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

Bipolar Disorder01:30

Bipolar Disorder

Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals.

Translational psychiatry·2026
Same author

How do network structures of depressive symptoms differ between asian patients with bipolar depression and those with unipolar depression?

Asian journal of psychiatry·2026
Same author

Is Richard Baxter's Melancholia Still Relevant Today?: Comparative Network Structures of Depressive Symptoms in Asian Individuals With Guilt-Rich and Guilt-Free Depression.

Psychiatry investigation·2026
Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026
Same author

Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia.

medRxiv : the preprint server for health sciences·2026
Same author

Accuracy of Machine Learning Models in Predicting Clinical Outcomes in Bipolar Disorder: A Systematic Review.

Brain sciences·2026

Related Experiment Video

Updated: Jun 16, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

White matter abnormalities in bipolar disorder: insights from diffusion tensor imaging studies.

Serene Heng1, Allen W Song, Kang Sim

  • 1Institute of Mental Health, Woodbridge Hospital, 10 Buangkok View, Singapore.

Journal of Neural Transmission (Vienna, Austria : 1996)
|January 29, 2010
PubMed
Summary

This review examines how advanced brain imaging techniques reveal structural changes in the white matter of patients with bipolar disorder. Researchers found that connectivity between specific brain regions is often disrupted, particularly in frontal areas. These insights help clarify the biological basis of the condition and guide future investigations into brain health.

Keywords:
neuroimagingbrain connectivitywhite matter integritypsychiatric disorders

Frequently Asked Questions

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Jun 16, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Area of Science:

  • Neuroimaging research within Diffusion tensor imaging clinical applications
  • Psychiatric neuroscience and mood disorder diagnostics

Background:

No prior work had resolved the full extent of structural brain changes in mood disorders. That uncertainty drove researchers to investigate white matter integrity using specialized scanning tools. It was already known that bipolar disorder involves complex emotional and cognitive shifts. Prior research has shown that traditional imaging often misses subtle microstructural differences. This gap motivated the application of advanced magnetic resonance techniques to map neural pathways. Scientists previously struggled to identify consistent patterns across diverse patient populations. Previous studies often reported conflicting data regarding specific brain regions. This review addresses the need to synthesize scattered evidence into a coherent framework for understanding disease pathology.

Purpose Of The Study:

This review aims to synthesize existing literature on structural brain imaging in patients with mood instability. The researchers seek to clarify the pathophysiology of the condition through detailed analysis. They intend to highlight brain regions that consistently show structural alterations. The study addresses the need to summarize current knowledge regarding fiber tract integrity. The authors propose to identify potential future directions for advanced scanning research. They aim to reconcile conflicting reports regarding the direction of observed changes. The team explores the influence of sample heterogeneity on reported results. This work provides a foundation for understanding the neural mechanisms underlying the disorder.

Main Methods:

Review Approach involved a comprehensive synthesis of existing scientific literature regarding brain structural integrity. Investigators systematically gathered data from published studies utilizing advanced magnetic resonance scanning. The team focused on identifying consistent patterns of fiber tract abnormalities across various patient groups. They evaluated the methodology of each included study to assess potential biases. The researchers categorized findings based on anatomical regions to determine spatial consistency. They compared results across different cohorts to address issues of sample diversity. The team assessed the limitations inherent in current scanning protocols to explain conflicting data. This systematic evaluation provided a structured overview of the current state of the field.

Main Results:

Key Findings From the Literature indicate that loss of white matter network connectivity represents a primary structural phenomenon in patients. The analysis identifies prefrontal and frontal regions as the most consistently affected areas. Evidence confirms that projection, associative, and commissural fibers are frequently involved in these structural changes. The review reveals that data regarding subcortical and non-frontal lobes remain sparse and inconsistent. Researchers observed notable differences in the direction of white matter index changes across various studies. These variations are linked to significant sample heterogeneity and technical constraints of the scanning equipment. The findings show that bipolar disorder research currently trails behind studies of schizophrenia. Despite this lag, the literature demonstrates that investigation into these neural pathways is accelerating rapidly.

Conclusions:

Synthesis and Implications suggest that white matter network connectivity loss characterizes the pathology of bipolar disorder. Authors propose that frontal and prefrontal regions represent the primary sites of structural alteration. The review indicates that projection and associative fibers exhibit consistent changes across multiple investigations. Synthesis and Implications highlight that evidence for subcortical involvement remains sparse and requires more rigorous testing. Researchers note that variations in reported directional changes likely stem from sample heterogeneity and technical constraints. The literature review underscores the necessity of standardizing imaging protocols to improve data reliability. Authors advocate for future work to clarify the specific roles of temporal and occipital lobes. This synthesis confirms that while bipolar research lags behind schizophrenia studies, the field is rapidly expanding its diagnostic capacity.

The authors propose that a primary mechanism involves the loss of white matter network connectivity. This structural disruption commonly affects prefrontal and frontal regions, as well as projection and associative fibers, rather than being isolated to a single localized brain structure.

Diffusion tensor imaging serves as the primary tool for this analysis. This technique allows researchers to map the orientation and integrity of white matter tracts by measuring the diffusion of water molecules within the brain tissue.

The prefrontal and frontal regions are necessary for consistent findings, as these areas show the most robust evidence of connectivity loss. In contrast, subcortical and non-frontal lobes lack sufficient, consistent data to confirm their role in the disorder.

The researchers utilize existing literature to synthesize findings. This meta-analytical approach allows for the comparison of diverse patient cohorts, helping to identify trends that individual studies might miss due to small sample sizes or varying methodologies.

The researchers measure white matter indices to track structural changes. They observe differences in the direction of these changes, which they attribute to factors such as sample heterogeneity and the inherent limitations of the scanning technology used across different studies.

The authors propose that future research must focus on clinico-anatomical correlations. They suggest that unraveling these underlying neural mechanisms is vital for better understanding the disease, especially as the field gains momentum compared to schizophrenia research.