TY - GEN
T1 - Age-Stratified Differences in Morphological Connectivity Patterns in ASD
T2 - 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
AU - Manoj, Gokul
AU - Saini, Pranay
AU - Ratnaik, Ravi
AU - Sengar, Sandeep Singh
AU - Ganapathy, Nagarajan
AU - Pa, Karthick
AU - Agastinose Ronickom, Jac Fredo
PY - 2025/12/3
Y1 - 2025/12/3
N2 - Autism spectrum disorder (ASD) is one of the most common neurological disorders, and its early detection is extremely difficult. Researchers use different physiological and medical imaging signals to diagnose ASD based on the severity level and the age of the patient. In this study, morphological features (MF) and morphological connectivity features (MCF) are used to investigate the influence of age on the diagnosis of autism spectrum disorders (ASD). In this work, we have utilized structural magnetic resonance imaging (sMRI) data from ABIDE-I and ABIDE-II databases, divided into 6-11, 11-18, and 6-18 age groups, were pre processed and yielded 592 MF and 10,878 MCF per subject. As a result, the 6-11 age group outperformed the others in both feature types, especially in MCF, with a random forest (RF) classifier achieving 75.8% accuracy, 83.1% F1 score, 86% recall, and 80.4% precision, respectively. Based on this, it can be concluded that an age-specific morphological connectivity approach holds promise for effective diagnosis of autism spectrum disorders.
AB - Autism spectrum disorder (ASD) is one of the most common neurological disorders, and its early detection is extremely difficult. Researchers use different physiological and medical imaging signals to diagnose ASD based on the severity level and the age of the patient. In this study, morphological features (MF) and morphological connectivity features (MCF) are used to investigate the influence of age on the diagnosis of autism spectrum disorders (ASD). In this work, we have utilized structural magnetic resonance imaging (sMRI) data from ABIDE-I and ABIDE-II databases, divided into 6-11, 11-18, and 6-18 age groups, were pre processed and yielded 592 MF and 10,878 MCF per subject. As a result, the 6-11 age group outperformed the others in both feature types, especially in MCF, with a random forest (RF) classifier achieving 75.8% accuracy, 83.1% F1 score, 86% recall, and 80.4% precision, respectively. Based on this, it can be concluded that an age-specific morphological connectivity approach holds promise for effective diagnosis of autism spectrum disorders.
KW - Adolescent
KW - Age Factors
KW - Autism Spectrum Disorder/diagnostic imaging
KW - Brain/pathology
KW - Child
KW - Female
KW - Humans
KW - Machine Learning
KW - Magnetic Resonance Imaging/methods
KW - Male
UR - https://www.scopus.com/pages/publications/105023715883
U2 - 10.1109/EMBC58623.2025.11252956
DO - 10.1109/EMBC58623.2025.11252956
M3 - Conference contribution
C2 - 41336018
AN - SCOPUS:105023715883
SN - 9798331586195
VL - 2025
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
SP - 1
EP - 5
BT - 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 July 2025 through 18 July 2025
ER -