Aberrant Functional Connectivity Architecture in Participants with Chronic Insomnia Disorder Accompanying Cognitive Dysfunction: A Whole-Brain, Data-Driven Analysis

Abstract

Objectives: Although it is widely observed that chronic insomnia disorder (CID) is associated with cognitive impairment, the neurobiological mechanisms underlying this remain unclear. Prior neuroimaging studies have confirmed that a close correlation exists between functional connectivity and cognitive impairment. Based on this observation, in this study we used resting-state functional magnetic resonance imaging (rs-fMRI) to study the relationship between whole brain functional connectivity and cognitive function in CID.Methods: We included 39 patients with CID and 28 age-, gender-, and education-matched healthy controls (HC). Abnormalities in functional connectivity were identified by comparing the correlation coefficients for each pair of 116 brain regions between CID and HC.Results: Cognitive impairment was associated with reduced subjective insomnia scores after controlling for age, gender, and educational effects. Compared with HC, patients with CID had larger negative correlations within the task-negative network [medial prefrontal cortex (mPFC), precuneus, inferior temporal gyrus, cerebellum, and superior parietal gyrus], and between two intrinsic anti-correlation networks (mPFC and middle temporal gyrus; supplementary motor area and cerebellum). Patients with CID also had decreased positive correlations within the default mode network (DMN), and between the cerebellum and DMN, which mainly comprises the mPFC and posterior cingulated cortex. There were positive correlations of decreased positive connectivity with subjective sleep scores and MMSE scores, and increased negative correlations between the task-negative-network and MMSE scores in CID.Conclusions: Using rs-fMRI, our results support previous observations of cortical disconnection in CID in the prefrontal and DMN networks. Moreover, abnormal correlations within the task-negative network, and between two intrinsically anti-correlation networks, might be important neurobiological indicators of CID and associated cognitive impairment.

Disrupted brain network topology in chronic insomnia disorder: A resting-state fMRI study

“…Moreover, CID patients were required not have taken medicine that would influence brain function two weeks before experiment. The inclusion criteria for CID patients were as follows: ( Spiegelhalder et al, 2015 ) duration of insomnia symptoms, such as fatigue, testiness, or cognitive decline, were required no less than three months; ( Kay and Buysse, 2017 ) PSQI score ≥8; ( Li et al, 2016 ) no neurological or psychiatric disorders, such as stroke, depression (HAMD ≤ 17), and anxiety (HAMA ≤ 14) ( Pang et al, 2017 ) etc. ; ( Altena et al, 2008 ) no other sleep disorders (such as sleep-related movement disorders, hypersomnia, or parasomnia); ( Baglioni et al, 2014 ) right-hand dominance (determined by Chinese Handedness Inventory that suits Chinese people, including 10 test items) and native chinese speakers; ( Wang et al, 2012 ) age 20–60 years; ( Biswal et al, 1995 ) no medication or substance abuse, such as caffeine, nicotine, or alcohol; ( Fox and Raichle, 2007 ) no abnormal signal found by T2-weighted dark-fluid and T1-weighted MR images.…”

“…Some other studies have found disrupted resting-state FC using seed-based region-to-region FC, such as in the amygdala, insula, posterior cingulate cortex, and hippocampus ( Huang et al, 2012 ; Wang et al, 2017 ). Li and Pang et al divided the brain into 116 regions and identified abnormal FCs by comparing the Pearson's correlation coefficients of each pair, and their results indicated aberrant FCs in widely distributed regions ( Li et al, 2017 ; Pang et al, 2017 ). Many previous findings in fMRI described above were focused on the dysfunctions of the circumscribed brain regions or FC changes between two different brain regions.…”

Reduced structural connectivity in Insomnia Disorder

“…Modern neuroimaging methods allow for a non-invasive mapping of structural and functional brain networks and provide a promising way to shed light on the neurobiological foundations of insomnia to achieve clinically relevant information that can help target interventions. An increasing number of studies have investigated functional connectivity alterations related to insomnia (Huang et al, 2012;Killgore et al, 2013;Chen et al, 2014;Li et al, 2014;Nie et al, 2015;Zhou et al, 2016;Li et al, 2017;Lu et al, 2017b;Pang et al, 2017;, but as a number of recent reviews point out, the results are relatively inconsistent so far (Spiegelhalder et al, 2015;Kay and Buysse, 2017;Tagliazucchi and van Someren, 2017). This inconsistency may stem from the variety of methods used (Spiegelhalder et al, 2015), but may also be influenced by sleep confounds (Tagliazucchi and Laufs, 2014) and insomnia heterogeneity (Chee, 2013;Benjamins et al, 2017;Kay and Buysse, 2017).…”

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

“…Neuroimaging studies for insomnia have made substantial effort to understand the neuromechanisms of insomnia. Previous studies found aberrant brain metabolism and connectivity related to the prefrontal cortex, insular cortex, amygdala, precuneus and caudate in primary insomnia [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For example, using PET, Nofzinger et al [4] found smaller decrease in relative metabolism from waking to non-REM sleep states in the ascending reticular activating system, hypothalamus, thalamus, insular cortex, amygdala, hippocampus, anterior cingulate and medial prefrontal cortices, which supports the CNS hyperarousal hypothesis.…”