fNIRS Cerebral Signal Isolation via Scaling Factor Subtraction
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Solution Overview
Problem
Existing functional near-infrared spectroscopy (fNIRS) methods struggle to reliably disentangle hemodynamic responses due to neurovascular coupling from confounding components such as systemic hemodynamic activity, local blood flow changes, and instrumental noise, which interfere with brain activity measurements.
Innovation Solution
A method involving cyclic cerebral stimulation at a given task frequency using multi-distance near-infrared recordings, where a scaling factor is applied to subtract shallow-signal components from deep-signal components to isolate the cerebral signal, leveraging periodic hemodynamic fluctuations for effective frequency-domain analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-distance NIRS recordings are used to separate cerebral and extracerebral signals, then measurement precision is improved, but device complexity increases due to multiple detectors and scaling factor calculations
Solution Approach 1:
The NIRS signal is segmented into two distinct components: shallow-signal (extracerebral) and deep-signal (cerebral), measured by separate detectors at different distances from the scalp. This segmentation allows independent analysis and subtraction of confounding components from the cerebral signal of interest.
Solution Approach 2:
The shallow-signal acts as an intermediary measurement that captures the extracerebral confounding components. By measuring this intermediate signal and applying a scaling factor, the system can subtract the confounding components from the deep-signal to isolate the pure cerebral response.
2Difficulty of detecting and measuring
If cyclic cerebral stimulation at task frequency is applied, then signal detection capability is improved through frequency-domain analysis, but ease of operation deteriorates due to protocol complexity
Solution Approach 1:
The protocol employs cyclic cerebral stimulation at a specific task frequency, creating periodic hemodynamic fluctuations. This periodic action allows the use of frequency-domain analysis to distinguish task-related cerebral signals from aperiodic confounding components, improving detection capability.
Solution Approach 2:
The method changes the temporal parameter of stimulation by applying cyclic tasks at a defined frequency rather than using continuous or random stimulation. This parameter change enables frequency-domain separation of signals, where the cerebral response occurs at the task frequency while confounding components appear at different frequencies.
3Reliability
If scaling factor is calculated from baseline recording to subtract shallow-signal components, then reliability of cerebral signal is improved, but loss of time increases due to additional baseline recording stage
Solution Approach 1:
A baseline recording stage is performed before the actual stimulation to calculate the scaling factor between shallow and deep signals. This preliminary action establishes the relationship between extracerebral and cerebral components under resting conditions, which is then applied during the stimulation phase to improve reliability without requiring continuous complex adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise assessment of neurovascular dynamics, providing a clean cerebral signal that can be used for diagnosing conditions like Alzheimer's disease, autism, ADHD, and autonomic dysfunctions by accurately separating cerebral and extracerebral responses.
Implementation Method 1
functional near-infrared spectroscopy (fNIRS) aims at detecting the hemodynamic changes evoked by neuronal oxygen consumption
Implementation Method 2
Based on the neurovascular coupling principle, functional near-infrared spectroscopy (fNIRS) aims at detecting the hemodynamic changes evoked by neuronal oxygen consumption
Data Source
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AI summary
A method to obtain a near-infrared spectroscopy (fNIRS) cerebral signal (SCS) in a subject (1), comprising the steps of: placing a near-infrared emitter (2) and respective proximal and distal near-infrared detectors (3) on the skin of the head of the subject (1);during a baseline recording stage (t1) with the subject in resting-state, record near-infrared signals, the recorded signals comprising a baseline deep-signal (BDS) and a baseline shallow-signal (BSS); calculate a scaling factor (K) between the amplitude of the baseline deep-signal and the baseline shallow-signal at a given task-frequency (ft); with the subject undergoing a cyclic cerebral stimulation at the task-frequency during a stimulation recording stage (t2), record near-infrared signals, the recorded signals comprising a shallow-signal (SSS) and a deep-signal (SDS); and applying the scaling factor to the shallow-signal, calculating the cerebral signal at the task-frequency as the difference between the deep-signal and the scaled shallow-signal, at the task-frequency (ft).