scholarly journals Brain network dynamics codify heterogeneity in seizure propagation

2021 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Claudia Lainscsek ◽  
Sydney S Cash ◽  
Javier O Garcia ◽  
Terrence J Sejnowski ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Brain Connectivity ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Brain Network ◽  
Functional Brain ◽  
Graph Theoretical Approach ◽  
Network Properties ◽  
Adaptive Cognition ◽  
And Behavior

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.

scholarly journals Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy

SLEEP ◽  
2020 ◽  
Vol 43 (8) ◽  
Author(s):  
Xiao Fulong ◽  
Spruyt Karen ◽  
Lu Chao ◽  
Zhao Dianjiang ◽  
Zhang Jun ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Neuropsychological Assessment ◽  
Resting State ◽  
Brain Connectivity ◽  
Small World ◽  
Theoretical Method ◽  
Brain Network ◽  
Topological Properties ◽  
Depressive Feelings ◽  
Network Properties

Abstract Study Objectives To evaluate functional connectivity and topological properties of brain networks, and to investigate the association between brain topological properties and neuropsychiatric behaviors in adolescent narcolepsy. Methods Resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessment were applied in 26 adolescent narcolepsy patients and 30 healthy controls. fMRI data were analyzed in three ways: group independent component analysis and a graph theoretical method were applied to evaluate topological properties within the whole brain. Lastly, network-based statistics was utilized for group comparisons in region-to-region connectivity. The relationship between topological properties and neuropsychiatric behaviors was analyzed with correlation analyses. Results In addition to sleepiness, depressive symptoms and impulsivity were detected in adolescent narcolepsy. In adolescent narcolepsy, functional connectivity was decreased between regions of the limbic system and the default mode network (DMN), and increased in the visual network. Adolescent narcolepsy patients exhibited disrupted small-world network properties. Regional alterations in the caudate nucleus (CAU) and posterior cingulate gyrus were associated with subjective sleepiness and regional alterations in the CAU and inferior occipital gyrus were associated with impulsiveness. Remodeling within the salience network and the DMN was associated with sleepiness, depressive feelings, and impulsive behaviors in narcolepsy. Conclusions Alterations in brain connectivity and regional topological properties in narcoleptic adolescents were associated with their sleepiness, depressive feelings, and impulsive behaviors.

scholarly journals Variability in Infants' Functional Brain Network Connectivity Is Associated With Differences in Affect and Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Caroline M. Kelsey ◽  
Katrina Farris ◽  
Tobias Grossmann
Keyword(s):  
Individual Differences ◽  
Long Range ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Brain ◽  
Default Mode ◽  
Behavioral Traits ◽  
Functional Brain Network ◽  
Brain Network Connectivity ◽  
And Behavior

Variability in functional brain network connectivity has been linked to individual differences in cognitive, affective, and behavioral traits in adults. However, little is known about the developmental origins of such brain-behavior correlations. The current study examined functional brain network connectivity and its link to behavioral temperament in typically developing newborn and 1-month-old infants (M [age] = 25 days; N = 75) using functional near-infrared spectroscopy (fNIRS). Specifically, we measured long-range connectivity between cortical regions approximating fronto-parietal, default mode, and homologous-interhemispheric networks. Our results show that connectivity in these functional brain networks varies across infants and maps onto individual differences in behavioral temperament. Specifically, connectivity in the fronto-parietal network was positively associated with regulation and orienting behaviors, whereas connectivity in the default mode network showed the opposite effect on these behaviors. Our analysis also revealed a significant positive association between the homologous-interhemispheric network and infants' negative affect. The current results suggest that variability in long-range intra-hemispheric and cross-hemispheric functional connectivity between frontal, parietal, and temporal cortex is associated with individual differences in affect and behavior. These findings shed new light on the brain origins of individual differences in early-emerging behavioral traits and thus represent a viable novel approach for investigating developmental trajectories in typical and atypical neurodevelopment.

scholarly journals Effects of spatial smoothing on group-level differences in functional brain networks

2020 ◽  
Vol 4 (3) ◽  
pp. 556-574
Author(s):  
Ana María Triana ◽  
Enrico Glerean ◽  
Jari Saramäki ◽  
Onerva Korhonen
Keyword(s):  
Brain Connectivity ◽  
Autism Spectrum ◽  
Brain Network ◽  
Group Level ◽  
Spatial Smoothing ◽  
Link Length ◽  
Functional Brain ◽  
Gaussian Kernels ◽  
Smoothing Kernel ◽  
Functional Brain Network

Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.

scholarly journals Functional brain connectivity phenotypes for schizophrenia drug discovery

2015 ◽  
Vol 29 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Neil Dawson ◽  
Brian J Morris ◽  
Judith A Pratt
Keyword(s):  
Drug Discovery ◽  
Brain Connectivity ◽  
Neural System ◽  
Brain Dysfunction ◽  
Brain Network ◽  
Attrition Rate ◽  
Rodent Models ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Electrophysiological Studies

While our knowledge of the pathophysiology of schizophrenia has increased dramatically, this has not translated into the development of new and improved drugs to treat this disorder. Human brain imaging and electrophysiological studies have provided dramatic new insight into the mechanisms of brain dysfunction in the disease, with a swathe of recent studies highlighting the differences in functional brain network and neural system connectivity present in the disorder. Only recently has the value of applying these approaches in preclinical rodent models relevant to the disorder started to be recognised. Here we highlight recent findings of altered functional brain connectivity in preclinical rodent models and consider their relevance to those alterations seen in the brains of schizophrenia patients. Furthermore, we highlight the potential translational value of using the paradigm of functional brain connectivity phenotypes in the context of preclinical schizophrenia drug discovery, as a means both to understand the mechanisms of brain dysfunction in the disorder and to reduce the current high attrition rate in schizophrenia drug discovery.

scholarly journals EEG Resting-State Brain Topological Reorganization as a Function of Age

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Manuela Petti ◽  
Jlenia Toppi ◽  
Fabio Babiloni ◽  
Febo Cincotti ◽  
Donatella Mattia ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Brain Network ◽  
Graph Theoretical Approach ◽  
Network Properties ◽  
Filter Approach ◽  
Effects Of Aging ◽  
Middle Aged Adults ◽  
Resting State Connectivity

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20–63 years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document