Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry.

IF 3.4 2区医学 Q2 NEUROIMAGING Neuroimage-Clinical Pub Date : 2025-01-16 DOI:10.1016/j.nicl.2025.103736

Linn B Norbom, Bilal Syed, Rikka Kjelkenes, Jaroslav Rokicki, Antoine Beauchamp, Stener Nerland, Azadeh Kushki, Evdokia Anagnostou, Paul Arnold, Jennifer Crosbie, Elizabeth Kelley, Robert Nicolson, Russell Schachar, Margot J Taylor, Lars T Westlye, Christian K Tamnes, Jason P Lerch

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Abstract

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6-23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions.

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利用T1w/ t2w比值和形态计量学的皮质特征探测自闭症和ADHD亚型。

自闭症谱系障碍（ASD）和注意力缺陷/多动障碍（ADHD）是两种具有共同遗传病因且经常同时发生的神经发育疾病。考虑到这种合并症和公认的临床异质性，识别具有相似大脑特征的个体可能对预测临床结果和定制治疗策略有价值。皮质髓鞘形成是一个突出的发育过程，其破坏是这两种疾病的候选机制。然而，没有研究试图使用T1w/ t2w比率（一种基于磁共振成像（MRI）的皮质内髓磷脂替代指标）来识别亚型。此外，皮质变异性源于许多生物学途径，多模式方法可以将皮质指标整合到一个单一的网络中。我们分析了310名年龄在2.6-23.6岁之间的个体的数据，这些数据来自安大略省神经发育（POND）网络，由诊断为ASD （n = 136）、ADHD （n = 100）和典型发育（TD）个体（n = 74）组成。我们首先测试了诊断类别和对照组之间T1w/ t2w比率的差异。然后，我们进行单峰（T1w/ t2w比）和多峰（T1w/ t2w比、皮质厚度和表面积）光谱聚类来识别诊断盲亚群。线性模型显示T1w/ t2w比的病例-对照差异无统计学意义。单峰聚类主要是孤立的单个或少数簇，由图像质量和强度异常值驱动。多模态聚类表明了三个不同的亚群，它们超越了诊断界限，表现出不同的皮层模式，但相似的临床和认知特征。T1w/ t2w比值特征与划分最相关，其次是表面积。虽然我们的分析显示没有显著的病例对照差异，但结合皮层特征之间的T1w/ t2w比率的多模态聚类有望识别具有神经发育条件的个体的生物学相似亚群。

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来源期刊

Neuroimage-Clinical NEUROIMAGING-

CiteScore

7.50

自引率

4.80%

发文量

368

审稿时长

52 days

期刊介绍： NeuroImage: Clinical, a journal of diseases, disorders and syndromes involving the Nervous System, provides a vehicle for communicating important advances in the study of abnormal structure-function relationships of the human nervous system based on imaging. The focus of NeuroImage: Clinical is on defining changes to the brain associated with primary neurologic and psychiatric diseases and disorders of the nervous system as well as behavioral syndromes and developmental conditions. The main criterion for judging papers is the extent of scientific advancement in the understanding of the pathophysiologic mechanisms of diseases and disorders, in identification of functional models that link clinical signs and symptoms with brain function and in the creation of image based tools applicable to a broad range of clinical needs including diagnosis, monitoring and tracking of illness, predicting therapeutic response and development of new treatments. Papers dealing with structure and function in animal models will also be considered if they reveal mechanisms that can be readily translated to human conditions.