Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.
Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.
复制标题
静息状态下的频率特异性共激活模式及其在精神分裂症中的改变：一项功能磁共振成像研究
DOI：
10.1002/hbm.25884
复制
发表时间：
2022-08-15
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
人类大脑的静息状态是一个动态系统，显示出频率依赖的特征。最近的研究表明，共激活模式（CAP）分析可以识别具有相似共激活配置的反复出现的大脑状态。然而，目前尚不清楚cap是否以及如何取决于频段。除了典型的低频范围（0.01-0.08 Hz）外，本研究还研究了慢5 （0.01-0.027 Hz）、慢4 （0.027-0.073 Hz）、慢3 （0.073-0.198 Hz）和慢2 （0.198-0.25 Hz）四个频段的cap时空特征。在健康受试者中，每个频带得到6个CAP状态，与我们之前的研究一致。在慢- 5、4和3中观察到与典型范围相似的空间格局，但在慢- 2中没有。随着频率的增加，所有的CAP状态都表现出较短的持久性，这导致了更多的状态间转换。具体来说，从慢- 5到慢- 4，不仅在分布式皮质网络中发生了显著变化，而且在基底神经节和杏仁核中也有所增加。精神分裂症患者在慢- 5的cap持续性方面表现出显著的改变。通过leave - one - pair - out、hold - out和重采样验证，在不同频带中，slow - 4获得了最高的分类准确率（84%）。总之，我们的研究结果提供了不同频段CAP状态的时空特征的新信息，有助于更好地理解精神分裂症和其他疾病的生物标志物的频率方面。在健康的大脑中，静息状态CAP状态在不同的频率亚带中具有逐渐变化的时空模式。特别是，与慢- 5相比，慢- 4的CAP状态在几个皮质下区域表现出更强的共激活。由于慢- 5表现出更大的受试者间差异，慢- 4获得了更好的精神分裂症预测准确性。
The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders. The resting‐state CAP states have gradually varying spatial and temporal patterns across frequency sub‐bands in the healthy brain. Particularly, CAP states in slow‐4 exhibited stronger coactivation in several subcortical regions than slow‐5. As slow‐5 showed larger inter‐subject differences, slow‐4 achieved a better schizophrenia prediction accuracy.
登录
查看更多内容
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
4.8
作者：
Gohel, Suril;Gallego, Juan A.;Szeszko, Philip R.
通讯作者：
Szeszko, Philip R.
影响因子：
16.2
作者：
Calhoun, Vince D.;Miller, Robyn;Pearlson, Godfrey;Adali, Tulay
通讯作者：
Adali, Tulay
影响因子：
10.6
作者：
Brandl, Felix;Avram, Mihai;Sorg, Christian
通讯作者：
Sorg, Christian
DOI：
10.1073/pnas.0701519104
发表时间：
2007-06-12
影响因子：
11.1
作者：
Honey, Christopher J.;Koetter, Rolf;Sporns, Olaf
通讯作者：
Sporns, Olaf