Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia

McNabb, Carolyn Beth; Tait, Roger; McIlwain, Meghan; Anderson, Valerie; Suckling, John; Kydd, Robert R.; Russell, Bruce

doi:10.1016/j.schres.2017.10.015

Cited by 41 publications

(37 citation statements)

References 53 publications

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“…Dopaminergic and glutamatergic signaling have also been consistently implicated in reward processing and effort cost computation in healthy subjects and individuals with schizophrenia (29,46,53,(141)(142)(143)(144)(145). Evidence suggests downstream effects of dopamine and glutamate on c o n n e c t i v i t y i n b r a i n c i r c u i t r y i n p a t i e n t s w i t h schizophrenia (146,147) including disrupted resting state networks (148,149).…”

Section: Inflammation Dopaminergic/ Glutamatergic Signaling and Rewmentioning

confidence: 99%

Inflammation and Negative Symptoms of Schizophrenia: Implications for Reward Processing and Motivational Deficits

Goldsmith

Rapaport

2020

Front. Psychiatry

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Negative symptoms of schizophrenia are debilitating and chronic in nature, are difficult to treat, and contribute to poor functional outcomes. Motivational deficits are a core negative symptom and may involve alterations in reward processing, which involve subcortical regions such as the basal ganglia. More specifically, dopamine-rich regions like the ventral striatum, have been implicated in these reward-processing deficits. Inflammation is one mechanism that may underlie negative symptoms, and specifically motivational deficits, via the effects of inflammatory cytokines on the basal ganglia. Previous work has demonstrated that inflammatory stimuli decrease neural activity in the ventral striatum and decrease connectivity in reward-relevant neural circuitry. The immune system has been shown to be involved in the pathophysiology of schizophrenia, and inflammatory cytokines have been shown to be altered in patients with the disorder. This paper reviews the literature on associations between inflammatory markers and negative symptoms of schizophrenia as well as the role of anti-inflammatory drugs to target negative symptoms. We also review the literature on the role of inflammation and reward processing deficits in both healthy controls and individuals with depression. We use the literature on inflammation and depression as a basis for a model that explores potential mechanisms responsible for inflammation modulating certain aspects of negative symptoms in patients with schizophrenia. This approach may offer novel targets to treat these symptoms of the disorder that are significant barriers to functional recovery and do not respond well to available antipsychotic medications.

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Section: Inflammation Dopaminergic/ Glutamatergic Signaling and Rewmentioning

confidence: 99%

Inflammation and Negative Symptoms of Schizophrenia: Implications for Reward Processing and Motivational Deficits

Goldsmith

Rapaport

2020

Front. Psychiatry

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“…Previous group-level statistical analysis of neuroimaging data uncovered some neuroanatomical and functional 6 Computational Intelligence and Neuroscience differences between SZ patients and NCs [66][67][68]. Nevertheless, those findings had limited clinical applications.…”

Section: Discussionmentioning

confidence: 99%

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

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“…Analysis of pre-processed data is illustrated in Figure 1. First, motion-corrected fMRI data were divided into 272 parcels using a whole-brain parcellation scheme 44 . This parcellation scheme combines parcels from the Human Brainnetome Atlas 45 , a modification of the Desikan-Killiany (DK) atlas 46 and the probabilistic MR atlas of the human cerebellum 47 .…”

Section: Functional Mri Data Analysismentioning

confidence: 99%

No evidence for a relationship between social closeness and similarity in resting-state functional brain connectivity in schoolchildren

McNabb

Burgess

Fancourt³

et al. 2019

Preprint

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Previous research suggests that the proximity of individuals in a social network predicts how similarly their brains respond to naturalistic stimuli. However, the relationship between social connectedness and brain connectivity in the absence of external stimuli has not been examined. To investigate whether neural homophily between friends exists at rest we collected resting-state functional magnetic resonance imaging (fMRI) data from 68 school-aged girls, along with social network information from all pupils in their year groups (total 5,066 social dyads). Participants were asked to rate the amount of time they voluntarily spent with each person in their year group, and directed social network matrices and community structure were then determined from these data. No statistically significant relationships between social distance, community homogeneity and similarity of global-level resting-state connectivity were observed. Nor were we able to predict social distance using a machine learning technique (i.e. elastic net regression based on the local-level similarities in resting-state whole-brain connectivity between participants). Although neural homophily between friends exists when viewing naturalistic stimuli, this finding did not extend to functional connectivity at rest in our population. Instead, resting-state connectivity may be less susceptible to the influences of a person’s social environment.

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Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia

Cited by 41 publications

References 53 publications

Inflammation and Negative Symptoms of Schizophrenia: Implications for Reward Processing and Motivational Deficits

Inflammation and Negative Symptoms of Schizophrenia: Implications for Reward Processing and Motivational Deficits

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

No evidence for a relationship between social closeness and similarity in resting-state functional brain connectivity in schoolchildren

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