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Brain Imaging

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...

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Updated: Dec 11, 2025

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Finding specificity in structural brain alterations through Bayesian reverse inference.

Franco Cauda^1,2,3, Andrea Nani^1,2,3, Donato Liloia^1,2,3

¹GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.

Human Brain Mapping

|August 24, 2020

Summary

This summary is machine-generated.

This study introduces a new method using Bayes' factor analysis to identify brain alterations specific to diseases like schizophrenia and Alzheimer's disease. Pathology-specific brain areas are found to be altered earlier in disease progression.

Keywords:

Alzheimer's disease Bayes' factor alteration specificity brain disorders pain reverse probability schizophrenia voxel-based morphometry

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Last Updated: Dec 11, 2025

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Published on: August 12, 2019

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Area of Science:

Neuroimaging
Pathology
Brain Disorders

Background:

Reverse inferences in neuroimaging can link brain activity to cognitive processes.
Similar logic applies to brain alterations and disorders, but overlapping alterations complicate specificity.
Forward inference struggles to distinguish general from pathology-specific brain alterations.

Purpose of the Study:

To develop a method for identifying brain areas with alterations specific to particular pathologies.
To address the limitations of forward inference in distinguishing disease-specific brain changes.
To introduce a novel analytical instrument for brain pathology investigation.

Main Methods:

Employed Bayes' factor technique on voxel-based morphometry data.
Applied the method to schizophrenia and Alzheimer's disease datasets.
Performed temporal simulations of alteration spread for different pathologies.

Main Results:

Bayes' factor analysis successfully calculated the ratio of specific versus non-specific alteration likelihoods.
Simulated data analysis revealed that disease-specific brain areas are altered earlier.
The technique effectively identified pathology-specific altered brain regions.

Conclusions:

The Bayes' factor technique offers a novel approach to identify pathology-specific brain alterations.
Early alteration in specific brain areas is a key indicator of disease specificity.
This method has the potential to innovate the investigation of brain pathologies.