On the pros and cons of using temporal derivatives to assess brain functional connectivity

Ochab, Jeremi K.; Tarnowski, Wojciech; Nowak, Maciej A.; Chialvo, Dante R.

doi:10.1016/j.neuroimage.2018.09.063

Cited by 8 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Encouragingly, we found a similar pattern of information with high spatial correlations between the two methods across multiple studies (Lim et al 2016, Wang et al 2016. MTD has been found to be less sensitive to low frequency drifts, due to the inherent nature of differencing acting as a high-pass filter, but also to have less signal-to-noise ratio (Ochab et al 2019). As a result, the susceptibility of MTD to higher frequency signal (Shine et al 2015) as compared to SWPC necessarily means that the properties of the connectivity information it contains are different.…”

Section: Dcss Are Comparable Across Sliding Window and Temporal Diffesupporting

confidence: 60%

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Teng

Ong

Patanaik

et al. 2019

Preprint

View full text Add to dashboard Cite

Running title: Vulnerability to sleep deprivation is associated with connectivity statesThe authors of this manuscript have no financial conflicts of interest to disclose. AbstractRobustly linking dynamic functional connectivity (DFC) states to behaviour is an important goal of the fledgling research using these methods. Here, we demonstrate in an independent dataset that two connectivity states previously linked to arousal and sustained attention are associated with individual differences in vulnerability to sleep deprivation. 32 healthy participants underwent two sets of resting-state functional magnetic resonance imaging (fMRI) scans, once in a well-rested state and once after a single night of total sleep deprivation. Individual differences in vulnerability to this manipulation were measured using changes in Psychomotor Vigilance Test (PVT) performance. DFC and clustering analysis on the resting state fMRI data revealed five centroids that were highly correlated with those found in previous work, including two states associated with high and low arousal. Changes in these arousal states from the well-rested to the sleep-deprived condition specifically were correlated with changes in PVT performance. Global signal was also correlated with this change in univariate analysis, but was not a significant incremental predictor in a multivariate model. Although head motion increased following sleep deprivation, this did not predict change in PVT performance. Our results provide good evidence of the reliability, validity, and reproducibility of DFC measures, particularly with regard to measuring arousal and attention.

show abstract

Section: Dcss Are Comparable Across Sliding Window and Temporal Diffesupporting

confidence: 60%

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Teng

Ong

Patanaik

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, functional activity in general has been intensely analysed with a variety of methods 6 . However, blood oxygen-level-dependent (BOLD) signals have a nontrivially associated autocorrelation and cross-correlation structure 7 and remain notoriously challenging to analyse due to their very low temporal resolution. Motivated by prior work, we applied the fractal methodology to test for regional differences in BOLD scaling properties between the tasks and experimental phases of a working memory experiment.…”

Section: Introductionmentioning

confidence: 99%

Task-dependent fractal patterns of information processing in working memory

Ochab

Wątorek

Ceglarek

et al. 2022

Sci Rep

View full text Add to dashboard Cite

We applied detrended fluctuation analysis, power spectral density, and eigenanalysis of detrended cross-correlations to investigate fMRI data representing a diurnal variation of working memory in four visual tasks: two verbal and two nonverbal. We show that the degree of fractal scaling is regionally dependent on the engagement in cognitive tasks. A particularly apparent difference was found between memorisation in verbal and nonverbal tasks. Furthermore, the detrended cross-correlations between brain areas were predominantly indicative of differences between resting state and other tasks, between memorisation and retrieval, and between verbal and nonverbal tasks. The fractal and spectral analyses presented in our study are consistent with previous research related to visuospatial and verbal information processing, working memory (encoding and retrieval), and executive functions, but they were found to be more sensitive than Pearson correlations and showed the potential to obtain other subtler results. We conclude that regionally dependent cognitive task engagement can be distinguished based on the fractal characteristics of BOLD signals and their detrended cross-correlation structure.

show abstract

“…Next, a threshold for detecting strong activity is chosen, (typically the results remain unchanged when using a range of 1 − 2 SDs) and for each time series, the timing of each upward threshold crossing is determined ( Figure 1A ). Note that the number of threshold crossings depends on the auto-correlation of the BOLD signals (which stays in the range 0.6–0.85 Ochab et al, 2019 ) and more generally on the exponent of the 1/ f α frequency spectrum. Empirically, for the threshold of 1σ, in a BOLD signal we find on average 8.5 ± 2.8 upward crossings per 4 min of fMRI scan.…”

Section: Methodsmentioning

confidence: 99%

Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

et al. 2021

Self Cite

View full text Add to dashboard Cite

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

show abstract

On the pros and cons of using temporal derivatives to assess brain functional connectivity

Cited by 8 publications

References 24 publications

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Task-dependent fractal patterns of information processing in working memory

Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

Contact Info

Product

Resources

About