# Analysis of Nonstationary Point Process Data

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $373,940

## Abstract

SUMMARY
Much mental health research uses neurophysiological measurements to describe the
way neural activity within and across brain regions is related to behavioral function and
dysfunction. One kind of signal, known as a spike train, comes from an individual
neuron. Another, the local field potential (LFP), is based on activity from large numbers
of neurons within specified parts of the brain. For both kinds of data, scientifically
rigorous statistical analysis must accommodate unstable fluctuations, associated with
movement or thought, known in statistics as non-stationarity. The continuing research
program of this grant is to develop methods for analyzing non-stationary neural data.
The number of neural signals that can be recorded simultaneously has been increasing
rapidly. Because neural network dysfunction is widely considered to be associated with
psychopathology, improvements in recording technologies offer exciting opportunities,
but they also create big statistical challenges due to greatly increased complexity. To
provide the most useful information for designing novel therapies it is important to
characterize the interactions among different parts of the brain, and the timing of these
interactions relative to behavior. The research in this grant aims to develop methods for
analyzing the ways that a network of brain areas may change with particular variables,
including those that help characterize behavior. This involves the transmission of neural
information at multiple timescales: slower timescales can provide insight into states of
the brain, such as the extent to which a subject is paying attention to a task; fast
timescales include oscillations and neural synchrony, which could provide an essential
mechanism of neural network information flow and may be a marker that distinguishes
normal from diseased states. New methods investigated in this research program can
accommodate both faster and slower timescales, and they can also accommodate
relationships arising from the spatial configuration of electrodes that record neural
signals. Because a neural spike train is a set of times at which a neuron fired, it is
common to consider it to be a point process, which is the statistical model set up to
handle sequences of event times. The research supported by this grant concerns
development and investigation of statistical techniques involving both multi-dimensional
continuous time series (for LFPs) and multi-dimensional point processes (for spike
trains).

## Key facts

- **NIH application ID:** 10896296
- **Project number:** 5R01MH064537-21
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** ROBERT E KASS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $373,940
- **Award type:** 5
- **Project period:** 2001-09-26 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896296

## Citation

> US National Institutes of Health, RePORTER application 10896296, Analysis of Nonstationary Point Process Data (5R01MH064537-21). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10896296. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*