Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.10.30.564780

A new family of statistical tests for responses in point-event and time-series data for one- and two-sample comparisons

Heimel, J. A.·Meijer, G. T.·Montijn, J. S.
View original source ↗Export metadata ↓

Abstract

Quantifying whether and when signals are modulated by autonomous or external events is ubiquitous in the field of neuroscience. Existing statistical approaches, however, are not ideally suited to do this, especially when the signals under scrutiny show temporal autocorrelations. For example, a standard approach in the analysis of calcium imaging data is to use a t-test on predetermined time-windows to quantify whether neurons respond (differently) to an event of interest. While this is attractive because of its simplicity, only average signal differences can be detected. In practice, neurons often show complex response dynamics which are missed by conventional statistical tests. More advanced methods, such as bin-wise ANOVAs, do not share this drawback, but can suffer from high false-positive rates or "ghost correlations" when applied to temporally autocorrelated data. To solve this issue, we have developed three novel statistical tests extending the original ZETA-test to other use cases: 1) a test for time-series data; 2) a two-sample test to detect differences in neural responses between two conditions; and 3) a two-sample test to detect differences in time-series data between two conditions. In addition, we have improved upon the original ZETA-test. We show that our methods have a statistical sensitivity superior to t-tests and ANOVAs and work well with temporally autocorrelated data where other approaches fail. Our methods are widely applicable and we present example applications to Neuropixels data, two-photon GCaMP imaging data, and human electrocorticogram data. Open-source code for implementations in MATLAB and Python is available on GitHub and PyPi. Significance StatementNeurophysiology involves the detection of weak signals in a noisy background. Often these signals, as well as the background, are temporally autocorrelated. This means that many statistical methods, such as the ANOVA, are inappropriate. In some cases, these statistical tests produce "ghost" signals that mislead researchers into thinking there is an effect of their experiment, when in reality there is none. We have therefore developed a new family of statistical tests, the ZETA-tests, that are not negatively impacted by temporal autocorrelations. They do not use arbitrary (hyper)parameters, like bin size, and show superior statistical sensitivity compared to other methods like t-tests and ANOVAs. We provide easy-to-use implementations and expect our methods to be useful for many neuroscientists from various disciplines.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Heimel, J. A., Meijer, G. T., Montijn, J. S.. 2023-11-02. A new family of statistical tests for responses in point-event and time-series data for one- and two-sample comparisons. https://doi.org/10.1101/2023.10.30.564780

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Attention Across Scales: From Individual Variation to Social Hierarchies and Brain Networks in Semi-Free-Ranging Macaques

Attention is a fundamental brain function supporting perception, decision-making, and social behavior, and its dysfunction profoundly impairs daily life. It is both dynamic and stable, varying across observations and individuals, changing across the lifespan, and being shaped by social and environmental experience. Yet capturing this complexity remains a central challenge in neuroscience. Here, we integrated longitudinal behavioral assessments of semi-free-ranging macaques living in naturalistic social groups with resting-state fMRI. We quantified performance across days, ages, and social hierarchies and related it to intrinsic brain organization. Distinct attentional phenotypes emerged, including individuals with reduced attentional control. Performance followed an inverted-U lifespan trajectory, improving from childhood to adulthood before declining. Social status modulated attentional performance. Critically, nonlinear lifespan trajectories and associations with individual attentional differences were most clearly expressed in frontoparietal connectivity. Together, these findings reveal how sustained attention is organized across scales, providing a biological framework for its individual diversity, social modulation, and neural basis.

neuroscience↗
bioRxiv2026

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.

neuroscience↗
bioRxiv2026

Why Is Spontaneous Blink Timing Informative? An Adaptive Scheduling Perspective

Spontaneous eye blinks have long been linked to cognitive processing, yet how task demands shape blink timing and its relationship to behavioral performance remains unclear. We examined spontaneous blink behavior in 576 adults performing two variants of the Continuous Performance Task (CPT). Blink occurrence and timing were most strongly modulated by the experimental condition in the more demanding CPT-AX task, whereas their association with response time was stronger in the CPT-X task, where more consistent blink timing predicted faster responses. This dissociation suggests that task structure changes not only blink behavior but also the behavioral relevance of blink timing. These findings are consistent with an adaptive scheduling account of spontaneous blinking and provide a conceptual framework for understanding when and why blink timing contains chronometric information about ongoing cognition.

neuroscience↗