Mapping the landscape of brain functional dynamics in autism spectrum disorder

绘制自闭症谱系障碍大脑功能动态图谱

• 批准号: 2886713
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886713
• 关键词: Mapping; landscape; brain; functional; dynamics

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.

自闭症谱系障碍 (ASD) 是一种日益普遍的神经发育疾病，包括多种表型和异质性疾病，例如自闭症和阿斯伯格综合症。自闭症谱系障碍患者在一生中必须面对各种功能挑战，例如社会认知、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育造成的。例如，发现尾状核体积的增加与复杂的重复运动行为相关[4]。非典型大脑活动在与心理理论、社会认知和执行功能相关的大脑区域被广泛观察到[5]-[7]。因此，了解自闭症谱系障碍的神经生物学基础对于提高认识并为提供更有针对性的干预措施提供信息至关重要。最近，有人认为自闭症谱系障碍是一种导致信息处理改变的大脑连接障碍[5]。关于大脑结构连接，有人暗示自闭症谱系障碍患者白质连接的改变与功能障碍有关[8]、[9]。同时，有充分的证据表明自闭症谱系障碍患者存在广泛的功能失调[10]、[11]。例如，伏核（NAcc）（形成社会功能的神经生物学基础）与其他大脑区域（例如丘脑和前扣带皮层）之间发现连接不足[12]。然而，尽管 FC 研究数量庞大且增长迅速，但有关改变 FC 的结果（可能受到自闭症谱系障碍特征异质性的困扰）仍然不一致 [13]。此外，大多数评估自闭症谱系障碍中 FC 的研究都是基于 FC 保持静态的前提进行的。静态 FC 分析无法捕获网络或区域之间功能连接的时间分辨转变，并且先前研究的“平均”结果可能掩盖动态方面的重要差异。动态 FC (dFC) 方法最近受到越来越多的关注，重点关注 RS 或基于任务的 fMRI 期间功能连接的瞬时变化。脑电图 (EEG) 和功能磁共振成像联合研究表明 dFC 具有坚实的生物学起源 [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测量方法。表征 dFC 的指标，例如停留时间和时间变异性，已被暗示与 ASD 的功能损伤显着相关 [16] [17]。调查 dFC 的研究还可以验证通过静态 FC 发现的非典型大脑连接。例如，Fu 等人。发现下丘脑/下丘脑和感觉区域之间的 dFNC 增加，这扩展了先前基于静态 FC 分析的丘脑和感觉皮层之间超连接性的发现 [18]。通过将动态 FC 聚类为一组离散的状态，研究人员试图识别以其非典型动态模式为特征的大脑疾病。 Hyatt 和他们的同事已经确定了四种具有 ASD 的 dFC 状态，与 TD 相比显示出不同的停留时间 [19]。他们还发现 dFC 测量值与自闭症谱系障碍 (ASD) 的社交认知能力得分之间存在显着关联。此外，通过 dFC，机器学习已被用于诊断 ASD。一项基于自闭症脑成像数据交换 (ABIDE) 数据库的研究使用支持向量机 (SVM) 分类器从多级 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，自闭症谱系障碍 (ASD) dFC 异常的证据由于方法的异质性而仍然有限。应使用精心选择的分析方法和零模型进行进一步的研究。