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

¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

1.2K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
1.2K
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

1.3K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
1.3K

You might also read

Related Articles

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

Sort by
Same author

From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes - CORRIGENDUM.

The British journal of nutrition·2026
Same author

Maintenance of symptoms and weight-related gains following residential eating disorder treatment at discharge and one-year follow-up.

Eating disorders·2026
Same author

From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.

The British journal of nutrition·2026
Same author

Acceptance and Commitment Therapy for Generalized Anxiety Disorder: A Narrative Review.

Behavior modification·2026
Same author

Who benefits most? Exploring demographic and psychological predictors of misophonia psychotherapy outcomes.

Cognitive behaviour therapy·2026
Same author

Parent Questions About Childhood Hearing Loss: An Evaluation of ChatGPT Response Accuracy, Completeness, and Repeatability.

American journal of audiology·2026

Related Experiment Video

Updated: May 23, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

358

Recognizing individual variability in misophonia: Identifying symptom-based subgroups with Gaussian mixture modeling.

Mercedes G Woolley1, Amanda M Ramos1, Emily M Bowers1

  • 1Utah State University, United States.

Journal of Psychiatric Research
|March 8, 2025
PubMed
Summary
This summary is machine-generated.

This study identified two misophonia subgroups: anticipatory and reactive. The anticipatory group experiences distress before sounds, while the reactive group reacts during sounds, highlighting the need for personalized misophonia treatments.

Keywords:
Cluster analysisHeterogeneityInternalizing symptomsMisophoniaSub-type

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
10:48

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

Published on: June 3, 2013

22.1K

Related Experiment Videos

Last Updated: May 23, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

358
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
10:48

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

Published on: June 3, 2013

22.1K

Area of Science:

  • Psychology
  • Neuroscience
  • Audiology

Background:

  • Misophonia involves strong emotional and physiological reactions to specific sounds.
  • Previous research has primarily focused on defining misophonia, with less attention to individual symptom variations.

Purpose of the Study:

  • To identify distinct subgroups within the misophonia population.
  • To explore the heterogeneity of symptom profiles using statistical modeling.

Main Methods:

  • A Gaussian finite mixture model was applied to data from 60 treatment-seeking individuals with misophonia.
  • The Duke Misophonia Interview assessed behavioral, affective, and cognitive symptoms.

Main Results:

  • Two distinct clusters emerged: an anticipatory group and a reactive group.
  • The anticipatory group exhibited pre-sound distress and avoidance, while the reactive group showed in-sound responses.
  • The anticipatory group reported more internalizing symptoms like rumination and social isolation.

Conclusions:

  • Findings suggest misophonia is not monolithic, with identifiable subgroups.
  • Tailored interventions are recommended to address the specific needs of each misophonia subgroup.
  • Future research should use larger samples and broader models to encompass all misophonia symptom presentations.