Nonparametric trend estimation in the presence of fractal noise: Application to fMRI time-series analysis

Afshin-Pour, Babak; Hossein-Zadeh, Gholam-Ali; Soltanian-Zadeh, Hamid

doi:10.1016/j.jneumeth.2008.03.017

Cited by 5 publications

(5 citation statements)

References 24 publications

(35 reference statements)

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“…One possible reason is that the noise obtained from the scanner (e.g. pink noise or 1/f noise) might be flattened out when concatenating multiple scans together (Afshinpour et al, 2008;Akhrif et al, 2018). When a single long scan is acquired, however, the noise is maintained as the main variation between test and retest sessions, thereby reducing reliability.…”

Section: Discussionmentioning

confidence: 99%

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Cho

Korchmaros

Vogelstein

et al. 2020

Preprint

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2Compelling evidence suggests the need for more data per individual to reliably map the functional 3 organization of the human connectome. As the notion that 'more data is better' emerges as a golden 4 rule for functional connectomics, researchers find themselves grappling with the challenges of how 5 to obtain the desired amounts of data per participant in a practical manner, particularly for 6 retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans 7 available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, 8 task, movie). A number of open questions exist regarding the aggregation process and the impact 9 of different decisions on the reliability of resultant aggregate data. We leveraged the availability 10 of highly sampled test-retest datasets to systematically examine the impact of data aggregation 11 strategies on the reliability of whole-brain functional connectomics. Specifically, we compared 12 functional connectivity estimates derived after concatenating from: 1) multiple scans under the 13 same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), 14 and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal 15 regression, ICA-FIX, and task regression) and estimation procedures. When the total number of 16 time points is equal, and the scan state held constant, concatenating multiple shorter scans had a 17 clear advantage over a single long scan. However, this was not necessarily true when concatenating 18 across different fMRI states (i.e. task conditions), where the reliability from the aggregate data 19 varied across states. Concatenating fewer numbers of states that are more reliable tends to yield 20 higher reliability. Our findings provide an overview of multiple dependencies of data 21 concatenation that should be considered to optimize reliability in analysis of functional 22 connectivity data.

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Section: Discussionmentioning

confidence: 99%

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Cho

Korchmaros

Vogelstein

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Among standard preprocessing steps, it appears that regressing out the global mean signal, detrending, motion correction, and frequency exclusion most notably improves the reliability of fractal measures (Rubin et al, 2013). While machine or physiological noise may interfere with the signals' fractal properties, numerous studies report that fractal analysis adequately disentagles noise from signal (Afshinpour et al, 2008;Hu et al, 2008Hu et al, , 2006 and that observed variations in SNR do not affect fractal estimations (Herman et al, 2011) with appropriate pre-processing.…”

Section: Snrmentioning

confidence: 99%

Monofractal analysis of functional magnetic resonance imaging: An introductory review

Campbell

Weber

2022

Human Brain Mapping

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The following review will aid readers in providing an overview of scale-free dynamics and monofractal analysis, as well as its applications and potential in functional magnetic resonance imaging (fMRI) neuroscience and clinical research. Like natural phenomena such as the growth of a tree or crashing ocean waves, the brain expresses scale-invariant, or fractal, patterns in neural signals that can be measured. While neural phenomena may represent both monofractal and multifractal processes and can be quantified with many different interrelated parameters, this review will focus on monofractal analysis using the Hurst exponent (H). Monofractal analysis of fMRI data is an advanced analysis technique that measures the complexity of brain signaling by quantifying its degree of scale-invariance. As such, the H value of the blood oxygenation level-dependent (BOLD) signal specifies how the degree of correlation in the signal may mediate brain functions. This review presents a brief overview of the theory of fMRI monofractal analysis followed by notable findings in the field. Through highlighting the advantages and challenges of the technique, the article provides insight into how to best conduct fMRI fractal analysis and properly interpret the findings with physiological relevance. Furthermore, we identify the future directions necessary for its progression towards impactful functional neuroscience discoveries and widespread clinical use. Ultimately, this presenting review aims to build a foundation of knowledge among readers to facilitate greater understanding, discussion, and use of this unique yet powerful imaging analysis technique.

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“…If we do not want to use any fMRI toolbox for simulating datasets, we can create these data sets as previous works do by this way. [ 17 18 19 20 21 22 23 24 25 26 27 28 29 30 ] In block designed inputs, two conditions are considered as the “state of during task” and “state of being idle.” These kinds of inputs have been shown in Figure 1 . Note that these inputs are only used for simulation datasets, and we consider that we do not have any prior knowledge about inputs as we do not know in real datasets, because our proposed method have been considered as a non-parametric method.…”

Section: Simulated Datasetsmentioning

confidence: 99%

“…[ 1 ] We also add trends by different shapes as introduced in Afshinpour et al . [ 30 ] to time series data. Gaussian noise with variance of 9 and mean of 0 were added to time series.…”

Section: Simulated Datasetsmentioning

confidence: 99%

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine

Bahrami

Shamsi

2017

J Med Signals Sens

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Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1–4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

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Nonparametric trend estimation in the presence of fractal noise: Application to fMRI time-series analysis

Cited by 5 publications

References 24 publications

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Monofractal analysis of functional magnetic resonance imaging: An introductory review

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine

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