Flexible Brain Transitions Between Hierarchical Network Segregation and Integration Associated With Cognitive Performance During a Multisource Interference Task

Wang, Rong; Su, Xiaoli; Chang, Zhao; Lin, Pan; Wu, Ying

doi:10.1109/jbhi.2021.3119940

Cited by 17 publications

(19 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to classical graph theory, the NSP-based method had superior performance in predicting SANS and SAPS scores, reflecting the advantages of our method based on hierarchical modules in brain FC networks. This result is highly consistent with a series of our works wherein the NSP-based method is more powerful in linking the brain to diverse cognitive abilities 32 , task performance 31 , stress conditions 78 , ADHD symptoms 33 and bipolar disorder symptoms 79 . All these findings demonstrated that the NSP-based features detected across multiple levels are promising biomarkers for schizophrenia and other brain disorders.…”

Section: Biomarkers For Positive and Negative Symptomssupporting

confidence: 90%

“…Recently, a nested-spectral partition (NSP) method based on eigenmodes was proposed to detect hierarchical modules in brain networks and describe segregation and integration across multiple levels 29 , different from the classical graph measures (e.g., modularity and the participant coefficient) at a single level 30 . More importantly, the NSP method has been found to yield better neural signatures than graph theory for linking brain features to cognitive functions and attention-deficit/hyperactivity disorder (ADHD) symptoms 31,32,33 . It is thus expected that an NSP-based analysis may better reveal the opposite neural biomarkers that underlie positive and negative symptoms in schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Wang¹,

Zhao²,

Wang

2022

Preprint

View full text Add to dashboard Cite

Schizophrenia is a severe psychotic disorder clinically characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional connectivity networks in schizophrenia patients and healthy controls, and we studied the dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable global integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Attention systems were associated with hallucinations, and default-mode network (DMN) and control systems were related to avolition. In a machine-learning model, NSP-based features outperformed classical graph measures at predicting positive and negative symptom scores. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms on brain networks was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.

show abstract

Section: Biomarkers For Positive and Negative Symptomssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Wang¹,

Zhao²,

Wang

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This was proposed by [3], arguing that simpler tasks requires the coordination of few brain subnetworks, and in this case a segregated network topology should be desirable for an optimal in-task performance. For example, enhanced segregation was reported in motor learning [58], visual-attentional [59] and sustained attention tasks [60]. In the specific case of [60], segregation increases in task-related networks (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…To identify functional modules in both empirical and simulated data, we employed the hierarchical modular analysis (HMA) method following [64, 65]. Briefly, the method uses single value decomposition to find FC matrices’ eigenvectors and eigenvalues.…”

Section: Methodsmentioning

confidence: 99%

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Coronel-Oliveros

Gießing

Medel

et al. 2022

Preprint

View full text Add to dashboard Cite

Integration and segregation are accepted as two fundamental principles of brain organization. The brain manages the transitions between different functional states, more segregated or integrated, through neuromodulatory systems. Recently computational and experimental works suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology of healthy subjects during resting-state conditions and during an attentional task. Then, to find causal mechanisms about these changes, we employed a whole-brain neural mass model embedded into a human connectome for simulating the effects of nicotine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during task, but not in resting-state. In-task performance correlates with functional segregation, discovering a link between functional network topology and behavior. In the model, the drug was modeled decreasing both the global coupling and local feedback inhibition parameters of the model, consistent with the known cellular effects of acetylcholine. Also, we found that the regional density of the nicotinic acetylcholine α4β2 receptor explains better the effects of nicotine at the whole-brain level. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks. In addition, we propose biophysical mechanisms to simulate cholinergic neuromodulation in whole-brain models.

show abstract

“…Quantifying network integration, that refers to how interconnected different parts of the brain are, as well as network segregation, that refers to how independent different parts of the brain are, is tractable in network neuroscience. The balance between segregation and integration of brain networks has been studied in the setting of normal brain activity [22][23][24][25] , aging 26 , Alzheimer's disease 27 , and several neuropsychiatric disorders 28,29 . In epilepsy, both animal 30 and human studies 19,31 have shown increased segregation of epileptic networks relative to controls.…”

Section: Introductionmentioning

confidence: 99%

Resting State Functional Connectivity Demonstrates Increased Segregation in Bilateral Temporal Lobe Epilepsy

Lucas

Cornblath

Sinha

et al. 2022

Preprint

View full text Add to dashboard Cite

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe involvement during seizures. Bilateral TLE (BiTLE) remains understudied, likely due to its complex underlying pathophysiology and heterogeneous clinical presentation. In this study, using a multicenter resting state functional MRI (rs-fMRI) dataset, we constructed whole brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities ranging from 0.10-0.50. Curves of network density versus each network metric were compared between groups. Finally, we derived a combined metric, which we term the integration-segregation axis, by combining whole brain average clustering coefficient and global efficiency curves and applying principal component analysis (PCA)-based dimensionality reduction. Compared to UTLE, BiTLE had decreased global efficiency (p=0.026), increased whole brain average clustering coefficient (p=0.035), and decreased whole brain average participation coefficient across a range of network densities (p=0.001). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p=0.016). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis: 68% of patients with BiTLE were identified within the high segregation region, while only 41% of the UTLE patients were identified in the same region (p=0.042). Along the integration-segregation axis, UTLE patients with poor surgical outcomes were more similar to BiTLE than those with good surgical outcomes (p=0.72). Increased interictal whole brain network segregation, as measured by rs-fMRI, is specific to BiTLE, and may assist in non-invasively identifying this patient population prior to intracranial EEG or device implantation.

show abstract

Flexible Brain Transitions Between Hierarchical Network Segregation and Integration Associated With Cognitive Performance During a Multisource Interference Task

Cited by 17 publications

References 78 publications

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Resting State Functional Connectivity Demonstrates Increased Segregation in Bilateral Temporal Lobe Epilepsy

Contact Info

Product

Resources

About