Functional Near-Infrared Spectroscopy Reveals Stable Visually-Evoked Cortical Responses across Circadian Phases

Functional Near-Infrared Spectroscopy Reveals Stable Visually-Evoked Cortical Responses across Circadian Phases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional Near-Infrared Spectroscopy Reveals Stable Visually-Evoked Cortical Responses across Circadian Phases Camilla Simoncelli, Elena Scaffei, Roberta Battini, Raffaele Mario Mazziotti, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8590476/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Circadian rhythms shape physiology and behavior. However, their influence on sensory-evoked neural activity remains poorly understood. Although visual perception fluctuates across the day, it is unclear whether these fluctuations reflect changes in early neural processing or downstream cognitive states. Here, we tested whether visually-evoked cortical responses measured with functional near-infrared spectroscopy (fNIRS) are modulated by circadian phase or by repeated measurements across time. Hemodynamic responses over occipital cortex were recorded in healthy adults during a flickering visual stimulation paradigm. Participants were assessed either (i) at three circadian timepoints within a single day and again on separate days at matched hours, or (ii) across short-, medium-, and long-term test–retest intervals, including five minutes, two months, and two years. We found that visually-evoked response amplitudes remain stable across circadian phases and longitudinal intervals. These results indicate that early visual cortical responses measured with fNIRS are largely resilient to both circadian modulation and repeated stimulation. The marked temporal stability of fNIRS signals supports their reliability for longitudinal sensory research and highlights the potential of fNIRS as a non-invasive biomarker of cortical function. Cognitive Neuroscience circadian rhythm neural signal fNIRS biomarker visual cortex Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Circadian rhythms shape alertness, arousal, and cognitive performance, influencing perceptual sensitivity and decision-making across the day. Although behavioral studies report time-of-day effects on visual performance, it remains unclear whether such variability reflects genuine circadian modulation of early sensory processing or instead arises from fluctuations in attentional state and higher-order cognitive control. Circadian phase is known to modulate cortical excitability through coordinated changes in neurotransmission [ 1 ], [ 2 ], [ 3 ], neuromodulatory systems [ 4 ], and thalamocortical rhythms [ 5 ], [ 6 ]. Neural responsiveness typically peaks during the biological morning and declines toward the circadian evening or under elevated sleep pressure [ 7 ], [ 8 ]. Within the visual system, circadian regulation operates at multiple hierarchical levels, from retinal physiology to cortical processing. Retinal sensitivity and synaptic transmission follow circadian rhythms [ 9 ], [ 10 ], [ 11 ], and time-of-day–dependent changes propagate through early visual pathways, influencing electrophysiological and hemodynamic responses. Consistently, visual evoked potentials and BOLD responses exhibit circadian modulation, with attenuated amplitudes and delayed latencies in the biological evening [ 6 ], [ 12 ]. Despite this evidence, few studies have systematically examined whether visually evoked cortical responses remain stable across circadian phases or over extended time intervals. This gap is particularly relevant for neuroimaging approaches increasingly used in longitudinal and clinical research, where temporal stability is a prerequisite for biomarker validity. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive optical imaging technique that infers neural activity from either spontaneous or stimulus-evoked changes in cerebral hemodynamics. By quantifying changes in oxygenated and deoxygenated hemoglobin, fNIRS is increasingly recognized for its suitability in monitoring brain function in naturalistic settings [ 13 ], [ 14 ], [ 15 ]. Its robustness to movement, ease of deployment, and excellent feasibility for repeated measurements make fNIRS particularly well suited for longitudinal designs and for studies conducted across variable times of day. Importantly, fNIRS is emerging as a promising biomarker of brain function across neurological and neurodevelopmental conditions [ 16 ], [ 17 ], [ 18 ], underscoring the need to establish its temporal stability. In this study, we investigated whether visually evoked hemodynamic responses measured with fNIRS are modulated by circadian phase and whether they remain stable across repeated measurements. Neurotypical adults completed a reversing-checkerboard stimulation paradigm at three circadian timepoints within the same day, as well as on separate days at matched hours. To further assess signal reliability, additional cohorts were tested at short (five minutes) medium (two months), and long (two years) retest intervals. Methods Participants Seventy-one neurotypical adults were recruited and assigned to two experimental groups: The Circadian group (N = 21; mean age = 30.85 years ± 4.27; 11 women, 10 men) completed three testing sessions within a single day, morning (T10), afternoon (T14), and evening (T18), and were subsequently reassessed on separate days at the same corresponding timepoints. This design allowed evaluation of potential circadian-phase effects on visually evoked responses while controlling for time-of-day consistency across sessions. In this framework, T10, T14 and T18 coincide with the approximate testing hours, corresponding to 10 a.m., 2 p.m. and 6 p.m. respectively. The Interval group (N = 50; mean age = 31.64 years ± 4.44; 25 women, 25 men) underwent three testing sessions: at baseline (T0), after five minutes (T1), and at a delayed follow-up. Half of the participants were reassessed after two months (T2), whereas the remaining half were retested after two years (T3). In this framework, T1 was defined as the short-term retest, T2 as the mid-term retest, and T3 as the long-term retest. All participants had normal or corrected-to-normal vision, volunteered to take part in the study, and provided written consent (authorization numbers 116/2022 and 77/2023, Regional Ethics Committee for Clinical Research of the Tuscany Region). To ensure consistency across sessions, each participant viewed the same movie selected during their first visit in all subsequent experimental sessions. All procedures comply with the ethical guidelines of the relevant national and institutional committees and adhered to the principles of the Helsinki Declaration ( https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct2008/ ) of 1975, as revised in 2008. Stimuli and Procedure The experimental design was based on the paradigm described in Mazziotti et al. (2022), with minor methodological adaptations tailored to the aims of the present study. Visual stimuli were generated using Python 3 and PsychoPy3 [ 19 ] and displayed on a 31.5-inch LG UltraGear LCD monitor (spatial resolution: 2560 × 1440 pixels, refresh rate: 180 Hz, mean luminance: 400 cd/m²). The monitor was fully calibrated prior to testing, and gamma correction was performed using a Datacolor Spyder X Pro (SXP100) colorimeter to ensure accurate luminance and contrast reproduction. Hemodynamic Responses (HDRs) were recorded during both baseline and stimulation conditions. During baseline, participants watched a video presented on a full-field, isoluminant grey background. The stimulation consisted of a reversing black-and-white, square-wave radial checkerboard presented for 5 seconds [ 20 ], [ 21 ] [ 22 ]. The checkerboard (spatial frequency: 0.33 cycles per degree, temporal frequency: 4 Hz) was presented 20 times with a randomized Interstimulus Interval (ISI). The entire experimental block lasted 10 minutes (Fig. 1 ). fNIRS Data Acquisition Task-evoked changes in total hemoglobin concentration (HbT) and relative oxygenation levels, oxygenated (HbO) and deoxigenated (HbR) hemoglobin, were measured over the occipital cortex usinga continuous-wave fNIRS system (NIRSPort2, NIRx Medical Technologies). The system consisted of eight dual-wavelength light sources (760 and 850 nm) and seven detectors embedded in different-sized EEG caps, yielding 22 multidistance channels covering the visual cortex. To ensure consistent positioning across participants, probe arrays were secured within each cap using stabilizers and positional labels. Before data acquisition, the optode-equipped cap was positioned on the participant’s head and a light-coupling calibration between sources and detectors was performed. Participants were seated 70 cm from the monitor, and each experimental session lasted approximately 20 minutes. Data were recorded using Aurora software (version 1.4.1.1) at a sampling rate of 10.2 Hz. Visual stimulation events were synchronized with the NIRSPort2 system via wired LAN communication using the Python version of LabStreamingLayer ( https://github.com/sccn/labstreaminglayer ). Data Pre-processing and Statistical Analysis Data pre-processing followed established fNIRS pipelines [ 23 ] and procedures described in Mazziotti et al. (2022). Pre-processing steps included conversion of raw light intensities to optical density (OD), channel pruning, motion-artifact detection and correction, band-pass filtering, and transformation of OD signals into ΔHbO and ΔHbR concentration changes [ 24 ]. For each participant, HDRs were averaged across all trials for each condition and channel within a temporal window extending from 2 seconds before stimulus onset to 20 seconds after. For statistical analysis, we focused on the “best channel,” defined as the channel exhibiting maximal neural responsiveness, namely the largest positive peak for HbO and HbT and the largest negative peak for HbR. Averaging across channels would dilute the focal visual response by incorporating uninvolved or noisier channels, thereby reducing sensitivity. Selecting the best channel maximizes signal-to-noise ratio, minimizes the impact of individual anatomical variability, and yields a more stable and physiologically meaningful measure. These features are critical when assessing the reliability of a candidate biomarker of brain function, such as visually evoked fNIRS responses. To assess the robustness and temporal stability of visually evoked responses, paired t-tests, repeated-measures ANOVAs, post-hoc comparisons, and correlation analyses were performed across all experimental conditions, including circadian phase comparisons, multi-day assessment at matched timepoints, short-interval test–retest measurements, and mid- and long-interval retests. For paired Δ-amplitude correlation analyses, we used the Shepherd method, which provides a robust estimate of association while explicitly accounting for outliers, identified and removed using a median absolute deviation (MAD) criterion. This approach is particularly well suited for fNIRS amplitude data, where occasional motion artifacts, vascular fluctuations, or imperfect optode coupling can generate extreme values despite careful pre-processing. All statistical analyses and data visualizations were performed using the Pingouin statistical package, Matplotlib, and SciPy [ 25 ], [ 26 ], [ 27 ]. For age-correlation analyses, we used the Spearman method because this approach is robust to non-normal distributions and unequal variances, which are frequently observed in neurophysiological measures across age. To ensure data quality and reduce the influence of extreme values, outliers were identified and removed using the Interquartile Range (IQR) method. For each variable of interest, the first quartile (Q1) and third quartile (Q3) were calculated, and the IQR was defined as Q3 - Q1. Data points falling below Q1–1.5 × IQR or above Q3 + 1.5 × IQR were classified as outliers and excluded from subsequent analyses [ 28 ]. Across all three time points (T10, T14, T18) and hemodynamic measures (HbO, HbT, HbR), the removal of outliers did not substantially alter the overall pattern of results, confirming that our findings were robust and not driven by extreme observations. Results We applied the same statistical analysis to both experimental groups, performing a comprehensive reliability assessment that combined correlation analyses, corrected for multiple comparisons using the Benjamini–Hochberg method, with paired t-test and repeated-measures ANOVAs. Across all participants and recording sessions, visual stimulation consistently elicited the expected occipital fNIRS response: robust increase in HbO and HbT accompanied by a reliable decrease in HbR. No modulation of hemoglobin oxygenation was observed during the baseline condition. This canonical hemodynamic pattern was preserved across all experimental conditions, confirming the reliability of visually evoked responses obtained with our video-embedded stimulation protocol. Figure 2 illustrates a representative example of the hemodynamic signal during baseline versus stimulation, together with group-level peak responses during the T0 session. A comparable response profile was observed at all timepoints in same-day and multi-day recordings, as well as across short-, mid- and long- term retest sessions, highlighting the robustness and reproducibility of the signal at different timepoints and interval repetitions. Detailed results of paired-sample t-tests comparing baseline and stimulation conditions for HbO, HbR, and HbT across all sessions are reported in the Supplementary Material (Tables S1 and S3). No meaningful correlations were found between response amplitude and participant age in either group, indicating that visually evoked hemodynamic response is preserved across the adult lifespan and are not influenced by inter-individual age variability (Table S5 and Figure S1 in Supplementary Material). Circadian modulation Repeated-measures ANOVAs were conducted to assess whether visually evoked HbO, HbR, and HbT amplitudes differed across circadian timepoints (T10, T14, T18). No significant main effect of Timepoint was observed for any hemoglobin metric, indicating that response magnitude remained stable across the day. Individual trajectories showed no systematic trends, and estimation plots revealed paired mean differences centred near zero with confidence intervals spanning the null. Despite a slight increase of HbO and HbT amplitude at T14 in the multi-day design, this pattern was consistent both within single-day sessions (Fig. 3 A-C) and across sessions acquired on different days (Fig. 3 D-F), demonstrating strong multi-day reliability of visually evoked hemodynamic response. Top panels : Peak amplitudes for HbO (A), HbR (B) and HbT (C) during visual stimulation at T10, T14, and T18 in same day recordings and for HbO (D), HbR (E) and HbT (F) of recordings collected on separate days at matched circadian timepoints. Green dots represent individual participants; connecting lines indicate repeated measurements within the same participant. Grey rectangles denote the interquartile range at each timepoint highlighting the limited dispersion of group-level responses. All Hb metrics show comparable stability across the three circadian phases, with no systematic time-of-day modulation. Repeated-measures ANOVAs revealed no significant main effect of timepoint on the dependent measure, either for same-day recordings (HbO: F (2, 40) = 0.06, p = .93, η_g² = .002, HbR: F (2, 40) = 0.98, p = .38, η_g² = .012, HbT: F (2, 40) = 0.43, p = .65, η_g² = .012) or for multi-day recordings (HbO: F (2, 36) = 0.79, p = .46, η_g² = .029, HbR: F (2, 36) = 1.9, p = .16, η_g² = .069, HbT: F (2, 36) = 2.76, p = .077, η_g² = .084), indicating that response amplitude did not differ across timepoints. Bottom panels Paired mean difference plots comparing T14–T10 and T18–T10. Green markers indicate within-participant differences, while the black dot and line represent the mean difference 0. confidence interval. Differences are centred around zero, indicating no significant changes in hemodynamic response amplitudes across circadian timepoints. In panels A-C (same-day recordings) 6.3% of data points were removed, whereas in panels D-F (multi-day recordings) 8.8% of points corresponded to outliers. Robust Shepherd correlation analyses confirmed temporal stability, with significant global associations observed across most inter-timepoint comparisons, underscoring the reproducibility of the measured responses over time. Participants exhibiting stronger or weaker responses at one session tended to maintain similar response magnitudes across sessions, indicating high within-subject reliability and preserved inter-individual ranking over time (Table S2 and Figures S2 and S3 in Supplementary Material). Interval replication Significant correlations of signal amplitude were also observed between recordings separated by short, medium, and long intervals (Table S4 and Figures S4 and S5 in Supplementary Material), demonstrating strong test-retest reliability and robust within-subject consistency of visually evoked fNIRS responses. The uniformly low p-values and high statistical power indicate that these correlations reflect genuine neural reproducibility rather than measurement noise. Consistent with these findings, repeated-measures ANOVAs revealed no significant effect of interval length on HbO, HbR, or HbT amplitudes (Fig. 4 ). Altogether these findings indicate the absence of systematic changes across repeated sessions arguing against habituation or adaptation effects attributable to task repetition. Despite repeated exposure to the same visual stimulation protocol over intervals spanning minutes to years, neural response neither significantly attenuated nor amplified. This stability underscores the suitability of visually evoked fNIRS measures for longitudinal and multi-session designs, where consistency over time is essential for reliable interpretation. Top panels : Peak amplitudes for HbO (A, D), HbR (B, E) and HbT (C, F) during visual stimulation for recordings at T0, T1, T2 (A-C, mid-term interval) and T0, T1, T3 (D-F, long-term interval). Green dots represent individual participants with connecting lines indicating repeated measurements within subjects. Grey rectangles correspond to the interquartile range for each timepoint, highlighting the limited dispersion of group-level responses. Across all intervals HbO, HbR and HbT amplitudes remain stable, with no systematic changes over time. Repeated measures ANOVAs revealed no significant main effect of timepoint on the dependent measure for either the mid-term interval (HbO: F (2, 52) = 0.82, p = .44, η_g² = .01, HbR: F (2, 52) = 1.05, p = .35, η_g² = .014, HbT: F (2, 52) = 0.99, p = .38, η_g² = .012) or the long-term interval recordings (HbO: F (2, 44) = 2.01, p = .14, η_g² = .018, HbR: F (2, 44) = 0.43, p = .64, η_g² = .008, HbT: F (2, 44) = 1.43, p = .25, η_g² = .019), supporting the stability of the measured response across conditions. Bottom panels Paired mean difference plots comparing T1–T0, T2–T1 and T3-T1 for HbO, HbR and HbT. Green markers represent individual within-participant differences, while the black dot and line denote the mean difference and its confidence interval. In all comparisons, differences are centered around zero, indicating no significant modulation in hemodynamic response amplitudes. In mid-term interval recordings (A-C) 6.6% of data points were outliers and removed, whereas in long-term interval recordings (D-F) 7.2% of points were outliers. Discussion The present study investigated whether visually evoked cortical responses measured with fNIRS are modulated by circadian phase or by the temporal distance between repeated measurements. Across two complementary cohorts, a circadian group and an interval-based test-retest group, we consistently observed that both the morphology and the amplitude of visually evoked HDR in occipital cortex remain remarkably stable. These findings provide convergent evidence that early visual cortical processing is largely resilient to circadian fluctuations and temporally robust and further demonstrate that fNIRS-measured HDR is invariant across minutes, hours, days, months, and even years. Contrary to the hypothesis that circadian phase might influence early sensory processing through fluctuations in cortical excitability, neuromodulatory tone, or thalamocortical dynamics, we found no systematic variation in fNIRS responses across morning, afternoon, and evening sessions. At every timepoint, visual stimulation elicited the canonical HDR. While paired t-tests consistently confirmed robust stimulus-driven activation [ 16 ], repeated-measures ANOVAs revealed no main effect of circadian phase on response amplitude. Small fluctuations in effect size and correlation strength, particularly the tendency for stronger correlations within same-day recordings compared with multi-day recordings, are likely attributable to expected experimental and physiological variability, including differences in optode coupling, environmental conditions, or vascular tone. Importantly, these sources of noise did not undermine the central result: visually evoked HDRs remained robustly stable across both short and long temporal intervals. Although T14 exhibited slightly greater variability and larger Δ-peak amplitudes, this pattern likely reflects a transitional circadian phase in which competing homeostatic and circadian processes interact. Such state-dependent fluctuations may amplify response dispersion without indicating systematic modulation of early sensory processing. Notably, increased variability at T14 did not compromise reliability, further underscoring the robustness of the signal. These results indicate that the magnitude of cortical activation does not drift across the day, despite well-established circadian influences on alertness, reaction time, and perceptual performance reported in electrophysiological and behavioral studies [ 12 ], [ 29 ], [ 30 ]. A plausible explanation for this apparent discrepancy lies in the fundamentally different neural processes captured by fNIRS compared with EEG. While EEG indexes fast, synchronous neuronal firing and membrane potential fluctuations that are highly sensitive to arousal, vigilance, and excitability, fNIRS primarily reflects “slow signals” like activity-dependent metabolic demand mediated by neurovascular coupling which integrates synaptic and local processing over longer temporal windows. As a result, hemodynamic responses may be less sensitive to transient fluctuations in arousal or vigilance and instead reflect more stable aspects of cortical processing. The interval-based cohort further allowed us to assess test-retest reliability of visually evoked hemodynamic responses across increasingly longer temporal windows. We found that visually evoked HDRs remained robust at short-, mid-, and long-term retest. Collectively, these results demonstrate that visually evoked HDR are highly stable across time and repetition. The absence of attenuation or amplification across repeated exposures indicates that the hemodynamic response does not undergo measurable habituation or adaptation within the parameters of this experiment. These findings have important implications for both basic neurophysiology and fNIRS methodology. First, they indicate that the metabolic demands of early visual processing remain constant across circadian phases and repeated exposures. Second, they support the suitability of fNIRS for longitudinal sensory research, as the absence of signal drift or attenuation across long intervals validates its use for multi-session visual studies. Third, our results are compatible with the possibility that circadian-related variability in behavior and cognition reflects modulation of higher-order processes, including attention or executive function, rather than systematic changes in early sensory encoding, which appears relatively stable in primary visual cortex. Crucially, the observed stability defines visually evoked fNIRS responses as a reliable biomarker of brain function. In clinical contexts, such stability is essential: a dependable visual response can serve as a reference against which pathological changes, disease progression, or therapeutic effects can be quantified. For example, in neurodegenerative disorders, the presence of a stable sensory-evoked HDR would make it possible to reliably identify meaningful functional decline over time. In rehabilitation or intervention studies, it allows treatment-related changes to be distinguished from measurement variability. Moreover, despite known circadian variations in cardiovascular physiology that could theoretically affect fNIRS signals [ 31 ], our results indicate that visually evoked responses remain stable across the day, reinforcing the interpretation that these signals primarily reflect genuine neural–metabolic activity rather than systemic noise. Our study provides, to our knowledge, the most comprehensive characterization to date of visually evoked cortical responses measured with fNIRS. In contrast with previous studies [ 32 ], [ 33 ], [ 34 ], which have generally relied on small sample sizes and/or limited test-retest intervals, our work uniquely integrates a relatively large cohort with same-day, multi-day, and long-term repeated measurements within a single, unified experimental design. This combination of extensive sampling and longitudinal assessment substantially extends prior visual fNIRS research and establishes a robust reference framework for evaluating the temporal stability, reproducibility, and biomarker potential of visually evoked hemodynamic responses. Limitations and future directions Although the present findings strongly support the stability of early visual processing, several limitations need to be considered. First, our conclusions are specific to the occipital cortex; circadian modulation may be more pronounced in frontal, parietal, or subcortical networks involved in attention, executive control, or sleep-wake regulation. Multimodal approaches, combining fNIRS with EEG, would help clarify whether oscillatory dynamics, particularly in alpha or sigma bands, exhibit circadian sensitivity alongside stable hemodynamic responses. Second, these findings may not generalize to populations with disrupted circadian organization, such as shift workers or individuals with psychiatric or neurodevelopmental conditions. Third, our cohort included only young-to-middle-aged adults. Given that circadian physiology and neurovascular coupling substantially change across development and aging [ 35 ], [ 36 ], [ 37 ], age-stratified cohorts are essential to determine whether circadian resilience of hemodynamic processing in the early visual system is preserved across lifespan. Extending test–retest fNIRS protocols to pediatric and older populations would also enable dissociation of true developmental changes from measurement variability, enhancing the utility of fNIRS for longitudinal developmental and clinical research. Conclusions This study demonstrates that visually evoked cortical responses measured with fNIRS are highly stable across circadian phases and over a wide range of retest intervals. The absence of circadian modulation in the HDR, together with strong within-subject reliability, indicates that early visual cortical processing is largely resistant to circadian fluctuations. These findings support the robustness of fNIRS as a neuroimaging tool for longitudinal, time-sensitive, and multi-session research, and strengthen its potential as a reliable biomarker of brain function. Future work integrating multimodal imaging, age-stratified cohorts, and clinical populations will further clarify the interaction between circadian biology and neural function and guide the design of optimized, temporally informed neuroimaging protocols. Declarations Competing Interests statement The authors declare no competing interests. Author Contributions CS and ES collected the data; CS and RM analysed the data; CS, RB and LB created the experimental design; LB conceived the experiment; CS and LB wrote the manuscript, ES, RM and RB participated to the editing. Acknowledgements This work was supported by the EJP RD JTC 2022 program "Development of new analytic tools and pathways to accelerate diagnosis and facilitate diagnostic monitoring of rare diseases" , funded under the EJP-Cofund Action on Rare Diseases (Grant Agreement No. 825575) and by the Italian Ministry of Health. Additional support was provided by RC2025. References Brancaccio M et al (Jan. 2019) Cell-autonomous clock of astrocytes drives circadian behavior in mammals. Science 363(6423):187–192. 10.1126/science. aat4104 Farajnia S, van Westering TLE, Meijer JH, Michel S (2014) Seasonal induction of GABAergic excitation in the central mammalian clock, Proc. Natl. Acad. Sci. , vol. 111, no. 26, pp. 9627–9632, July 10.1073/pnas.1319820111 Pracucci E et al (Nov. 2023) Daily rhythm in cortical chloride homeostasis underpins functional changes in visual cortex excitability. Nat Commun 14(1):7108. 10.1038/s41467-023-42711-7 Aston-Jones G, Cohen JD, AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION (2005) : Adaptive Gain and Optimal Performance, Annu. Rev. Neurosci. , vol. 28, no. Volume 28, pp. 403–450, July 2005. 10.1146/annurev.neuro.28.061604.135709 Cajochen C, Brunner DP, Krauchi K, Graw P, Wirz-Justice A (1995) Power Density in Theta/Alpha Frequencies of the Waking EEG Progressively Increases During Sustained Wakefulness, Sleep , vol. 18, no. 10, pp. 890–894, Dec. 10.1093/sleep/18.10.890 Muto V et al (Aug. 2016) Local modulation of human brain responses by circadian rhythmicity and sleep debt. Science 353(6300):687–690. 10.1126/science.aad2993 Cajochen C, Foy R, Dijk D-J Frontal Predominance of a Relative Increase in Sleep Delta and Theta EEG Activity after Sleep Loss in Humans Münch M et al (2004) The frontal predominance in human EEG delta activity after sleep loss decreases with age, Eur. J. Neurosci. , vol. 20, no. 5, pp. 1402–1410. 10.1111/j.1460-9568.2004.03580 . x Berson DM, Dunn FA, Takao M (2002) Phototransduction by Retinal Ganglion Cells That Set the Circadian Clock, Science , vol. 295, no. 5557, pp. 1070–1073, Feb. 10.1126/science.1067262 Wong KY (Aug. 2012) Ganglion Cell That Can Signal Irradiance Continuously for 10 Hours. J Neurosci 32(33):11478–11485. 10.1523/JNEUROSCI.1423-12.2012 McMahon DG, Iuvone PM, Tosini G (Mar. 2014) Circadian organization of the mammalian retina: From gene regulation to physiology and diseases. Prog Retin Eye Res 39:58–76. 10.1016/j.preteyeres.2013.12.001 Cajochen C, Zeitzer JM, Czeisler CA, Dijk D-J (Oct. 2000) Dose-response relationship for light intensity and ocular and electroencephalographic correlates of human alertness. Behav Brain Res 115(1):75–83. 10.1016/S0166-4328(00)00236-9 Boas DA, Elwell CE, Ferrari M, Taga G (Jan. 2014) Twenty years of functional near-infrared spectroscopy: introduction for the special issue. NeuroImage 85:1–5. 10.1016/j.neuroimage.2013.11.033 Scholkmann F et al (Jan. 2014) A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage 85:6–27. 10.1016/j.neuroimage.2013.05.004 Obrig H, Villringer A (Jan. 2003) Beyond the Visible-Imaging the Human Brain with Light. J Cereb Blood Flow Metab 23(1):1–18. 10.1097/01.WCB.0000043472.45775.29 Mazziotti R et al (2022) The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children, Transl. Psychiatry , vol. 12, no. 1, p. 53, Feb. 10.1038/s41398-022-01820-5 Scaffei E et al (June 2023) A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers. Brain Sci 13(6):951. 10.3390/brainsci13060951 Scaffei E, Bosetti C, Battini R, Fagiolini M, Baroncelli L (May 2025) fNIRS as a biomarker for X-linked neurodevelopmental disorders: leveraging visual processing to assess brain function? Front Neurosci 19. 10.3389/fnins.2025.1560786 Peirce J et al (Feb. 2019) PsychoPy2: Experiments in behavior made easy. Behav Res Methods 51(1):195–203. 10.3758/s13428-018-01193-y Wijeakumar S, Shahani U, Simpson WA, McCulloch DL (2012) Localization of Hemodynamic Responses to Simple Visual Stimulation: An fNIRS Study, Invest. Ophthalmol. Vis. Sci. , vol. 53, no. 4, pp. 2266–2273, Apr. 10.1167/iovs.11-8680 Pinti P et al (2020) The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci 1464(1):5–29. 10.1111/nyas.13948 Chen L-C, Sandmann P, Thorne JD, Herrmann CS, Debener S (2015) Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli, Brain Topogr. , vol. 28, no. 5, pp. 710–725, Sept. 10.1007/s10548-015-0424-8 Zimeo Morais GA, Balardin JB, Sato JR (Feb. 2018) fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci Rep 8(1):3341. 10.1038/s41598-018-21716-z Di Lorenzo R et al (2019) Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems, NeuroImage , vol. 200, pp. 511–527, Oct. 10.1016/j.neuroimage.2019.06.056 Gollapudi S (2019) OpenCV with Python. In: Gollapudi S (ed) Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs. A, Berkeley, CA, pp 31–50. doi: 10.1007/978-1-4842-4261-2_2 . Vallat R (Nov. 2018) Pingouin: statistics in Python. J Open Source Softw 3(31):1026. 10.21105/joss.01026 Hunter JD Matplotlib: A 2D Graphics Environment, Accessed: Dec. 19, 2025. [Online]. Available: https://www.computer.org/csdl/magazine/cs/2007/03/c3090/13rRUwbJD0A Páez A, Boisjoly G (2022) Exploratory Data Analysis. In: Páez A, Boisjoly G (eds) Discrete Choice Analysis with R. Springer International Publishing, Cham, pp 25–64. doi: 10.1007/978-3-031-20719-8_2 . Campbell R, Pascalis O, Coleman M, Wallace S, Benson P (1997) Are faces of different species perceived categorically by human observers? Proc. Biol. Sci. , vol. 264, pp. 1429–34, Nov. 10.1098/rspb.1997.0199 Wright KP, Hull JT, Czeisler CA (Dec. 2002) Relationship between alertness, performance, and body temperature in humans. Am J Physiol -Regul Integr Comp Physiol 283(6):R1370–R1377. 10.1152/ajpregu.00205.2002 Minati L, Visani E, Dowell NG, Medford N, Critchley HD (2011) Variability comparison of simultaneous brain near-infrared spectroscopy (NIRS) and functional MRI (fMRI) during visual stimulation. J Med Eng Technol 35(0):370–376. 10.3109/03091902.2011.595533 Schroeter ML, Bücheler MM, Scheid R (2006) Circadian variability is negligible in primary visual cortices as measured by fNIRS, Int. J. Psychophysiol. , vol. 62, no. 1, pp. 9–13, Oct. 10.1016/j.ijpsycho.2005.11.003 Plichta MM, Heinzel S, Ehlis A-C, Pauli P, Fallgatter AJ (2007) Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study, NeuroImage , vol. 35, no. 2, pp. 625–634, Apr. 10.1016/j.neuroimage.2006.11.028 Wagner JC et al (Nov. 2025) Reproducibility of fNIRS within subject for visual and motor tasks. NeuroImage 321:121492. 10.1016/j.neuroimage.2025.121492 Ş, Akpınar, Tek NA (2023) Age-Related Changes in Circadian Rhythm and Association with Nutrition, Curr. Nutr. Rep. , vol. 12, no. 3, pp. 376–382, Sept. 10.1007/s13668-023-00474-z Myung J, Silver R, Jones JR, Nakamura TJ, Ono D (July 2024) Editorial: Development of circadian clock functions, II. Front Neurosci 18. 10.3389/fnins.2024.1453328 Van Drunen R, Eckel-Mahan K (Jan. 2023) Circadian rhythms as modulators of brain health during development and throughout aging. Front Neural Circuits 16:1059229. 10.3389/fncir.2022.1059229 Additional Declarations The authors declare no competing interests. Supplementary Files ArticleCircadianRythmwithfNIRS.SuppMaterialDEF.docx Supplementary figures and tables Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8590476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573830518,"identity":"744d8db2-d5e2-43c6-a636-3bd32fe97b53","order_by":0,"name":"Camilla Simoncelli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3Rv4rCMBzA8Z8E6uCvdi0o9hUCDi7SZzEU3DwKLsIJVhw6uuYxPISbAwFvuHN3LHQ+qPgAXppSbzAV3BzyHUozfPjlD4DN9qKJLFZfUi36XUKEqP5bSSOZ0H+CDnEmNVk3Gk2gJoC3pXGMl34zNSWEIO2wPF6O0WnjWcYQvo0SkmYG4v/MdopEQKW7H/LDVG3M3UkO0bwvzBujoAkBStzPnpvIiiAIxhvOQr3fkqwg2GhyVQSzx8TXUySA1ESUBB4S/6SnfKE+Cx6i8pKpRBoxTlprbrqx7WxfFIv3QbA9fuS4DAeeJ/MLLkLG22lWGEgd3l3L7XFtNpvN9nx/t4Ba5YFNr18AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0135-8293","institution":"IRCCS Stella Maris Foundation, Pisa, Italy","correspondingAuthor":true,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Simoncelli","suffix":""},{"id":573837843,"identity":"0e06bbb0-dead-466c-b374-73213782f21d","order_by":1,"name":"Elena Scaffei","email":"","orcid":"","institution":"USL Toscana Centro, Florence, Italy","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Scaffei","suffix":""},{"id":573837844,"identity":"9e5b2dfa-cb8e-4495-be44-41eb04893da2","order_by":2,"name":"Roberta Battini","email":"","orcid":"","institution":"University of Pisa, Pisa, Italy \u0026 IRCCS Stella Maris Foundation, Pisa, Italy","correspondingAuthor":false,"prefix":"","firstName":"Roberta","middleName":"","lastName":"Battini","suffix":""},{"id":573837845,"identity":"617fce27-34e0-4cae-b5d9-6b23a9b9cb45","order_by":3,"name":"Raffaele Mario Mazziotti","email":"","orcid":"","institution":"University of Florence, Italy \u0026 Institute of Neuroscience, National Research Council (CNR), Pisa, Italy","correspondingAuthor":false,"prefix":"","firstName":"Raffaele","middleName":"Mario","lastName":"Mazziotti","suffix":""},{"id":573837846,"identity":"dc4af1e5-ea68-445e-aebd-fe89ca9561b5","order_by":4,"name":"Laura Baroncelli","email":"","orcid":"","institution":"Institute of 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13:06:19","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102289,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/a66cdee1a17fe1a1f6e998be.html"},{"id":100408545,"identity":"8c5346cc-685f-48a2-ab1d-37cc0c6a5018","added_by":"auto","created_at":"2026-01-16 13:06:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisual stimulation paradigm.\u003c/em\u003e During the baseline condition (off; left), participants viewed an isoluminant cartoon/movie/video presented at constant luminance. During the stimulation condition (on; right), a high-contrast reversing radial checkerboard was presented to elicit visually evoked hemodynamic responses. Each experimental block lasted 10 minutes and included 20 checkerboard presentations, with baseline and stimulation epochs alternating according to the timing described in the Methods.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/66f20146cd2f842c97c5aed1.png"},{"id":100408724,"identity":"0258edfb-9826-4ae4-81c9-c823d87a83c2","added_by":"auto","created_at":"2026-01-16 13:06:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHemodynamic responses to visual stimulation compared with baseline at T0.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGrand-average changes in HbO (A), HbR (B) and HbT (C) concentrations are shown for the best channel at baseline (grey) and during visual stimulation (red) at T0. Visual stimulation elicited the canonical occipital HDR, characterized by increase in HbO and HbT and a concomitant decrease in HbR. Bottom panels display average ΔHbO, ΔHbR and ΔHbT values for each metrics during baseline and stimulation. Paired t-tests revealed significant differences between conditions for all hemoglobin metrics.\u003c/p\u003e\n\u003cp\u003eData are shown as average ± s.e.m; dots represent individual values. *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/f734b4fc3d6140170caa3369.png"},{"id":100409054,"identity":"dd0e1baa-e3a8-4c70-9ab2-cc461cb568c5","added_by":"auto","created_at":"2026-01-16 13:06:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1949199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStability of visually evoked HbO, HbR, and HbT amplitudes across circadian timepoints in same-day and multi-day recordings.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/a4468f36993408cc7af39b01.png"},{"id":100409147,"identity":"f130d0a6-73e5-49e0-aa50-f8a4b4a21458","added_by":"auto","created_at":"2026-01-16 13:06:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1756835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStability of visually evoked HbO, HbR and HbT amplitudes across mid- and long-term intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/7c3a44def99bda57d756c110.png"},{"id":100414835,"identity":"dea6ed4c-f0d3-4df0-be61-20cc4e07f122","added_by":"auto","created_at":"2026-01-16 13:20:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5001130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/3778bb6f-1058-42a7-a55a-8475ca161983.pdf"},{"id":100409140,"identity":"ad48b07a-3eeb-4d41-a1e2-65b144cd60d4","added_by":"auto","created_at":"2026-01-16 13:06:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2241048,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figures and tables\u003c/p\u003e","description":"","filename":"ArticleCircadianRythmwithfNIRS.SuppMaterialDEF.docx","url":"https://assets-eu.researchsquare.com/files/rs-8590476/v1/42f7dc20caacd009905101f5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFunctional Near-Infrared Spectroscopy Reveals Stable Visually-Evoked Cortical Responses across Circadian Phases\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCircadian rhythms shape alertness, arousal, and cognitive performance, influencing perceptual sensitivity and decision-making across the day. Although behavioral studies report time-of-day effects on visual performance, it remains unclear whether such variability reflects genuine circadian modulation of early sensory processing or instead arises from fluctuations in attentional state and higher-order cognitive control.\u003c/p\u003e \u003cp\u003eCircadian phase is known to modulate cortical excitability through coordinated changes in neurotransmission [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], neuromodulatory systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and thalamocortical rhythms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Neural responsiveness typically peaks during the biological morning and declines toward the circadian evening or under elevated sleep pressure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Within the visual system, circadian regulation operates at multiple hierarchical levels, from retinal physiology to cortical processing. Retinal sensitivity and synaptic transmission follow circadian rhythms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and time-of-day\u0026ndash;dependent changes propagate through early visual pathways, influencing electrophysiological and hemodynamic responses. Consistently, visual evoked potentials and BOLD responses exhibit circadian modulation, with attenuated amplitudes and delayed latencies in the biological evening [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this evidence, few studies have systematically examined whether visually evoked cortical responses remain stable across circadian phases or over extended time intervals. This gap is particularly relevant for neuroimaging approaches increasingly used in longitudinal and clinical research, where temporal stability is a prerequisite for biomarker validity.\u003c/p\u003e \u003cp\u003eFunctional Near-infrared Spectroscopy (fNIRS) is a non-invasive optical imaging technique that infers neural activity from either spontaneous or stimulus-evoked changes in cerebral hemodynamics. By quantifying changes in oxygenated and deoxygenated hemoglobin, fNIRS is increasingly recognized for its suitability in monitoring brain function in naturalistic settings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Its robustness to movement, ease of deployment, and excellent feasibility for repeated measurements make fNIRS particularly well suited for longitudinal designs and for studies conducted across variable times of day. Importantly, fNIRS is emerging as a promising biomarker of brain function across neurological and neurodevelopmental conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], underscoring the need to establish its temporal stability.\u003c/p\u003e \u003cp\u003eIn this study, we investigated whether visually evoked hemodynamic responses measured with fNIRS are modulated by circadian phase and whether they remain stable across repeated measurements. Neurotypical adults completed a reversing-checkerboard stimulation paradigm at three circadian timepoints within the same day, as well as on separate days at matched hours. To further assess signal reliability, additional cohorts were tested at short (five minutes) medium (two months), and long (two years) retest intervals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eSeventy-one neurotypical adults were recruited and assigned to two experimental groups:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003eCircadian group\u003c/b\u003e (N\u0026thinsp;=\u0026thinsp;21; mean age\u0026thinsp;=\u0026thinsp;30.85 years\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27; 11 women, 10 men) completed three testing sessions within a single day, morning (T10), afternoon (T14), and evening (T18), and were subsequently reassessed on separate days at the same corresponding timepoints. This design allowed evaluation of potential circadian-phase effects on visually evoked responses while controlling for time-of-day consistency across sessions. In this framework, T10, T14 and T18 coincide with the approximate testing hours, corresponding to 10 a.m., 2 p.m. and 6 p.m. respectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003eInterval group\u003c/b\u003e (N\u0026thinsp;=\u0026thinsp;50; mean age\u0026thinsp;=\u0026thinsp;31.64 years\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44; 25 women, 25 men) underwent three testing sessions: at baseline (T0), after five minutes (T1), and at a delayed follow-up. Half of the participants were reassessed after two months (T2), whereas the remaining half were retested after two years (T3). In this framework, T1 was defined as the short-term retest, T2 as the mid-term retest, and T3 as the long-term retest.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e All participants had normal or corrected-to-normal vision, volunteered to take part in the study, and provided written consent (authorization numbers 116/2022 and 77/2023, Regional Ethics Committee for Clinical Research of the Tuscany Region). To ensure consistency across sessions, each participant viewed the same movie selected during their first visit in all subsequent experimental sessions. All procedures comply with the ethical guidelines of the relevant national and institutional committees and adhered to the principles of the Helsinki Declaration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct2008/\u003c/span\u003e\u003cspan address=\"https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct2008/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) of 1975, as revised in 2008.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStimuli and Procedure\u003c/h3\u003e\n\u003cp\u003eThe experimental design was based on the paradigm described in Mazziotti et al. (2022), with minor methodological adaptations tailored to the aims of the present study. Visual stimuli were generated using Python 3 and PsychoPy3 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and displayed on a 31.5-inch LG UltraGear LCD monitor (spatial resolution: 2560 \u0026times; 1440 pixels, refresh rate: 180 Hz, mean luminance: 400 cd/m\u0026sup2;). The monitor was fully calibrated prior to testing, and gamma correction was performed using a Datacolor Spyder X Pro (SXP100) colorimeter to ensure accurate luminance and contrast reproduction. Hemodynamic Responses (HDRs) were recorded during both baseline and stimulation conditions. During baseline, participants watched a video presented on a full-field, isoluminant grey background. The stimulation consisted of a reversing black-and-white, square-wave radial checkerboard presented for 5 seconds [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The checkerboard (spatial frequency: 0.33 cycles per degree, temporal frequency: 4 Hz) was presented 20 times with a randomized Interstimulus Interval (ISI). The entire experimental block lasted 10 minutes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003efNIRS Data Acquisition\u003c/h3\u003e\n\u003cp\u003eTask-evoked changes in total hemoglobin concentration (HbT) and relative oxygenation levels, oxygenated (HbO) and deoxigenated (HbR) hemoglobin, were measured over the occipital cortex usinga continuous-wave fNIRS system (NIRSPort2, NIRx Medical Technologies). The system consisted of eight dual-wavelength light sources (760 and 850 nm) and seven detectors embedded in different-sized EEG caps, yielding 22 multidistance channels covering the visual cortex. To ensure consistent positioning across participants, probe arrays were secured within each cap using stabilizers and positional labels. Before data acquisition, the optode-equipped cap was positioned on the participant\u0026rsquo;s head and a light-coupling calibration between sources and detectors was performed. Participants were seated 70 cm from the monitor, and each experimental session lasted approximately 20 minutes. Data were recorded using Aurora software (version 1.4.1.1) at a sampling rate of 10.2 Hz. Visual stimulation events were synchronized with the NIRSPort2 system via wired LAN communication using the Python version of LabStreamingLayer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sccn/labstreaminglayer\u003c/span\u003e\u003cspan address=\"https://github.com/sccn/labstreaminglayer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData Pre-processing and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData pre-processing followed established fNIRS pipelines [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and procedures described in Mazziotti et al. (2022). Pre-processing steps included conversion of raw light intensities to optical density (OD), channel pruning, motion-artifact detection and correction, band-pass filtering, and transformation of OD signals into ΔHbO and ΔHbR concentration changes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For each participant, HDRs were averaged across all trials for each condition and channel within a temporal window extending from 2 seconds before stimulus onset to 20 seconds after. For statistical analysis, we focused on the \u0026ldquo;best channel,\u0026rdquo; defined as the channel exhibiting maximal neural responsiveness, namely the largest positive peak for HbO and HbT and the largest negative peak for HbR. Averaging across channels would dilute the focal visual response by incorporating uninvolved or noisier channels, thereby reducing sensitivity. Selecting the best channel maximizes signal-to-noise ratio, minimizes the impact of individual anatomical variability, and yields a more stable and physiologically meaningful measure. These features are critical when assessing the reliability of a candidate biomarker of brain function, such as visually evoked fNIRS responses.\u003c/p\u003e \u003cp\u003eTo assess the robustness and temporal stability of visually evoked responses, paired t-tests, repeated-measures ANOVAs, post-hoc comparisons, and correlation analyses were performed across all experimental conditions, including circadian phase comparisons, multi-day assessment at matched timepoints, short-interval test\u0026ndash;retest measurements, and mid- and long-interval retests. For paired Δ-amplitude correlation analyses, we used the Shepherd method, which provides a robust estimate of association while explicitly accounting for outliers, identified and removed using a median absolute deviation (MAD) criterion. This approach is particularly well suited for fNIRS amplitude data, where occasional motion artifacts, vascular fluctuations, or imperfect optode coupling can generate extreme values despite careful pre-processing. All statistical analyses and data visualizations were performed using the Pingouin statistical package, Matplotlib, and SciPy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor age-correlation analyses, we used the Spearman method because this approach is robust to non-normal distributions and unequal variances, which are frequently observed in neurophysiological measures across age. To ensure data quality and reduce the influence of extreme values, outliers were identified and removed using the Interquartile Range (IQR) method. For each variable of interest, the first quartile (Q1) and third quartile (Q3) were calculated, and the IQR was defined as Q3 - Q1. Data points falling below Q1\u0026ndash;1.5 \u0026times; IQR or above Q3\u0026thinsp;+\u0026thinsp;1.5 \u0026times; IQR were classified as outliers and excluded from subsequent analyses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Across all three time points (T10, T14, T18) and hemodynamic measures (HbO, HbT, HbR), the removal of outliers did not substantially alter the overall pattern of results, confirming that our findings were robust and not driven by extreme observations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe applied the same statistical analysis to both experimental groups, performing a comprehensive reliability assessment that combined correlation analyses, corrected for multiple comparisons using the Benjamini\u0026ndash;Hochberg method, with paired t-test and repeated-measures ANOVAs.\u003c/p\u003e \u003cp\u003e Across all participants and recording sessions, visual stimulation consistently elicited the expected occipital fNIRS response: robust increase in HbO and HbT accompanied by a reliable decrease in HbR. No modulation of hemoglobin oxygenation was observed during the baseline condition. This canonical hemodynamic pattern was preserved across all experimental conditions, confirming the reliability of visually evoked responses obtained with our video-embedded stimulation protocol.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates a representative example of the hemodynamic signal during baseline versus stimulation, together with group-level peak responses during the T0 session. A comparable response profile was observed at all timepoints in same-day and multi-day recordings, as well as across short-, mid- and long- term retest sessions, highlighting the robustness and reproducibility of the signal at different timepoints and interval repetitions. Detailed results of paired-sample t-tests comparing baseline and stimulation conditions for HbO, HbR, and HbT across all sessions are reported in the Supplementary Material (Tables S1 and S3). No meaningful correlations were found between response amplitude and participant age in either group, indicating that visually evoked hemodynamic response is preserved across the adult lifespan and are not influenced by inter-individual age variability (Table S5 and Figure S1 in Supplementary Material).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCircadian modulation\u003c/h2\u003e \u003cp\u003eRepeated-measures ANOVAs were conducted to assess whether visually evoked HbO, HbR, and HbT amplitudes differed across circadian timepoints (T10, T14, T18). No significant main effect of Timepoint was observed for any hemoglobin metric, indicating that response magnitude remained stable across the day. Individual trajectories showed no systematic trends, and estimation plots revealed paired mean differences centred near zero with confidence intervals spanning the null. Despite a slight increase of HbO and HbT amplitude at T14 in the multi-day design, this pattern was consistent both within single-day sessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C) and across sessions acquired on different days (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F), demonstrating strong multi-day reliability of visually evoked hemodynamic response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTop panels\u003c/b\u003e: Peak amplitudes for HbO (A), HbR (B) and HbT (C) during visual stimulation at T10, T14, and T18 in same day recordings and for HbO (D), HbR (E) and HbT (F) of recordings collected on separate days at matched circadian timepoints. Green dots represent individual participants; connecting lines indicate repeated measurements within the same participant. Grey rectangles denote the interquartile range at each timepoint highlighting the limited dispersion of group-level responses. All Hb metrics show comparable stability across the three circadian phases, with no systematic time-of-day modulation. Repeated-measures ANOVAs revealed no significant main effect of timepoint on the dependent measure, either for same-day recordings (HbO: \u003cem\u003eF\u003c/em\u003e (2, 40)\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.93, η_g\u0026sup2; = .002, HbR: \u003cem\u003eF\u003c/em\u003e (2, 40)\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38, η_g\u0026sup2; = .012, HbT: \u003cem\u003eF\u003c/em\u003e (2, 40)\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.65, η_g\u0026sup2; = .012) or for multi-day recordings (HbO: \u003cem\u003eF\u003c/em\u003e (2, 36)\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.46, η_g\u0026sup2; = .029, HbR: \u003cem\u003eF\u003c/em\u003e (2, 36)\u0026thinsp;=\u0026thinsp;1.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.16, η_g\u0026sup2; = .069, HbT: \u003cem\u003eF\u003c/em\u003e (2, 36)\u0026thinsp;=\u0026thinsp;2.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.077, η_g\u0026sup2; = .084), indicating that response amplitude did not differ across timepoints.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBottom panels\u003c/strong\u003e \u003cp\u003ePaired mean difference plots comparing T14\u0026ndash;T10 and T18\u0026ndash;T10. Green markers indicate within-participant differences, while the black dot and line represent the mean difference 0. confidence interval. Differences are centred around zero, indicating no significant changes in hemodynamic response amplitudes across circadian timepoints. In panels A-C (same-day recordings) 6.3% of data points were removed, whereas in panels D-F (multi-day recordings) 8.8% of points corresponded to outliers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eRobust Shepherd correlation analyses confirmed temporal stability, with significant global associations observed across most inter-timepoint comparisons, underscoring the reproducibility of the measured responses over time. Participants exhibiting stronger or weaker responses at one session tended to maintain similar response magnitudes across sessions, indicating high within-subject reliability and preserved inter-individual ranking over time (Table S2 and Figures S2 and S3 in Supplementary Material).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInterval replication\u003c/h3\u003e\n\u003cp\u003eSignificant correlations of signal amplitude were also observed between recordings separated by short, medium, and long intervals (Table S4 and Figures S4 and S5 in Supplementary Material), demonstrating strong test-retest reliability and robust within-subject consistency of visually evoked fNIRS responses. The uniformly low p-values and high statistical power indicate that these correlations reflect genuine neural reproducibility rather than measurement noise. Consistent with these findings, repeated-measures ANOVAs revealed no significant effect of interval length on HbO, HbR, or HbT amplitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAltogether these findings indicate the absence of systematic changes across repeated sessions arguing against habituation or adaptation effects attributable to task repetition. Despite repeated exposure to the same visual stimulation protocol over intervals spanning minutes to years, neural response neither significantly attenuated nor amplified. This stability underscores the suitability of visually evoked fNIRS measures for longitudinal and multi-session designs, where consistency over time is essential for reliable interpretation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTop panels\u003c/b\u003e: Peak amplitudes for HbO (A, D), HbR (B, E) and HbT (C, F) during visual stimulation for recordings at T0, T1, T2 (A-C, mid-term interval) and T0, T1, T3 (D-F, long-term interval). Green dots represent individual participants with connecting lines indicating repeated measurements within subjects. Grey rectangles correspond to the interquartile range for each timepoint, highlighting the limited dispersion of group-level responses. Across all intervals HbO, HbR and HbT amplitudes remain stable, with no systematic changes over time. Repeated measures ANOVAs revealed no significant main effect of timepoint on the dependent measure for either the mid-term interval (HbO: \u003cem\u003eF\u003c/em\u003e (2, 52)\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.44, η_g\u0026sup2; = .01, HbR: \u003cem\u003eF\u003c/em\u003e (2, 52)\u0026thinsp;=\u0026thinsp;1.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.35, η_g\u0026sup2; = .014, HbT: \u003cem\u003eF\u003c/em\u003e (2, 52)\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38, η_g\u0026sup2; = .012) or the long-term interval recordings (HbO: \u003cem\u003eF\u003c/em\u003e (2, 44)\u0026thinsp;=\u0026thinsp;2.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14, η_g\u0026sup2; = .018, HbR: \u003cem\u003eF\u003c/em\u003e (2, 44)\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.64, η_g\u0026sup2; = .008, HbT: \u003cem\u003eF\u003c/em\u003e (2, 44)\u0026thinsp;=\u0026thinsp;1.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.25, η_g\u0026sup2; = .019), supporting the stability of the measured response across conditions.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBottom panels\u003c/strong\u003e \u003cp\u003ePaired mean difference plots comparing T1\u0026ndash;T0, T2\u0026ndash;T1 and T3-T1 for HbO, HbR and HbT. Green markers represent individual within-participant differences, while the black dot and line denote the mean difference and its confidence interval. In all comparisons, differences are centered around zero, indicating no significant modulation in hemodynamic response amplitudes. In mid-term interval recordings (A-C) 6.6% of data points were outliers and removed, whereas in long-term interval recordings (D-F) 7.2% of points were outliers.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigated whether visually evoked cortical responses measured with fNIRS are modulated by circadian phase or by the temporal distance between repeated measurements. Across two complementary cohorts, a circadian group and an interval-based test-retest group, we consistently observed that both the morphology and the amplitude of visually evoked HDR in occipital cortex remain remarkably stable. These findings provide convergent evidence that early visual cortical processing is largely resilient to circadian fluctuations and temporally robust and further demonstrate that fNIRS-measured HDR is invariant across minutes, hours, days, months, and even years.\u003c/p\u003e \u003cp\u003eContrary to the hypothesis that circadian phase might influence early sensory processing through fluctuations in cortical excitability, neuromodulatory tone, or thalamocortical dynamics, we found no systematic variation in fNIRS responses across morning, afternoon, and evening sessions. At every timepoint, visual stimulation elicited the canonical HDR. While paired t-tests consistently confirmed robust stimulus-driven activation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], repeated-measures ANOVAs revealed no main effect of circadian phase on response amplitude. Small fluctuations in effect size and correlation strength, particularly the tendency for stronger correlations within same-day recordings compared with multi-day recordings, are likely attributable to expected experimental and physiological variability, including differences in optode coupling, environmental conditions, or vascular tone. Importantly, these sources of noise did not undermine the central result: visually evoked HDRs remained robustly stable across both short and long temporal intervals. Although T14 exhibited slightly greater variability and larger Δ-peak amplitudes, this pattern likely reflects a transitional circadian phase in which competing homeostatic and circadian processes interact. Such state-dependent fluctuations may amplify response dispersion without indicating systematic modulation of early sensory processing. Notably, increased variability at T14 did not compromise reliability, further underscoring the robustness of the signal.\u003c/p\u003e \u003cp\u003eThese results indicate that the magnitude of cortical activation does not drift across the day, despite well-established circadian influences on alertness, reaction time, and perceptual performance reported in electrophysiological and behavioral studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A plausible explanation for this apparent discrepancy lies in the fundamentally different neural processes captured by fNIRS compared with EEG. While EEG indexes fast, synchronous neuronal firing and membrane potential fluctuations that are highly sensitive to arousal, vigilance, and excitability, fNIRS primarily reflects \u0026ldquo;slow signals\u0026rdquo; like activity-dependent metabolic demand mediated by neurovascular coupling which integrates synaptic and local processing over longer temporal windows. As a result, hemodynamic responses may be less sensitive to transient fluctuations in arousal or vigilance and instead reflect more stable aspects of cortical processing.\u003c/p\u003e \u003cp\u003eThe interval-based cohort further allowed us to assess test-retest reliability of visually evoked hemodynamic responses across increasingly longer temporal windows. We found that visually evoked HDRs remained robust at short-, mid-, and long-term retest. Collectively, these results demonstrate that visually evoked HDR are highly stable across time and repetition. The absence of attenuation or amplification across repeated exposures indicates that the hemodynamic response does not undergo measurable habituation or adaptation within the parameters of this experiment.\u003c/p\u003e \u003cp\u003eThese findings have important implications for both basic neurophysiology and fNIRS methodology. First, they indicate that the metabolic demands of early visual processing remain constant across circadian phases and repeated exposures. Second, they support the suitability of fNIRS for longitudinal sensory research, as the absence of signal drift or attenuation across long intervals validates its use for multi-session visual studies. Third, our results are compatible with the possibility that circadian-related variability in behavior and cognition reflects modulation of higher-order processes, including attention or executive function, rather than systematic changes in early sensory encoding, which appears relatively stable in primary visual cortex.\u003c/p\u003e \u003cp\u003eCrucially, the observed stability defines visually evoked fNIRS responses as a reliable biomarker of brain function. In clinical contexts, such stability is essential: a dependable visual response can serve as a reference against which pathological changes, disease progression, or therapeutic effects can be quantified. For example, in neurodegenerative disorders, the presence of a stable sensory-evoked HDR would make it possible to reliably identify meaningful functional decline over time. In rehabilitation or intervention studies, it allows treatment-related changes to be distinguished from measurement variability. Moreover, despite known circadian variations in cardiovascular physiology that could theoretically affect fNIRS signals [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], our results indicate that visually evoked responses remain stable across the day, reinforcing the interpretation that these signals primarily reflect genuine neural\u0026ndash;metabolic activity rather than systemic noise. Our study provides, to our knowledge, the most comprehensive characterization to date of visually evoked cortical responses measured with fNIRS. In contrast with previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which have generally relied on small sample sizes and/or limited test-retest intervals, our work uniquely integrates a relatively large cohort with same-day, multi-day, and long-term repeated measurements within a single, unified experimental design. This combination of extensive sampling and longitudinal assessment substantially extends prior visual fNIRS research and establishes a robust reference framework for evaluating the temporal stability, reproducibility, and biomarker potential of visually evoked hemodynamic responses.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eAlthough the present findings strongly support the stability of early visual processing, several limitations need to be considered. First, our conclusions are specific to the occipital cortex; circadian modulation may be more pronounced in frontal, parietal, or subcortical networks involved in attention, executive control, or sleep-wake regulation. Multimodal approaches, combining fNIRS with EEG, would help clarify whether oscillatory dynamics, particularly in alpha or sigma bands, exhibit circadian sensitivity alongside stable hemodynamic responses. Second, these findings may not generalize to populations with disrupted circadian organization, such as shift workers or individuals with psychiatric or neurodevelopmental conditions. Third, our cohort included only young-to-middle-aged adults. Given that circadian physiology and neurovascular coupling substantially change across development and aging [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], age-stratified cohorts are essential to determine whether circadian resilience of hemodynamic processing in the early visual system is preserved across lifespan. Extending test\u0026ndash;retest fNIRS protocols to pediatric and older populations would also enable dissociation of true developmental changes from measurement variability, enhancing the utility of fNIRS for longitudinal developmental and clinical research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that visually evoked cortical responses measured with fNIRS are highly stable across circadian phases and over a wide range of retest intervals. The absence of circadian modulation in the HDR, together with strong within-subject reliability, indicates that early visual cortical processing is largely resistant to circadian fluctuations. These findings support the robustness of fNIRS as a neuroimaging tool for longitudinal, time-sensitive, and multi-session research, and strengthen its potential as a reliable biomarker of brain function. Future work integrating multimodal imaging, age-stratified cohorts, and clinical populations will further clarify the interaction between circadian biology and neural function and guide the design of optimized, temporally informed neuroimaging protocols.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests statement\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eCS and ES collected the data; CS and RM analysed the data; CS, RB and LB created the experimental design; LB conceived the experiment; CS and LB wrote the manuscript, ES, RM and RB participated to the editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the EJP RD JTC 2022 program \u003cem\u003e\"Development of new analytic tools and pathways to accelerate diagnosis and facilitate diagnostic monitoring of rare diseases\"\u003c/em\u003e, funded under the EJP-Cofund Action on Rare Diseases (Grant Agreement No. 825575) and by the Italian Ministry of Health. Additional support was provided by RC2025.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrancaccio M et al (Jan. 2019) Cell-autonomous clock of astrocytes drives circadian behavior in mammals. Science 363(6423):187\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science. aat4104\u003c/span\u003e\u003cspan address=\"10.1126/science. aat4104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarajnia S, van Westering TLE, Meijer JH, Michel S (2014) Seasonal induction of GABAergic excitation in the central mammalian clock, \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e, vol. 111, no. 26, pp. 9627\u0026ndash;9632, July \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1319820111\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1319820111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePracucci E et al (Nov. 2023) Daily rhythm in cortical chloride homeostasis underpins functional changes in visual cortex excitability. Nat Commun 14(1):7108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-023-42711-7\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-42711-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAston-Jones G, Cohen JD, AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION (2005) : Adaptive Gain and Optimal Performance, \u003cem\u003eAnnu. Rev. Neurosci.\u003c/em\u003e, vol. 28, no. Volume 28, pp. 403\u0026ndash;450, July 2005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev.neuro.28.061604.135709\u003c/span\u003e\u003cspan address=\"10.1146/annurev.neuro.28.061604.135709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCajochen C, Brunner DP, Krauchi K, Graw P, Wirz-Justice A (1995) Power Density in Theta/Alpha Frequencies of the Waking EEG Progressively Increases During Sustained Wakefulness, \u003cem\u003eSleep\u003c/em\u003e, vol. 18, no. 10, pp. 890\u0026ndash;894, Dec. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/sleep/18.10.890\u003c/span\u003e\u003cspan address=\"10.1093/sleep/18.10.890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuto V et al (Aug. 2016) Local modulation of human brain responses by circadian rhythmicity and sleep debt. Science 353(6300):687\u0026ndash;690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aad2993\u003c/span\u003e\u003cspan address=\"10.1126/science.aad2993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCajochen C, Foy R, Dijk D-J Frontal Predominance of a Relative Increase in Sleep Delta and Theta EEG Activity after Sleep Loss in Humans\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;nch M et al (2004) The frontal predominance in human EEG delta activity after sleep loss decreases with age, \u003cem\u003eEur. J. Neurosci.\u003c/em\u003e, vol. 20, no. 5, pp. 1402\u0026ndash;1410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1460-9568.2004.03580\u003c/span\u003e\u003cspan address=\"10.1111/j.1460-9568.2004.03580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerson DM, Dunn FA, Takao M (2002) Phototransduction by Retinal Ganglion Cells That Set the Circadian Clock, \u003cem\u003eScience\u003c/em\u003e, vol. 295, no. 5557, pp. 1070\u0026ndash;1073, Feb. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1067262\u003c/span\u003e\u003cspan address=\"10.1126/science.1067262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong KY (Aug. 2012) Ganglion Cell That Can Signal Irradiance Continuously for 10 Hours. J Neurosci 32(33):11478\u0026ndash;11485. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.1423-12.2012\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1423-12.2012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMahon DG, Iuvone PM, Tosini G (Mar. 2014) Circadian organization of the mammalian retina: From gene regulation to physiology and diseases. Prog Retin Eye Res 39:58\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.preteyeres.2013.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.preteyeres.2013.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCajochen C, Zeitzer JM, Czeisler CA, Dijk D-J (Oct. 2000) Dose-response relationship for light intensity and ocular and electroencephalographic correlates of human alertness. Behav Brain Res 115(1):75\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0166-4328(00)00236-9\u003c/span\u003e\u003cspan address=\"10.1016/S0166-4328(00)00236-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoas DA, Elwell CE, Ferrari M, Taga G (Jan. 2014) Twenty years of functional near-infrared spectroscopy: introduction for the special issue. NeuroImage 85:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2013.11.033\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2013.11.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScholkmann F et al (Jan. 2014) A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage 85:6\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2013.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2013.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObrig H, Villringer A (Jan. 2003) Beyond the Visible-Imaging the Human Brain with Light. J Cereb Blood Flow Metab 23(1):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.WCB.0000043472.45775.29\u003c/span\u003e\u003cspan address=\"10.1097/01.WCB.0000043472.45775.29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazziotti R et al (2022) The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children, \u003cem\u003eTransl. Psychiatry\u003c/em\u003e, vol. 12, no. 1, p. 53, Feb. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41398-022-01820-5\u003c/span\u003e\u003cspan address=\"10.1038/s41398-022-01820-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScaffei E et al (June 2023) A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers. Brain Sci 13(6):951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/brainsci13060951\u003c/span\u003e\u003cspan address=\"10.3390/brainsci13060951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScaffei E, Bosetti C, Battini R, Fagiolini M, Baroncelli L (May 2025) fNIRS as a biomarker for X-linked neurodevelopmental disorders: leveraging visual processing to assess brain function? Front Neurosci 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2025.1560786\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2025.1560786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeirce J et al (Feb. 2019) PsychoPy2: Experiments in behavior made easy. Behav Res Methods 51(1):195\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3758/s13428-018-01193-y\u003c/span\u003e\u003cspan address=\"10.3758/s13428-018-01193-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijeakumar S, Shahani U, Simpson WA, McCulloch DL (2012) Localization of Hemodynamic Responses to Simple Visual Stimulation: An fNIRS Study, \u003cem\u003eInvest. Ophthalmol. Vis. Sci.\u003c/em\u003e, vol. 53, no. 4, pp. 2266\u0026ndash;2273, Apr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1167/iovs.11-8680\u003c/span\u003e\u003cspan address=\"10.1167/iovs.11-8680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinti P et al (2020) The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci 1464(1):5\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/nyas.13948\u003c/span\u003e\u003cspan address=\"10.1111/nyas.13948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L-C, Sandmann P, Thorne JD, Herrmann CS, Debener S (2015) Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli, \u003cem\u003eBrain Topogr.\u003c/em\u003e, vol. 28, no. 5, pp. 710\u0026ndash;725, Sept. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10548-015-0424-8\u003c/span\u003e\u003cspan address=\"10.1007/s10548-015-0424-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimeo Morais GA, Balardin JB, Sato JR (Feb. 2018) fNIRS Optodes\u0026rsquo; Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci Rep 8(1):3341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-018-21716-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-21716-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Lorenzo R et al (2019) Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems, \u003cem\u003eNeuroImage\u003c/em\u003e, vol. 200, pp. 511\u0026ndash;527, Oct. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2019.06.056\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2019.06.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGollapudi S (2019) OpenCV with Python. In: Gollapudi S (ed) Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs. A, Berkeley, CA, pp 31\u0026ndash;50. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4842-4261-2_2\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4842-4261-2_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallat R (Nov. 2018) Pingouin: statistics in Python. J Open Source Softw 3(31):1026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21105/joss.01026\u003c/span\u003e\u003cspan address=\"10.21105/joss.01026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter JD Matplotlib: A 2D Graphics Environment, Accessed: Dec. 19, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.computer.org/csdl/magazine/cs/2007/03/c3090/13rRUwbJD0A\u003c/span\u003e\u003cspan address=\"https://www.computer.org/csdl/magazine/cs/2007/03/c3090/13rRUwbJD0A\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026aacute;ez A, Boisjoly G (2022) Exploratory Data Analysis. In: P\u0026aacute;ez A, Boisjoly G (eds) Discrete Choice Analysis with R. Springer International Publishing, Cham, pp 25\u0026ndash;64. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-20719-8_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-20719-8_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell R, Pascalis O, Coleman M, Wallace S, Benson P (1997) Are faces of different species perceived categorically by human observers? \u003cem\u003eProc. Biol. Sci.\u003c/em\u003e, vol. 264, pp. 1429\u0026ndash;34, Nov. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rspb.1997.0199\u003c/span\u003e\u003cspan address=\"10.1098/rspb.1997.0199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright KP, Hull JT, Czeisler CA (Dec. 2002) Relationship between alertness, performance, and body temperature in humans. Am J Physiol -Regul Integr Comp Physiol 283(6):R1370\u0026ndash;R1377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajpregu.00205.2002\u003c/span\u003e\u003cspan address=\"10.1152/ajpregu.00205.2002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinati L, Visani E, Dowell NG, Medford N, Critchley HD (2011) Variability comparison of simultaneous brain near-infrared spectroscopy (NIRS) and functional MRI (fMRI) during visual stimulation. J Med Eng Technol 35(0):370\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/03091902.2011.595533\u003c/span\u003e\u003cspan address=\"10.3109/03091902.2011.595533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchroeter ML, B\u0026uuml;cheler MM, Scheid R (2006) Circadian variability is negligible in primary visual cortices as measured by fNIRS, \u003cem\u003eInt. J. Psychophysiol.\u003c/em\u003e, vol. 62, no. 1, pp. 9\u0026ndash;13, Oct. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijpsycho.2005.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpsycho.2005.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlichta MM, Heinzel S, Ehlis A-C, Pauli P, Fallgatter AJ (2007) Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study, \u003cem\u003eNeuroImage\u003c/em\u003e, vol. 35, no. 2, pp. 625\u0026ndash;634, Apr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2006.11.028\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2006.11.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner JC et al (Nov. 2025) Reproducibility of fNIRS within subject for visual and motor tasks. NeuroImage 321:121492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2025.121492\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2025.121492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŞ, Akpınar, Tek NA (2023) Age-Related Changes in Circadian Rhythm and Association with Nutrition, \u003cem\u003eCurr. Nutr. Rep.\u003c/em\u003e, vol. 12, no. 3, pp. 376\u0026ndash;382, Sept. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13668-023-00474-z\u003c/span\u003e\u003cspan address=\"10.1007/s13668-023-00474-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyung J, Silver R, Jones JR, Nakamura TJ, Ono D (July 2024) Editorial: Development of circadian clock functions, II. Front Neurosci 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2024.1453328\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2024.1453328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Drunen R, Eckel-Mahan K (Jan. 2023) Circadian rhythms as modulators of brain health during development and throughout aging. Front Neural Circuits 16:1059229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fncir.2022.1059229\u003c/span\u003e\u003cspan address=\"10.3389/fncir.2022.1059229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"IRCCS Stella Maris Foundation, Pisa, Italy ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"circadian rhythm, neural signal, fNIRS, biomarker, visual cortex, ","lastPublishedDoi":"10.21203/rs.3.rs-8590476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8590476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCircadian rhythms shape physiology and behavior. However, their influence on sensory-evoked neural activity remains poorly understood. Although visual perception fluctuates across the day, it is unclear whether these fluctuations reflect changes in early neural processing or downstream cognitive states. Here, we tested whether visually-evoked cortical responses measured with functional near-infrared spectroscopy (fNIRS) are modulated by circadian phase or by repeated measurements across time. Hemodynamic responses over occipital cortex were recorded in healthy adults during a flickering visual stimulation paradigm. Participants were assessed either (i) at three circadian timepoints within a single day and again on separate days at matched hours, or (ii) across short-, medium-, and long-term test\u0026ndash;retest intervals, including five minutes, two months, and two years. We found that visually-evoked response amplitudes remain stable across circadian phases and longitudinal intervals. These results indicate that early visual cortical responses measured with fNIRS are largely resilient to both circadian modulation and repeated stimulation. The marked temporal stability of fNIRS signals supports their reliability for longitudinal sensory research and highlights the potential of fNIRS as a non-invasive biomarker of cortical function.\u003c/p\u003e","manuscriptTitle":"Functional Near-Infrared Spectroscopy Reveals Stable Visually-Evoked Cortical Responses across Circadian Phases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:06:10","doi":"10.21203/rs.3.rs-8590476/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fac88fc-9e51-4c76-a8b5-75f2354cb5ed","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61055835,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2026-01-16T11:06:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:06:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8590476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8590476","identity":"rs-8590476","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}