A systematic conceptual framework for time-series analysis in neuroscience
Poster No:
1575
Submission Type:
Abstract Submission
Authors:
Giulia Baracchini1, James Shine2, Ben Fulcher3
Institutions:
1The University of Sydney, Sydney, New South Wales, 2The University of Sydney, Sydney, NSW, 3University of Sydney, Sydney, Australia
First Author:
Co-Author(s):
Introduction:
A common endeavour across disparate fields of neuroscience (from micro- to macro-scale, from electrophysiology to fMRI) is understanding the temporal structure of complex multiscale neural processes via time-series analysis techniques[1]. While the use of these quantitative methods has enabled a deep characterisation of brain activity in health and disease [e.g., 2-3], there are stark inconsistencies in how these metrics are employed and interpreted, creating confusion and halting progress. This fragmentation has led to issues of generalisability and replicability, and, importantly, to difficulties in developing unified theories of brain dynamics. For instance, in the brain signal variability literature, measures of distributional moments (e.g., variance and standard deviation)[4-5] are often grouped, and interchangeably used, with stationary linear measures (e.g., signal entropy)[6] which rely on completely different theoretical assumptions, and therefore capture different aspects of a time-series. If we do not draw clear conceptual boundaries between the vast array of time-series methods in neuroscience, we risk heading down a theoretical cul-de-sac. Here, we introduce a systematic conceptual framework for time-series analyses that provides a logically structured hierarchical grouping of the most commonly used dynamics measures in neuroscience.
Methods:
We surveyed the literature (via Pubmed, Web of Science, and Google Scholar) and current time-series toolkits[7-8] to identify the most commonly used statistical measures of brain signal dynamics (univariate time-series that are uniformly sampled in time).
Results:
Our conceptual organization of the time-series literature is depicted in Figure 1. We first distinguish time-series metrics that are insensitive to time ordering of the data, and are thus properties of the distribution of time-series values. Under this category, we find distributional moments (mean, variance, skewness, kurtosis), outlier measures, and distributional fits. The remaining methods are all sensitive to the time-ordering of the measurements, from which much of the literature is derived from an assumption of stationarity. Statistics sensitive to this next concept of stationarity include measures of trend ('ramping'), relative spacing of outliers across the time-series, and measures of how local statistical estimates vary across the signal. Note that sources of non-stationarity can sometimes be removed via techniques including detrending or windowing/trimming. The remaining time-series techniques are those developed to analyze and quantify different types of correlation structures in stationary time-series. We first divide off methods that are derived from an assumption of linearity (linear time-series analysis), and include statistics derived from linear two-point correlation structure (e.g., Fourier power spectrum and the autocorrelation function) that capture linear correlation structure in the signal (from which statistics like intrinsic timescales, power in frequency bands, or aperiodic exponents can be computed). We label remaining methods as nonlinear time-series analysis, which are sensitive to more complex and nonlinear correlation structures in the time-series, including measures of signal entropy (e.g., Lempel–Ziv complexity), chaotic dynamics (e.g., Lyapunov exponent), and information-theoretic measures (e.g., automutual information).
Conclusions:
Here, we provide a timely and necessary systematic framework with which to disambiguate conceptually different time-series approaches in neuroscience. Our framework will help researchers across subfields of neuroscience better understand how existing methods fit in the broader literature, encouraging appropriate usage and interpretation of time-series metrics. It will additionally help solve issues of generalisability and replicability, ultimately paving the path for unified theories of brain dynamics more broadly.
Modeling and Analysis Methods:
Methods Development 1
Univariate Modeling 2
Other Methods
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Data analysis
Design and Analysis
Modeling
Statistical Methods
Univariate
Other - Time-series analyses
1|2Indicates the priority used for review
Abstract Information
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[2] Shafiei, G., Fulcher, B. D., Voytek, B., Satterthwaite, T. D., Baillet, S., & Misic, B. (2023). Neurophysiological signatures of cortical micro-architecture. Nature communications, 14(1), 6000.
[3] Bryant, A. G., Aquino, K., Parkes, L., Fornito, A., & Fulcher, B. D. (2024). Extracting interpretable signatures of whole-brain dynamics through systematic comparison. bioRxiv, 2024-01.
[4] Baracchini, G., Mišić, B., Setton, R., Mwilambwe-Tshilobo, L., Girn, M., Nomi, J. S., ... & Spreng, R. N. (2021). Inter-regional BOLD signal variability is an organizational feature of functional brain networks. NeuroImage, 237, 118149.
[5] Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2010). Blood oxygen level-dependent signal variability is more than just noise. Journal of Neuroscience, 30(14), 4914-4921.
[6] Wehrheim, M. H., Faskowitz, J., Schubert, A. L., & Fiebach, C. J. (2024). Reliability of variability and complexity measures for task and task‐free BOLD fMRI (Vol. 45, No. 10, p. e26778). Hoboken, USA: John Wiley & Sons, Inc..
[7] Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., & Jones, N. S. (2019). catch22: CAnonical Time-series CHaracteristics: Selected through highly comparative time-series analysis. Data Mining and Knowledge Discovery, 33(6), 1821-1852.
[8] Cliff, O. M., Bryant, A. G., Lizier, J. T., Tsuchiya, N., & Fulcher, B. D. (2023). Unifying pairwise interactions in complex dynamics. Nature Computational Science, 3(10), 883-893.