Non-invasive Optical Low-frequency Oscillation Study of the Prefrontal Cortex Based on fNIRS
torstai 6. elokuuta 2026 päivittänyt: Ting Li, Peking Union Medical College
The investigators aimed to investigate the effects of different cognitive load tasks (1-back, 2-back, 3-back) on low-frequency oscillations (LFOs) in the prefrontal cortex of healthy young individuals, analyze the relationship between LFO parameters (power, peak amplitude, peak frequency) and behavioral performance (reaction time, accuracy), and validate the feasibility of using LFOs as a quantitative measure of brain resting-state activity and a sensitive indicator of cognitive load.Low-frequency oscillations (LFO, 0.05-0.15
Hz) are a core feature of cerebral hemodynamics and are closely related to neural activity, blood pressure regulation, and metabolic function.
Existing studies suggest that LFO may be influenced by cognitive load during mental tasks, but the underlying mechanisms remain unclear.
Functional near-infrared spectroscopy (fNIRS), with its advantages of non-invasiveness, portability, and high temporal resolution, enables precise monitoring of hemodynamic parameters in the prefrontal cortex, including changes in oxygenated and deoxygenated hemoglobin concentrations (Δ[oxy-Hb], Δ[deoxy-Hb]).
This study combines the N-back working memory task to investigate how cognitive load modulates LFO, offering a novel approach for assessing brain function.In this study, the investigators used a portable 16-channel continuous-wave functional near-infrared spectroscopy (fNIRS) system to measure dynamic changes in prefrontal cortex (PFC) hemodynamic parameters-Δ[oxy-Hb], Δ[deoxy-Hb], and Δ[tot-Hb]-in 48 healthy participants under varying task loads.
The investigators then analyzed low-frequency oscillations (LFOs) based on the detected hemodynamic parameter fluctuations, following established methodologies from prior research.
Using Welch's method, LFOs were extracted from power spectral density (PSD) of different hemodynamic parameters across three task conditions (1-back, 2-back, and 3-back).
The principle of fNIRS relies on near-infrared light penetrating the skull and brain, being absorbed by chromophores [HbO₂] and [Hb] with distinct absorption spectra.
Assuming constant scattering and applying the modified Beer-Lambert law during calculations, sensitive light intensity detectors can detect concentration changes of these chromophores within the subject's brain tissue.
The experimental setup consisted of a 4.4 cm × 15 cm sensor array distributed across the forehead, a dedicated data acquisition control box with a sampling rate of 3.3 Hz, and a computer for data storage and analysis software.
The current flexible sensor comprises four light sources and ten detectors.
The light sources include three built-in LEDs with peak wavelengths at 735 nm, 805 nm, and 850 nm, while the detectors are designed to image the sub-frontal cortical regions (dorsolateral and ventromedial prefrontal cortices).
The distance between each source and its surrounding detectors is 2.89 cm, enabling hemodynamic measurements at depths of 2-3 cm beneath the scalp, focusing primarily on the cerebral cortex.
Each light source is arranged in an X-shape with four adjacent detectors, where the source is centered and detectors positioned at the corners, forming channels connecting each source to individual detectors.
The prefrontal cortex is a region strongly associated with cognitive processing and plays a critical role in working memory tasks.
Task load was implemented through working memory tasks-a mechanism by which the brain temporarily processes and stores information crucial for handling complex cognitive demands.
Understanding how the amplitude of hemodynamic oscillations is modulated by different cognitive loads is significant, as microvascular blood flow is regulated by myogenic mechanisms.
These foundational insights will help improve the analysis and interpretation of hemodynamic neuroimaging data.
This study employs fNIRS, a non-invasive neuroimaging technique highly sensitive to microvascular activity, to investigate the interactive effects of cognitive load on brain hemodynamic LFOs.
Tutkimuksen yleiskatsaus
Tila
Valmis
Interventio / Hoito
Opintotyyppi
Havainnollistava
Ilmoittautuminen (Todellinen)
47
Yhteystiedot ja paikat
Tässä osiossa on tutkimuksen suorittajien yhteystiedot ja tiedot siitä, missä tämä tutkimus suoritetaan.
Opiskelupaikat
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Tianjin Municipality
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Tianjin, Tianjin Municipality, Kiina, 300192
- 16 channel fNIRS
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Osallistumiskriteerit
Tutkijat etsivät ihmisiä, jotka sopivat tiettyyn kuvaukseen, jota kutsutaan kelpoisuuskriteereiksi. Joitakin esimerkkejä näistä kriteereistä ovat henkilön yleinen terveydentila tai aiemmat hoidot.
Kelpoisuusvaatimukset
Opintokelpoiset iät
- Aikuinen
Hyväksyy terveitä vapaaehtoisia
Joo
Näytteenottomenetelmä
Ei-todennäköisyysnäyte
Tutkimusväestö
47 right-handed volunteers (23 males and 24 females) were recruited.
Their ages ranged from 18 to 23 years, with an average age of 22.1 years for males and 21.5 years for females.
The participants were young adults, as the study aimed to investigate the effects of LFO on cognitive activation, which involved a relatively challenging 3-back working memory task.
All participants had normal or corrected-to-normal vision.
Female participants were excluded if they were within their menstrual cycle, as hormonal fluctuations during this period may affect certain prefrontal cortical functions.
None of the participants had a history of neurological or psychiatric disorders, nor were they taking any psychotropic medications.
Participants abstained from alcohol, caffeine, and nicotine for at least 24 hours prior to the experiment.
Kuvaus
Inclusion Criteria:
- Aged 18 to 23 years
- Right-hand
- Normal or corrected-to-normal vision
- Willing to abstain from alcohol, caffeine, and nicotine for at least 24 hours prior to the experiment
- Able to understand the study procedures and provide written informed consent
Exclusion Criteria:
- History of neurological or psychiatric disorders (e.g., epilepsy, severe insomnia, or anxiety)
- History of arrhythmia or other severe cardiovascular conditions
- Current or long-term use of psychotropic medications or other medications affecting the nervous system
- Contraindications for wearing skin-mounted optical devices
- Female participants currently in their menstrual cycle (due to potential hormonal effects on prefrontal cortical functions)
- Inability to follow experimental instructions or complete the testing tasks
Opintosuunnitelma
Tässä osiossa on tietoja tutkimussuunnitelmasta, mukaan lukien kuinka tutkimus on suunniteltu ja mitä tutkimuksella mitataan.
Miten tutkimus on suunniteltu?
Suunnittelun yksityiskohdat
Kohortit ja interventiot
Ryhmä/Kohortti |
Interventio / Hoito |
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No category
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The investigators employed the N-back visual working memory task, a continuous letter task with varying working memory load demands.
The task consisted of four fixation blocks and three task blocks, lasting a total of 377 seconds.
Each fixation block lasted 20 seconds, during which participants were instructed to fixate their gaze on a central point.
The working memory load gradually increased from 1-back to 3-back across the task blocks.
Participants were required to determine whether the current letter matched the previous letter (1-back), the letter two positions back (2-back), or the letter three positions back (3-back) in terms of features, responding with one button for "yes" and another for "no."
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Mitä tutkimuksessa mitataan?
Ensisijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
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Peak Amplitude of Low-Frequency Oscillations (LFO) Measured by functional Near-Infrared Spectroscopy (fNIRS)
Aikaikkuna: Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Peak frequency of Low-Frequency Oscillations (LFO) Measured by fNIRS
Aikaikkuna: Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Power of Low-Frequency Oscillations (LFO) Measured by fNIRS
Aikaikkuna: Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Day1, The assessment time point was after the n-back task, with spectral analysis performed on low-frequency oscillation data collected during the 377-second n-back experiment.
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Toissijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
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Reaction Time Assessed by N-back Behavioral Task System
Aikaikkuna: Day1, The assessment time point was after the n-back task, analyzing behavioral reaction times during the 377-second n-back experiment.
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Day1, The assessment time point was after the n-back task, analyzing behavioral reaction times during the 377-second n-back experiment.
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Accuracy Rate Assessed by N-back Behavioral Task System
Aikaikkuna: Day 1, the assessment time point was after the n-back task, analyzing behavioral accuracy rate during the 377-second n-back experiment.
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Day 1, the assessment time point was after the n-back task, analyzing behavioral accuracy rate during the 377-second n-back experiment.
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Correlation Between LFO Parameters and Task Behavioral Performance
Aikaikkuna: Day 1, the assessment time point was after the n-back task, analyzing behavioral correlation between low frequency oscillation parameters and task behavioral performance during the 377-second n-back experiment.
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Day 1, the assessment time point was after the n-back task, analyzing behavioral correlation between low frequency oscillation parameters and task behavioral performance during the 377-second n-back experiment.
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Yhteistyökumppanit ja tutkijat
Täältä löydät tähän tutkimukseen osallistuvat ihmiset ja organisaatiot.
Sponsori
Julkaisuja ja hyödyllisiä linkkejä
Tutkimusta koskevien tietojen syöttämisestä vastaava henkilö toimittaa nämä julkaisut vapaaehtoisesti. Nämä voivat koskea mitä tahansa tutkimukseen liittyvää.
Yleiset julkaisut
- [1] Biswal B, Zerrin Yetkin F, Haughton V M, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI[J]. Magnetic resonance in medicine, 1995, 34(4): 537-541. [2] Tong Y, Hocke L M, Licata S C, et al. Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals[J]. Journal of biomedical optics, 2012, 17(10): 106004-106004. [3] Auer D P. Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the 'resting'brain[J]. Magnetic resonance imaging, 2008, 26(7): 1055-1064. [4] Morita Y, Hardebo J E, Bouskela E. Influence of cerebrovascular sympathetic, parasympathetic, and sensory nerves on autoregulation and spontaneous vasomotion[J]. Acta physiologica scandinavica, 1995, 154(2): 121-130 [5] Vern B A, Schuette W H, Leheta B, et al. Low-frequency oscillations of cortical oxidative metabolism in waking and sleep[J]. Journal of Cerebral Blood Flow & Metabolism, 1988, 8(2): 215-226. [6] Lin PY, Roche-Labarbe N, Dehaes M, Carp S, Fenoglio A, Barbieri B, Hagan K, Grant PE, Franceschini MA. Non-invasive optical measurement of cerebral metabolism and hemodynamics in infants. J Vis Exp. 2013 Mar 14;(73):e4379. [7] Diehl R R, Linden D, Lücke D, et al. Spontaneous blood pressure oscillations and cerebral autoregulation[J]. Clinical Autonomic Research, 1998, 8(1): 7-12. [8] Katura T, Tanaka N, Obata A, et al. Quantitative evaluation of interrelations between spontaneous low-frequency oscillations in cerebral hemodynamics and systemic cardiovascular dynamics[J]. Neuroimage, 2006, 31(4): 1592-1600. [9] Cui X, Bray S, Bryant DM, Glover GH, Reiss AL. A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage. 2011 Feb 14;54(4):2808-21. [10] Biswal B B, Kylen J V, Hyde J S. Simultaneous assessment of flow and BOLD signals in resting-state fu
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