Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

718
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
718
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

547
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
547
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

338
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
338
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

814
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
814
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.7K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.

Journal of the American Statistical Association·2026
Same author

Bayesian Image Mediation Analysis.

Journal of the American Statistical Association·2026
Same author

Quantitative MRI Assessment of Bone Marrow Disease in Myelofibrosis: A Prospective Study.

Radiology. Imaging cancer·2025
Same author

Fat Fraction MRI for Longitudinal Assessment of Bone Marrow Heterogeneity in a Mouse Model of Myelofibrosis.

Tomography (Ann Arbor, Mich.)·2025
Same author

Engineering encapsulated living bacteria for advanced healthcare management.

Biotechnology advances·2025
Same author

CyberKnife Treatment of Medically Intractable Trigeminal Neuralgia: A Comparison of Isocentric and Non-isocentric Treatment Planning Outcomes.

Cureus·2025

Related Experiment Video

Updated: Feb 11, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Time series analysis of fMRI data: Spatial modelling and Bayesian computation.

Ming Teng1, Timothy D Johnson1, Farouk S Nathoo2

  • 1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.

Statistics in Medicine
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

Variational Bayes (VB) approximations are computationally efficient for fMRI analysis but may be inaccurate with low signal-to-noise ratios (SNR). Hamiltonian Monte Carlo (HMC) offers better accuracy in low SNR scenarios for neuroimaging data.

Keywords:
Hamiltonian Monte CarloSPMfMRIspatial modeltime seriesvariational Bayes

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.1K

Related Experiment Videos

Last Updated: Feb 11, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.1K

Area of Science:

  • Neuroimaging statistics
  • Medical statistics
  • Computational neuroscience

Background:

  • Time series analysis of fMRI data is crucial in neuroimaging.
  • Spatial and Bayesian models offer advantages over mass univariate approaches.
  • Approximate Bayesian inference using variational Bayes (VB) is widely adopted for computational efficiency in fMRI analysis.

Purpose of the Study:

  • To evaluate the accuracy of variational Bayes (VB) approximations in fMRI spatial models.
  • To compare VB with Hamiltonian Monte Carlo (HMC) for statistical inference.
  • To identify conditions under which VB approximations may be unreliable for neuroimaging data.

Main Methods:

  • Derivation of Hamiltonian Monte Carlo (HMC) for fMRI spatial models with spatially varying coefficients.
  • Simulation studies comparing VB and HMC on estimation accuracy, posterior variability, spatial smoothness, and computation time.
  • Application to real fMRI data examining the hemodynamic response to face perception.

Main Results:

  • VB is computationally faster than HMC.
  • VB and HMC yield similar results in high or moderate signal-to-noise ratio (SNR) settings.
  • In low SNR conditions, VB approximations show substantial differences from HMC, leading to larger mean squared errors.

Conclusions:

  • VB approximations are useful for fMRI spatiotemporal analysis when SNR is adequate.
  • HMC provides a more accurate alternative to VB in low SNR scenarios.
  • Understanding the limitations of VB is critical for reliable statistical inference in neuroimaging.