Mapping the landscape of brain functional dynamics in autism spectrum disorder

自闭症谱系障碍(ASD)是一种日益普遍的神经发育疾病，包括广泛的表型和异质性疾病，如自闭症和阿斯伯格综合征。ASD患者一生中必须面对各种功能挑战，如社会认知、语言技能、执行功能和运动能力方面的困难[1]-[3]。他们的行为和大脑功能与典型发育(TD)个体的偏差是由神经结构和大脑活动的非典型发展支撑的。例如，尾状核体积的增加被发现与复杂的重复运动行为相关[4]。在与心理理论、社会认知和执行功能相关的大脑区域中，已经广泛观察到非典型的大脑活动[5]-[7]。因此，了解ASD的神经生物学基础对于提高认识和提供更有针对性的干预措施是至关重要的。最近，有人提出ASD是一种导致信息处理改变的大脑连接障碍[5]。关于结构性脑连接，已经暗示ASD中白质连接的改变与功能损害有关[8]，[9]。同时，有充分的证据表明自闭症患者普遍存在功能障碍[10]、[11]。例如，伏隔核(NAcc)与其他大脑区域，如丘脑和前扣带皮质之间的连接不足[12]，这构成了社会功能的神经生物学基础。然而，尽管FC研究的数量庞大且快速增长，但关于改变FC的结果似乎受到ASD特征异质性的困扰，仍然不一致[13]。此外，大多数评估ASD患者FC的研究都是在FC保持不变的前提下进行的。静态FC分析未能捕获网络或区域之间的功能连接的时间分辨转换，并且先前研究的“平均”结果可能掩盖了动态中的重要差异。动态功能障碍(Dynamic FC，DFC)方法关注的是基于RS或任务的功能磁共振成像过程中功能连接性的瞬时变化，近年来受到越来越多的关注。联合脑电(EEG)和功能磁共振成像(FMRI)研究表明DFC有可靠的生物学起源[14]，[15]，最近的报告表明动态功能连接是探索ASD神经生物学基础的良好指标。表征DFC的指标，如停留时间和时间变异性，已被暗示与ASD的功能损害显著相关[16][17]。研究DFC的研究也可以验证用静态FC发现的非典型大脑连接。例如，傅成玉等人。发现下丘脑/下丘脑和感觉区域之间的dFNC增加，这扩展了之前基于静态FC分析的丘脑和感觉皮质之间的高度连接的发现[18]。通过将动态FC分成一组离散的状态，研究人员试图识别以非典型动态模式为特征的大脑疾病。凯悦和他们的同事已经确定了四个患有ASD的DFC州，与TD相比，它们的停留时间不同[19]。他们还发现，自闭症患者的DFC测量与社会认知能力得分之间存在显著关联。此外，利用DFC，实现了机器学习对ASD的诊断。一项基于自闭症脑成像数据交换数据库的研究使用支持向量机分类器从多层DFC中提取的中心矩特征对ASD进行分类，准确率达到83%[20]。作为一个新兴领域，ASD中DFC异常的证据仍然有限，方法学上的异质性。进一步的研究应该用精心挑选的分析方法和零模型进行。

Autism spectrum disorder (ASD) is an increasingly prevalent neurodevelopmental condition, comprising a wide range of phenotypes and heterogeneous conditions, such as autism and Asperger's syndrome. ASD patients have to face various functioning challenges across lifespan, such as difficulties in social cognition, language skills, executive functions, and motor abilities [1]-[3]. Their deviations in behaviours and brain functions from typically developed (TD) individuals are underpinned by the atypical development of neural structures and brain activity. For instance, increased caudate volume was found to be correlated with complex repetitive motor behaviours [4]. Atypical brain activity has been widely observed in brain regions associated with theory of mind, social cognition, and executive functions [5]-[7]. Therefore, it is crucial to understand the neurobiological underpinnings of ASD both to increase awareness and inform options to give more tailored interventions. Recently, it has been posited that ASD is a disorder of brain connectivity leading to altered information processing [5]. Regarding structural brain connectivity, it has been implied that altered white-matter connectivity in ASD is associated with function impairment [8], [9]. Meanwhile, there is ample evidence of widespread functional dysconnectivity in ASD [10], [11]. As an example, underconnectivity was found between nucleus accumbens (NAcc), which forms the neurobiological basis of social functions, and other brain regions, such as thalamus, and anterior cingulate cortex [12]. However, despite the large and fast growing number of FC studies, the results regarding altered FC, plausibly plagued by heterogeneity in traits of ASD, remains inconsistent [13]. In addition, most studies assessing FC in ASD have been conducted based on the premise that FC remained static. Static FC analysis fails to capture the time-resolved transitions of functional connectivity between networks or regions and the 'on average' results from previous studies may mask important differences in dynamics. Dynamic FC (dFC) approaches, which have gained increasing attention recently, focus on the transient changes of functional connectivity during an RS- or task-based fMRI. Joint electroencephalographic (EEG) and fMRI studies have suggested a solid biological origin for dFC [14], [15], and recent reports have suggested dynamic functional connectivity as a good measurement to explore the neurobiological basis of ASD. Metrics characterising dFC, such as dwelling times and temporal variability, have been implied to be significantly associated with function impairment in ASD [16] [17]. Studies investigating dFC can also validate atypical brain connectivity discovered with static FC. For instance, Fu et al. found increased dFNC between Hypothalamus/Subthalamus and Sensory Regions which extended previous findings of hyperconnectivity between thalamus and sensory cortex based on static FC analysis [18]. By clustering dynamic FC into a discrete set of states, researchers have attempted to identify brain disorders characterised by their atypical dynamic patterns. Hyatt and their colleagues have identified four dFC states with ASD showing different dwelling times compared with TD [19]. They also found significant associations between dFC measures and social cognitive ability scores in ASD. Furthermore, with dFC, machine learning has been implemented to diagnose ASD. A study based on the Autism Brain Imaging Data Exchange (ABIDE) database reached 83% accuracy to classify ASD using central moment features extracted from multilevel dFC by support vector machines (SVM) classifiers [20]. As an emerging area, evidence for dFC abnormalities in ASD remain limited with heterogeneity in methodology. Further research should be conducted with carefully selected analytic approaches and null models.