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

Structural Classification of Joints01:20

Structural Classification of Joints

7.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.1K
Language01:16

Language

898
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
898
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

542
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
542
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

19.3K
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...
19.3K
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

932
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
932

You might also read

Related Articles

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

Sort by
Same author

Data fusion of medical imaging in neurological disorders.

Reviews in the neurosciences·2025
Same author

Image-based food groups and portion prediction by using deep learning.

Journal of food science·2025
Same author

Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.

Journal of medical systems·2024
Same author

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning.

Journal of medical systems·2024
Same author

AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG.

IEEE journal of biomedical and health informatics·2023
Same author

Deep learning fuzzy immersion and invariance control for type-I diabetes.

Computers in biology and medicine·2022

Related Experiment Video

Updated: Jan 25, 2026

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice
06:15

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice

Published on: July 15, 2025

1.2K

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language

Hidir Selcuk Nogay1, Hojjat Adeli2

  • 1Bursa Uludag University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Bursa, Turkey.

Psychiatry Research. Neuroimaging
|January 23, 2026
PubMed
Summary

This study introduces a novel dual-representation structural MRI framework using deep learning for improved classification of psychiatric disorders like schizophrenia and bipolar disorder, enhancing diagnostic accuracy.

Keywords:
Convolutional neural networks (CNN)Data augmentation (DA)Large language model (LLM)Psychiatric disordersSegmentationStructural magnetic resonance imaging (SMRI)Transfer learning (TL)

More Related Videos

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

4.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Related Experiment Videos

Last Updated: Jan 25, 2026

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice
06:15

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice

Published on: July 15, 2025

1.2K
Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

4.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Psychiatry

Background:

  • Accurate differentiation between schizophrenia and bipolar disorder is challenging due to overlapping symptoms and subtle neuroanatomical differences.
  • Current diagnostic methods can be subjective and lack objective biomarkers.
  • Structural MRI offers potential for objective assessment but requires advanced analytical techniques.

Purpose of the Study:

  • To develop and evaluate a dual-representation structural MRI framework for improved classification of psychiatric disorders.
  • To compare the diagnostic utility of raw MRI slices versus tissue segmentation maps using deep learning.
  • To leverage Large Language Models (LLMs) for enhanced interpretability of neuroimaging findings.

Main Methods:

  • Utilized a dual-representation framework analyzing raw T1-weighted MRI slices and color-coded tissue segmentation maps.
  • Employed two independently trained ResNet-18 Convolutional Neural Networks (CNNs) for feature extraction.
  • Applied Transfer Learning (TL) and Domain Adaptation (DA) techniques to a dataset of 103 subjects across four groups (Healthy Controls, Schizophrenia Spectrum, Bipolar Disorder with Psychosis, Bipolar Disorder without Psychosis).
  • Integrated a Large Language Model (LLM) for post-hoc analysis and interpretation of CNN outputs.

Main Results:

  • The dual-representation approach demonstrated improved four-way classification performance compared to single-representation methods.
  • Systematic comparison revealed differential contributions of raw versus segmentation-based MRI inputs to classification accuracy.
  • LLM-assisted interpretation provided insights into the neuroanatomical features driving diagnostic predictions.

Conclusions:

  • The proposed dual-representation framework enhances diagnostic classification accuracy in psychiatric disorders.
  • Combining deep learning with LLM interpretability offers a promising avenue for transparent and informative psychiatric neuroimaging tools.
  • This approach has the potential to support more objective and reliable psychiatric diagnoses.