eroded segmentation masks. Pearson's correlation coefficients were generated by computing the correlations between the time series of the ROI and time series of the rest of the voxels in the brain volume. Whole-brain correlation maps from the seed ROI were then computed to generate first-level connectivity measures.
Group-level analyses: Whole-brain seed-to-voxel r-maps were transformed to z-maps (Fisher's r-to-z transform) and voxel wise GLM analysis was conducted on connectivity values for within-group and between-group comparisons. Results for the between group analyses (ASD vs. HC) were thresholded using a cluster-forming p < 0.001 voxel-level threshold, and a false discovery rate corrected p < 0.05 cluster-size threshold.
Results:
RsFc analysis from the cerebellar ROI revealed increased cerebello-cerebral connectivity in the ASD compared to HC, within the brain areas corresponding to DMN (Fig. 1 and Table 1). The two cerebellar clusters (bilateral Crus I and II), and the cerebral clusters (AG and mSTS), overlap with the regions attributed to social cognition/DMN.
Conclusions:
Whole-brain data-driven approaches have previously uncovered cerebellar involvement beyond motor function, influencing broader cognitive functions relevant to ASD [10]. In this study, our exploratory analyses employing cerebellar ROI, leveraging a whole-brain high-spatiotemporal resolution resting-state fMRI dataset demonstrate the alterations of cerebello-cerebral functional connectivity networks in ASD. Cerebellum-inclusive studies have the potential to unleash the power of functional neuroimaging in precision medicine approaches to ASD, providing vital insights into disease mechanisms and biomarker discovery.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
Autism
Cerebellum
Data analysis
FUNCTIONAL MRI
Other - Resting State Functional Connectivity
1|2Indicates the priority used for review
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