scholarly journals Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex

2021 ◽  
Vol 14 ◽  
Author(s):  
Hashini Wanniarachchi ◽  
Yan Lang ◽  
Xinlong Wang ◽  
Tyrell Pruitt ◽  
Sridhar Nerur ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Inventory Management ◽  
Brain Network ◽  
Cerebral Hemodynamics ◽  
Newsvendor Problem ◽  
Clustering Coefficient ◽  
Profit Margins ◽  
Network Properties ◽  
Local Brain

While many publications have reported brain hemodynamic responses to decision-making under various conditions of risk, no inventory management scenarios, such as the newsvendor problem (NP), have been investigated in conjunction with neuroimaging. In this study, we hypothesized (I) that NP stimulates the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) joined with frontal polar area (FPA) significantly in the human brain, and (II) that local brain network properties are increased when a person transits from rest to the NP decision-making phase. A 77-channel functional near infrared spectroscopy (fNIRS) system with wide field-of-view (FOV) was employed to measure frontal cerebral hemodynamics in response to NP in 27 healthy human subjects. NP-induced changes in oxy-hemoglobin concentration, Δ[HbO], were investigated using a general linear model (GLM) and graph theory analysis (GTA). Significant activation induced by NP was shown in both DLPFC and OFC+FPA across all subjects. Specifically, higher risk NP with low-profit margins (LM) activated left-DLPFC but deactivated right-DLPFC in 14 subjects, while lower risk NP with high-profit margins (HM) stimulated both DLPFC and OFC+FPA in 13 subjects. The local efficiency, clustering coefficient, and path length of the network metrics were significantly enhanced under NP decision making. In summary, multi-channel fNIRS enabled us to identify DLPFC and OFC+FPA as key cortical regions of brain activations when subjects were making inventory-management risk decisions. We demonstrated that challenging NP resulted in the deactivation within right-DLPFC due to higher levels of stress. Also, local brain network properties were increased when a person transitioned from the rest phase to the NP decision-making phase.

scholarly journals Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

scholarly journals Structural Network Alterations Induced by ART-naive and ART-treated Subjects Infected with HIV

2020 ◽  
Author(s):  
Jiaojiao Liu ◽  
Wei Wang ◽  
Yuanyuan Wang ◽  
Mingming Liu ◽  
Dan Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Immune Suppression ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Cd4 Count ◽  
Hiv Patients ◽  
Regional Characteristics ◽  
History Of ◽  
Network Properties ◽  
Cortical Regions

Abstract BackgroundTo investigate how the structural connectivity altered in cART-treated HIV patients and cART-naïve HIV patients by conducting Network analysis of Diffusion Tensor Imaging (DTI) data.MethodsWe enrolled 22 cART-naïve, 23 cART-treated and 28 normal controls in our current study. Firstly, the DTI imaging data pre-processing was conducted and the asymmetric 90 × 90 matrix for each participant from their DTI data was obtained with the use of PANDA. Then, we applied a graph-theoretical network analysis toolkit, GRETNA v2.0, to calculate metrics such as small-“worldness,” characteristic path length, clustering coefficient, global efficiency, local efficiency, and nodal “betweenness”. Finally, we took comparisons among the three groups to investigate topological alterations, and we also conducted relevant analysis with current CD4 T cell counts and neuropsychological evaluation.ResultsResults (1) the regional characteristics (nodal efficiency) were altered in CART- and CART+ patients predominantly in the frontal cortical regions; (2) changes in various network properties in CART- and CART+ patients were associated with the performance of behavior functions; (3) Reduced network segregation was associated with lower current CD4 count in cART- participants, suggesting that brain network segregation may be adversely affected by a history of greater immune suppression. (4) Hubs redistributed in HIV subjects especially in cART+ patients. Conclusion1) The regional characteristics (nodal efficiency) were altered in cART-naïve and cART-treated patients predominantly in the frontal cortical regions; (2) changes in various network properties in cART-naïve and cART-treated patients were associated with the performance of behavior functions; (3) reduced network segregation was associated with lower current CD4 count in cART-naïve participants, suggesting that brain network segregation may have been adversely affected by a history of greater immune suppression. (4) Hubs redistributed in HIV subjects especially in cART-treated patients.

scholarly journals Metabolic Brain Network Analysis With 18F-FDG PET in a Rat Model of Neuropathic Pain

2021 ◽  
Vol 12 ◽  
Author(s):  
Bei-Bei Huo ◽  
Mou-Xiong Zheng ◽  
Xu-Yun Hua ◽  
Jun Shen ◽  
Jia-Jia Wu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Entorhinal Cortex ◽  
Brain Regions ◽  
Brain Network ◽  
Caudate Putamen ◽  
Metabolic Connectivity ◽  
Network Properties ◽  
The Brain

Neuropathic pain has been found to be related to profound reorganization in the function and structure of the brain. We previously demonstrated changes in local brain activity and functional/metabolic connectivity among selected brain regions by using neuroimaging methods. The present study further investigated large-scale metabolic brain network changes in 32 Sprague–Dawley rats with right brachial plexus avulsion injury (BPAI). Graph theory was applied in the analysis of 2-deoxy-2-[18F] fluoro-D-glucose (18F-FDG) PET images. Inter-subject metabolic networks were constructed by calculating correlation coefficients. Global and nodal network properties were calculated and comparisons between pre- and post-BPAI (7 days) status were conducted. The global network properties (including global efficiency, local efficiency and small-world index) and nodal betweenness centrality did not significantly change for all selected sparsity thresholds following BPAI (p > 0.05). As for nodal network properties, both nodal degree and nodal efficiency measures significantly increased in the left caudate putamen, left medial prefrontal cortex, and right caudate putamen (p < 0.001). The right entorhinal cortex showed a different nodal degree (p < 0.05) but not nodal efficiency. These four regions were selected for seed-based metabolic connectivity analysis. Strengthened connectivity was found among these seeds and distributed brain regions including sensorimotor area, cognitive area, and limbic system, etc. (p < 0.05). Our results indicated that the brain had the resilience to compensate for BPAI-induced neuropathic pain. However, the importance of bilateral caudate putamen, left medial prefrontal cortex, and right entorhinal cortex in the network was strengthened, as well as most of their connections with distributed brain regions.

scholarly journals Hippocampal regenerative medicine: neurogenic implications for addiction and mental disorders

2021 ◽  
Author(s):  
Lee Peyton ◽  
Alfredo Oliveros ◽  
Doo-Sup Choi ◽  
Mi-Hyeon Jang
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Executive Function ◽  
General Population ◽  
Mental Disorders ◽  
Psychiatric Illness ◽  
Neural Circuitry ◽  
Brain Regions ◽  
Neuropsychiatric Disease ◽  
Motivated Behavior

AbstractPsychiatric illness is a prevalent and highly debilitating disorder, and more than 50% of the general population in both middle- and high-income countries experience at least one psychiatric disorder at some point in their lives. As we continue to learn how pervasive psychiatric episodes are in society, we must acknowledge that psychiatric disorders are not solely relegated to a small group of predisposed individuals but rather occur in significant portions of all societal groups. Several distinct brain regions have been implicated in neuropsychiatric disease. These brain regions include corticolimbic structures, which regulate executive function and decision making (e.g., the prefrontal cortex), as well as striatal subregions known to control motivated behavior under normal and stressful conditions. Importantly, the corticolimbic neural circuitry includes the hippocampus, a critical brain structure that sends projections to both the cortex and striatum to coordinate learning, memory, and mood. In this review, we will discuss past and recent discoveries of how neurobiological processes in the hippocampus and corticolimbic structures work in concert to control executive function, memory, and mood in the context of mental disorders.

scholarly journals Dissociable roles of cortical excitation-inhibition balance during patch-leaving versus value-guided decisions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luca F. Kaiser ◽  
Theo O. J. Gruendler ◽  
Oliver Speck ◽  
Lennart Luettgau ◽  
Gerhard Jocham
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neuronal Activity ◽  
Ventromedial Prefrontal Cortex ◽  
Anterior Cingulate ◽  
Mutual Inhibition ◽  
Dorsal Anterior Cingulate Cortex ◽  
Cortical Excitation ◽  
The Cost

AbstractIn a dynamic world, it is essential to decide when to leave an exploited resource. Such patch-leaving decisions involve balancing the cost of moving against the gain expected from the alternative patch. This contrasts with value-guided decisions that typically involve maximizing reward by selecting the current best option. Patterns of neuronal activity pertaining to patch-leaving decisions have been reported in dorsal anterior cingulate cortex (dACC), whereas competition via mutual inhibition in ventromedial prefrontal cortex (vmPFC) is thought to underlie value-guided choice. Here, we show that the balance between cortical excitation and inhibition (E/I balance), measured by the ratio of GABA and glutamate concentrations, plays a dissociable role for the two kinds of decisions. Patch-leaving decision behaviour relates to E/I balance in dACC. In contrast, value-guided decision-making relates to E/I balance in vmPFC. These results support mechanistic accounts of value-guided choice and provide evidence for a role of dACC E/I balance in patch-leaving decisions.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

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