fMRI as a Measure of Cognition Related Brain Circuitry in Schizophrenia

Libby, Laura A.; Ragland, J. Daniel

doi:10.1007/7854_2011_173

Cited by 6 publications

(5 citation statements)

References 54 publications

(64 reference statements)

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“…Hyperconnectivity in the resting-state default mode network (DMN) in patients with schizophrenia has been reported elsewhere. 16 , 38 Also, DMN deactivation during cognitive tasks may be deficient in schizophrenia, 41 which might relate to the overall higher functional connectivity in patients vs. controls, similar to our observations in this task-based fMRI study. In addition, some studies have reported increased thalamo-sensorimotor connectivity in schizophrenia, 18 , 33 , 34 although they also reported decreased thalamo-frontal connectivity in this context, which was not observed in our analysis (there were no thalamo-frontal links in the stable links subset).…”

Section: Discussionsupporting

confidence: 83%

“…16, 37, 38 Here, we observed that a large fraction of whole-brain connections had increased link-weight as well as nodal degrees in schizophrenic patients (Supplementary Figs. S3 and S5).…”

Section: Discussionmentioning

confidence: 64%

“…While there is a general consensus on disruption of functional connectivity (in several brain areas) in schizophrenia, so far there is no consensus on the direction of the alteration, with some studies reporting hyper-connectivity and others hypo-connectivity, as well as a mixture of the two. 16 , 37 , 38 Here, we observed that a large fraction of whole-brain connections had increased link-weight as well as nodal degrees in schizophrenic patients (Supplementary Figs. S3 and S5 ).…”

Section: Discussionmentioning

confidence: 64%

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Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

et al. 2017

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Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale datadriven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs. controls. We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Wholebrain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Linkweight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.

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Section: Discussionsupporting

confidence: 83%

“…16, 37, 38 Here, we observed that a large fraction of whole-brain connections had increased link-weight as well as nodal degrees in schizophrenic patients (Supplementary Figs. S3 and S5).…”

Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 64%

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Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

et al. 2017

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“…This seems in contrast with the results of previous studies that global/average connectivity strength was signi cantly reduced in SZ compared to controls [23,24]. However, it should be noted that there are many other studies reporting increased connectivities in the resting-state DMN [25], thalamo-sensorimotor link [26] and computational modeling [27]. The most prominent aberrant connections in SZ were between the cingulate and inferior frontal gyri.…”

Section: Discussioncontrasting

confidence: 91%

Diagnosis of Schizophrenia with Functional Connectome Data: A Graph-Based Convolutional Neural Network Approach

Tsogt

et al. 2021

SSRN Journal

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Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the Brain Graph Covariance Pooling Network (BrainGCPNet) by incorporating global covariance pooling and BrainNetCNN into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainGCPNet and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainGCPNet showed an accuracy of 83•13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also signi cant in the functional connectivity analysis. Our ndings suggest that the proposed BrainGCPNet can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information.These results highlight the potential use of the BrainGCPNet in the diagnosis of schizophrenia.

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“…The dorsolateral PFC exhibits perhaps the most consistent dysfunction across studies, but there is little commonality in other brain regions affected, and no evidence for a deficit in a single localized brain region that can explain all of the group differences in task performance ( i.e. , no ‘smoking gun’; Libby and Ragland, 2012 , p255).…”

Section: Introductionmentioning

confidence: 99%

Reality monitoring impairment in schizophrenia reflects specific prefrontal cortex dysfunction

Garrison

Fernández-Egea

Zaman

et al. 2017

NeuroImage: Clinical

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Reality monitoring impairment is often reported in schizophrenia but the neural basis of this deficit is poorly understood. Difficulties with reality monitoring could be attributable to the same pattern of neural dysfunction as other cognitive deficits that characterize schizophrenia, or might instead represent a separable and dissociable impairment. This question was addressed through direct comparison of behavioral performance and neural activity associated with reality monitoring and working memory in patients with schizophrenia and matched healthy controls. Participants performed a word-pair reality monitoring task and a Sternberg working memory task while undergoing fMRI scanning. Distinct behavioral deficits were observed in the patients during performance of each task, which were associated with separable task- and region-specific dysfunction in the medial anterior prefrontal cortex for reality monitoring and dorsolateral prefrontal cortex for working memory. The results suggest that reality monitoring impairment is a distinct neurocognitive deficit in schizophrenia. The findings are consistent with the presence of a range of dissociable cognitive deficits in schizophrenia which may be associated with variable functional and structural dysconnectivity in underlying processing networks.

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fMRI as a Measure of Cognition Related Brain Circuitry in Schizophrenia

Cited by 6 publications

References 54 publications

Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Diagnosis of Schizophrenia with Functional Connectome Data: A Graph-Based Convolutional Neural Network Approach

Reality monitoring impairment in schizophrenia reflects specific prefrontal cortex dysfunction

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