Structural Architecture of the Social Brain in Adults with Autism: A Combined Cortical Thickness and Similarity Network Analysis

Öz

Aim: Autism Spectrum Disorder (ASD) involves complex alterations in brain structure that persist across the lifespan. While structural brain alterations are known in children, the persistence of these neuroanatomical differences into adulthood remains less understood. This study examines the neuroanatomical basis of ASD in adulthood, specifically investigating how cortical thickness (CT) and structural similarity networks (SSN) are organized within the social brain network. Materials and Methods: T1-weighted MRI data were obtained for 24 adults with ASD and 24 neurotypical (NT) controls (ages 18–30) from the OpenNeuro dataset (ds002522). Image preprocessing was performed using the recon-all pipeline in FreeSurfer. We investigated CT and SSN at both: (1) the whole-brain, and (2) a hypothesis-driven level targeting 14 specific social brain network regions. CT was assessed using vertex-wise surface-based morphometry, while SSN were constructed using the Morphometric INverse Divergence (MIND) method. MIND quantifies morphological similarities based on the divergence of regional distributions for thickness, volume, surface area, mean curvature, and sulcal depth. Results: The SSN analysis revealed significantly increased nodal connectivity strength in the ASD group within the right posterior insula (p-FDR=0.04) and the orbital part of the right inferior frontal gyrus (p-FDR = 0.04). ROI-based CT comparisons and whole-brain SSN analyses showed no significant group differences. Conclusion: Our findings reveal a neuroanatomical signature in adults with ASD, characterized by localized structural hyper-connectivity within the inferior frontal gyrus and the insula. These results highlight that adult ASD is defined by persistent structural anomalies, manifesting as atypically high structural similarity within key social brain nodes rather than widespread, global network disruptions.

Anahtar Kelimeler

• Autism Spectrum Disorder
• Structural Similarity Networks
• Morphometric INverse Divergence
• Cortical Thickness
• Social Brain

Otizmli Yetişkinlerde Sosyal Beynin Yapısal Mimarisi: Kortikal Kalınlık ve Benzerlik Ağlarının Karşılaştırması

Öz

Otizm Spektrum Bozukluğu (OSB), yaşam boyu devam eden beyin yapısındaki karmaşık değişimleri içerir. Çocuklardaki yapısal beyin değişimleri iyi bilinse de, bu nöroanatomik farklılıkların yetişkinlikte devam edip etmediği iyi bilinmemektedir. Bu çalışma, yetişkinlikteki OSB'nin nöroanatomik temelini, özellikle kortikal kalınlık (KK) ve yapısal benzerlik ağlarının (YBA) sosyal beyin ağı içinde nasıl organize olduğunu araştırmaktadır. OpenNeuro veri setinden (ds002522) 24 OSB'li yetişkin ve 24 nörotipik (NT) kontrol grubuna (18-30 yaş arası) ait T1 ağırlıklı MRG verileri alınmıştır. Görüntü ön işleme süreçleri FreeSurfer'daki recon-all prosedürü kullanılarak gerçekleştirilmiştir. KK ve YBA değerleri (1) tüm beyin seviyesinde ve (2) 14 sosyal beyin ağı ilgi bölgesini hedefleyen hipotez odaklı seviyede incelenmiştir. KK, verteks tabanlı yüzey morfolojisi kullanılarak değerlendirilirken; YBA Morfometrik Ters Iraksama (Morphometric INverse Divergence, MIND) yöntemiyle hesaplanmıştır. MIND; kalınlık, hacim, yüzey alanı, ortalama eğrilik ve sulkal derinlik gibi bölgesel dağılımların ıraksamasına dayanarak morfolojik benzerlikleri nicelleştirmektedir. YBA analizi, OSB grubunda sağ posterior insula (p-FDR=0.04) ve sağ inferior frontal girusun orbital kısmında (p-FDR=0.04) düğüm bağlantı gücünün anlamlı derecede arttığını ortaya koymuştur. İlgi bölgesi temelli KK karşılaştırmaları ve tüm beyin YBA analizleri gruplar arasında anlamlı bir fark göstermemiştir. Bulgularımız, OSB'li yetişkinlerde inferior frontal girus ve insula içinde lokalize yapısal hiper-bağlantısallık ile karakterize bir nöroanatomik profil ortaya koymaktadır. Bu sonuçlar, yetişkinlikteki OSB'nin yaygın küresel ağ bozulmalarından ziyade, temel sosyal beyin düğümleri içinde atipik düzeyde yüksek yapısal benzerlik şeklinde tezahür eden kalıcı yapısal anomalilerle tanımlandığını öne sürmektedir.

Anahtar Kelimeler

• Otizm Spektrum Bozukluğu
• Yapısal Benzerlik Ağları
• Morfometrik Ters Iraksama
• Kortikal Kalınlık
• Sosyal Beyin

Kaynakça

1. 1. Association AP. Diagnostic and statistical manual of mental disorders: American psychiatric association; 2013.
2. 2. Kadak MT, Meral Y. Autism Spectrum Disorders - What is our current knowledge? Compreh Med. 2019;11(50):5-15.
3. 3. Courchesne E, Carper R, Akshoomoff N. Evidence of Brain Overgrowth in the First Year of Life in Autism. JAMA. 2003;290(3):337-44.
4. 4. Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, et al. Early Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years. Archives of General Psychiatry. 2011;68(5):467-76.
5. 5. Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and Anatomical Cortical Underconnectivity in Autism: Evidence from an fMRI Study of an Executive Function Task and Corpus Callosum Morphometry. Cerebral Cortex. 2006;17(4):951-61.
6. 6. Vissers ME, X Cohen M, Geurts HM. Brain connectivity and high functioning autism: A promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neuroscience & Biobehavioral Reviews. 2012;36(1):604-25.
7. 7. Ecker C, Bookheimer SY, Murphy DGM. Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. The Lancet Neurology. 2015;14(11):1121-34.
8. 8. Raznahan A, Toro R, Daly E, Robertson D, Murphy C, Deeley Q, et al. Cortical Anatomy in Autism Spectrum Disorder: An In Vivo MRI Study on the Effect of Age. Cerebral Cortex. 2009;20(6):1332-40.

9. 9. Wolff JJ, Jacob S, Elison JT. The journey to autism: Insights from neuroimaging studies of infants and toddlers. Development and Psychopathology. 2018;30(2):479-95.
10. 10. Zwaigenbaum L, Bryson S, Rogers T, Roberts W, Brian J, Szatmari P. Behavioral manifestations of autism in the first year of life. International Journal of Developmental Neuroscience. 2005;23(2):143-52.
11. 11. Lange N, Travers BG, Bigler ED, Prigge MB, Froehlich AL, Nielsen JA, et al. Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years. Autism Research. 2015;8(1):82-93.
12. 12. Yang X, Si T, Gong Q, Qiu L, Jia Z, Zhou M, et al. Brain gray matter alterations and associated demographic profiles in adults with autism spectrum disorder: A meta-analysis of voxel-based morphometry studies. Australian & New Zealand Journal of Psychiatry. 2016;50(8):741-53.
13. 13. Ecker C, Ginestet C, Feng Y, Johnston P, Lombardo MV, Lai M-C, et al. Brain Surface Anatomy in Adults With Autism: The Relationship Between Surface Area, Cortical Thickness, and Autistic Symptoms. JAMA Psychiatry. 2013;70(1):59-70.
14. 14. Doyle-Thomas KAR, Duerden EG, Taylor MJ, Lerch JP, Soorya LV, Wang AT, et al. Effects of age and symptomatology on cortical thickness in autism spectrum disorders. Research in Autism Spectrum Disorders. 2013;7(1):141-50.
15. 15. Sebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, et al. Robust estimation of cortical similarity networks from brain MRI. Nature Neuroscience. 2023;26(8):1461-71.
16. 16. Wang J, He Y. Toward individualized connectomes of brain morphology. Trends in Neurosciences. 2024;47(2):106-19.
17. 17. Sebenius I, Dorfschmidt L, Seidlitz J, Alexander-Bloch A, Morgan SE, Bullmore E. Structural MRI of brain similarity networks. Nature Reviews Neuroscience. 2025;26(1):42-59.
18. 18. Dong H, Wang M, Wang Y, Ma X, Wan H, Dong G, et al. Morphological inverse divergence reveals enhanced visual-attention structural similarity in internet gaming disorder. Addictive Behaviors. 2025;170:108437.
19. 19. Yu Y, He H, Yang R, Yang L, Liu Y, Yao D, et al. Shared and distinct patterns of cortical morphometric inverse divergence and their association with empathy in dancers and musicians. Scientific Reports. 2025;15(1):28572.
20. 20. Tamar Kolodny M-PS, and Scott O. Murray. Contrast Response Functions. OpenNeuro2020. 21. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of autism and developmental disorders. 1994;24(5):659-85.
21. 22. Hus V, Lord C. The autism diagnostic observation schedule, module 4: revised algorithm and standardized severity scores. Journal of autism and developmental disorders. 2014;44(8):1996-2012.
22. 23. Kolodny T, Schallmo M-P, Gerdts J, Bernier RA, Murray SO. Response Dissociation in Hierarchical Cortical Circuits: a Unique Feature of Autism Spectrum Disorder. The Journal of Neuroscience. 2020;40(11):2269-81.
23. 24. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences. 2000;97(20):11050-5.
24. 25. Patriquin MA, DeRamus T, Libero LE, Laird A, Kana RK. Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder. Human brain mapping. 2016;37(11):3957-78.
25. 26. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289-300.
26. 27. Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, et al. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron. 2018;97(1):231-47.e7.
27. 28. Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, et al. Salience network–based classification and prediction of symptom severity in children with autism. JAMA psychiatry. 2013;70(8).
28. 29. Kana RK, Maximo JO, Williams DL, Keller TA, Schipul SE, Cherkassky VL, et al. Aberrant functioning of the theory-of-mind network in children and adolescents with autism. Molecular autism. 2015;6(1):59.
29. 30. Barnes J, Ridgway GR, Bartlett J, Henley SM, Lehmann M, Hobbs N, et al. Head size, age and gender adjustment in MRI studies: a necessary nuisance? Neuroimage. 2010;53(4):1244-55.
30. 31. Wierenga LM, Langen M, Oranje B, Durston S. Unique developmental trajectories of cortical thickness and surface area. NeuroImage. 2014;87:120-6.