Reproducible Coactivation Patterns of Functional Brain Networks Reveal the Aberrant Dynamic State Transition in Schizophrenia

Yang, Hang; Zhang, Hong; Di, Xin; Wang, Shuai; Meng, Chun; Tian, Lin; Biswal, Bharat B.

doi:10.1101/2021.03.29.437611

Cited by 3 publications

(12 citation statements)

References 76 publications

(85 reference statements)

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“…It also has a better temporal resolution and does not require predefined parameters such as window length, as the analytical unit of CAP analysis is a single volume. Besides, our previous study has shown the robustness of CAPs across several technique flexibilities and independent cohorts, and reproducible alterations were also obtained between schizophrenia patients and healthy controls (Yang et al 2021). Recently, CAP analysis has been used to study the altered brain dynamics in patients with depression (Kaiser et al 2019) and Alzheimer’s disease (Ma et al 2020).…”

Section: Introductionmentioning

confidence: 60%

“…Similar to our previous study, the mean BOLD time series was extracted from 408 ROIs separately (Yang et al 2021), which includes 400 cortical regions (Schaefer et al 2018) and 8 subcortical regions (bilateral caudate nucleus, putamen, globus pallidus and amygdala) from the AAL template (Tzourio-Mazoyer et al 2002). The 400 cortical regions belong to 7 networks, including the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network, fronto-parietal network (FPN) and default mode network (DMN).…”

Section: Methodsmentioning

confidence: 99%

“…The coactivation pattern (CAP) analysis is a data-driven method based on the k-means clustering, and it is supposed to identify recurring whole-brain coactivation states. The same analysis pipeline from our previous work (Yang et al 2021) was used to detect the CAP states in different frequency bands.…”

Section: Methodsmentioning

confidence: 99%

“…The cluster number K was tested from 2 to 21, and the silhouette score (Rousseeuw 1987) was used to determine the cluster number. Our previous work identified six robust CAP states in the typical range (H. Yang et al, 2021). We found six clusters were also suitable for the four sub-frequency bands, and their silhouette score curves were shown in Supplementary Figure S1.…”

Section: Methodsmentioning

confidence: 99%

“…The abnormal dynamic brain graphs (Yu et al 2015) and network reconfigurations have also been identified in SZ (Reinen et al 2018). Our previous study has shown the altered CAP state dynamics of SZ patients were not only involved with the triple-network, but also extend to other primary and high-order networks (Yang et al 2021). Besides the dynamic brain changes, SZ patients also showed frequency-specific alterations in several aspects, including ALFF (Yu et al 2014; Gohel et al 2018; Meda et al 2015; Hare et al 2017), ReHo (Yu et al 2013), BOLD variability (Zhang, Yang, and Cai 2020), as well as functional connectivity (Wang et al 2017; Han et al 2017).…”

Section: Introductionmentioning

confidence: 93%

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Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study

Yang

Zhang

et al. 2021

Preprint

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The resting-state human brain is a dynamic system that shows frequency-specific characteristics. Coactivation pattern (CAP) analysis has been recently used to identify recurring brain states sharing similar coactivation configurations. However, whether and how CAPs differ across different sub-frequency bands are unknown. In the current study, in addition to the typical low-frequency range (0.01 - 0.08 Hz), the spatial and temporal characteristics of CAPs in four sub-frequency bands, slow-5 (0.01 - 0.027 Hz), slow-4 (0.027 - 0.073 Hz), slow-3 (0.073 - 0.198 Hz), and slow-2 (0.198 - 0.25 Hz), were studied. Six CAP states were obtained for each band., The CAPs from the typical frequency range were spatially largely overlapped with those in slow-5, slow-4 and slow-3 but not with those in slow-2. With the increase of frequency, the CAP state became more unstable and resulted in an overall shorter persistence. The spatial and temporal characteristics of slow-4 and slow-5 were further compared, because they constitute most power of the resting-state fMRI signals. In general, slow-4 showed stronger coactivations or co-deactivations in subcortical regions, while slow-5 showed stronger coactivations or co-deactivations in large-scale cortical networks such as the dorsal attention network. Lastly, frequency-dependent dynamic alterations were also observed in schizophrenia patients. Combining the information obtained from both slow-5 and slow-4 increased the classification accuracy of schizophrenia patients than only using the typical range. In conclusion, our results revealed that the spatial and temporal characteristics of CAP state varied at different frequency bands, which could be helpful for identifying brain alterations in schizophrenia.

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Section: Introductionmentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

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Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study

Yang

Zhang

et al. 2021

Preprint

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On co-activation pattern analysis and non-stationarity of resting brain activity

Matsui

Pham

Jimura

et al. 2021

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The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the non- stationary characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, whether these techniques adequately capture non-stationarity needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.

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Increased prevalence of a frontoparietal brain state is associated with better motor recovery after stroke affecting dominant-hand corticospinal tract

Olafson

Russello²,

Jamison

et al. 2022

Preprint

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Strokes cause lesions that damage brain tissue, disrupt normal brain activity patterns, and can lead to impairments in cognition and motor function. Although modulation of cortical activity is central to stimulation-based rehabilitative therapies, aberrant and adaptive patterns of brain activity after stroke have not yet been fully characterized. Here, we apply a brain dynamics analysis approach to study longitudinal brain activity patterns in individuals with ischemic pontine stroke. We first found 4 commonly occurring brain states largely characterized by high amplitude activations in the visual, frontoparietal, default mode, and motor networks. Stroke subjects spent less time in the frontoparietal state compared to controls, and, consistently, the default mode state had fewer transitions to the frontoparietal state in stroke subjects relative to controls. For individuals with dominant-hand CST damage, increased time spent in the frontoparietal state from 1 week to 3-6 months post-stroke was associated with better motor recovery over the same time period, an association which was independent of baseline impairment. Furthermore, the amount of time spent in brain states was linked theoretically and empirically to functional connectivity. This work suggests that when the dominant-hand CST is compromised in stroke, resting state configurations may shift to include increased activation of the frontoparietal network, which may facilitate compensatory neural pathways that support recovery of motor function when traditional motor circuits of the dominant-hemisphere CST are compromised. Determining subject- and stroke lesion-specific patterns of brain activity highly associated with recovery may help improve the design of clinical rehabilitation strategies, including non-invasive brain stimulation.

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Reproducible Coactivation Patterns of Functional Brain Networks Reveal the Aberrant Dynamic State Transition in Schizophrenia

Cited by 3 publications

References 76 publications

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

On co-activation pattern analysis and non-stationarity of resting brain activity

Increased prevalence of a frontoparietal brain state is associated with better motor recovery after stroke affecting dominant-hand corticospinal tract

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