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

Reliability and Validity01:29

Reliability and Validity

12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
Data Validation01:15

Data Validation

141
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
141
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

27.6K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
27.6K
Factorial Design02:01

Factorial Design

13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
Variability: Analysis01:11

Variability: Analysis

126
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
126
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

1.9K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Commonality and variability in functional networks in children under 5 years old.

Communications biology·2026
Same author

Racialized Heteroscedasticity in Neuroimaging Features, Behavior Measures, and Neuroimaging-Based Predictive Models.

Research square·2026
Same author

Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

Human brain mapping·2026
Same author

Patterns of brain-wide associations reflect socioeconomics.

Science (New York, N.Y.)·2026
Same author

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide Motifs.

bioRxiv : the preprint server for biology·2026
Same author

Blunted experience of pleasure, unsystematic subjective value representation, and disrupted goal-directed behaviors: an ecological momentary assessment study across mood and psychotic disorders.

Schizophrenia bulletin·2026
Same journal

Unlocking the capacity of Mn-based Prussian blue cathodes in capacitive deionization.

Nature communications·2026
Same journal

Scaling biodiversity-stability relationships from populations to meta-communities across trophic levels.

Nature communications·2026
Same journal

Thermodynamically programmed one-pot CRISPR platform for point-of-care SNP genotyping.

Nature communications·2026
Same journal

Engineering all-organic electrocatalysts with asymmetric dual-active sites for uncommon oxygen-evolving pathway.

Nature communications·2026
Same journal

Rapid GC content evolution in rice through GC-biased gene conversion and selection for translation efficiency.

Nature communications·2026
Same journal

Declines in organic matter persistence with increased soil carbon.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Jun 1, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K

A data-driven latent variable approach to validating the research domain criteria framework.

S K L Quah1, B Jo2, C Geniesse3

  • 1Department of Psychiatry & Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. shaunq@stanford.edu.

Nature Communications
|January 18, 2025
PubMed
Summary
This summary is machine-generated.

The Research Domain Criteria (RDoC) framework may need revision. A new bifactor model better reflects brain circuitry, suggesting improvements for psychiatric and neuroscience research.

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

682
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Related Experiment Videos

Last Updated: Jun 1, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

682
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Area of Science:

  • Neuroscience
  • Psychiatry
  • Cognitive Science

Background:

  • The Research Domain Criteria (RDoC) framework is widely used in neuroscience and psychiatry.
  • Concerns exist regarding the RDoC framework's specificity and breadth in relation to brain circuitry.

Purpose of the Study:

  • To address limitations of the RDoC framework by developing a more accurate model of brain circuitry.
  • To propose a data-driven revision of the RDoC framework based on functional neuroimaging data.

Main Methods:

  • Utilized a latent variable approach with bifactor analysis on 84 task-based fMRI (tfMRI) activation maps from 6192 participants.
  • Employed internal validation with a training/held-out set and external validation using Neurosynth meta-analysis data.
  • Compared the fit of a novel bifactor model against the existing RDoC framework.

Main Results:

  • A bifactor model, including a task-general domain and a split cognitive systems domain, demonstrated a superior fit to tfMRI data compared to the RDoC framework.
  • The arousal and regulatory systems domain was identified as underrepresented within the current RDoC structure.
  • Findings provide empirical support for refining the RDoC framework.

Conclusions:

  • The current RDoC framework may not optimally capture the underlying brain circuitry.
  • A revised RDoC framework, informed by neuroimaging data and bifactor modeling, could enhance specificity and accuracy in psychiatric and neuroscience research.
  • Future research should focus on data-driven validation and refinement of the RDoC framework.