ArticleNeurons, behavior, data analysis, and theory2020
Application of the hierarchical bootstrap to multi-level data in neuroscience.
Article in Neurons, behavior, data analysis, and theory, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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Who cites it
105 citing papers in PubMed.
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- Premotor cortex hemodynamic responses primarily reflect perceptual rather than specific motor aspects of decision making.PLoS biology · 2026Article
- Inflammation rewires the enteric nervous system through neurogenic monocyte recruitment.The Journal of experimental medicine · 2026Article
- Variance-calibrated cross-individual bootstrapping for small-sample neuroscience.Scientific reports · 2026Article
- Regional specialization in prefrontal cortex manifests in the reliability of task progression codes.Neuron · 2026Article
- A human electrophysiological signature of Fragile X pathophysiology is shared in V1 of Fmr1Nature communications · 2026Article
- TNEA Regulates Hippocampal Oscillation by Improving Inhibitory Synaptic Plasticity to Ameliorates Cognitive Impairment in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Novel Approach to Map the Causal Impact of Brain Stimulation on Semantic Processing With Language Models.Neurobiology of language (Cambridge, Mass.) · 2026Article
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- Adaptive communication between cell assemblies and "reader" neurons shapes flexible brain dynamics.PLoS biology · 2025Article
- Pre-existing and emergent cortical neuronal assembly sequences during learning.bioRxiv : the preprint server for biology · 2025Article
- Behavioral Timing of Interictal Spikes, But Not Rate, Correlates with Impaired Working Memory Performance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
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- Activity in serotonergic axons in visuomotor areas of cortex is modulated by the recent history of visuomotor coupling.Peer community journal · 2025Article
45 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
A common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g., Student's t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (
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33644783PMC7906290What OpenQuestion holds
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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.