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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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Related Experiment Video

Updated: Apr 17, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study.

Amy Kuceyeski¹, Babak B Navi, Hooman Kamel

¹Department of Radiology, Weill Cornell Medical College, New York; The Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York.

Human Brain Mapping

|February 7, 2015

Summary

This summary is machine-generated.

Structural connectome disruptions after stroke, measured by Change in Connectivity (ChaCo) scores, better predict clinical performance than lesion size. This quantitative approach offers new insights into brain-behavior relationships for stroke recovery.

Keywords:

biological markers cognition connectome infarction linear models magnetic resonance imaging outcome assessment (health care)stroke

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Neuroscience
Medical Imaging
Computational Biology

Background:

Cerebral infarction (stroke) causes structural connectome disruptions.
Clinical outcomes after stroke are complex and influenced by network changes.
Current methods may not fully capture the relationship between brain damage and function.

Purpose of the Study:

To quantitatively model the relationship between stroke-induced connectome disruptions and clinical performance.
To assess the predictive power of structural connectivity changes compared to traditional lesion metrics.
To identify brain regions crucial for predicting cognitive, physical, and daily living activities post-stroke.

Main Methods:

Used Change in Connectivity (ChaCo) scores derived from clinical MRI to quantify structural disruption.
Applied Partial Least Squares Regression (PLSR) to predict clinical measures using ChaCo scores and demographics.
Employed cross-validation, bootstrapping, and multiple comparisons correction for robust statistical analysis.

Main Results:

Models based on connectivity disruption (R(2): 0.26-0.92) outperformed lesion characteristic models (R(2): 0.06-0.50) in predicting clinical performance.
Identified specific brain regions critical for various functional outcomes.
Validated the method through mapping eloquent functions and replication across pathologies.

Conclusions:

Quantitative modeling of structural connectome disruptions provides superior prediction of clinical performance post-stroke.
This approach offers valuable insights into brain-behavior relationships, aiding prognostication and rehabilitation strategies.
The method highlights the importance of network integrity in complex functions like cognition and daily living.