scholarly journals The impact of physiological noise on hemodynamic-derived estimates of directed functional connectivity

2019 ◽  
Vol 224 (9) ◽  
pp. 3145-3157 ◽  
Author(s):  
F. Konrad Schumacher ◽  
Carmen Steinborn ◽  
Cornelius Weiller ◽  
Björn O. Schelter ◽  
Matthias Reinhard ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Physiological Noise ◽  
The Impact

scholarly journals Disambiguating the role of blood flow and global signal with Partial Information Decomposition

2019 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Functional Connectivity ◽  
Arrival Time ◽  
Partial Information ◽  
Physiological Noise ◽  
Global Signal ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

AbstractIn resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

scholarly journals The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Noise Reduction ◽  
Real Time ◽  
Brain Activity ◽  
Time Estimation ◽  
Blood Oxygenation ◽  
Physiological Noise ◽  
Global Signal ◽  
The Impact

AbstractObjectiveComprehensive denoising is imperative in fMRI analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.ApproachWe performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main resultsAll the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETORICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.SignificanceThe results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

scholarly journals Structural and functional brain-wide alterations in A350V Iqsec2 mutant mice displaying autistic-like behavior

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniela Lichtman ◽  
Eyal Bergmann ◽  
Alexandra Kavushansky ◽  
Nadav Cohen ◽  
Nina S. Levy ◽  
...  
Keyword(s):  
Social Behavior ◽  
Functional Connectivity ◽  
Mouse Model ◽  
Ampa Receptor ◽  
Social Preference ◽  
Autism Spectrum ◽  
Receptor Trafficking ◽  
Anterior Cingulate ◽  
The Impact ◽  
Ampa Receptor Trafficking

AbstractIQSEC2 is an X-linked gene that is associated with autism spectrum disorder (ASD), intellectual disability, and epilepsy. IQSEC2 is a postsynaptic density protein, localized on excitatory synapses as part of the NMDA receptor complex and is suggested to play a role in AMPA receptor trafficking and mediation of long-term depression. Here, we present brain-wide structural volumetric and functional connectivity characterization in a novel mouse model with a missense mutation in the IQ domain of IQSEC2 (A350V). Using high-resolution structural and functional MRI, we show that animals with the A350V mutation display increased whole-brain volume which was further found to be specific to the cerebral cortex and hippocampus. Moreover, using a data-driven approach we identify putative alterations in structure–function relations of the frontal, auditory, and visual networks in A350V mice. Examination of these alterations revealed an increase in functional connectivity between the anterior cingulate cortex and the dorsomedial striatum. We also show that corticostriatal functional connectivity is correlated with individual variability in social behavior only in A350V mice, as assessed using the three-chamber social preference test. Our results at the systems-level bridge the impact of previously reported changes in AMPA receptor trafficking to network-level disruption and impaired social behavior. Further, the A350V mouse model recapitulates similarly reported brain-wide changes in other ASD mouse models, with substantially different cellular-level pathologies that nonetheless result in similar brain-wide alterations, suggesting that novel therapeutic approaches in ASD that result in systems-level rescue will be relevant to IQSEC2 mutations.

scholarly journals A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Federico Calesella ◽  
Alberto Testolin ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Verbal Memory ◽  
Resting State ◽  
Component Analysis ◽  
Model Complexity ◽  
Stroke Patients ◽  
Prediction Ability ◽  
The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

scholarly journals Development of transport infrastructure as precondition of economy and tourism development: Case study municipality of Negotin

2013 ◽  
Vol 93 (3) ◽  
pp. 145-157
Author(s):  
Nikola Ristic ◽  
Bogdan Lukic ◽  
Dejan Filipovic ◽  
Velimir Secerov
Keyword(s):  
Functional Connectivity ◽  
Human Activities ◽  
Tourism Development ◽  
Transport Network ◽  
Transport Infrastructure ◽  
Mutual Interaction ◽  
The Impact

Developed transport network is a precondition for economic and tourism development of areas and largely follows and allows the development of human activities. If it is developing without plan, spontaneous and without coordination it may be a limit to the overall development. The aim of research was to define developmental basis for the revitalization, improvement and construction of transport infrastructure in the municipality of Negotin. The paper will present the mutual interaction and functional connectivity of planning solutions for development of transport infrastructure and development of economic and tourism, as well as the impact which planning solutions have on the evolvent of other spatial and city functions.

scholarly journals Attention network modulation via tRNS correlates with attention gain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Federica Contò ◽  
Grace Edwards ◽  
Sarah Tyler ◽  
Danielle Parrott ◽  
Emily Grossman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Random Noise ◽  
Cognitive Capacity ◽  
Resting State Functional Connectivity ◽  
Attention Network ◽  
Transcranial Random Noise Stimulation ◽  
The Impact ◽  
Behavioral Improvement

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

scholarly journals Differential resting state connectivity responses to glycemic state in type 1 diabetes

2019 ◽  
Author(s):  
Lisa Parikh ◽  
Dongju Seo ◽  
Cheryl Lacadie ◽  
Renata Belfort-DeAguiar ◽  
Derek Groskreutz ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Functional Connectivity ◽  
Resting State ◽  
Medical Center ◽  
Academic Medical Center ◽  
Angular Gyrus ◽  
Resting State Functional Connectivity ◽  
The Impact ◽  
Resting State Connectivity

Abstract Context Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study Setting Academic medical center Participants 27 individuals with T1DM and 12 healthy control volunteers participated in the study. Intervention All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp. Outcome Changes in resting state functional connectivity Results Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

scholarly journals The Impact of Math Anxiety on Working Memory: A Cortical Activations and Cortical Functional Connectivity EEG Study

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15027-15039 ◽  
Author(s):  
Manousos A. Klados ◽  
Evangelos Paraskevopoulos ◽  
Niki Pandria ◽  
Panagiotis D. Bamidis
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Math Anxiety ◽  
The Impact

scholarly journals Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Emiliano Santarnecchi ◽  
Chiara Del Bianco ◽  
Isabella Sicilia ◽  
Davide Momi ◽  
Giorgio Di Lorenzo ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Default Mode Network ◽  
Age Of Onset ◽  
Brain Connectivity ◽  
Disruptive Effect ◽  
Default Mode ◽  
Connectivity Patterns ◽  
The Impact

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients.

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