Connectivity dynamics from wakefulness to sleep

AbstractInterest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 seconds, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.

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Frequency Specific Effects of ApoE ε4 Allele on Resting-State Networks in Nondemented Elders

BioMed Research International ◽

10.1155/2017/9823501 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Ying Liang ◽

Zhenzhen Li ◽

Jing Wei ◽

Chunlin Li ◽

Xu Zhang ◽

...

Keyword(s):

Functional Connectivity ◽

Frequency Band ◽

Default Mode Network ◽

Resting State ◽

Brain Network ◽

Salience Network ◽

Default Mode ◽

Specific Effects ◽

Network Time ◽

Time Courses

We applied resting-state functional magnetic resonance imaging (fMRI) to examine the Apolipoprotein E (ApoE) ε4 allele effects on functional connectivity of the default mode network (DMN) and the salience network (SN). Considering the frequency specific effects of functional connectivity, we decomposed the brain network time courses into two bands: 0.01–0.027 Hz and 0.027–0.08 Hz. All scans were acquired by the Alzheimer’s Disease Neuroscience Initiative (ADNI). Thirty-two nondemented subjects were divided into two groups based on the presence (n=16) or absence (n=16) of the ApoE ε4 allele. We explored the frequency specific effects of ApoE ε4 allele on the default mode network (DMN) and the salience network (SN) functional connectivity. Compared to ε4 noncarriers, the DMN functional connectivity of ε4 carriers was significantly decreased while the SN functional connectivity of ε4 carriers was significantly increased. Many functional connectivities showed significant differences at the lower frequency band of 0.01–0.027 Hz or the higher frequency band of 0.027–0.08 Hz instead of the typical range of 0.01–0.08 Hz. The results indicated a frequency dependent effect of resting-state signals when investigating RSNs functional connectivity.

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Probing the association between resting state brain network dynamics and psychological resilience

10.1101/2021.07.20.452941 ◽

2021 ◽

Author(s):

Dominik Kraft ◽

Christian Fiebach

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Network ◽

Original Study ◽

Node Degree ◽

Psychological Resilience ◽

Time Varying ◽

Time Resolved ◽

The Difference ◽

Window Approach

This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience. To this end, we acquired multiband resting-state BOLD fMRI (TR = .675 sec) from 60 participants and assessed resilience with three psychological questionnaires. Time resolved functional connectivity was calculated by performing a sliding window approach on averaged time series from the Schaefer 100 parcellation, resulting in 753 connectivity matrices. Node degree was calculated as a proxy for time-varying connectivity. Multilayer modularity detection was performed to track network reconfigurations over time and to calculate node flexibility (the number of times a node changes community assignment) and node promiscuity (the fraction of communities a node participates in). We found no significant correlations between resilience and any of the measures of dynamic functional connectivity, neither at nodal, functional network, or global levels. Results were also robust against down-sampling the data to resemble a conventional TR of ~2 sec (as in the original study) and against the application of different windowing schemes (overlapping vs. non-overlapping, different window lengths). We discuss several methodological reasons that may account for the difference in results between the present and the original study, to inform future network neuroscience studies of mental health.

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Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

10.1101/2020.12.20.423696 ◽

2020 ◽

Author(s):

Andreas Anastasiou ◽

Ivor Cribben ◽

Piotr Fryzlewicz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Change Point ◽

Sliding Window ◽

Resting State Fmri ◽

Change Point Detection ◽

Change Points ◽

Fmri Data ◽

Cross Covariance ◽

Window Approach

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID is also applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from GitHub.

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Probing the association between resting state brain network dynamics and psychological resilience

Network Neuroscience ◽

10.1162/netn_a_00216 ◽

2021 ◽

pp. 1-45

Author(s):

Dominik Kraft ◽

Christian J. Fiebach

Keyword(s):

Resting State ◽

Brain Network ◽

Node Degree ◽

Psychological Resilience ◽

Time Resolved ◽

Temporal Sampling ◽

Specific Analysis ◽

The Face ◽

Network Flexibility ◽

Window Approach

Abstract This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, i.e., the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores, that were however very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.

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Marginal Spectrum Modulated Hilbert-Huang Transform: Application to Time Courses Extracted by Independent Vector Analysis of Resting-State fMRI Data

10.1007/978-3-030-92310-5_35 ◽

2021 ◽

pp. 299-306

Author(s):

Wei-Xing Li ◽

Chao-Ying Zhang ◽

Li-Dan Kuang ◽

Yue Han ◽

Huan-Jie Li ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Vector Analysis ◽

Fmri Data ◽

Independent Vector ◽

Hilbert Huang Transform ◽

Independent Vector Analysis ◽

Marginal Spectrum ◽

Time Courses

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Aging effect on head motion: A Machine Learning study on resting state fMRI data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109084 ◽

2021 ◽

Vol 352 ◽

pp. 109084

Author(s):

Valeria Saccà ◽

Alessia Sarica ◽

Andrea Quattrone ◽

Federico Rocca ◽

Aldo Quattrone ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Aging Effect ◽

Resting State Fmri ◽

Fmri Data ◽

Head Motion ◽

Learning Study

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Identifying patients with PTSD utilizing resting-state fMRI data and neural network approach

IEEE Access ◽

10.1109/access.2021.3098453 ◽

2021 ◽

pp. 1-1

Author(s):

Mirza Naveed Shahzad ◽

Haider Ali ◽

Tanzila Saba ◽

Amjad Rehman ◽

Hoshang Kolivand ◽

...

Keyword(s):

Neural Network ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Network Approach ◽

Neural Network Approach

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MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽

10.1016/j.dib.2020.105213 ◽

2020 ◽

Vol 29 ◽

pp. 105213 ◽

Cited By ~ 1

Author(s):

Pradyumna Lanka ◽

D. Rangaprakash ◽

Sai Sheshan Roy Gotoor ◽

Michael N. Dretsch ◽

Jeffrey S. Katz ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Clinical Diagnostics ◽

Fmri Data ◽

Matlab Toolbox

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Neurophysiology (fMRI)

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2015.173 ◽

2015 ◽

Vol 42 (S1) ◽

pp. S38-S38

Author(s):

ST Lang ◽

B Goodyear ◽

J Kelly ◽

P Federico

Keyword(s):

Cognitive Functioning ◽

Default Mode Network ◽

Resting State ◽

Attentional Control ◽

Brain Tumours ◽

Resting State Fmri ◽

Fmri Data ◽

Resting State Networks ◽

Default Mode ◽

Eloquent Cortex

Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.

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