22 Whole-brain functional connectivity based on the graph theory analysiisn Psychogenic Non-Epileptic Seizures (PNES)

2020 ◽  
Vol 91 (8) ◽  
pp. e17.1-e17
Author(s):  
M Arbabi ◽  
S Amiri ◽  
F Badragheh ◽  
MM Mirbagheri ◽  
AA Asadi-Pooya
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Networks ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Control Subjects ◽  
Wide Range ◽  
Healthy Control ◽  
Left And Right ◽  
Brain Functional Connectivity

ObjectiveDespite being the subject of many studies over the past two decades, mechanisms underlying psychogenic non-epileptic seizures (PNES) are still poorly understood. We tried to address this issue by utilizing brain functional connectivity analysis to identify brain regions with abnormal activities in patients with PNES. In a case-control study, we performed graph based network analysis, a robust technique that determines the organization of brain connectivity and characterizes topological properties of the brain networks.MethodsTwelve individuals with PNES and twenty-one healthy control subjects were examined. Resting state functional magnetic resonance imaging (rsfMRI) was acquired. All subjects were asked to keep their eyes open during the scanning process. The rsfMRI analysis consisted of pre-processing, extracting the functional connectivity matrix (FCM) based on the AAL atlas, threshold for binary FCM, constructing a graph network from FCM and extracting graph features, and finally statistical analysis. For all cortical and subcortical regions of the AAL atlas, we calculated measures of ‘degree,’ which is one of the features of the graph theory. Results: Our results revealed that, as compared to the healthy control subjects, patients with PNES had a significantly lower degree in some brain regions including their left and right insula (INS), right Putamen (PUT), left and right Supramarginal gyrus (SMG), right Middle occipital gyrus (MOG), and left and right Rolandic operculum (ROL). In contrast, degree was significantly greater in two regions [i.e., right Caudate (CAU) and left Inferior frontal gyrus orbital part (ORBinf)] in patients with PNES compared to that in controls.ConclusionOur findings suggest that functional connectivity of several major brain regions are different in patients with PNES compared with that in healthy individuals. While there is hypoactivity in regions important in perception, motor control, self- awareness, and cognitive functioning (e.g., insula) and also movement regulation (e.g., putamen), there is hyperactivity in areas involved in feedback processing (i.e., using information from past experiences to influence future actions and decisions) (e.g., caudate) in patients with PNES. The observation that individuals with PNES suffer from a wide range of abnormal activities in functional connectivity of their brain networks is consistent with the fact that PNES occur in a heterogeneous patient population; no single mechanism or contributing factor could explain PNES in all patients.

scholarly journals Tiagabine Increases [11C]flumazenil Binding in Cortical Brain Regions in Healthy Control Subjects

2008 ◽  
Vol 34 (3) ◽  
pp. 624-633 ◽  
Author(s):  
W Gordon Frankle ◽  
Raymond Y Cho ◽  
Rajesh Narendran ◽  
N Scott Mason ◽  
Shivangi Vora ◽  
...  
Keyword(s):  
Brain Regions ◽  
Control Subjects ◽  
Healthy Control

scholarly journals Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

2018 ◽  
Vol 29 (10) ◽  
pp. 4208-4222 ◽  
Author(s):  
Yuehua Xu ◽  
Miao Cao ◽  
Xuhong Liao ◽  
Mingrui Xia ◽  
Xindi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Network Architecture ◽  
Brain Networks ◽  
Individual Variability ◽  
Distance Constraint ◽  
Behavioral Differences ◽  
Sensorimotor Network ◽  
Middle Range ◽  
Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

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 The Human Intraparietal Sulcus Modulates Task-Evoked Functional Connectivity

2019 ◽  
Vol 30 (3) ◽  
pp. 875-887
Author(s):  
Kai Hwang ◽  
James M Shine ◽  
Dillan Cellier ◽  
Mark D’Esposito
Keyword(s):  
Functional Connectivity ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Intraparietal Sulcus ◽  
Top Down ◽  
Adaptive Communication ◽  
Wide Range ◽  
Functional Connectivity Strength ◽  
Ventral Temporal Cortex ◽  
Cortical Regions

Abstract Past studies have demonstrated that flexible interactions between brain regions support a wide range of goal-directed behaviors. However, the neural mechanisms that underlie adaptive communication between brain regions are not well understood. In this study, we combined theta-burst transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging to investigate the sources of top-down biasing signals that influence task-evoked functional connectivity. Subjects viewed sequences of images of faces and buildings and were required to detect repetitions (2-back vs. 1-back) of the attended stimuli category (faces or buildings). We found that functional connectivity between ventral temporal cortex and the primary visual cortex (VC) increased during processing of task-relevant stimuli, especially during higher memory loads. Furthermore, the strength of functional connectivity was greater for correct trials. Increases in task-evoked functional connectivity strength were correlated with increases in activity in multiple frontal, parietal, and subcortical (caudate and thalamus) regions. Finally, we found that TMS to superior intraparietal sulcus (IPS), but not to primary somatosensory cortex, decreased task-specific modulation in connectivity patterns between the primary VC and the parahippocampal place area. These findings demonstrate that the human IPS is a source of top-down biasing signals that modulate task-evoked functional connectivity among task-relevant cortical regions.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

2021 ◽  
Author(s):  
Etienne Combrisson ◽  
Michele Allegra ◽  
Ruggero Basanisi ◽  
Robin A. A. Ince ◽  
Bruno L Giordano ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Scientific Practice ◽  
Local Level ◽  
Random Effect ◽  
Brain Networks ◽  
Brain Regions ◽  
Group Level ◽  
Starting Point ◽  
Neurophysiological Data ◽  
Statistical Approaches

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuity in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present a open-source Python toolbox called Frites that includes all of the methods used here, from functional connectivity estimations to the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

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