Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model

Shan, Xiaolong; Uddin, Lucina Q.; Xiao, Jinming; He, Changchun; Ling, Zihan; Li, Lei; Huang, Xiaoping; Chen, Huafu; Duan, Xujun

doi:10.1016/j.biopsych.2022.01.011

Cited by 35 publications

(26 citation statements)

References 72 publications

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“…This approach has been increasingly used to map variations between demographic, cognitive, clinical, or behavioral variables and quantitative brain readouts derived from neuroimaging (such as brain volume (Marquand et al, 2019, Wolfers et al, 2020, Ziegler et al, 2014), cortical thickness (Bethlehem et al, 2018; Zabihi et al, 2019), brain activity derived from task fMRI (Marquand et al, 2016) and rsFC (Kessler et al, 2016)), providing statistical inferences at the individual level based on the extent to which each individual deviate from the normative range. Importantly, previous multisite studies demonstrated stability and robustness of normal models across the life span (Bethlehem et al, 2022; Shan et al, 2022). However, measurement using in the current study might not be able to effectively capture the life‐span developmental changes in ASD, and a potential reason might be the state‐dependent nature of the moment‐to moment activity cofluctuations in high‐amplitude frames.…”

Section: Discussionmentioning

confidence: 89%

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Zheng

et al. 2022

Human Brain Mapping

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Resting‐state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently‐proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment‐to‐moment‐activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting‐state functional magnetic resonance imaging (rs‐fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high‐amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.

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Section: Discussionmentioning

confidence: 89%

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Zheng

et al. 2022

Human Brain Mapping

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“…Importantly, the model can recognize all sources of variance and reduce overly optimistic inferences and thus obtain more accurate and patient-specific individual deviations for patients. Given its great advantage, the normative model has recently been used to characterize the individual abnormalities and intersubject differences in neuroimaging features in multiple psychiatric disorders, such as autism [22-24], attention deficit/hyperactivity disorder [25], and schizophrenia [26]. Here, based on the normative model, our study investigated the individual FCS deviations for each patient and explored the heterogeneity of FCS deviations among patients.…”

Section: Discussionmentioning

confidence: 99%

“…Similar to the widely-used normative growth charts in pediatric medicine, where a child’s height or weight is compared to the normative distribution for that particular age and gender [21], the normative model can be used to evaluate individuals in relation to a neuroimaging normative feature at a particular age and gender. Recently, the normative model has gained increased attention in the field of psychiatric disorders, as it has been applied to characterize individual abnormalities and intersubject differences in neuroimaging features in disorders, such as autism [22-24], attention deficit/hyperactivity disorder [25], and schizophrenia [26]. Unlike the traditional case-control analysis that only provide information on group-level abnormities, the normative model takes into account intersubject differences within the patient and control groups and allows for measuring individual deviation from a large reference cohort.…”

Section: Introductionmentioning

confidence: 99%

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

Sun

Liu

et al. 2023

Preprint

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Major depressive disorder (MDD) is the most burdensome psychiatric disorder characterized by remarkably heterogeneous clinical phenotypes. It remains challenging to delineate the heterogeneity of neurobiological abnormalities underlying the clinical variance and, on this basis, to identify neurophysiological subtypes of MDD patients. Here, using a large multisite resting-state functional MRI data from 1,148 MDD patients and 1,079 healthy controls, we generated lifespan normative models of functional connectivity strengths, mapped the heterogeneity of patients' individual deviations, and identified neurobiological MDD subtypes. MDD patients showed positive deviations mainly in the default mode and subcortical areas, and negative deviations widely distributed over the cortex. However, there was a great inter-subject heterogeneity as indicated by that no more than 3.14% of patients deviated from the normative range for any brain region. Two neurophysiological MDD subtypes were identified. Subtype 1 showed severe deviations with positive deviations in the default mode, limbic, and subcortical areas, and negative deviations in the sensorimotor, dorsal and ventral attention areas, while subtype 2 showed a moderate but conversed deviation pattern. The severe-deviation subtype had older age, higher medicated proportion, and higher Suicide item score, while the moderate-deviation subtype showed higher Work and Activities and Depressed Mood item scores. Moreover, the baseline deviations in the severe-deviation subtype were predictive of 6-month antidepressant treatment effects in a subsample. To our knowledge, the current study is the largest multisite analysis of neurophysiological MDD subtyping to date and the findings shed light on our understanding of the biological mechanisms underlying the intersubject heterogeneity of clinical phenotypes, which are informative for the development of personalized treatments for this disorder.

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“…Although nonlinear methods such as deep neural networks implicitly handle heterogeneity, there is a gap between these models (requiring large sample size) and human interpretability and clinical need of seeking discrete subtypes. Thus it is worth the effort to discover the heterogeneity in brain structure or function with different clustering algorithms [7][8][9][10]. It has been recently shown by Wen et al [11,12] that the use of disease information to perform a semi-supervised clustering method enabled to dissect diseased populations into subgroups based on their neuroanatomical heterogeneity in semisimulated population, aging individuals with Alzheimer's disease and mild cognitive impairment, and patients with schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised clustering for neuro-subtyping of autism spectrum disorder

Wang

Geng

2023

Medical Imaging 2023: Image Processing

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with heterogeneous symptoms and neural characteristics, which has hindered the finding of reliable biomarkers. Until recently, ASD subtyping using neuroimaging has emerged to detect underlying neural features in each subgroup. However, not only the existing findings are preliminary, but also the methodologies are diverse with no conclusions on which are superior. In this study, we implemented neurosubtyping of ASD using a semi-supervised clustering method, HeterogeneitY through DiscRiminative Analysis (HYDRA), together with multi-scale dimension reduction of high-dimensional input features. We first performed orthogonal projective non-negative matrix factorization to objectively search down-sampled feature dimension, then utilized labelling information of ASD/controls to guide the clustering of ASD subjects so that the subtyping can consider the intrinsic disease-specific differentiations. Subjects were from a large ASD repository ABIDE. Functional connectivity was chosen as neural features for subtyping. Systematic evaluation of clustering performance, estimated by validity and reliability indicies, was conducted through a three-layer approach with random-stratification in the inner layer. We further compared HYDRA with unsupervised k-means, commonly-used for neuro-subtyping of mental disorders. Results showed that HYDRA with dimension reduction successfully detected two distinct clusters with high reliability. Both semi-supervised and unsupervised clustering have discovered similar connectivity patterns under each cluster. Hypo-connectivity in default mode network, hyper-connectivity in visual recognition, subcortical and cerebellum networks were observed in one cluster; and the opposite patterns were in another. Yet HYDRA produced more distinct patterns across clusters compared to kmeans. Statistical analyses suggest that neural connectivity patterns were associated with core ASD symptoms differently across each cluster. Our findings show that semi-supervised clustering is promising to subtype ASD into reliable and distinct clusters that can dissociate the heterogeneous neural markers and neural-behavioral relationships, which is one step forward for individual-based diagnosis and treatment in the future.

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Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model

Cited by 35 publications

References 72 publications

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

Semi-supervised clustering for neuro-subtyping of autism spectrum disorder

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