scholarly journals BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

2017 ◽  
Author(s):  
Mite Mijalkov ◽  
Ehsan Kakaei ◽  
Joana B. Pereira ◽  
Eric Westman ◽  
Giovanni Volpe ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Local Network ◽  
Healthy Controls ◽  
The Brain

AbstractThe brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH – BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

scholarly journals The Effects of Acupuncture at Real or Sham Acupoints on the Intrinsic Brain Activity in Mild Cognitive Impairment Patients

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Baohui Jia ◽  
Zhishun Liu ◽  
Baoquan Min ◽  
Zhenchang Wang ◽  
Aihong Zhou ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Healthy Controls ◽  
The Difference ◽  
The Brain

Accumulating neuroimaging studies in humans have shown that acupuncture can modulate a widely distributed brain network in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients. Acupuncture at different acupoints could exert different modulatory effects on the brain network. However, whether acupuncture at real or sham acupoints can produce different effects on the brain network in MCI or AD patients remains unclear. Using resting-state fMRI, we reported that acupuncture at Taixi (KI3) induced amplitude of low-frequency fluctuation (ALFF) change of different brain regions in MCI patients from those shown in the healthy controls. In MCI patients, acupuncture at KI3 increased or decreased ALFF in the different regions from those activated by acupuncture in the healthy controls. Acupuncture at the sham acupoint in MCI patients activated the different brain regions from those in healthy controls. Therefore, we concluded that acupuncture displays more significant effect on neuronal activities of the above brain regions in MCI patients than that in healthy controls. Acupuncture at KI3 exhibits different effects on the neuronal activities of the brain regions from acupuncture at sham acupoint, although the difference is only shown at several regions due to the close distance between the above points.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

scholarly journals Individual Variation in Brain Network Topology Predicts Emotional Intelligence

2018 ◽  
Author(s):  
Ling George ◽  
Lee Ivy ◽  
Guimond Synthia ◽  
Lutz Olivia ◽  
Tandon Neeraj ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
Social Cognition ◽  
Network Topology ◽  
Resting State ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Connectivity ◽  
Distance Matrix ◽  
Resting State Fmri ◽  
Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

scholarly journals Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 297-311
Author(s):  
Jae-Sung Lim ◽  
Jae-Joong Lee ◽  
Choong-Wan Woo
Keyword(s):  
Cognitive Impairment ◽  
Large Scale ◽  
Imaging Techniques ◽  
Brain Network ◽  
Clinical Neurology ◽  
Post Stroke ◽  
Large Scale Networks ◽  
High Level ◽  
New Treatments ◽  
The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

scholarly journals Brain network topology predicts participant adherence to mental training programs

2020 ◽  
Vol 4 (3) ◽  
pp. 528-555
Author(s):  
Marzie Saghayi ◽  
Jonathan Greenberg ◽  
Christopher O’Grady ◽  
Farshid Varno ◽  
Muhammad Ali Hashmi ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Local Level ◽  
Resting State Fmri ◽  
Brain Network ◽  
Learning Performance ◽  
Local Network ◽  
P Value ◽  
Mental Training ◽  
Wide Range ◽  
Organizational Patterns

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.

scholarly journals Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gretel Sanabria-Diaz ◽  
Lester Melie-Garcia ◽  
Bogdan Draganski ◽  
Jean-Francois Demonet ◽  
Ferath Kherif
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Late Onset ◽  
Brain Regions ◽  
Brain Network ◽  
Theory Approach ◽  
Characteristic Path Length ◽  
Cross Sectional ◽  
Adaptive Mechanisms ◽  
The Brain

AbstractThe Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer’s Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers’) and ApoE4-negative (‘non-Carriers’). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.

scholarly journals Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and Mild Cognitive Impairment: A systematic review

2020 ◽  
Author(s):  
BUHARI IBRAHIM ◽  
Nisha Syed Nasser ◽  
NORMALA IBRAHIM ◽  
Mazlyfarina Mohamed ◽  
Hasyma Abu Hassan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Cingulate Cortex ◽  
Resting State Fmri ◽  
Anterior Cingulate ◽  
Healthy Controls ◽  
Diagnostic Power

Resting state fMRI (rs-fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). FC of the default mode network (DMN), which is involved in memory consolidation, is commonly impaired in AD and MCI. We aimed to determine the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN, which help to distinguish patients with AD or MCI from healthy controls (HCs). We searched articles in PubMed and Scopus databases using the search terms such as AD, MCI, resting-state fMRI, sensitivity and specificity through to 27th March 2020 and removed duplicate papers. We screened 390 published articles, and shortlisted 12 articles for the final analysis. The range of sensitivity of DMN FC at the posterior cingulate cortex (PCC) for diagnosing AD was between 65.7% - 100% and specificity ranged from 66 - 95%. Reduced DMN FC between the PCC and anterior cingulate cortex (ACC) in the frontal lobes was observed in MCI patients. AD patients had impaired FC in most regions of the DMN; particularly the PCC in early AD. This indicates that DMN's rs-fMRI FC can offer moderate to high diagnostic power to distinguish AD and MCI patients. fMRI detected abnormal DMN FC, particularly in the PCC that helps to differentiate AD and MCI patients from healthy controls (HCs). Combining multivariate method of analysis with other MRI parameters such as structural changes improve the diagnostic power of rs-fMRI in distinguishing patients with AD or MCI from HCs.

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