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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 26, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Mapping brain activity at scale with cluster computing.

Jeremy Freeman¹, Nikita Vladimirov¹, Takashi Kawashima¹

¹Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.

|July 29, 2014

Summary

This summary is machine-generated.

Neuroscience research faces challenges with large neural datasets. Thunder, an open-source tool on Apache Spark, analyzes complex brain activity, enabling faster biological insights from neural data.

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Last Updated: Apr 26, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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Published on: March 8, 2024

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Published on: October 13, 2023

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Published on: May 12, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Bioinformatics

Background:

Understanding brain function necessitates analyzing large-scale neural network activity during behavior.
Rapid advancements in neural recording technologies generate increasingly vast and complex datasets.
Analyzing these large neural datasets presents a significant bottleneck for the field of neuroscience.

Purpose of the Study:

To introduce Thunder, a novel library of analytical tools designed for large-scale distributed computing.
To provide a modular and extendable framework for interactive exploration and development of neural data analyses.
To demonstrate the capability of Thunder in uncovering structure within large-scale neural datasets.

Main Methods:

Development of Thunder, a library built on the Apache Spark platform for distributed computing.
Implementation of diverse univariate and multivariate analytical approaches within a modular structure.
Application of Thunder to analyze whole-brain light-sheet imaging data from larval zebrafish and two-photon imaging data from behaving mice.

Main Results:

Thunder successfully identifies structure in large-scale neural data, including whole-brain and two-photon imaging datasets.
Analyses performed by Thunder correlate neuronal responses with sensory input and behavior.
The implemented analyses are efficient, completing in minutes, and are deployable on private clusters or cloud environments.

Conclusions:

Thunder offers a powerful, open-source framework for analyzing large-scale neural data.
This framework facilitates the interpretation of complex neural activity, accelerating the translation of brain activity mapping into biological insights.
The tool's efficiency and scalability address the growing challenge of big data in neuroscience research.