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

Cluster Sampling Method01:20

Cluster Sampling Method

14.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.7K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.9K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.9K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.6K
3.6K
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

19.4K
Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
19.4K
Nursing Code of Ethics01:29

Nursing Code of Ethics

4.5K
The Nursing Code of Ethics sets the ethical benchmark for the profession, and guides nurses in ethical analysis and decision making at the societal, organizational, and clinical levels. The code encompasses showing compassion and respect for the patient, their families, and communities in all circumstances while committing to providing patient-centered care. In addition, the code states that nurses must advocate for the patient by defending a cause or recommendation to protect their rights,...
4.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

963
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
963

You might also read

Related Articles

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

Sort by
Same author

The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).

Neuroinformatics·2026
Same author

Neptune: a toolbox for spinal cord functional MRI data processing and quality assurance.

bioRxiv : the preprint server for biology·2026
Same author

Non-invasive Electrophysiological Characterization of Distinctive Meditative States in a Yogi during <i>Samaadhi</i>.

International journal of yoga·2026
Same author

Augmenting an Allograft for Anterior Cruciate Ligament Reconstruction With a Collagen Matrix and Bone Marrow Aspirate Concentrate Injection Appears Safe and Produces Favorable Clinical Outcomes at 2-Year Follow-Up.

Arthroscopy, sports medicine, and rehabilitation·2026
Same author

Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability.

IEEE access : practical innovations, open solutions·2026
Same author

Effects of seasonal factors on brain function: Systematic review and future perspectives.

iScience·2025

Related Experiment Video

Updated: Jan 31, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

Xinyu Zhao1, D Rangaprakash1,2, Thomas S Denney1,3,4,5

  • 1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.

Data in Brief
|January 11, 2019
PubMed
Summary
This summary is machine-generated.

This study offers fMRI data and analysis code for five neuropsychiatric disorders, enabling unsupervised machine learning to explore neurobiological clusters corresponding to clinical diagnoses.

Keywords:
ClusteringEffective connectivityFunctional connectivityFunctional magnetic resonance imagingGenetic algorithmPsychiatric disordersUnsupervised learning

More Related Videos

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
05:51

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

26.4K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.3K

Related Experiment Videos

Last Updated: Jan 31, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K
A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
05:51

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

26.4K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.3K

Area of Science:

  • Neuroscience
  • Psychiatry
  • Data Science

Background:

  • Neuroimaging data, including resting-state functional magnetic resonance imaging (fMRI) connectivity, is crucial for understanding brain disorders.
  • Accurate classification and understanding of neuropsychiatric disorders require robust analytical methods and accessible datasets.

Purpose of the Study:

  • To provide a comprehensive dataset of fMRI connectivity, phenotypic variables, and clinical diagnostic labels for Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome.
  • To offer source MATLAB codes for advanced data analysis, including unsupervised clustering and genetic algorithm-based feature selection.
  • To facilitate research into the relationship between clinical diagnostic groups and underlying neurobiological/phenotypic clusters using fMRI data.

Main Methods:

  • The dataset includes resting-state fMRI connectivity features, phenotypic variables, and clinical diagnostic labels for individuals with five neuropsychiatric disorders and healthy controls.
  • Provided MATLAB code incorporates three clustering methods that do not require a priori specification of the number of clusters.
  • A genetic algorithm-based feature selection method is included for simultaneous feature selection and data clustering.

Main Results:

  • The dataset enables the investigation of unsupervised machine learning approaches on fMRI data.
  • The provided codes facilitate the exploration of neurobiological clusters within the data.
  • The findings support the potential for identifying distinct neurobiological patterns associated with different neuropsychiatric conditions.

Conclusions:

  • This contribution serves as a valuable resource for researchers in neuroscience and psychiatry.
  • The dataset and associated codes empower unsupervised machine learning analyses of fMRI data.
  • It aids in investigating the correspondence between clinical diagnoses and neurobiological/phenotypic clusters.