Convolutional Sparse Coded Dynamic Brain Functional Connectivity

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.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2017100104 ◽

2017 ◽

Vol 9 (4) ◽

pp. 46-66 ◽

Cited By ~ 1

Author(s):

Bhuvaneshwari Bhaskaran ◽

Kavitha Anandan

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Default Mode Network ◽

Brain Connectivity ◽

Brain Network ◽

Brain Disorder ◽

Default Mode ◽

Graph Metrics ◽

Functional Topology ◽

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/130985 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13 ◽

Cited By ~ 43

Author(s):

J. Toppi ◽

F. De Vico Fallani ◽

G. Vecchiato ◽

A. G. Maglione ◽

F. Cincotti ◽

...

Keyword(s):

Functional Connectivity ◽

Adjacency Matrix ◽

Brain Connectivity ◽

Small World ◽

Connectivity Pattern ◽

Edge Density ◽

Statistical Validation ◽

Common Procedure ◽

Connectivity Patterns ◽

Definition Of

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

The effect of fatigue on brain connectivity networks

Brain Science Advances ◽

10.26599/bsa.2020.9050008 ◽

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 ◽

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.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

Research Anthology on Diagnosing and Treating Neurocognitive Disorders ◽

10.4018/978-1-7998-3441-0.ch030 ◽

2021 ◽

pp. 589-610

Author(s):

Bhuvaneshwari Bhaskaran ◽

Kavitha Anandan

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Default Mode Network ◽

Brain Connectivity ◽

Brain Network ◽

Brain Disorder ◽

Default Mode ◽

Graph Theoretic ◽

Functional Topology ◽

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

SLEEP ◽

10.1093/sleep/zsaa116 ◽

2020 ◽

Vol 43 (12) ◽

Cited By ~ 1

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 ◽

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.

Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/452503 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 21

Author(s):

Andreas A. Ioannides ◽

Stavros I. Dimitriadis ◽

George A. Saridis ◽

Marotesa Voultsidou ◽

Vahe Poghosyan ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Clinical Applications ◽

Brain Areas ◽

Connectivity Patterns ◽

The Brain ◽

Typical Subject ◽

Different Parts ◽

Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

The Differences in the Whole-Brain Functional Network between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals

Brain Sciences ◽

10.3390/brainsci11030310 ◽

2021 ◽

Vol 11 (3) ◽

pp. 310

Author(s):

Xiaoxuan Fan ◽

Yujia Wu ◽

Lei Cai ◽

Jingwen Ma ◽

Ning Pan ◽

...

Keyword(s):

Functional Connectivity ◽

Semantic Processing ◽

Brain Connectivity ◽

Occipital Cortex ◽

Brain Network ◽

Network Efficiency ◽

Whole Brain ◽

Strongly Connected ◽

The Right ◽

Brain Functional Connectivity

Cantonese-Mandarin bilinguals are logographic-logographic bilinguals that provide a unique population for bilingual studies. Whole brain functional connectivity analysis makes up for the deficiencies of previous bilingual studies on the seed-based approach and helps give a complete picture of the brain connectivity profiles of logographic-logographic bilinguals. The current study is to explore the effect of the long-term logographic-logographic bilingual experience on the functional connectivity of the whole-brain network. Thirty Cantonese-Mandarin bilingual and 30 Mandarin monolingual college students were recruited in the study. Resting state functional magnetic resonance imaging (rs-fMRI) was performed to investigate the whole-brain functional connectivity differences by network-based statistics (NBS), and the differences in network efficiency were investigated by graph theory between the two groups (false discovery rate corrected for multiple comparisons, q = 0.05). Compared with the Mandarin monolingual group, Cantonese-Mandarin bilinguals increased functional connectivity between the bilateral frontoparietal and temporal regions and decreased functional connectivity in the bilateral occipital cortex and between the right sensorimotor region and bilateral prefrontal cortex. No significant differences in network efficiency were found between the two groups. Compared with the Mandarin monolinguals, Cantonese-Mandarin bilinguals had no significant discrepancies in network efficiency. However, the Cantonese-Mandarin bilinguals developed a more strongly connected subnetwork related to language control, inhibition, phonological and semantic processing, and memory retrieval, whereas a weaker connected subnetwork related to visual and phonology processing, and speech production also developed.

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

F1000Research ◽

10.12688/f1000research.6570.1 ◽

2015 ◽

Vol 4 ◽

pp. 144 ◽

Cited By ~ 1

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 (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.

Large-scale intrinsic connectivity is consistent across varying task demands

10.1101/407205 ◽

2018 ◽

Author(s):

Paulina Kieliba ◽

Sasidhar Madugula ◽

Nicola Filippini ◽

Eugene P. Duff ◽

Tamar R. Makin

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Brain Activation ◽

Whole Brain ◽

Activation Patterns ◽

Intrinsic Connectivity ◽

Connectivity Patterns ◽

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

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

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v7i3.4618 ◽

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 ◽

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.