ArticleNeuroImage2019
Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 citing papers in PubMed.
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- Efficacy of Personalized Connectivity-Guided Accelerated Brain Stimulation in a Naturalistic Treatment-Resistant Depression Population.medRxiv : the preprint server for health sciences · 2026Article
- Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.Network neuroscience (Cambridge, Mass.) · 2026Article
- Regression and alignment for functional data and network topology.Biostatistics (Oxford, England) · 2025Article
- Trends in the global and Chinese burden of rabies from 1990 to 2021 and forecasted to 2040: evidence from the Global Burden of Disease study 2021.BMC infectious diseases · 2025Article
- Subpolar North Atlantic decadal cooling may have aggravated recent Eastern Siberian wildfires.Nature communications · 2025Article
- Feature similarity, a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior.Communications biology · 2025Article
- Attenuated cognitive control network connectivity as a mechanism in mother-daughter intergenerational transmission of depression: a preliminary study.Scientific reports · 2025Article
- Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall.PLoS computational biology · 2025Article
- Dynamic Causal Tractography Analysis of Auditory Descriptive Naming: An Intracranial Study of 106 Patients.NeuroImage · 2025Article
- Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Associative conditioning in gene regulatory network models increases integrative causal emergence.Communications biology · 2025Article
- Identifying Features of Resilience to Childhood Maltreatment in Resting-State Connectivity Data From Adults With and Without a History of Mood Disorder.Biological psychiatry global open science · 2025Article
- Evaluating the effects of volume censoring on fetal functional connectivity.Scientific reports · 2025Article
- Maximizing the accuracy of genetic variance estimation and using a novel generalized effective sample size to improve simulations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Insights into the epidemiological analysis of subarachnoid hemorrhage burden and trends in middle-aged and elderly populations: a global perspective from the Global Burden of Disease Study 2021.Frontiers in neurology · 2025Article
- Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Claustrum modulation drives altered prefrontal cortex dynamics and connectivity.Communications biology · 2024Article
- Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.American journal of epidemiology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.