Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

Nawaz, Uzma; Lee, Ivy; Beermann, Adam; Eack, Shaun M.; Keshavan, Matcheri S.; Brady, Roscoe O.

doi:10.1093/schbul/sbaa088

Cited by 19 publications

(16 citation statements)

References 35 publications

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“…Individual differences in network topography have been previously observed (18,(34)(35)(36)(37)(38)(39). Only recently have these topographic variations been linked to behavioral/cognitive phenotypes (24,25,40). Our report of a link between network topography and substance use in schizophrenia is novel.…”

Section: Discussionsupporting

confidence: 55%

“…A multivariate pattern analysis of whole-connectome data (MDMR) was used to identify the strongest links between daily cigarette consumption and functional connectivity (17). This analysis occurs in two steps: the first step identifies any regions where daily cigarette consumption correlates with functional connectivity, and the second step (posthoc testing) involves seed-based analysis of the identified region (see ROI-Based Connectivity Analysis) to determine the spatial pattern of connectivity it represents (17,24,25).…”

Section: Cohort 1: Network Discovery Using Multivariate Distance Matrix Regressionmentioning

confidence: 99%

“…This voxelwise threshold was selected to maximize the replicability potential. This approach has been used to examine the relationship between psychiatric pathology and connectivity (24,25). We modeled the effect of daily cigarette consumption on functional connectivity while covarying for effects of chlorpromazine equivalents, age, and sex (Figure 1F).…”

Section: Cohort 1: Network Discovery Using Multivariate Distance Matrix Regressionmentioning

confidence: 99%

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Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

et al. 2022

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Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = −0.50; 95% CI −0.75 to −0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.

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Section: Discussionsupporting

confidence: 55%

Section: Cohort 1: Network Discovery Using Multivariate Distance Matrix Regressionmentioning

confidence: 99%

Section: Cohort 1: Network Discovery Using Multivariate Distance Matrix Regressionmentioning

confidence: 99%

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Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

et al. 2022

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“…Several studies from our group and others have previously reported a link between sFC among the ACC, PCC, and PCu and symptom severity in schizophrenia (Whitfield-Gabrieli et al, 2009;Hare et al, 2019;Nawaz et al, 2021). One of those studies reported a positive correlation between PCu/PCC connectivity and symptom severity as measured by the scale for the assessment of positive symptoms (SAPS) in a relatively small number of subjects (Whitfield-Gabrieli et al, 2009).…”

Section: Introductionmentioning

confidence: 81%

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Sendi

Zendehrouh

Ellis

et al. 2021

Front. Neural Circuits

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Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized.Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects.Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity.Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

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“…Brady et al (2019) recently showed that transcranial magnetic stimulation (TMS) of the dlPFC-cerebellum network improved negative symptoms in schizophrenia, highlighting links between brain function and illness severity within a specific disorder. Schizophrenia symptom severity is also linked to resting state functional connectivity of the default mode network and the task-positive network (Nawaz et al, 2020). There is also evidence from MRI literature that an increased number of prior episodes of major depression is associated with reduced volume of dentate gyrus and cortical thinning of the left medial PFC (Treadway et al, 2015).…”

Section: Evidence For Pathoplasty Modelsmentioning

confidence: 99%

Integrating dynamic interactionism into the brain–stress–psychopathology relationship.

Kopala‐Sibley¹,

Stein²,

Bray³

et al. 2021

Canadian Psychology / Psychologie canadienne

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Public Significance StatementMuch research has examined associations between brain structure or function and psychopathology. However, less research has examined dynamic interactional models of the brain-stresspsychopathology relationship. In this paper, drawing on a long history of personality-stresspsychopathology research, we selectively review clinical neuroscience evidence regarding different models of the brain-stress-psychopathology relationship and propose a dynamic-interactionist perspective of this relationship.

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Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

Cited by 19 publications

References 35 publications

Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Integrating dynamic interactionism into the brain–stress–psychopathology relationship.

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