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 Novel Inferences in a Multidimensional Social Network Use a Grid-like Code

2020 ◽  
Author(s):  
Seongmin A. Park ◽  
Douglas S. Miller ◽  
Erie D. Boorman
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Entorhinal Cortex ◽  
Brain Regions ◽  
Cognitive Map ◽  
General Mechanism ◽  
Temporoparietal Junction ◽  
Flexible Behavior ◽  
Discrete Problems

ABSTRACTGeneralizing experiences to guide decision making in novel situations is a hallmark of flexible behavior. It has been hypothesized such flexibility depends on a cognitive map of an environment or task, but directly linking the two has proven elusive. Here, we find that discretely sampled abstract relationships between entities in an unseen two-dimensional (2-D) social hierarchy are reconstructed into a unitary 2-D cognitive map in the hippocampus and entorhinal cortex. We further show that humans utilize a grid-like code in several brain regions, including entorhinal cortex and medial prefrontal cortex, for inferred direct trajectories between entities in the reconstructed abstract space during discrete decisions. Moreover, these neural grid-like codes in the entorhinal cortex predict neural decision value computations in the medial prefrontal cortex and temporoparietal junction area during choice. Collectively, these findings show that grid-like codes are used by the human brain to infer novel solutions, even in abstract and discrete problems, and suggest a general mechanism underpinning flexible decision making and generalization.

scholarly journals Integrating memories: Congruency and reactivation aid memory integration through reinstatement of prior knowledge

2019 ◽  
Author(s):  
Marlieke T.R. van Kesteren ◽  
Paul Rignanese ◽  
Pierre G. Gianferrara ◽  
Lydia Krabbendam ◽  
Martijn Meeter
Keyword(s):  
Prefrontal Cortex ◽  
Prior Knowledge ◽  
Medial Prefrontal Cortex ◽  
Medial Temporal Lobe ◽  
Knowledge Building ◽  
Activity Patterns ◽  
Brain Regions ◽  
Memory Integration ◽  
The Brain ◽  
Parahippocampal Place Area

AbstractBuilding consistent knowledge schemas that organize information and guide future learning is of great importance in everyday life. Such knowledge building is suggested to occur through reinstatement of prior knowledge during new learning in stimulus-specific brain regions. This process is proposed to yield integration of new with old memories, supported by the medial prefrontal cortex (mPFC) and medial temporal lobe (MTL). Possibly as a consequence, congruency of new information with prior knowledge is known to enhance subsequent memory. Yet, it is unknown how reactivation and congruency interact to optimize memory integration processes that lead to knowledge schemas. To investigate this question, we here used an adapted AB-AC inference paradigm in combination with functional Magnetic Resonance Imaging (fMRI). Participants first studied an AB-association followed by an AC-association, so B (a scene) and C (an object) were indirectly linked through their common association with A (an unknown pseudoword). BC-associations were either congruent or incongruent with prior knowledge (e.g. a bathduck or a hammer in a bathroom), and participants were asked to report subjective reactivation strength for B while learning AC. Behaviorally, both the congruency and reactivation measures enhanced memory integration. In the brain, these behavioral effects related to univariate and multivariate parametric effects of congruency and reactivation on activity patterns in the MTL, mPFC, and Parahippocampal Place Area (PPA). Moreover, mPFC exhibited larger connectivity with the PPA for more congruent associations. These outcomes provide insights into the neural mechanisms underlying memory integration enhancement, which can be important for educational learning.Significance statementHow does our brain build knowledge through integrating information that is learned at different periods in time? This question is important in everyday learning situations such as educational settings. Using an inference paradigm, we here set out to investigate how congruency with, and active reactivation of previously learned information affects memory integration processes in the brain. Both these factors were found to relate to activity in memory-related regions such as the medial prefrontal cortex (mPFC) and the hippocampus. Moreover, activity in the parahippocampal place area (PPA), assumed to reflect reinstatement of the previously learned associate, was found to predict subjective reactivation strength. These results show how we can moderate memory integration processes to enhance subsequent knowledge building.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

2021 ◽  
Vol 15 ◽  
Author(s):  
Paolo Finotelli ◽  
Carlo Piccardi ◽  
Edie Miglio ◽  
Paolo Dulio
Keyword(s):  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Induced Subgraphs ◽  
Topological Characterization ◽  
Network Information ◽  
The Subject ◽  
Euclidean Distances ◽  
The Brain

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

Large-scale plastic changes of the brain network in an animal model of neuropathic pain

NeuroImage ◽  
2014 ◽  
Vol 98 ◽  
pp. 203-215 ◽  
Author(s):  
Chang-Eop Kim ◽  
Yu Kyeong Kim ◽  
Geehoon Chung ◽  
Jae Min Jeong ◽  
Dong Soo Lee ◽  
...  
Keyword(s):  
Animal Model ◽  
Neuropathic Pain ◽  
Large Scale ◽  
Brain Network ◽  
Plastic Changes ◽  
The Brain

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

scholarly journals Altered neural activity in brain cough suppression networks in cigarette smokers

2019 ◽  
Vol 54 (3) ◽  
pp. 1900362 ◽  
Author(s):  
Ayaka Ando ◽  
Stuart B. Mazzone ◽  
Michael J. Farrell
Keyword(s):  
Prefrontal Cortex ◽  
Neural Circuits ◽  
Brain Regions ◽  
Smoking Behaviour ◽  
Brain Network ◽  
Functional Brain Imaging ◽  
Cigarette Smokers ◽  
Dorsolateral Prefrontal ◽  
Cortical Regions ◽  
Airway Irritation

Cough is important for airway defence, and studies in healthy animals and humans have revealed multiple brain networks intimately involved in the perception of airway irritation, cough induction and cough suppression. Changes in cough sensitivity and/or the ability to suppress cough accompany pulmonary pathologies, suggesting a level of plasticity is possible in these central neural circuits. However, little is known about how persistent inputs from the lung might modify the brain processes regulating cough.In the present study, we used human functional brain imaging to investigate the central neural responses that accompany an altered cough sensitivity in cigarette smokers.In nonsmokers, inhalation of the airway irritant capsaicin induced a transient urge-to-cough associated with the activation of a distributed brain network that included sensory, prefrontal and motor cortical regions. Cigarette smokers demonstrated significantly higher thresholds for capsaicin-induced urge-to-cough, consistent with a reduced sensitivity to airway irritation. Intriguingly, this was accompanied by increased activation in brain regions known to be involved in both cough sensory processing (primary sensorimotor cortex) and cough suppression (dorsolateral prefrontal cortex and the midbrain nucleus cuneiformis). Activations in the prefrontal cortex were highest among participants with the least severe smoking behaviour, whereas those in the midbrain correlated with more severe smoking behaviour.These outcomes suggest that smoking-induced sensitisation of central cough neural circuits is offset by concurrently enhanced central suppression. Furthermore, central suppression mechanisms may evolve with the severity of smoke exposure, changing from initial prefrontal inhibition to more primitive midbrain processes as exposure increases.

scholarly journals Neural correlates of integrated self and social processing

2020 ◽  
Vol 15 (9) ◽  
pp. 941-949
Author(s):  
Laura Finlayson-Short ◽  
Christopher G Davey ◽  
Ben J Harrison
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Processing Condition ◽  
Emotional Valence ◽  
Neural Correlates ◽  
Brain Regions ◽  
Affective Processing ◽  
Social Processing ◽  
Referential Processing ◽  
Core Self

Abstract Self-referential and social processing are often engaged concurrently in naturalistic judgements and elicit activity in overlapping brain regions. We have termed this integrated processing ‘self-other referential processing’ and developed a task to measure its neural correlates. Ninety-eight healthy young people aged 16–25 (M = 21.5 years old, 67% female) completed our novel functional magnetic resonance imaging task. The task had two conditions, an active self-other referential processing condition in which participants rated how much they related to emotional faces and a control condition. Rating relatedness required thinking about oneself (self-referential processing) and drawing a comparison to an imagined other (social processing). Self-other referential processing elicited activity in the default mode network and social cognition system; most notably in the ‘core self’ regions of the medial prefrontal cortex and posterior cingulate cortex. Relatedness and emotional valence directly modulated activity in these core self areas, while emotional valence additionally modulated medial prefrontal cortex activity. This shows the key role of the medial prefrontal cortex in constructing the ‘social-affective self’. This may help to unify disparate models of medial prefrontal cortex function, demonstrating its role in coordinating multiple processes—self-referential, social and affective processing—to allow the self to exist in a complex social world.

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