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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 Stimulation (TMS).

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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

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Published on: February 4, 2022

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework.

Steven L Small¹, Michael Wilde, Sarah Kenny

¹Department of Neurology, The University of Chicago, United States. small@uchicago.edu

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology

|February 24, 2009

Summary

This summary is machine-generated.

A new computational framework, the Computational Neuroscience Applications Research Infrastructure (CNARI), enables large-scale, statistically valid functional magnetic resonance imaging (fMRI) experiments and analysis for human neuroscience research.

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Area of Science:

Neuroscience
Cognitive Neuroscience
Computational Neuroscience

Background:

Functional magnetic resonance imaging (fMRI) has driven significant growth in cognitive neuroanatomy and human neuroscience.
Advances in hardware and software promise a bright future for fMRI research.

Purpose of the Study:

To introduce a novel computational framework, the Computational Neuroscience Applications Research Infrastructure (CNARI).
To demonstrate CNARI's capability in facilitating large-scale, naturalistic, and statistically valid fMRI experiments and analysis.

Main Methods:

Development of the Computational Neuroscience Applications Research Infrastructure (CNARI).
Incorporation of novel methods for maintaining, serving, and analyzing massive fMRI datasets.
Application of CNARI to cognitive neuroscience and neurological research examples.

Main Results:

CNARI enables large-scale, network-based, statistically valid experiments in systems neuroscience.
The framework simplifies data manipulation and analysis within reasonable computational timeframes.
Illustrative examples showcase CNARI's utility in cognitive and neurological research.

Conclusions:

Advanced computational approaches, exemplified by CNARI, are poised to revolutionize cognitive brain imaging with fMRI.
CNARI facilitates efficient and powerful analysis of large-scale fMRI data.
The infrastructure supports rethinking experimental design and analysis in fMRI studies.