Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task

Godwin, Douglass; Ji, A.B.; Kandala, Sridhar; Mamah, Daniel

doi:10.3389/fpsyt.2017.00294

Cited by 44 publications

(32 citation statements)

References 82 publications

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“…Furthermore, by decomposing the connectivity into connectional strength and connectional connectivity (namely, nodal connectivity diversity), we unveiled two different properties of the DMN and FPN under different cognitive loads (2‐back and 0‐back), that is, flexibility and strength, respectively (Zuo et al, ). These results agree with our findings that the DMN and the FPN simultaneously showed strong ASS and SASS with other networks, signifying that these two networks globally modulated the functions of the brain in the WM test (Cassidy et al, ; Douw, Wakeman, Tanaka, Liu, & Stufflebeam, ; Godwin, Ji, Kandala, & Mamah, ).…”

Section: Discussionsupporting

confidence: 92%

Activation‐based association profiles differentiate network roles across cognitive loads

Zuo

Salami

Yang

et al. 2019

Human Brain Mapping

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Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level‐dependent (BOLD) response across several large‐scale brain networks, the unique and shared contributions of each large‐scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared‐association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared‐association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2‐back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared‐associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared‐associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

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Section: Discussionsupporting

confidence: 92%

Activation‐based association profiles differentiate network roles across cognitive loads

Zuo

Salami

Yang

et al. 2019

Human Brain Mapping

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“…Our findings are in line with previous literature describing schizophrenia as a disorder of brain connectivity (5,6,11). Along with the growing body of evidence linking connectivity directly to task-evoked brain activity (17,18,20,42) and cognitive function (13,43), the current study provides support to the claim that abnormal brain activation patterns and altered cognitive function, which are widely reported in schizophrenia, may correspond directly to changes in the architecture of connectivity-derived brain networks. The successful predictions of taskevoked brain activity from task-free functional connectivity measures suggest that the abnormalities observed in SCZ in task-performance and the related brain activity are manifested at the level of functional connectivity and may even derive directly from connectivity alterations.…”

Section: Discussionsupporting

confidence: 92%

“…Alterations in various RSN's which are associated with high cognitive functions, such as the default-mode (11), salience (12) and fronto-parietal control (13) networks are consistently reported in schizophrenia, and are in line with the substantial cognitive deficit and behavioral changes that are among the hallmarks of this condition (1,14).…”

Section: Introductionmentioning

confidence: 82%

Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

Tik

Livny

Gal

et al. 2020

Preprint

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BACKGROUND:Patients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia. METHODS:We generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in taskevoked brain activation in 20 patients diagnosed with schizophrenia. RESULTS:We demonstrated a successful prediction of individual variability in the taskevoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps ( = 0.589, = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test ( = 0.25, < 0.0001). CONCLUSIONS:A Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task. -50.64 (12.83) PANSS general Mean (STD) 23.47(5.47) PANSS positive Mean (STD) -11 (4.99) PANSS negative Mean (STD) -16.18 (5.16)

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“…We hypothesized that these deficits may in part be caused by altered long‐distance connectivity in functional networks that serve these cognitive functions (Bartzokis, ; Bartzokis, et al, ; Felts, Baker, & Smith, ; Waxman & Bennett, ). Magnetic resonance imaging (MRI) exploring the “resting‐state” and “task‐based” functionality of individuals with schizophrenia suggests reduced connectivity in networks that interconnect frontal, parietal, and temporal functions and are activated during tasks of higher cognitive function (Fornito & Bullmore, ; Godwin, Ji, Kandala, & Mamah, ; Kelly et al, ; Li et al, ; Liang et al, ; Whitfield‐Gabrielia et al, ). Prior studies suggest that reduced functional connectivity (FC) is caused by complex interactions between brain maturation and genetic and environmental etiologies for schizophrenia (Blokland, ; Calhoun, Eichele, & Pearlson, ; Karbasforoushan & Woodward, ; Whitfield‐Gabrieli & Ford, ).…”

Section: Introductionmentioning

confidence: 99%

Functional network connectivity impairments and core cognitive deficits in schizophrenia

Adhikari

Hong

Sampath

et al. 2019

Human Brain Mapping

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Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting‐state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta‐ and mega‐analyses were used to evaluate patient‐control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting‐state FC (rsFC) deficits across three cohorts. Patient‐control differences in rsFC calculated using seed‐based and dual‐regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10−4). RsFC measures explained 12–17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default‐mode networks. The pattern of association between rsFC (within‐network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between‐network) and PS (r = .52, p = 8.4 × 10−3) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient‐control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity‐cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.

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Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task

Cited by 44 publications

References 82 publications

Activation‐based association profiles differentiate network roles across cognitive loads

Activation‐based association profiles differentiate network roles across cognitive loads

Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

Functional network connectivity impairments and core cognitive deficits in schizophrenia

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