Estimating c-level partial correlation graphs with application to brain imaging

Biostatistics ◽  
2018 ◽  
Vol 21 (4) ◽  
pp. 641-658
Author(s):  
Yumou Qiu ◽  
Xiao-Hua Zhou
Keyword(s):  
Functional Connectivity ◽  
Partial Correlation ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Higher Power ◽  
Correlation Graph ◽  
Partial Correlations ◽  
Large P Small N ◽  
Brain Functional Connectivity ◽  
The Brain

Summary Alzheimer’s disease (AD) is a chronic neurodegenerative disease that changes the functional connectivity of the brain. The alteration of the strong connections between different brain regions is of particular interest to researchers. In this article, we use partial correlations to model the brain connectivity network and propose a data-driven procedure to recover a $c$-level partial correlation graph based on PET data, which is the graph of the absolute partial correlations larger than a pre-specified constant $c$. The proposed procedure is adaptive to the “large p, small n” scenario commonly seen in whole brain studies, and it incorporates the variation of the estimated partial correlations, which results in higher power compared to the existing methods. A case study on the FDG-PET images from AD and normal control (NC) subjects discovers new brain regions, Sup Frontal and Mid Frontal in the frontal lobe, which have different brain functional connectivity between AD and NC.

scholarly journals The effect of fatigue on brain connectivity networks

2020 ◽  
Vol 6 (2) ◽  
pp. 120-131
Author(s):  
Shangen Zhang ◽  
Jingnan Sun ◽  
Xiaorong Gao
Keyword(s):  
Functional Connectivity ◽  
Normal State ◽  
Brain Connectivity ◽  
Memory Task ◽  
Visual Presentation ◽  
Mental States ◽  
Parietal Region ◽  
Brain Response ◽  
Brain Functional Connectivity ◽  
The Brain

In the fatigue state, the neural response characteristics of the brain might be different from those in the normal state. Brain functional connectivity analysis is an effective tool for distinguishing between different brain states. For example, comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states. The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity. In particular, the phase‐scrambling method was used to generate images with two noise levels, while the N‐back working memory task was used to induce the fatigue state in subjects. The paradigm of rapid serial visual presentation (RSVP) was used to present visual stimuli. The analysis of brain connections in the normal and fatigue states was conducted using the open‐source eConnectome toolbox. The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states. Compared to the normal state, the brain connectivity power in the parietal region was significantly weakened under the fatigue state, which indicates that the control ability of the brain is reduced in the fatigue state.

scholarly journals Brain functional connectivity upon awakening from sleep predicts interindividual differences in dream recall frequency

SLEEP ◽  
2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Raphael Vallat ◽  
Alain Nicolas ◽  
Perrine Ruby
Keyword(s):  
Functional Connectivity ◽  
Short Term Memory ◽  
Brain Connectivity ◽  
Dream Recall ◽  
Mental Calculation ◽  
Interindividual Differences ◽  
Sleep Inertia ◽  
Early Afternoon ◽  
Brain Functional Connectivity ◽  
The Brain

Abstract Why do some individuals recall dreams every day while others hardly ever recall one? We hypothesized that sleep inertia—the transient period following awakening associated with brain and cognitive alterations—could be a key mechanism to explain interindividual differences in dream recall at awakening. To test this hypothesis, we measured the brain functional connectivity (combined electroencephalography–functional magnetic resonance imaging) and cognition (memory and mental calculation) of high dream recallers (HR, n = 20) and low dream recallers (LR, n = 18) in the minutes following awakening from an early-afternoon nap. Resting-state scans were acquired just after or before a 2 min mental calculation task, before the nap, 5 min after awakening from the nap, and 25 min after awakening. A comic was presented to the participants before the nap with no explicit instructions to memorize it. Dream(s) and comic recall were collected after the first post-awakening scan. As expected, between-group contrasts of the functional connectivity at 5 min post-awakening revealed a pattern of enhanced connectivity in HR within the default mode network (DMN) and between regions of the DMN and regions involved in memory processes. At the behavioral level, a between-group difference was observed in dream recall, but not comic recall. Our results provide the first evidence that brain functional connectivity right after awakening is associated with interindividual trait differences in dream recall and suggest that the brain connectivity of HR at awakening facilitates the maintenance of the short-term memory of the dream during the sleep–wake transition.

scholarly journals Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 144 ◽  
Author(s):  
A. Erramuzpe ◽  
J. M. Encinas ◽  
A. Sierra ◽  
M. Maletic-Savatic ◽  
A.L. Brewster ◽  
...  
Keyword(s):  
Case Report ◽  
Status Epilepticus ◽  
Functional Connectivity ◽  
Mouse Model ◽  
Latent Period ◽  
Brain Connectivity ◽  
Low Frequency ◽  
Longitudinal Variations ◽  
Brain Functional Connectivity ◽  
The Brain

Brain Functional Connectivity (FC) quantifies statistical dependencies between areas of the brain.FC has been widely used to address altered function of brain circuits in control conditions compared to different pathological states, including epilepsy, a major neurological disorder. However, FC also has the as yet unexplored potential to help us understand the pathological transformation of the brain circuitry.Our hypothesis is that FC can differentiate global brain interactions across a time-scale of days. To this end, we present a case report study based on a mouse model for epilepsy and analyze longitudinal intracranial electroencephalography data of epilepsy to calculate FC across three stages:  1, the initial insult (status epilepticus); 2, the latent period, when epileptogenic networks emerge; and 3, chronic epilepsy, when unprovoked seizures occur as spontaneous events.We found that the overall network FC at low frequency bands decreased immediately after status epilepticus was provoked, and increased monotonously later on during the latent period. Overall, our results demonstrate the capacity  of FC to address longitudinal variations of brain connectivity across the establishment of pathological states.

scholarly journals Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages

2021 ◽  
Vol 288 (1944) ◽  
pp. 20202866
Author(s):  
Yoosik Youm ◽  
Junsol Kim ◽  
Seyul Kwak ◽  
Jeanyung Chey
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Functional Connectivity ◽  
Cross Validation ◽  
Brain Connectivity ◽  
External Validation ◽  
Gender Role Attitudes ◽  
Brain Regions ◽  
Individual Level ◽  
The Brain

To avoid polarization and maintain small-worldness in society, people who act as attitudinal brokers are critical. These people maintain social ties with people who have dissimilar and even incompatible attitudes. Based on resting-state functional magnetic resonance imaging ( n = 139) and the complete social networks from two Korean villages ( n = 1508), we investigated the individual-level neural capacity and social-level structural opportunity for attitudinal brokerage regarding gender role attitudes. First, using a connectome-based predictive model, we successfully identified the brain functional connectivity that predicts attitudinal diversity of respondents' social network members. Brain regions that contributed most to the prediction included mentalizing regions known to be recruited in reading and understanding others’ belief states. This result was corroborated by leave-one-out cross-validation, fivefold cross-validation and external validation where the brain connectivity identified in one village was used to predict the attitudinal diversity in another independent village. Second, the association between functional connectivity and attitudinal diversity of social network members was contingent on a specific position in a social network, namely, the structural brokerage position where people have ties with two people who are not otherwise connected.

scholarly journals A Comparative Study of the Effect of Weighted or Binary Functional Brain Networks in fMRI Data Analysis

2020 ◽  
Author(s):  
Hesam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Correlation Coefficients ◽  
Clustering Coefficient ◽  
Fmri Data ◽  
Threshold Method ◽  
P Values ◽  
Fixed Threshold ◽  
Brain Functional Connectivity ◽  
The Brain

Purpose: Graph theory is a widely used and reliable tool to quantify brain connectivity. Brain functional connectivity is modeled as graph edges employing correlation coefficients. The correlation coefficients can be used as the weight that shows the power of connectivity between two nodes or can be binarized to show the existence of a connection regardless of its strength. To binarize the brain graph two approaches, namely fixed threshold and fixed density are often used. Materials and Methods: This paper aims to investigate the difference between weighted or binarized graphs in brain functional connectivity analysis. To achieve this goal, the brain connectivity matrices are generated employing the functional Magnetic Resonance Imaging (fMRI) data of Alzheimer's Disease (AD). After preprocessing the data, weighted and binarized connectivity matrices are constructed using a fixed threshold and fixed density techniques. Graph global features are extracted and a non-parametric statistical test is performed to analyze the performance of the methods. Results: Results show that all three methods are powerful in distinguishing the healthy group from AD subjects. The P-Values of the weighted graph is close to the fixed threshold method. Conclusion: Also, it is worthwhile mentioning that the fixed threshold method is robust in changing the threshold while the fixed density method is very sensitive. On the other hand, graph global measures such as clustering coefficient and transitivity, regardless of the method, show significant differences between the control and AD groups. Furthermore, the P-Values of modularity measure are very varied according to the method and the selected threshold.

scholarly journals Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 144
Author(s):  
A. Erramuzpe ◽  
J. M. Encinas ◽  
A. Sierra ◽  
M. Maletic-Savatic ◽  
A.L. Brewster ◽  
...  
Keyword(s):  
Case Report ◽  
Status Epilepticus ◽  
Functional Connectivity ◽  
Mouse Model ◽  
Latent Period ◽  
Brain Connectivity ◽  
Low Frequency ◽  
Longitudinal Variations ◽  
Brain Functional Connectivity ◽  
The Brain

Brain Functional Connectivity (FC) quantifies statistical dependencies between areas of the brain. FC has been widely used to address altered function of brain circuits in control conditions compared to different pathological states, including epilepsy, a major neurological disorder. However, FC also has the as yet unexplored potential to help us understand the pathological transformation of the brain circuitry. Our hypothesis is that FC can differentiate global brain interactions across a time-scale of days. To this end, we present a case report study based on a mouse model for epilepsy and analyze longitudinal intracranial electroencephalography data of epilepsy to calculate FC changes from the initial insult (status epilepticus) and over the latent period, when epileptogenic networks emerge, and at chronic epilepsy, when unprovoked seizures occur as spontaneous events. We found that the overall network FC at low frequency bands decreased immediately after status epilepticus was provoked, and increased monotonously later on during the latent period. Overall, our results demonstrate the capacity of FC to address longitudinal variations of brain connectivity across the establishment of pathological states.

scholarly journals Convolutional Sparse Coded Dynamic Brain Functional Connectivity

2018 ◽  
Author(s):  
Jin Yan ◽  
Yingying Zhu
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Sliding Window ◽  
Brain Network ◽  
Clinical Applications ◽  
Connectivity Pattern ◽  
Brain Disorder ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractFunctional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.

scholarly journals Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum

2019 ◽  
Vol 13 ◽  
pp. 117906951985180 ◽  
Author(s):  
Tonya White ◽  
Vince D. Calhoun
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Dynamic Functional Connectivity ◽  
Age Related ◽  
Autistic Symptoms ◽  
Magnetic Resonance Imaging Mri ◽  
Dynamic Measures ◽  
The Brain

The ability to measure the intrinsic functional architecture of the brain has grown exponentially over the last 2 decades. Measures of intrinsic connectivity within the brain, typically measured using resting-state functional magnetic resonance imaging (MRI), have evolved from primarily “static” approaches, to include dynamic measures of functional connectivity. Measures of dynamic functional connectivity expand the assumptions to allow brain regions to have temporally different patterns of communication between different regions. That is, connections within the brain can differentially fire between different regions at different times, and these differences can be quantified. Applying approaches that measure the dynamic characteristics of functional brain connectivity have been fruitful in identifying differences during brain development and psychopathology. We provide a brief overview of static and dynamic measures of functional connectivity and illustrate the synergy in applying these approaches to identify both age-related differences in children and differences between typically developing children and children with autistic symptoms.

Type 1 diabetes affects the brain functional connectivity underlying working memory processing

2021 ◽  
Author(s):  
Geisa B. Gallardo‐Moreno ◽  
Francisco J. Alvarado‐Rodríguez ◽  
Rebeca Romo‐Vázquez ◽  
Hugo Vélez‐Pérez ◽  
Andrés A. González‐Garrido
Keyword(s):  
Working Memory ◽  
Type 1 Diabetes ◽  
Functional Connectivity ◽  
Memory Processing ◽  
Brain Functional Connectivity ◽  
The Brain

scholarly journals Category representations in the brain are both discretely localized and widely distributed

2018 ◽  
Vol 119 (6) ◽  
pp. 2256-2264 ◽  
Author(s):  
Zarrar Shehzad ◽  
Gregory McCarthy
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Strong Support ◽  
Brain Regions ◽  
Distributed Representations ◽  
Domain Specific ◽  
Category Information ◽  
Shared Information ◽  
Unique Domain ◽  
The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

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