Looking at comorbidity through the glasses of neuroscientific memory research: A brain-network perspective

2010 ◽  
Vol 33 (2-3) ◽  
pp. 170-171 ◽  
Author(s):  
Angelica Staniloiu ◽  
Hans J. Markowitsch
Keyword(s):  
Brain Imaging ◽  
Psychiatric Comorbidity ◽  
Theoretical Models ◽  
Brain Network ◽  
Psychiatric Diseases ◽  
Network Approach ◽  
Memory Research ◽  
Psychiatric Illnesses ◽  
The Brain ◽  
Structural Brain

AbstractAs psychiatric illnesses have correlates in the brain, it is surprising that Cramer et al. make almost no reference to the brain's network character when proposing a network approach to comorbidity of psychiatric diseases. We illustrate how data from combined neuropsychological and functional and structural brain-imaging investigations could inform theoretical models about the role played by overlapping symptoms in the etiology of psychiatric comorbidity and the pathways from one disorder to another.

Epilepsy in later childhood and adulthood

2010 ◽  
pp. 4810-4826
Author(s):  
G.D. Perkin ◽  
M.R. Johnson
Keyword(s):  
Brain Imaging ◽  
Neuronal Activity ◽  
Case History ◽  
Epileptic Seizure ◽  
Neurological Disease ◽  
Epileptic Seizures ◽  
Social Consequences ◽  
Definition Of ◽  
The Brain ◽  
Structural Brain

Case History—A 33 yr old woman, known to have epilepsy, now presenting with odd behaviour. An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is defined as a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiological, cognitive, psychological, and social consequences of this condition. The definition of epilepsy requires the occurrence of at least one epileptic seizure and evidence for an enduring alteration in the brain that increases the likelihood of future seizures such as an ‘epileptiform’ EEG abnormality, an appropriate lesion on structural brain imaging (CT or MRI), or the presence of recurrent (two or more) seizures. Epilepsy is a common, serious neurological disease, with prevalence 1% and a cumulative lifetime risk of 5%....

P.187 Genetic vulnerability to various psychiatric illnesses is associated with a shared structural brain network

2019 ◽  
Vol 29 ◽  
pp. S143-S144
Author(s):  
M. Taquet ◽  
S.M. Smith ◽  
A.K. Prohl ◽  
J.M. Peters ◽  
S.K. Warfield ◽  
...  
Keyword(s):  
Brain Network ◽  
Genetic Vulnerability ◽  
Psychiatric Illnesses ◽  
Structural Brain Network ◽  
Structural Brain

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

2021 ◽  
pp. 2250029
Author(s):  
Geng Zhang ◽  
Qi Zhu ◽  
Jing Yang ◽  
Ruting Xu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Medical Image Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Structural Brain Network ◽  
The Brain ◽  
Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

scholarly journals Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

2020 ◽  
Author(s):  
Lu Wang ◽  
Feng Vankee Lin ◽  
Martin Cole ◽  
Zhengwu Zhang
Keyword(s):  
Oral Reading ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Learning Differences ◽  
Structural Brain Network ◽  
Levels Of Functioning ◽  
The Brain ◽  
Variable Selection Problem ◽  
Structural Brain

AbstractStructural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

scholarly journals A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer’s Disease

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Sang Wook Yoo ◽  
Cheol E. Han ◽  
Joseph S. Shin ◽  
Sang Won Seo ◽  
Duk L. Na ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
High Education ◽  
Network Flow ◽  
Cognitive Reserve ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Education Levels ◽  
The Brain ◽  
Structural Brain

Abstract Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer’s disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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