Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach

Abstract

Default mode network (DMN) has been reported altered in schizophrenia (SZ) using static connectivity analysis. However, the studies on dynamic characteristics of DMN in SZ are still limited. In this work, we compare dynamic connectivity within DMN between 82 healthy controls (HC) and 82 SZ patients using resting-state fMRI. Firstly, dynamic DMN was computed using a sliding time window method for each subject. Then, the overall connectivity strengths were compared between two groups. Furthermore, we estimated functional connectivity states using K-means clustering, and then investigated group differences with respect to the connectivity strengths in states, the dwell time in each state, and the transition times between states. Finally, graph metrics of time-varying connectivity patterns and connectivity states were assessed. Results suggest that measured by the overall connectivity, HC showed stronger inter-subsystem interaction than patients. Compared to HC, patients spent less time in the states with nodes tightly connected. For each state, SZ patients presented relatively weaker connectivity strengths mainly in inter-subsystem. Patients also exhibited lower values in averaged node strength, clustering coefficient, global efficiency, and local efficiency than HC. In summary, our findings indicate that SZ showed impaired interaction among DMN subsystems, with a reduced central role for posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC) hubs as well as weaker interaction between dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. For SZ, decreased integration of DMN may be associated with impaired ability in making self-other distinctions and coordinating present mental states with episodic decisions about future.

Reduced Default Mode Connectivity in Adolescents With Conduct Disorder

“…All seeds were 8mm radius spheres created using FSL. Consistent with previous studies 11,27,28 , only left lateralized and midline regions were investigated in this study to prevent biasing connectivity to mirrored ROIs.…”

“…This takes advantage of the functionally dissociable nature of DMN subsystems and has the potential to provide a more straightforward interpretation in terms of underlying cognitive impairments. Indeed, such an approach has already been used to identify DMN subsystem-specific abnormalities in schizophrenia and major depression [26][27][28] .…”

“…However, it is possible that the nature of the DMN dysfunction in CD is different in nature from that observed in other disorders. For example, schizophrenia appears to be associated with reduced DMN connectivity in all subsystems of the network 27 , and this has been correlated with subsystem-specific deficits in cognitive processes 28 . Likewise, in MDD 64 , OCD, 65 and autistic spectrum disorders 66 , respectively, there is emerging evidence of disorder-specific abnormalities that may be tied to individual subcomponents of the DMN.…”

Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases

“…Novel analyses: some recent advances in analyzing resting state activity in psychosis include dynamic connectivity approaches 119,120 group information guided independent component analysis 121 , inclusion of low-frequency fluctuation measures such as ALFF/fALFF 122 . Sometimes these are coupled with machine learning approaches 121 .…”

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

“…Investigators have pursued DFC methods in a wide variety of brain disorders, including schizophrenia 38,131,132133134 , bipolar disorder 131 , autism spectrum disorder 135,136 , major depression, mild cognitive impairment 137138 , Alzheimer’s disease 139 and dementia with Lewy bodies 95 , post-traumatic stress disorder 78 , epilepsy 61,140 and multiple sclerosis, among others 79 . Encouragingly, these studies have begun to suggest that dynamic features are more sensitive or specific than static connectivity in differentiating between healthy and control populations.…”