scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

scholarly journals The Ubiquitous Vigilance Signal in fMRI Time Series Data

2021 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
Isabel Fernandez ◽  
Daniel A Handwerker ◽  
Peter A Bandettini
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Time Series Data ◽  
Resting State Fmri ◽  
Series Data ◽  
Similar Frequency ◽  
Global Signal ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
Eye Closure

Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.

scholarly journals Functional connectivity networks with and without global signal correction

2013 ◽  
Author(s):  
Satoru Hayasaka
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Correlation Coefficients ◽  
Blood Oxygenation ◽  
Fmri Data ◽  
Series Data ◽  
Network Analyses ◽  
Global Signal ◽  
Signal Correction ◽  
The Impact

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.

scholarly journals Empirical Analysis of Demand for Real Money Balances in Africa: Panel Evidence from Nigeria and Ghana

2020 ◽  
Vol 19 (4) ◽  
pp. 363-376
Author(s):  
Chigozie Nelson Nkalu
Keyword(s):  
Exchange Rates ◽  
Interest Rates ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Real Income ◽  
Seemingly Unrelated Regression ◽  
Series Data ◽  
Real Money ◽  
Development Indicators ◽  
Preference Theory

Abstract This study investigates demand for real money balances in Africa using panel time-series data from Nigeria and Ghana between 1970 and 2014. The study employs Levin, Lin, Chu common unit root process and Pedroni Residual Cointegration Test which the results reveal that all the variables in the model are stationary and cointegrated respectively. Data sourced from the World Development Indicators (WDI) were analyzed using Panel Two-Stage Estimated Generalized Least Squares (cross-section Seemingly Unrelated Regression model (SURE)) with Instrumental Variables (IV). The results conform to the liquidity preference theory, with all the variables – inflation, real interest rates, and official exchange rates are statistically significant except real income. It is recommended that the monetary authorities in Africa especially the economies of Nigeria and Ghana should adopt appropriate monetary policies by placing interest rates, inflation and official exchange rates at acceptable levels to boost income through private sector investments.

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

2010 ◽  
Vol 663 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Sonja Peters ◽  
Hans-Gerd Janssen ◽  
Gabriel Vivó-Truyols
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Novel Method ◽  
Analysis Of Time Series

The Triple Entente and the Triple Alliance 1880–1914: A Collective Goods Approach

1990 ◽  
Vol 84 (4) ◽  
pp. 1197-1206 ◽  
Author(s):  
John A. C. Conybeare ◽  
Todd Sandler
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Generalized Least Squares ◽  
Series Data ◽  
Accurate Estimation ◽  
Product Model ◽  
Public And Private ◽  
Static Tests ◽  
Joint Product ◽  
Triple Entente

The distribution of burdens in alliances may be explained in terms of public and private outputs. A joint product model is applied to the Triple Alliance and Triple Entente, using generalized least squares-auto regressive moving average (GLS-ARMA) techniques and time series data. Results indicate that countries regarded allies' military effort more as complements than as substitutes, though several examples of free-riding behavior are noted. The method used here may provide more accurate estimation of publicness problems than is found in the usual static tests.

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

2018 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data ◽  
Extensive Literature ◽  
Topographic Organization ◽  
Lateral Organization

AbstractIn accordance with the concept of topographic organization of neuroanatomical structures, there is an increased interest in estimating and delineating continuous changes in the functional connectivity patterns across neighboring voxels within a region of interest using resting-state fMRI data. Fundamental to this functional connectivity gradient analysis is the assumption that the functional organization is stable and uniform across the region of interest. To evaluate this assumption, we developed a model testing procedure to arbitrate between overlapping, shifted, or different topographic connectivity gradients across subdivisions of a structure. We tested the procedure using the striatum, a subcortical structure consisting of the caudate nucleus and putamen, in which an extensive literature, primarily from rodents and non-human primates, suggest to have a shared topographic organization of a single diagonal gradient. We found, across multiple resting state fMRI data samples of different spatial resolutions in humans, and one macaque resting state fMRI data sample, that the models with different functional connectivity gradients across the caudate and putamen was the preferred model. The model selection procedure was validated in control conditions of checkerboard subdivisions, demonstrating the expected overlapping gradient. More specifically, while we replicated the diagonal organization of the functional connectivity gradients in both the caudate and putamen, our analysis also revealed a medial-lateral organization within the caudate. Not surprisingly, performing the same analysis assuming a unitary gradient obfuscates the medial-lateral organization of the caudate, producing only a diagonal gradient. These findings demonstrate the importance of testing basic assumptions and evaluating interpretations across species. The significance of differential topographic gradients across the putamen and caudate and the medial-lateral gradient of the caudate in humans should be tested in future studies.

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

scholarly journals The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

2019 ◽  
Author(s):  
William Hedley Thompson ◽  
Jessey Wright ◽  
James M. Shine ◽  
Russell A. Poldrack
Keyword(s):  
Time Series Data ◽  
Dynamic Network ◽  
Fmri Data ◽  
Series Data ◽  
Time Varying ◽  
Trajectory Clustering ◽  
Single Trial ◽  
Neuroimaging Data ◽  
New Perspective ◽  
Static Connectivity

AbstractInteracting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.

scholarly journals Whole brain mapping of somatosensory responses in awake marmosets investigated with ultra-high-field fMRI

2020 ◽  
Vol 124 (6) ◽  
pp. 1900-1913
Author(s):  
Justine C. Cléry ◽  
Yuki Hori ◽  
David J. Schaeffer ◽  
Joseph S. Gati ◽  
J. Andrew Pruszynski ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Mapping ◽  
Resting State ◽  
High Field ◽  
Body Representation ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Ultra High Field ◽  
Somatosensory Stimulation ◽  
Somatosensory Responses

We used somatosensory stimulation combined with functional MRI (fMRI) in awake marmosets to reveal the topographic body representation in areas S1, S2, thalamus, and putamen. We showed the existence of a body representation organization within the thalamus and the cingulate cortex by computing functional connectivity maps from seeds defined in S1/S2 using resting-state fMRI data. This noninvasive approach will be essential for chronic studies by guiding invasive recording and manipulation techniques.

Sign in / Sign up

Export Citation Format

Share Document