Functional network changes and cognitive control in schizophrenia

Ray, Kimberly L.; Lesh, Tyler A.; Howell, Amber; Salo, Taylor; Ragland, J. Daniel; MacDonald, Angus W.; Gold, James M.; Silverstein, Steven M.; Deana, M; Carter, Cameron S.

doi:10.1016/j.nicl.2017.05.001

Cited by 39 publications

(43 citation statements)

References 75 publications

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“…In other words, the DPX was most modular (Q) and contained the greatest mutual information (I) whereas RiSE encoding was least modular (Q) and contained the least amount of mutual information (I). Incorporating the current findings with previous studies investigating FPN specific functional connectivity changes associated with cognitive control on the same sample (Ray et al (2017), the DPX task displayed the greatest change in within FPN functional connectivity between high and low CC conditions (Ray et al, 2017) and our current work suggests it is also most modular (i.e. greatest network segregation) and experiences the least amount of network reorganization.…”

Section: Network Reorganizationsupporting

confidence: 73%

“…Previous fMRI whole-brain analyses from our group using a different healthy sample have shown that contrasting B-cues relative to A-cues in the DPX task elicits widespread activations in the cognitive control FPN, including bilateral DLPFC, bilateral fusiform gyri, and right inferior parietal gyrus (Lopez-Garcia et al, 2015). These activation findings have been replicated in the current sample (Poppe et al, 2016) and subsequent functional connectivity analyses have also demonstrated network specific engagement of the FPN during the DPX task (Ray et al, 2017).…”

Section: Data Acquisitionsupporting

confidence: 73%

“…greatest network segregation) and experiences the least amount of network reorganization. Conversely, the RiSE encoding task displayed the least within FPN change in function connectivity between high and low CC conditions (Ray et al, 2017) while current work shows RiSE encoding to be least modular yet undergoes the most network reorganization. All the while, performance on these tasks is significantly correlated, suggesting that they share common processes associated with cognitive control (Sheffield et al, 2014) and they elicit increased activity in the dorsolateral prefrontal and parietal cortices in fMRI studies (Lopez-Garcia et al, 2015;Ragland et al, 2015).…”

Section: Network Reorganizationcontrasting

confidence: 48%

“…Previous RiSE fMRI studies contrasting relational (high cognitive control) against item-specific (low cognitive control) in the same sample have identified robust activation increases in the bilateral DLPFC, VLPFC, parietal, and occipital cortices (Ragland et al, 2015). Furthermore, functional connectivity analyses examined in this sample have demonstrated network specific engagement of the FPN during the RiSE task (Ray et al, 2017).…”

Section: Data Acquisitionmentioning

confidence: 50%

“…variation in connectivity states) involved in each of these cognitive tasks. Prior work from this sample used the Network Based Statistic (Zalesky et al, 2010) to identify increases in functional connections within the FPN associated with cognitive control demand (Ray et al, 2017). However, it is not clear how within network FC changes correspond to brain wide network organization.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Reorganization of the Frontal Parietal Network During Cognitive Control and Episodic Memory

Ray

Ragland

Macdonald

et al. 2019

Preprint

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Higher cognitive functioning is supported by adaptive reconfiguration of large-scale functional brain networks. Cognitive control (CC), which plays a vital role in flexibly guiding cognition and behavior in accordance with our goals, supports a range of executive functions via distributed brain networks. These networks process information dynamically and can be represented as functional connectivity changes between network elements.Using graph theory, we explored context-dependent network reorganization in 56 healthy adults performing fMRI tasks from two cognitive domains that varied in CC and episodic-memory demands. We examined whole-brain modular structure during the DPX task, which engages proactive CC in the frontal-parietal cognitive control network (FPN), and the RiSE task, which manipulates CC demands at encoding and retrieval during episodic-memory processing, and engages FPN, the medial-temporal lobe and other memory related networks in a context dependent manner.Analyses revealed different levels of network integration and segregation. High CC conditions exhibited greater integration across tasks. Moreover, nodes associated with higher cognitive functioning displayed the greatest amount of dynamic module reorganization across tasks.Specifically, the FPN displayed a high level of segregation in the DPX task, where only demands for proactive control varied, and more complex network integration with default mode and salience networks in the RiSE task, where CC is differentially integrated during memory encoding and retrieval. These findings provide insight into how brain networks reorganize to support differing task contexts, suggesting that the FPN flexibly segregates during focused proactive control and integrates to support control in other domains such as episodic-memory.

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Section: Network Reorganizationsupporting

confidence: 73%

Section: Data Acquisitionsupporting

confidence: 73%

Section: Network Reorganizationcontrasting

confidence: 48%

Section: Data Acquisitionmentioning

confidence: 50%

Section: Introductionmentioning

confidence: 99%

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Dynamic Reorganization of the Frontal Parietal Network During Cognitive Control and Episodic Memory

Ray

Ragland

Macdonald

et al. 2019

Preprint

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Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks

Polat

2023

J Applied Clin Med Phys

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The complexity of symptoms of schizophrenia (SZ) complicate traditional and effective diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time‐consuming, and error‐prone. Thus, there is a requirement to develop automated systems for timely and accurate diagnosis of SZ. This paper proposes an automated SZ diagnosis pipeline based on residual neural networks (ResNet). To exploit the superior image processing capabilities of the ResNet models, multi‐channel electroencephalogram (EEG) signals were converted into functional connectivity representations (FCRs). The functional connectivity of multiple regions in the cerebral cortex is critical for a better understanding of the mechanisms of SZ. In creating the FCR input images, the phase lag index (PLI) was calculated based on 16‐channel EEG signals from 45 SZ patients and 39 healthy control (HC) subjects to reduce and avoid the volume conduction effect. The experimental results showed that satisfactory classification performance (accuracy = 96.02%, specificity = 94.85%, sensitivity = 97.03%, precision = 95.70%, and F1‐score = 96.33%) was achieved by combining FCR inputs of beta oscillatory and the ResNet‐50 model. The statistical analyses also confirmed that there is a significant difference between SZ patients and HC subjects (p < 0.001, one‐way ANOVA). More specifically, the average connectivity strengths between nodes in the parietal cortex and those in the central, occipital, and temporal regions were significantly reduced in SZ patients compared to HC subjects. Overall results demonstrated that this paper not only provided an automated diagnostic model whose classification performance is superior to most previous studies but also valuable biomarkers for clinical use.

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Nonsocial and social cognition in schizophrenia: current evidence and future directions

2019

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Cognitive impairment in schizophrenia involves a broad array of nonsocial and social cognitive domains. It is a core feature of the illness, and one with substantial implications for treatment and prognosis. Our understanding of the causes, consequences and interventions for cognitive impairment in schizophrenia has grown substantially in recent years. Here we review a range of topics, including: a) the types of nonsocial cognitive, social cognitive, and perceptual deficits in schizophrenia; b) how deficits in schizophrenia are similar or different from those in other disorders; c) cognitive impairments in the prodromal period and over the lifespan in schizophrenia; d) neuroimaging of the neural substrates of nonsocial and social cognition, and e) relationships of nonsocial and social cognition to functional outcome. The paper also reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches. In the final section, we consider areas that are emerging and have the potential to provide future insights, including the interface of motivation and cognition, the influence of childhood adversity, metacognition, the role of neuroinflammation, computational modelling, the application of remote digital technology, and novel methods to evaluate brain network organization. The study of cognitive impairment has provided a way to approach, examine and comprehend a wide range of features of schizophrenia, and it may ultimately affect how we define and diagnose this complex disorder.

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Functional network changes and cognitive control in schizophrenia

Cited by 39 publications

References 75 publications

Dynamic Reorganization of the Frontal Parietal Network During Cognitive Control and Episodic Memory

Dynamic Reorganization of the Frontal Parietal Network During Cognitive Control and Episodic Memory

Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks

Nonsocial and social cognition in schizophrenia: current evidence and future directions

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