John R Pruett, Alexandre A Todorov, Zoë W Hawks, Muhamed Talovic, Tomoyuki Nishino, Steven E Petersen, Savannah Davis, Lyn Stahl, Kelly N Botteron, John N Constantino, Stephen R Dager, Jed T Elison, Annette M Estes, Alan C Evans, Guido Gerig, Jessica B Girault, Heather Hazlett, Leigh MacIntyre, Natasha Marrus, Robert C McKinstry, Juhi Pandey, Robert T Schultz, William D Shannon, Mark D Shen, Abraham Z Snyder, Martin Styner, Jason J Wolff, Lonnie Zwaigenbaum, Joseph Piven
{"title":"Brain functional connectivity correlates of autism diagnosis and familial liability in 24-month-olds.","authors":"John R Pruett, Alexandre A Todorov, Zoë W Hawks, Muhamed Talovic, Tomoyuki Nishino, Steven E Petersen, Savannah Davis, Lyn Stahl, Kelly N Botteron, John N Constantino, Stephen R Dager, Jed T Elison, Annette M Estes, Alan C Evans, Guido Gerig, Jessica B Girault, Heather Hazlett, Leigh MacIntyre, Natasha Marrus, Robert C McKinstry, Juhi Pandey, Robert T Schultz, William D Shannon, Mark D Shen, Abraham Z Snyder, Martin Styner, Jason J Wolff, Lonnie Zwaigenbaum, Joseph Piven","doi":"10.1186/s11689-025-09621-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD.</p><p><strong>Methods: </strong>fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses.</p><p><strong>Results: </strong>OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99%, PPV = 96%, NPV = 100%), based on connectivity involving many networks. SVM accurately classified (non-training) LLN subjects with 100% accuracy. Enrichment analyses identified a cross-group fcMRI difference in the posterior cingulate default mode network 1 (pcDMN1)- temporal default mode network (tDMN) pair (p = 0.0070). Functional connectivity for implicated connections in these networks was consistently lower in HLP and HLN than in LLN (p = 0.0461 and 0.0004). HLP did not differ from HLN (p = 0.2254). Secondary testing showed HL children with low ASD behaviors still differed from LLN (p = 0.0036).</p><p><strong>Conclusions: </strong>24-month-old high-familial-likelihood infants show reduced intra-DMN connectivity, a potential neural finding related to familial liability, while widely distributed functional connections correlate with ASD diagnosis.</p>","PeriodicalId":16530,"journal":{"name":"Journal of Neurodevelopmental Disorders","volume":"17 1","pages":"40"},"PeriodicalIF":4.1000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12275292/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neurodevelopmental Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s11689-025-09621-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD.
Methods: fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses.
Results: OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99%, PPV = 96%, NPV = 100%), based on connectivity involving many networks. SVM accurately classified (non-training) LLN subjects with 100% accuracy. Enrichment analyses identified a cross-group fcMRI difference in the posterior cingulate default mode network 1 (pcDMN1)- temporal default mode network (tDMN) pair (p = 0.0070). Functional connectivity for implicated connections in these networks was consistently lower in HLP and HLN than in LLN (p = 0.0461 and 0.0004). HLP did not differ from HLN (p = 0.2254). Secondary testing showed HL children with low ASD behaviors still differed from LLN (p = 0.0036).
Conclusions: 24-month-old high-familial-likelihood infants show reduced intra-DMN connectivity, a potential neural finding related to familial liability, while widely distributed functional connections correlate with ASD diagnosis.
期刊介绍:
Journal of Neurodevelopmental Disorders is an open access journal that integrates current, cutting-edge research across a number of disciplines, including neurobiology, genetics, cognitive neuroscience, psychiatry and psychology. The journal’s primary focus is on the pathogenesis of neurodevelopmental disorders including autism, fragile X syndrome, tuberous sclerosis, Turner Syndrome, 22q Deletion Syndrome, Prader-Willi and Angelman Syndrome, Williams syndrome, lysosomal storage diseases, dyslexia, specific language impairment and fetal alcohol syndrome. With the discovery of specific genes underlying neurodevelopmental syndromes, the emergence of powerful tools for studying neural circuitry, and the development of new approaches for exploring molecular mechanisms, interdisciplinary research on the pathogenesis of neurodevelopmental disorders is now increasingly common. Journal of Neurodevelopmental Disorders provides a unique venue for researchers interested in comparing and contrasting mechanisms and characteristics related to the pathogenesis of the full range of neurodevelopmental disorders, sharpening our understanding of the etiology and relevant phenotypes of each condition.