scholarly journals Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks

2016 ◽  
Vol 63 (12) ◽  
pp. 2540-2549 ◽  
Author(s):  
Dustin Scheinost ◽  
Fuyuze Tokoglu ◽  
Xilin Shen ◽  
Emily S. Finn ◽  
Stephanie Noble ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Brain Activity ◽  
Functional Networks ◽  
Whole Brain ◽  
Global Brain

Abstract W P387: Neurological Correlates Of Brain Function After Acute Stroke--A Dense Array EEG Study

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Jennifer Wu ◽  
Ramesh Srinivasan ◽  
Ana Solodkin ◽  
Steven L Small ◽  
Steven C Cramer
Keyword(s):  
Motor Cortex ◽  
Acute Stroke ◽  
Brain Function ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Dense Array ◽  
Whole Brain ◽  
Regional Brain ◽  
Eeg Connectivity ◽  
Regional Brain Activity

INTRODUCTION: Measures of brain function can complement assessment of injury to inform clinical decision-making after stroke, but the most useful metrics remain uncertain. An acute stroke alters brain function in widespread areas. We therefore reasoned that a whole brain measure of brain function would be better related to behavioral deficits than a regional measure of brain function. METHODS: In 24 patients hospitalized for acute stroke, resting EEG (256 leads) was recorded for 3 min at the bedside and analyzed offline. Two EEG measures of brain function were extracted: [1] whole brain connectivity, which found the EEG frequency (from 1-30 Hz) and seed point (from among the 256 leads) that best fit whole brain coherence with total NIHSS scores, using a partial least squares regression model; and [2] regional brain activity, which found the EEG frequency and lead where spectral power was most strongly correlated with total NIHSS scores. Analyses were repeated focused on NIHSS motor subscores (Q4-6). All models were validated using a leave-one-out approach. RESULTS: The 24 patients were age 60.9±13.1yr, 3.5 ± 2.9 d post-onset (range 3hr-12d), and were studied in settings that included ER, ICU, and stroke ward. Whole brain EEG connectivity explained a large fraction of the variance in total NIHSS scores (r^2=0.72); this was achieved in the 2-4 Hz range, with seed over ipsilesional motor cortex, and with model predicting higher NIHSS score when this seed had greater coherence with contralesional frontal/motor regions. Regional brain activity, by comparison, explained a smaller fraction of variance (r^2=0.51), with maximal correlation between total NIHSS and regional EEG power found using a lead over contralesional motor cortex, at 2 Hz. Similar results for whole brain EEG connectivity were obtained when modeling NIHSS motor subscores in the 14 subjects with motor deficits (validated r^2=0.71). CONCLUSIONS: Dense array EEG recordings could be obtained early after stroke, rapidly and reliably, and at the bedside in widespread hospital settings. Whole brain connectivity measures corresponded to behavioral state better than measures of regional brain activity do. Results support the utility of EEG as a bedside method for evaluating brain functional status after stroke.

scholarly journals Pipeline for 4D whole-brain microscopy preprocessing and analysis.

2021 ◽  
Author(s):  
Valeria Bonapersona ◽  
Heike Schuler ◽  
Ruth D. Damsteeg ◽  
Youri Adolfs ◽  
R. J. Pasterkamp ◽  
...  
Keyword(s):  
Brain Activity ◽  
R Package ◽  
Functional Networks ◽  
High Resolution Imaging ◽  
Promising Technique ◽  
Intact Mouse ◽  
Whole Brain ◽  
Resolution Imaging ◽  
Brain Data ◽  
Machine Readable

Whole-brain microscopy allows for high-resolution imaging of intact mouse brains. It is a promising technique to relate behaviour to underlying brain activity, for example when combined with staining of immediate early genes. Multiple tools have been developed to transform images to machine-readable numbers, but these do not address the statistical requirements for robust data analysis. We present a novel pre-processing and analytical pipeline to analyze whole-brain data in 4 dimensions, from macro- to micro-scale and over time. This practical pipeline guides researchers through the appropriate cleaning, normalization and transformation steps for each analysis type. We developed analyses at different spatial resolutions, from macroscale functional networks to the single cell. We validate and showcase the advantages of the method by analysing activity changes at various time-points after foot-shock in male mice. All data can be visualized in our interactive web-portal (https://utrecht-university.shinyapps.io/brain_after_footshock/). The pipeline is available as R-package, abc4d.

scholarly journals Multivariate Brain Functional Connectivity Through Regularized Estimators

2020 ◽  
Vol 14 ◽  
Author(s):  
Raymond Salvador ◽  
Norma Verdolini ◽  
Beatriz Garcia-Ruiz ◽  
Esther Jiménez ◽  
Salvador Sarró ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Random Forest ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Brain Activity ◽  
The Other ◽  
Linear Functions ◽  
Other Hand ◽  
Age Related ◽  
Global Brain

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models.

scholarly journals Instantaneous radiated power of brain activity: application to prepulse inhibition and facilitation for body dysmorphic disorder

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Anastasios E. Giannopoulos ◽  
Sotirios T. Spantideas ◽  
Christos Capsalis ◽  
Panos Papageorgiou ◽  
Nikolaos Kapsalis ◽  
...  
Keyword(s):  
Prepulse Inhibition ◽  
Body Dysmorphic Disorder ◽  
Time Window ◽  
Brain Activity ◽  
Control Group ◽  
Whole Brain ◽  
Radiated Power ◽  
Spatial Dimensions ◽  
Global Brain ◽  
The Brain

Abstract Background Global measures of neuronal activity embrace the advantage of a univariate, holistic and unique description of brain activity, reducing the spatial dimensions of electroencephalography (EEG) analysis at the cost of lower precision in localizing effects. In this work, the instantaneous radiated power (IRP) is proposed as a new whole-brain descriptor, reflecting the cortical activity from an exclusively electromagnetic perspective. Considering that the brain consists of multiple elementary dipoles, the whole-brain IRP takes into account the radiational contribution of all cortical sources. Unlike conventional EEG analyses that evaluate a large number of scalp or source locations, IRP reflects a whole-brain, event-related measure and forces the analysis to focus on a single time-series, thus efficiently reducing the EEG spatial dimensions and multiple comparisons. Results To apply the developed methodology in real EEG data, two groups (25 controls vs 30 body dysmorphic disorder, BDD, patients) were matched for age and sex and tested in a prepulse inhibition (PPI) and facilitation (PPF) paradigm. Two global brain descriptors were extracted for between-groups and between-conditions comparison purposes, namely the global field power (GFP) and the whole-brain IRP. Results showed that IRP can replicate the expected condition differences (with PPF being greater than PPI responses), exhibiting also reduced levels in BDD compared to control group overall. There were also similar outcomes using GFP and IRP, suggesting consistency between the two measures. Finally, regression analysis showed that the PPI-related IRP (during N100 time-window) is negatively correlated with BDD psychometric scores. Conclusions Investigating the brain activity with IRP significantly reduces the data dimensionality, giving insights about global brain synchronization and strength. We conclude that IRP can replicate the existing evidence regarding sensorimotor gating effects, revealing also group electrophysiological alterations. Finally, electrophysiological IRP responses exhibited correlations with BDD psychometrics, potentially useful as supplementary tool in BDD symptomatology.

Replicating Global Brain Connectivity As an Imaging Marker for Depression - Influence of Preprocessing Strategies and Placebo-Controlled Ketamine Treatment

2019 ◽  
Author(s):  
Christoph Kraus ◽  
Anahit Mkrtchian ◽  
Bashkim Kadriu ◽  
Allison C. Nugent ◽  
Carlos A. Zarate Jr. ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Global Brain

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

Decoupling of Global Brain Activity and Cerebrospinal Fluid Flow in Parkinson's Disease Cognitive Decline

2021 ◽  
Author(s):  
Feng Han ◽  
Gregory L. Brown ◽  
Yalin Zhu ◽  
Aaron E. Belkin‐Rosen ◽  
Mechelle M. Lewis ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Fluid Flow ◽  
Cognitive Decline ◽  
Brain Activity ◽  
Cerebrospinal Fluid Flow ◽  
Global Brain

scholarly journals Using Regional Homogeneity to Reveal Altered Spontaneous Activity in Patients with Mild Cognitive Impairment

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yumei Wang ◽  
Xiaochuan Zhao ◽  
Shunjiang Xu ◽  
Lulu Yu ◽  
Lan Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Spontaneous Activity ◽  
Healthy Aging ◽  
Early Stage ◽  
Brain Activity ◽  
Regional Homogeneity ◽  
Whole Brain ◽  
Spontaneous Brain Activity ◽  
Regional Brain Activity

Most patients with mild cognitive impairment (MCI) are thought to be in an early stage of Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging reflects spontaneous brain activity and/or the endogenous/background neurophysiological process of the human brain. Regional homogeneity (ReHo) rapidly maps regional brain activity across the whole brain. In the present study, we used the ReHo index to explore whole brain spontaneous activity pattern in MCI. Our results showed that MCI subjects displayed an increased ReHo index in the paracentral lobe, precuneus, and postcentral and a decreased ReHo index in the medial temporal gyrus and hippocampus. Impairments in the medial temporal gyrus and hippocampus may serve as important markers distinguishing MCI from healthy aging. Moreover, the increased ReHo index observed in the postcentral and paracentral lobes might indicate compensation for the cognitive function losses in individuals with MCI.

scholarly journals Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
Topological Organization

Error-related Persistence of Motor Activity in Resting-state Networks

2018 ◽  
Vol 30 (12) ◽  
pp. 1883-1901 ◽  
Author(s):  
Nicolò F. Bernardi ◽  
Floris T. Van Vugt ◽  
Ricardo Ruy Valle-Mena ◽  
Shahabeddin Vahdat ◽  
David J. Ostry
Keyword(s):  
Motor Learning ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Neural Activation ◽  
Resting State Functional Connectivity ◽  
Movement Training ◽  
Movement Error ◽  
Human Participants

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

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