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Related Concept Videos

Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

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Related Experiment Video

Updated: May 17, 2026

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation
07:11

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Published on: December 8, 2023

Fast optical signal in visual cortex: Improving detection by General Linear Convolution Model.

Antonio Maria Chiarelli1, Assunta Di Vacri1, Gian Luca Romani1

  • 1Infrared Imaging Lab, ITAB - Institute for Advanced Biomedical Technologies, Foundation University G. d'Annunzio, Chieti, Italy; Department of Neurosciences and Imaging, University G. d'Annunzio, Chieti-Pescara, Italy.

Neuroimage
|November 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a General Linear Convolution Model for fast optical signals (FOS), effectively detecting brain activity even with low signal-to-noise ratios. The method accurately identified visual cortex activation in human participants.

Keywords:
Fast optical signalsGeneral Linear ModelHemoglobinNear infrared spectroscopyOptical imaging

Related Experiment Videos

Last Updated: May 17, 2026

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation
07:11

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Published on: December 8, 2023

Area of Science:

  • Neuroscience
  • Biomedical Optics
  • Signal Processing

Background:

  • Fast optical signals (FOS) offer a promising avenue for studying neural activity.
  • Existing methods like the grand average approach have limitations in detecting subtle or rapid brain activations, especially under noisy conditions.

Purpose of the Study:

  • To develop and validate a General Linear Convolution Model for analyzing fast optical signals (FOS).
  • To assess the model's capability in detecting simulated and real cortical activations with improved sensitivity compared to traditional methods.

Main Methods:

  • Application of the General Linear Convolution Model, defining the Impulse Response Function (IRF) as a 30ms rectangular function with variable delay.
  • Validation using simulated data with low Signal to Noise Ratio (SNR) and testing on 10 healthy volunteers undergoing hemi-field visual stimulation.
  • Analysis of FOS data, including hemodynamic intensity, phase, and intensity, to differentiate between diffusive and absorption changes.

Main Results:

  • Simulated data demonstrated the model's feasibility and superior performance over the grand average method, even in unfavorable SNR conditions.
  • Experimental data revealed an IRF time delay of 80-100ms post-stimulus onset, consistent with established visual evoked potential literature.
  • Confirmed contralateral activation in the occipital region and suggested that observed FOS changes are primarily due to diffusive processes related to neuronal activity.

Conclusions:

  • The General Linear Convolution Model provides a feasible and effective method for detecting fast cortical activations using FOS.
  • This approach enhances the sensitivity of optical imaging for neural activity detection, particularly in challenging signal conditions.
  • The findings support the interpretation of FOS changes as indicators of neuronal activity through diffusive mechanisms.