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Related Concept Videos

Cerebrum: Anatomical Overview II01:11

Cerebrum: Anatomical Overview II

Each cerebral hemisphere can be divided into three main regions. The outermost region, the cerebral cortex, is a thin layer (2 to 4 millimeters thick) made up of gray matter, consisting of neuron cell bodies, dendrites, glial cells, and blood vessels. The middle region, or white matter, is primarily composed of myelinated nerve fibers organized into three types of large tracts: association fibers, commissures, and projection fibers. Association fibers connect different areas within the same...

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Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex.

Stephane Doyen1, Peter Nicholas1, Anujan Poologaindran2,3

  • 1Omniscient Neurotechnology, Sydney, New South Wales, Australia.

Human Brain Mapping
|November 26, 2021
PubMed
Summary

This study introduces a novel machine learning approach for human cortical parcellation, creating patient-specific brain maps even with distortions from tumors or surgery. This method improves neurosurgical precision by adapting to individual brain anatomy.

Keywords:
DTIconnectivitygliomamachine learningparcellationtractography

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Cortical parcellation is crucial for understanding brain function.
  • Existing methods are limited in neurosurgical applications due to anatomical distortions from pathology and resection.
  • Personalized parcellation is needed for accurate surgical planning.

Purpose of the Study:

  • To develop a novel connectivity-based parcellation approach for single-subject application.
  • To create patient-specific cortical maps that are independent of brain shape and pathological distortion.
  • To overcome limitations of standard atlas-based registration in neurosurgery.

Main Methods:

  • Developed a machine learning (ML) classifier using normative diffusion data to learn healthy structural connectivity patterns.
  • Utilized the Glasser HCP atlas as a prior to calculate voxel-to-parcel streamline connectivity.
  • Applied the ML classifier to neurosurgical patients (n=40) to determine voxel parcel identity and iteratively adjust the prior, creating patient-specific maps.

Main Results:

  • The ML classifier re-parcellated an average of 2.65% of cortical voxels in a healthy dataset (n=178) and 5.5% in neurosurgical patients.
  • Demonstrated the creation of patient-specific maps independent of brain shape and pathological distortion.
  • Validated the approach's validity and practical utility in patients with supratentorial infiltrating gliomas.

Conclusions:

  • A rapid and effective ML parcellation approach was developed for the human cortex during anatomical distortion.
  • This voxel-wise connectivity approach based on individual data overcomes limitations of applying healthy atlases to distorted brains.
  • The method offers improved accuracy for personalized neurosurgical applications.