Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Ogive Graph

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...

Graphing Antiderivatives

Graphing Antiderivatives

The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Bar Graph

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...

Time-Series Graph

Time-Series Graph

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...

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

You might also read

Related Articles

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

Sort by

Same author

Converting negative symptom dimension scores across SANS and PANSS.

Schizophrenia research·2026

Same author

Sensorimotor recovery and neuropathic pain reduction after remotely delivered cognitive multisensory rehabilitation or remotely delivered exercise in adults with spinal cord injury: a pilot clinical trial.

medRxiv : the preprint server for health sciences·2026

Same author

AI-associated delusions: terminology.

The lancet. Psychiatry·2026

Same author

Largely Typical Neural Activation During Monetary Reward Receipt in People With Psychosis-Spectrum Disorders and First-Degree Relatives.

Schizophrenia bulletin·2026

Same author

Electroencephalography Microstate Instability and Clinical Outcomes in Individuals at Clinical High Risk of Psychosis.

JAMA psychiatry·2026

Same author

Cerebellar structure is abnormal in schizophrenia and deviates from bipolar disorder.

NeuroImage. Clinical·2026

Search research articles

Related Experiment Video

Updated: Jan 27, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

A method for building a genome-connectome bipartite graph model.

Qingbao Yu¹, Jiayu Chen¹, Yuhui Du²

¹The Mind Research Network, Albuquerque, NM, 87106, USA.

Journal of Neuroscience Methods

|March 24, 2019

Summary

Genomic factors influence schizophrenia risk and brain connectivity. A novel genome-connectome model reveals specific genes impacting brain networks, offering insights into schizophrenia pathology.

Keywords:

Bipartite graph FNC SNPs fMRI

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Published on: December 3, 2016

Related Experiment Videos

Last Updated: Jan 27, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Published on: December 3, 2016

Area of Science:

Neuroscience
Genetics
Psychiatry

Background:

Genomic factors are known to influence schizophrenia risk and functional brain connectivity.
Schizophrenia is characterized by disruptions in brain connectivity patterns.

Purpose of the Study:

To propose and validate a novel genome-connectome bipartite graph model for imaging genomic analysis in schizophrenia.
To investigate the association between single nucleotide polymorphisms (SNPs) and functional network connectivity (FNC) in patients with schizophrenia (SZ) and healthy controls (HC).

Main Methods:

Resting-state functional magnetic resonance imaging (fMRI) data from HC and SZ were analyzed using group independent component analysis (G-ICA).
A bipartite graph was constructed with 83 FNC connections (differing between HC and SZ) as fMRI nodes and 81 schizophrenia-related SNPs as genetic nodes.
SNP-FNC associations were evaluated using a general linear model to define edges in the bipartite graph.

Main Results:

The genome-connectome bipartite graph identified influential SNP nodes modulating brain connectivity and associated with schizophrenia risk.
Bi-clustering analysis revealed a significant cluster of 15 SNPs interacting with 38 FNC connections, primarily within somato-motor and visual brain areas.
These findings suggest a link between specific genetic variations and the functional activity of these brain regions in schizophrenia.

Conclusions:

The SNP-FNC bipartite graph approach provides a novel framework for exploring genetic influences on functional brain connectivity in mental illnesses like schizophrenia.
The identified SNP-FNC interactions offer valuable insights into the neurobiological underpinnings and genetic etiology of schizophrenia.