1
|
Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med 2024:1-12. [PMID: 38563297 DOI: 10.1017/s0033291724000138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.
Collapse
Affiliation(s)
- Anhang Jiang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Shuang Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| | - Shizhen Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Mei Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Center for Mental Health Education and Counselling, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| |
Collapse
|
2
|
Yang C, Wang XK, Ma SZ, Lee NY, Zhang QR, Dong WQ, Zang YF, Yuan LX. Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study. J Affect Disord 2024; 347:608-618. [PMID: 38070748 DOI: 10.1016/j.jad.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet. METHODS The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs. RESULTS For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula. CONCLUSIONS Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.
Collapse
Affiliation(s)
- Chen Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Xing-Ke Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Sheng-Zhi Ma
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Nathan Yee Lee
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Qiu-Rong Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Wen-Qiang Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China; TMS Center, Hangzhou Normal University Affiliated Deqing Hospital, Huzhou, China
| | - Li-Xia Yuan
- School of Physics, Zhejiang University, Hangzhou, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China.
| |
Collapse
|
3
|
Ma X, Zhou W, Zheng H, Ye S, Yang B, Wang L, Wang M, Dong GH. Connectome-based prediction of the severity of autism spectrum disorder. PSYCHORADIOLOGY 2023; 3:kkad027. [PMID: 38666105 PMCID: PMC10917386 DOI: 10.1093/psyrad/kkad027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/14/2023] [Accepted: 11/23/2023] [Indexed: 04/28/2024]
Abstract
Background Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD. Objective This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD. Methods Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results. Results The CPM successfully identified negative networks that significantly predicted ADOS total scores [r (df = 150) = 0.19, P = 0.008 in all patients; r (df = 104) = 0.20, P = 0.040 in classic autism] and communication scores [r (df = 150) = 0.22, P = 0.010 in all patients; r (df = 104) = 0.21, P = 0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles. Conclusions A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.
Collapse
Affiliation(s)
- Xuefeng Ma
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province 650500, China
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
| | - Weiran Zhou
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Shuer Ye
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Bo Yang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
| | - Min Wang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province 650500, China
| | - Guang-Heng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province 650500, China
| |
Collapse
|