Differences in brain networks between ADHD and ASD using a novel metric in resting-state fMRI
Poster No:
288
Submission Type:
Abstract Submission
Authors:
Satoru Kohno1, Taisei Kashiwa1, Kyohei Maekawa1, Yuki Matsumoto1, Tatsuo Mori1, Masafumi Harada1
Institutions:
1Institute of Biomedical Sciences, Tokushima University, Tokushima, Tokushima
First Author:
Co-Author(s):
Introduction:
Recently, some voxel-to-voxel connectome analyses of resting state functional MRI (rfMRI) have provided promising methods that does not require a priori assumption such as region of interest (ROI) setting to explore the neural network mechanisms. Of these, radial correlation contrast (RCC) characterizes the spatial asymmetry of the local connectivity pattern between each voxel and its neighbors, and it has been reported that RCC can detect neuronal firing in animal studies (Goelman, 2004). However, human applications of RCC had not yet been reported before our group reported that applying RCC to dystonia patients detects its network abnormalities with high sensitivity (Kohno et al., 2021). To further improve the sensitivity of RCC, we implemented a novel RCC in polar coordinates and the RCC revealed abnormal brain network activities in prodromal Parkinson's Disease that could not be found in other local brain network metrics (Maekawa et al. 2023).
Neuroimaging studies have shown that attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with abnormalities in resting-state brain functional connectivity (Di Martino A et al. 2013, Jung M et al. 2019). For instance, compared to typical development, ADHD patients exhibit disrupted functional connectivity patterns in brain regions involved in attention and sensory processing, while ASD patients show increased resting-state functional connectivity in the posterior cingulate cortex and salience network, with the strength of this connectivity being linked to the severity of social interaction deficits. However, many aspects of the mechanisms underlying the comorbidity and distinctions between the two disorders remain unclear. It is believed that resting-state fMRI techniques with high sensitivity are essential for elucidating the mechanisms that differentiate these disorders. This study aimed to investigate whether the RCC method, compared to the other metrics, can detect differences in local brain networks between ADHD and ASD with high sensitivity and validity.
Neuroimaging studies have shown that attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with abnormalities in resting-state brain functional connectivity (Di Martino A et al. 2013, Jung M et al. 2019). For instance, compared to typical development, ADHD patients exhibit disrupted functional connectivity patterns in brain regions involved in attention and sensory processing, while ASD patients show increased resting-state functional connectivity in the posterior cingulate cortex and salience network, with the strength of this connectivity being linked to the severity of social interaction deficits. However, many aspects of the mechanisms underlying the comorbidity and distinctions between the two disorders remain unclear. It is believed that resting-state fMRI techniques with high sensitivity are essential for elucidating the mechanisms that differentiate these disorders. This study aimed to investigate whether the RCC method, compared to the other metrics, can detect differences in local brain networks between ADHD and ASD with high sensitivity and validity.
Methods:
Eleven participants (ADHD: N = 4, age = 7.9 ± 1.3; ASD: N = 7, age = 6.2 ± 5.5,) were analyzed. The rfMRI data were acquired with a 3T MRI (Discovery MR750 GE Healthcare) using a gradient-echo EPI (TR/TE = 2000/27 ms, pixel size = 3.75 × 3.75 × 3 mm^3 and number of volumes = 235). During the fMRI scan, participants were instructed to rest quietly with their eyes closed and not fall asleep. Additionally, structure images (IR-SPGR: TR/TE/TI = 8.6/3.5/400 ms) were acquired for anatomical reference and spatial normalization. Structural and functional images were preprocessed and analyzed using the CONN toolbox ver. 21a (https://web.conn-toolbox.org). We statistically evaluated the differences in brain networks between ADHD and ASD using the polar coordinate-based RCC, the conventional orthogonal coordinate-based RCC, fractional amplitude of low-frequency fluctuations (fALFF), and intrinsic connectivity (IC) (voxel p < 0.01, cluster FDR q < 0.05).
Results:
The polar coordinate-based RCC detected the largest number of local brain network cluster regions (precentral gyrus, cerebellum, superior frontal gyrus, middle frontal gyrus, precuneus, caudate, amygdala and orbitofrontal cortex, etc.) with statistical significance between ADHD and ASD, compared to other network metrics (the number of clusters: polar RCC: 19, orthogonal RCC: 5, fALFF: 3 and ICC: 5) (Fig.1). Many of these brain regions were associated with the functional abnormality networks of ADHD and ASD that have been previously reported.
·Fig. 1. Differences in brain networks between ADHD and ASD identified by the polar coordinate-based RCC.
Conclusions:
We succeeded to detect differences in local brain between ADHD and ASD with high sensitivity and validity using the polar coordinate-based RCC. Our results indicate that the polar coordinate-based RCC may be an imaging biomarker for detecting differences in brain networks between ADSD and ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Attention Deficit Disorder
Autism
Data analysis
FUNCTIONAL MRI
Modeling
1|2Indicates the priority used for review
Abstract Information
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.
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:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Kohno S, Sato D, Sumida N, Matsumoto Y, Harada M and Fujita K, Radial Correlation and Radial Similarity Contrast Reveal Abnormal Brain Networks in Dystonia. 27th Annual Meeting of The Organization for Human Brain Mapping, Jun. 2021.
3. Maekawa K, Kohno S, Matsumoto Y, Harada M and Fujita K, Imaging biomarker in Prodromal Parkinson’s Disease using a novel network index of resting-state fMRI. 29th Annual Meeting of The Organization for Human Brain Mapping, Jul. 2023.
4. Di Martino A, Zuo X-N, Kelly C, Grzadzinski R, Mennes M, Schvarcz A, et al. Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biol Psychiatry 2013; 74: 623–32.
5. Jung M, Tu Y, Park J, Jorgenson K, Lang C, Song W, et al. Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. Brit J Psych. 2019;214(6):339-44.