Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects.
Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects.
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DOI：
10.3389/fnhum.2014.00897
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发表时间：
2014
影响因子：
2.9
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Rashid B;Damaraju E;Pearlson GD;Calhoun VD
精神分裂症(SZ)和双相情感障碍(BP)在临床症状、脑特征和危险基因上有显著重叠，都与大规模脑网络之间的连接障碍有关。静息状态功能磁共振成像(RsfMRI)数据有助于研究远距离大脑区域之间的宏观连接。识别这种连通性的标准方法包括基于种子的相关性和数据驱动的聚类法，例如独立分量分析(ICA)，但通常侧重于平均连通性。在这项研究中，我们利用rsfMRI数据的ICA来获得健康对照(HCS)和年龄匹配的SZ和BP患者队列中的内在连接网络(ICN)。随后，我们研究了功能网络连接性的差异，其定义为ICN的时间进程之间的成对相关性，以及HCS和患者之间的差异。我们量化了在整个扫描持续时间内静态(平均)和动态(窗口)连接的差异。不同动态状态下的连通性存在疾病特异性差异。值得注意的是，结果表明，与HCS相比，患者向某些状态(状态1、2和4)的转换较少，大多数此类差异仅限于一种状态。SZ患者比双极患者表现出更多的差异，包括在一个共同的连接状态(动态3)下的高连接和低连接。此外，SZ患者和双相患者在涉及额叶(动态1)和额顶区(动态3)的连接模式(状态)上也存在组内差异。我们的结果提供了关于这些疾病的新信息，并强烈表明，基于状态的分析对于避免将有助于区分这些临床组的重要因素平均在一起至关重要。
Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.
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