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Related Concept Videos

Brain Imaging01:14

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).
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...
Auditory Perception01:17

Auditory Perception

The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the cochlea, a...

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Related Experiment Video

Updated: Jul 4, 2026

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging
10:09

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Published on: September 12, 2012

CNN-Based Modelling Reveals Temporal Brain Dynamics of Auditory Intensity Processing.

J Esmaelpoor, D Mao, J Wunderlich

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 2, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study used a novel deep learning model to analyze brain activity, revealing that the human brain uses consistent, time-based patterns to understand sound intensity. This advances our understanding of auditory processing in normal-hearing individuals.

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    fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
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    Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging
    10:09

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    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

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    fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
    11:15

    fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

    Published on: May 23, 2017

    Area of Science:

    • Neuroscience
    • Auditory Neuroscience
    • Cognitive Neuroscience

    Background:

    • Decoding auditory intensity from brain signals is challenging, especially at the single-trial hemodynamic response level.
    • Traditional functional near-infrared spectroscopy (fNIRS) analysis methods may miss crucial temporal information by averaging trials.

    Purpose of the Study:

    • To develop and validate a deep learning approach for decoding auditory intensity from full hemodynamic response waveforms.
    • To investigate the temporal structure and cross-participant consistency of cortical responses to auditory intensity.

    Main Methods:

    • A Siamese convolutional neural network was trained to decode auditory intensity levels from fNIRS HbO trial waveforms.
    • Data from 18 normal-hearing adults were analyzed using leave-one-subject-out cross-validation.
    • Training augmentation with cross-subject trial pairs was employed to enhance generalizability.

    Main Results:

    • The deep learning model achieved high accuracy (>85%) in discriminating auditory intensity levels.
    • Intensity decoding accuracy significantly decreased when temporal structure was disrupted, emphasizing its importance.
    • Intensity-related hemodynamic patterns were found to be consistent across participants.

    Conclusions:

    • Auditory intensity perception in normal-hearing listeners is supported by consistent and temporally structured cortical responses.
    • Dynamic, waveform-based deep learning models offer a more sensitive approach to fNIRS data analysis.
    • This study provides a foundation for more physiologically grounded models of auditory processing.