Altered Hippocampo-Cerebello-Cortical Circuit in Schizophrenia by a Spatiotemporal Consistency and Causal Connectivity Analysis

Chen, Xi; Jiang, Yuchao; Chen, Lin; He, Hong; Dong, Li; Hou, Changyue; Duan, Mingjun; Yang, Maolin; Yao, Dezhong; Chen, Luo

doi:10.3389/fnins.2017.00025

Cited by 23 publications

(18 citation statements)

References 33 publications

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“…While considerable research has used temporal coupling of BOLD signals (i.e., FC) to reveal information exchange among brain areas (Jiang et al, ; Jiang, Luo, Gong, Peng, et al, ; Zhu et al, ), rich high‐order information interactions such as dynamic (He et al, ; Hutchison et al, ; Klugah‐Brown et al, ), causal (Chen, Jiang, et al, ; Jiang, Luo, Li, Duan, et al, ), hierarchical (Smith et al, ), and many‐to‐many (Pessoa, ) have not been fully explored yet. To characterize a complex relationship between white‐matter and gray‐matter regions, this study measured the functional resemblance between any two white‐matter networks based on their connectivity profiles with gray‐matter regions.…”

Section: Discussionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Human Brain Mapping

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Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.

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Section: Discussionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Human Brain Mapping

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“…Some of the patients were part of our previous studies (Chen et al 2015(Chen et al , 2016(Chen et al , 2017Duan et al 2015;He et al 2018;. Twenty-eight of the 96 patients were included in a prior study in which the authors identified abnormal functional integration in the basal ganglia network by using spatial independent component analysis in schizophrenia (Duan et al 2015).…”

Section: Participantsmentioning

confidence: 99%

“…Forty-six of the 96 patients were previously included in a prior article in which authors demonstrated dysfunction in the insula with datadriven clustering and functional connectivity analysis in schizophrenia (Chen et al 2016). Sixty-nine of the 96 patients were included in a prior article in which the authors identified an altered hippocampus-cerebellum cortical circuit with a combination of local consistency analysis and Causal Connectivity analysis in schizophrenia (Chen et al 2017). Thirty-six (data before the intervention) of the 96 patients were included in a prior study in which the authors found increased insular connectivity after music intervention in schizophrenia (He et al 2018).…”

Section: Participantsmentioning

confidence: 99%

Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia

et al. 2018

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Schizophrenia is thought as a self-disorder with dysfunctional brain connectivity. This self-disorder is often attributed to high-order cognitive impairment. Yet due to the frequent report of sensorial and perceptual deficits, it has been hypothesized that self-disorder in schizophrenia is dysfunctional communication between sensory and cognitive processes. To further verify this assumption, the present study comprehensively examined dynamic reconfigurations of resting-state functional connectivity (rsFC) in schizophrenia at voxel level, region level, and network levels (102 patients vs. 124 controls). We found patients who show consistently increased rsFC variability in sensory and perceptual system, including visual network, sensorimotor network, attention network, and thalamus at all the three levels. However, decreased variability in high-order networks, such as default mode network and frontal–parietal network were only consistently observed at region and network levels. Taken together, these findings highlighted the rudimentary role of elevated instability of information communication in sensory and perceptual system and attenuated whole-brain integration of high-order network in schizophrenia, which provided novel neural evidence to support the hypothesis of disrupted perceptual and cognitive function in schizophrenia. The foci of effects also highlighted that targeting perceptual deficits can be regarded as the key to enhance our understanding of pathophysiology in schizophrenia and promote new treatment intervention.

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“…The voxelwise, residual-based GCA was performed on the gray matter mask using REST toolbox (http://www.restfmri.net) 37 . This GCA method has been used in our previous study which identified an altered hippocampo-cerebello-cortical circuit in SCH 38 . This previous study indicated that the GCA could be an effective measure to investigate the abnormal effective connectivity in psychiatry disorders.…”

Section: Causal Causality Analysismentioning

confidence: 99%

“…Different from the previous study, the current study applied the GCA to investigate the altered connectivity based on the structural deficits in a different sample of SCH, DEP and HC. Same as our previous study 38 , three cause parameters (FX→Y, FY→X and Fnet) including inflow-to-seed, outflow-from-seed and out-inflow were calculated in the current study. For each seed, three maps were acquired between the seed and each voxel in the gray matter mask.…”

Section: Causal Causality Analysismentioning

confidence: 99%

Aberrant Prefrontal–Thalamic–Cerebellar Circuit in Schizophrenia and Depression: Evidence From a Possible Causal Connectivity

Jiang

Duan

Chen

et al. 2019

Int. J. Neur. Syst.

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Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal-thalamic-cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal-thalamic-cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.

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Altered Hippocampo-Cerebello-Cortical Circuit in Schizophrenia by a Spatiotemporal Consistency and Causal Connectivity Analysis

Cited by 23 publications

References 33 publications

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia

Aberrant Prefrontal–Thalamic–Cerebellar Circuit in Schizophrenia and Depression: Evidence From a Possible Causal Connectivity

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