Poster No:
311
Submission Type:
Abstract Submission
Authors:
Yumi Shikauchi1, Ryuta Aoki1,2,4, Takashi Itahashi1, Masaaki Shimizu3, Taiga Naoe1, Tsukasa Okimura1, Haruhisa Ota1, Ryu-ichiro Hashimoto1,4, Motoaki Nakamura1
Institutions:
1Showa University, Setagaya-ku, Tokyo, 2Kyoto University, Sakyo-ku, Kyoto, 3Institute of Science Tokyo, Bunkyo-ku, Tokyo, 4Tokyo Metropolitan University, Hachioji-shi, Tokyo
First Author:
Co-Author(s):
Introduction:
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by symptoms such as social communication difficulties and restricted, repetitive behaviors. Sensory processing abnormalities are common, with individuals often experiencing sensory hypersensitivity and numbness, disrupting daily life through distractions from faint sounds or unrecognized hunger. Despite the significance of sensory issues in ASD, the neural mechanisms remain unclear.
Temporal information processing is crucial for interpreting sensory stimuli over time. MRI provides metrics for studying this in the brain, including intrinsic neural timescale (INT), which reflects the decay of autocorrelation in the BOLD signal, and myelin content, estimated from T1-weighted (T1w)/T2-weighted (T2w) ratio. These measures correspond to neural timescale hierarchies.
This study explores the relationship between sensory traits, INT and myelin content in Japanese adults with ASD and typical development (TD).
Methods:
Participants
The study included two groups: 92 individuals diagnosed with ASD and 127 TD controls. The inclusion criteria for the ASD group were based on a confirmed clinical diagnosis following DSM-5 criteria. Participants provided written informed consent, and this study was approved by the institutional ethics committee of Showa University.
Assessment of Sensory Traits
To evaluate sensory traits, participants completed the Adolescent and Adult Sensory Profile (AASP), a widely used self-report questionnaire. The AASP categorizes sensory behaviors into four subscores: Low Registration, reduced responsiveness to stimuli; Sensation Seeking, active seeking of sensory experiences; Sensory Sensitivity, heightened awareness or reaction to sensory stimuli; Sensation Avoiding, active effort to reduce sensory input.
Neuroimaging Measures
All MRI data were acquired using an MR scanner (a 3T Skyra fit, Siemens Healthcare GmbH, Erlangen, Germany) at Showa University, employing a high-quality scanning protocol, Harmonization Protocol (HARP) (Koike et al. 2021).
Data Analysis
The k-means clustering in a two-dimensional space with t-SNE analyses were performed on the AASP subscores to identify sensory traits. The T1w/T2w ratio was used for myelin content (Glasser and Van Essen 2011). INT was calculated from resting-state fMRI (Raut et al. 2020).
Results:
Clustering participants based on their AASP subscores revealed three distinct sensory profiles: High Low Registration and Sensory Sensitivity, predominantly observed in the ASD group (76.1% of individuals in this cluster were ASD); Low scores across all subscores (indicating minimal sensory processing abnormalities), largely seen in the TD group (82.8% TD); High Sensation Seeking, also largely seen in the TD group (68.4% TD). These findings confirmed that the coexistence of sensory hypersensitivity and sensory numbness is a hallmark feature of the ASD group.
In the ASD group, INT showed a significant negative correlation with Sensation Avoiding across a wide range of brain regions, including the primary sensory areas. In the TD group, INT was positively correlated with Sensation Avoiding, particularly in association areas such as the prefrontal lobe. Myelin content was not significantly correlated in both groups in those sensory areas. These results suggest that the influence of INT on sensory traits is complex, mediated by the presence or absence of ASD, and that further investigation from functional measures is important.
Conclusions:
This study highlights the complex interplay between sensory traits, brain structure, and brain function. The findings underscore the significance of temporal information processing as a key factor in understanding sensory abnormalities. This study contributes to a better understanding of the neural basis of sensory properties in ASD and lays the foundation for future research aimed at improving the quality of life of individuals with ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular
Perception: Tactile/Somatosensory
Perception: Visual
Keywords:
ADULTS
Autism
Data analysis
DISORDERS
FUNCTIONAL MRI
Myelin
Perception
Somatosensory
STRUCTURAL MRI
Vision
1|2Indicates the priority used for review
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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:
Functional MRI
Structural MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
FSL
Free Surfer
Provide references using APA citation style.
Glasser, M.F. and Van Essen, D.C. (2011) ‘Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI’, The Journal of neuroscience: the official journal of the Society for Neuroscience, 31(32), pp. 11597–11616.
Koike, S. et al. (2021) ‘Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan’, NeuroImage: Clinical, 30, p. 102600.
Raut, R.V., Snyder, A.Z. and Raichle, M.E. (2020) ‘Hierarchical dynamics as a macroscopic organizing principle of the human brain’, Proceedings of the National Academy of Sciences of the United States of America, 117(34), pp. 20890–20897.
No