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Related Concept Videos

Cerebrum: Anatomical Overview I01:26

Cerebrum: Anatomical Overview I

The main and largest component of the human brain is the cerebrum. The cerebrum consists of two main parts: the cerebral cortex, an outer layer with wrinkles or folds known as gyri and shallow grooves called sulci, and a deeper region beneath it. The cerebrum divides into two distinct hemispheres and contains five different lobes: the frontal, parietal, temporal, occipital, and insula. The central sulcus separates the frontal and parietal lobes and two functionally important gyri — the...
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The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
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Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
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Published on: February 15, 2021

Structural simplexity of the brain.

Tuan D Pham1, Heba Z Elfiqi, Stefan Knecht

  • 1School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia. t.pham@adfa.edu.au

Journal of Neuroscience Methods
|February 6, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new complexity analysis framework for brain MRI scans. It quantifies white matter changes, aiding in understanding cognitive decline and neurological disorders.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • The human brain is exceptionally complex, and understanding its intricate functions is key to unraveling neurological diseases.
  • White matter changes in the brain are linked to cognitive decline, dementia (e.g., Alzheimer's disease), and neuropsychiatric disorders (e.g., multiple sclerosis, depression).
  • Manual analysis of white matter changes in MRI scans can be challenging and vary in sensitivity.

Purpose of the Study:

  • To propose an entropy-based framework for analyzing complexity in human brain MRI scans.
  • To apply this framework to study white matter changes and their spatial characteristics.
  • To develop a tool for automated quantification and comparison of age-related white matter alterations.

Main Methods:

  • Developed an entropy-based framework to analyze brain complexity.
  • Integrated morphological structure and image intensity values from MRI scans.
  • Constructed complexity profiles of the brain, focusing on white matter.

Main Results:

  • The framework quantifies complexity by considering both structure and intensity in MRI data.
  • It generates complexity profiles that capture the spatial distribution and morphology of white matter.
  • Demonstrated potential for automated analysis of white matter changes.

Conclusions:

  • The proposed entropy-based framework offers a novel approach to quantify brain complexity.
  • This method can aid in the automated analysis of white matter changes relevant to cognitive function and neurological disorders.
  • The tool facilitates objective comparison of white matter alterations across different spatial distributions and orientations.