Familial risk and illness burden jointly shape prefrontal hypoactivation in depression: An fNIRS study

Familial risk and illness burden jointly shape prefrontal hypoactivation in depression: An fNIRS study | 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 Article Familial risk and illness burden jointly shape prefrontal hypoactivation in depression: An fNIRS study Georg Kranz, Bella Zhang, Sharie Wang, Vera Wai Man Lam, Rebecca Kan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537776/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Prefrontal dysfunction is a well-documented deficit in major depressive disorder (MDD), yet it remains unclear whether this represents a genetic endophenotype or a persistent "scar" of the illness. This study aimed to disentangle familial risk from disease chronicity by simultaneously examining remitted MDD (rMDD) patients, unaffected high-risk (HR) individuals, and healthy controls (HC). We recruited 87 participants: 35 with rMDD, 15 unaffected HR individuals, and 37 HCs. Hemodynamic responses in the bilateral prefrontal cortex (PFC) were recorded using functional near-infrared spectroscopy (fNIRS) during an emotional Verbal Fluency Task (VFT). Resting-state network topology (global/local efficiency) was analyzed via graph theory. The rMDD group exhibited significantly attenuated PFC activation compared to HCs (p = 0.024) during VFT. The HR group displayed an intermediate level of activation that did not statistically differ from either HCs (p = 0.700) or the rMDD group (p = 1.000). Behaviorally, rMDD patients showed blunted emotional modulation of performance compared to HCs (p < 0.05). No significant group differences were found in resting-state network topology. In conclusion, prefrontal hypoactivation during emotional processing appears to be a graded deficit shaped by both familial vulnerability and illness burden, rather than by a simple heritable trait. Combined with blunted behavioral reactivity to emotional cues in remission, these measures shift in their utility from predicting onset to tracking illness impact. Thus, fNIRS-derived prefrontal activation may serve as a biomarker for monitoring incomplete neural recovery rather than predicting initial disease onset. Health sciences/Biomarkers/Prognostic markers Health sciences/Diseases/Psychiatric disorders/Depression Depression prefrontal cortex functional near-infrared spectroscopy trait marker verbal fluency task Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Major depressive disorder (MDD) is a prevalent and disabling condition that imposes a substantial disease burden [ 1 – 3 ]. Despite the availability of various therapeutic strategies, a substantial proportion of patients exhibit treatment resistance or high recurrence rates, resulting in chronic disability and severe psychosocial impairment [ 1 , 4 ]. Given the heritable component of MDD risk [ 5 , 6 ], discerning the predispositions that confer vulnerability to both disease onset and recurrence is paramount. Yet current diagnostic frameworks rely primarily on symptom-based interviewing and subjective clinical judgment, which limits biological specificity [ 7 , 8 ]. Therefore, the identification of objective trait markers that persist independently of the depressive state via scalable clinical tools is central to biomarker guided prevention and care. Converging evidence implicates dysregulated brain function in MDD [ 9 – 12 ]. Studies utilizing positron emission tomography (PET) [ 13 , 14 ] and functional magnetic resonance imaging (fMRI) [ 15 , 16 ] have frequently reported hypoactivation and dysfunction in the prefrontal cortex (PFC) among depressed patients. While these modalities have been instrumental in mapping brain abnormalities, their application in routine clinical practice is often restricted by high cost and environmental constraints. In this context, functional near-infrared spectroscopy (fNIRS) has emerged as a promising alternative, offering a non-invasive, cost-effective, and ecologically valid neuroimaging method for monitoring brain activity [ 17 ]. Based on the principle of neurovascular coupling, fNIRS measures the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) within the near-infrared spectrum (700-900nm). Unlike the restrictive environments of fMRI or PET, fNIRS allows patients to be comfortably seated in well-lit, ordinary clinical settings, thereby facilitating the measurement of spatiotemporal brain characteristics in a naturalistic environment [ 18 ]. Over the past two decades, fNIRS has been increasingly adopted to assess brain function and screen for psychiatric disorders, most commonly in conjunction with the verbal fluency task (VFT) [ 19 , 20 ]. By requiring word generation under time constraints, the VFT engages executive functions like working memory and cognitive flexibility, reliably recruiting the PFC [ 21 – 23 ]. However, as MDD involves fundamental emotional dysregulation beyond general cognitive deficits, standard VFT may overlook these affective dimensions. To address this, emotional VFT, which requires word generation based on emotional categories, was developed [ 24 , 25 ]. This paradigm probes the interaction between executive control and emotional processing, capturing the emotion-cognition integration deficits central to depression. Extensive research using the fNIRS-VFT paradigm has consistently reported attenuated task-evoked HbO increases in the PFC among depressed patients [ 26 – 31 ]. While this hypoactivation is a robust signature of the acute state, its status as a stable neurobiological trait remains debated due to inconsistent findings in remitted MDD (rMDD) [ 32 – 34 ] and high-risk (HR) populations. [ 35 , 36 ]. This implies that localized task-evoked activation alone may be insufficient to fully capture the vulnerability to depression. Given that depression is increasingly conceptualized as a disconnection syndrome [ 1 ], incorporating the characterization of intrinsic brain organization via resting state graph theory as a complementary dimension may provide a more comprehensive framework to understand neural dysregulation and identify potential endophenotypes of depression. Isolating the mechanisms of risk across the clinical spectrum requires a strategic group comparison. Remitted patients provide a unique opportunity to identify trait markers in a symptom free state, yet their neural profiles remain difficult to interpret as they may reflect either preexisting vulnerability or the cumulative impact of prior episodes. The inclusion of unaffected HR individuals is therefore essential to isolate premorbid vulnerability from such confounding history. Therefore, by comparing these distinct cohorts with healthy controls (HC), the present study aims to investigate whether prefrontal disorganization constitutes stable trait markers of depression. We hypothesized that both rMDD and HR groups would exhibit attenuated activation and disrupted topological efficiency, thereby reflecting a core endophenotypic feature of MDD risk. 2. Methods 2.1 Study design and Participants This cross-sectional, observational study involved three distinct groups: rMDD, HR, and healthy controls (HC). Participants were recruited from The Hong Kong Polytechnic University between July 2021 and November 2024. The study was approved by the Institutional Review Board of The Hong Kong Polytechnic University (HSEARS2021032300) and was registered at clinicaltrials.gov (NCT05427578). All procedures were conducted in accordance with the Declaration of Helsinki. Prospective participants first completed a telephone or online screening to assess their eligibility. Those who met the criteria were then invited to the laboratory, where they provided written informed consent before participating. To be included, participants were required to be aged 18–65 years, right-handed, have completed at least six years of formal education, possess normal or corrected-to-normal vision, and be capable of understanding verbal instructions. Right-handedness was confirmed using the Edinburgh Handedness Inventory (laterality quotient > + 40) [ 37 , 38 ]. Clinical characteristics were rigorously assessed by a trained therapist on the day of testing. Current depressive symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) [ 39 ], with a score of ≤ 4 required for all participants to ensure the absence of significant current depressive symptoms. Lifetime and current psychiatric diagnoses were evaluated using the Chinese version of Mini-International Neuropsychiatry Interview [ 40 ]. Detailed family psychiatric history was collected via a customized questionnaire to verify the presence of first-degree biological relatives with MDD. Specific inclusion criteria were defined for each study group. The rMDD group comprised individuals with a history of at least one major depressive episode according to DSM/ICD criteria, who had been in remission for a minimum of two months prior to enrollment [ 1 ]. The HR group consisted of individuals with no personal lifetime history of any psychiatric disorder but who reported having at least one first-degree biological relative with a confirmed lifetime diagnosis of MDD. Finally, the HC group included individuals with neither a personal lifetime history of any psychiatric disorder nor a known first-degree family history of MDD [ 41 ]. General exclusion criteria for all groups included a history of neurological disorders, unstable medical conditions requiring treatment, or an inability to tolerate the experimental procedure. The participant screening workflow is summarized in Fig. 1 . Demographic characteristics and clinical details are presented in Table 1 . Table 1 Demographic and clinical characteristics of participants. Characteristic Overall N = 87 1 RMDD N = 35 1 HR N = 15 1 HC N = 37 1 p -value 2 Statistic Age (years) 26.98 (9.79) 27.94 (10.62) 24.20 (3.95) 27.19 (10.58) 0.462 F = 0.779 Education Level 0.910 χ ² = 5.384 Elementary (0-6y) 2 (2.3%) 0 (0%) 0 (0%) 2 (5.4%) High School (6-12y) 5 (5.7%) 2 (5.7%) 1 (6.7%) 2 (5.4%) Associates (13-14y) 3 (3.4%) 2 (5.7%) 0 (0%) 1 (2.7%) Bachelor (15-16y) 39 (45%) 15 (43%) 8 (53%) 16 (43%) Master (17-18y) 33 (38%) 13 (37%) 5 (33%) 15 (41%) Doctor (> 19y) 5 (5.7%) 3 (8.6%) 1 (6.7%) 1 (2.7%) PHQ-9 Score 2.79 (2.51) 3.06 (3.04) 3.60 (2.44) 2.22 (1.83) 0.143 F = 1.99 MDD Episodes 2.06 (2.31) Laterality Quotient 89.25 (13.15) 89.57 (13.08) 86.67 (15.89) 90.00 (12.25) 0.703 F = 0.355 Gender 0.354 χ ² = 2.244 F 65 (75%) 27 (77%) 13 (87%) 25 (68%) M 22 (25%) 8 (23%) 2 (13%) 12 (32%) 1 Mean (SD); n (%) 2 One-way analysis of means; Exact tests with Monte Carlo simulation 2.2 Experimental Procedure Upon arrival at the laboratory, participants provided informed consent and completed the confirmation assessments for handedness and clinical status. Following eligibility confirmation, participants were seated comfortably in a quiet, dimly lit room, positioned approximately 50 cm in front of a computer screen. The fNIRS probe was then positioned and calibrated. Participants were instructed to keep their head and body as motionless as possible throughout the entire recording session to reduce motion artifacts. Data collection commenced once the fNIRS signals stabilized. The session began with a 2min resting-state measurement, during which participants were instructed to fixate on a central crosshair “+” displayed on the screen, remain in a state of relaxed wakefulness, and avoid mind wandering. This was followed by the VFT, where participants were instructed to follow the specific visual cues presented on the screen during the task. 2.3 Verbal fluency task We used an emotion semantic version of VFT with a block design [ 42 – 45 ]. The paradigm consisted of four experimental blocks and four control blocks, with each block lasting 60 seconds. The categories used for the experimental blocks were Animals, Positive Emotions, Supermarket Items, and Negative Emotions. The task sequence commenced with a control block. A 20-second resting period was inserted between the control block and the experimental block. During this resting period, participants were instructed to fixate on a central crosshair (“+”) displayed on the screen and remain motionless. During the experimental condition, participants were instructed to generate as many unique words as possible belonging to the specific semantic category displayed on the screen without repetition. During the control block, participants were required to repeat the numbers “1, 2, 3, 4, 1, 2, 3, 4…” at a steady pace. This control condition served as a baseline to account for hemodynamic changes induced by the motor act of articulation. Prior to the start of the task, a practice trial (using the category "Flowers" and the number sequence) was conducted to ensure participants fully understood the task requirements. The overall duration of the VFT session was 740 seconds. All stimuli were presented using E-Prime 2 (Psychology Software Tools, Pittsburgh, PA, USA). 2.4 fNIRS measurement Hemodynamic responses were measured using a 52-channel continuous wave fNIRS system (ETG-4000, Hitachi Medical Co., Tokyo, Japan) with dual-wavelength emitters (695 and 830 nm) and a sampling rate of 10 Hz. The optode montage consisted of a 3×11 array with 17 sources and 16 detectors (Fig. 2 ). A fixed source-detector separation of 3.0 cm was maintained to ensure the detection of hemodynamic signals from the cerebral cortex. Each pair of adjacent source and detector was defined as a channel (Ch). The probe array was positioned on the forehead with its lower edge aligned with the T4-Fpz-T3 line of the International 10–20 system, and the sixth column centered on the midsagittal line. During measurement, the probe holder was securely fastened to the participant's head to ensure stable optode-scalp coupling. Following the fNIRS recording, a Polhemus 3D digitizer (Patriot™, Polhemus, USA) was used to record the 3D positions of all sources and detectors for every participant. NIRS_SPM was used to perform probabilistic anatomical localization, mapping each channel to MNI coordinates and corresponding Brodmann areas (BA) [ 46 ]. This spatial registration confirmed that the probe covered the bilateral dorsolateral prefrontal cortex (DLPFC; BA 9, 46), frontopolar area (BA 10), anterior superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21). The MNI coordinates for the midpoint of each channel and their estimated BAs, derived from a representative participant, are detailed in Supplementary Table S1 . 2.5 Data analysis 2.5.1 fNIRS data analysis fNIRS data analysis was performed using the HOMER 2 toolbox and custom scripts developed in MATLAB 2013b (The MathWorks, Inc., Natick, MA) [ 47 ]. For preprocessing, channels with SNR lower than 2 were excluded (38). The raw fNIRS data was then converted to optical density (OD) [ 48 ]. Motion artifacts were corrected using spline interpolation and wavelet-based algorithms [ 49 , 50 ]. A bandpass filter (0.006–0.05 Hz) was then employed to low-frequency drift and high-frequency physiological noise (e.g., heartbeat and respiration) [ 51 ]. These preprocessed signals were converted to the concentration change of HbO (ΔHbO) based on the modified Beer-Lambert Law with a differential pathlength factor of six [ 52 , 53 ]. For the activation analysis, block-averaged hemodynamic responses (0–60 s) were calculated for each block. Baseline correction was applied to each block by subtracting the mean signal of the 10 seconds preceding the block onset. Task-related activation was determined by calculating the contrast between conditions (i.e., Experimental Blocks minus Control Blocks). We defined bilateral prefrontal cortex (PFC) as our region of interest (ROI) which including channels 25, 26, 27, 28, 36, 38, 46, 47, 48, and 49 [ 54 ]. We focused primarily on HbO signals as an indicator of hemodynamic response due to its greater signal-to-noise ratio [ 55 ]. The mean of HbO concentration change for different groups were used for further analysis. 2.5.2 Graph-Theoretical Network Analysis Graph-theoretical network analysis was performed using the MATLAB-based FC-NIRS toolbox [ 56 ]. Data preprocessing proceeded in several steps. First, channels with a SNR lower than 2 were identified as low-quality and excluded from further analysis. Second, motion artifacts were detected and corrected using a spline interpolation method combined with a moving standard deviation (MSD), applying a sliding window of 2 s and a threshold of 5 standard deviations (SD). Third, a principal component analysis (PCA) was applied to filter out the first principal component, thereby reducing global noise. Finally, a band-pass filter (0.01–0.1 Hz) was applied to remove physiological noise and high-frequency interference. The concentration changes of HbO and HbR were calculated based on the modified Beer-Lambert Law. The HbO signals were subsequently used to construct functional networks and calculate topological characteristics. Resting-state functional connectivity was estimated using Pearson correlation analysis between the time series of all pairs of measurement channels (nodes), resulting in a correlation matrix for each participant per session. Given the ambiguous biological basis for negative correlations in functional connectivity analyses, we set all negative correlations to zero and restricted our analysis to positive correlations. To improve the normality distribution of the correlations in the matrix, we converted correlation coefficients into z values via Fisher’s r-to-z transformation. To define the topological organization of the functional networks, these correlation matrices were thresholded into binary matrices. To mitigate potential bias induced by a single threshold, the networks were binarized across a range of sparsity thresholds (0.1 to 0.5) with an increment of 0.01[ 57 ]. Network topology was characterized using global efficiency (E glob ) and local efficiency (E loc ). E glob reflects the average inverse shortest path length between all node pairs, representing the capacity for overall network integration and global information transfer. Conversely, E loc assesses network segregation and resilience; it is defined as the average efficiency of the subgraphs formed by the neighbors of a given node, capturing the efficiency of local communication in the event of node failure [ 58 ]. Both metrics were computed across the entire range of sparsity thresholds, and the area under the curve (AUC) was calculated for statistical analysis [ 59 ]. 2.5.3 Statistical analysis Statistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA). The statistical significance threshold was set at α = 0.05 (two-tailed). Descriptive statistics for continuous variables are reported as mean ± standard deviation (SD), while model-adjusted data are presented as estimated marginal means (EMMs) ± standard error (SE). One-way analysis of variance (ANOVA) was used to compare continuous demographic and clinical variables (age, PHQ-9 scores, and Laterality Quotient) among the three groups (HC, HR, and RMDD). For categorical variables (gender and education level), exact tests with Monte Carlo simulation were employed to account for small cell counts in some categories. To investigate group differences in behavioral performance (VFT accuracy) and hemodynamic responses (mean ∆HbO in the bilateral DLPFC) across emotional contexts, a 3 × 3 repeated measures analysis of covariance (ANCOVA) was conducted. The model included group (HC, HR, RMDD) as the between-subjects factor, emotional condition (neutral, positive, negative) as the within-subjects factor, and age, gender, and education level as covariates to control for potential confounding effects. We utilized Type III Sum of Squares to account for the unbalanced design. Effect sizes for main effects and interactions were estimated using partial eta squared ( \(\:{\eta}_{\text{P}}^{\text{2}}\) ).The assumption of sphericity was assessed using Mauchly’s test; if violated, degrees of freedom were adjusted using the Greenhouse-Geisser correction. To further decompose significant group × condition interactions, we calculated emotion contrast scores (i.e., neutral-positive, neutral-negative, and positive–negative) to quantify the magnitude of emotional modulation. These contrast scores were entered into a one-way multivariate analysis of covariance (MANOVA) controlling age, gender and education level. This was followed by covariate-adjusted univariate F -tests and Bonferroni-corrected post-hoc pairwise comparisons. Finally, to explore the relationship between brain activation and behavioral performance while accounting for confounders, partial Pearson correlation analyses were performed. We examined the associations between mean ∆HbO and VFT accuracy within each group, controlling for age, gender, and education level. 3. Results 3.1 Participant Characteristics A total of 87 participants were included in the final data analysis (HC: n = 37; HR: n = 15; RMDD: n = 35). Prior to the main analysis, baseline demographic and clinical variables were compared among the three groups (Table 1 ). ANOVA indicated no significant differences in age [ F (2, 84) = 0.779, p = 0.462, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.018], laterality quotient (LQ) [ F (2, 84) = 0.355, p = 0.703, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.008], and depressive symptom severity (PHQ-9 scores) [ F (2, 84) = 1.99, p = 0.143, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.045]. Education level [χ² (10) = 5.384, df = 10, Monte Carlo p = 0.910] and gender distribution [χ² (2) = 2.244, df = 2, Monte Carlo p = 0.354] were also comparable among the three groups. 3.2 VFT Performance ANCOVA analysis revealed a significant main effect of emotional condition on VFT accuracy [ F (1.77, 143.59) = 8.462, p ˂ 0.001, \(\:{\eta}_{\text{P}}^{\text{2}}\:\) = 0.095], and critically, a significant group × condition interaction was observed [ F (3.55, 143.59) = 4.013, p = 0.006, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.090], although the main effect of group was non-significant [ F (2, 81) = 0.594, p = 0.554, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.014] (Fig. 3 a). To decompose the significant interaction, Bonferroni-corrected pairwise comparisons were performed within each group, confirmed that VFT accuracy was significantly modulated by emotional condition. Specifically, all groups consistently showed highest accuracy in the neutral condition, followed by the negative and then positive condition (Fig. 3 b). Given this consistent ordinal pattern, we further tested whether the magnitude of emotional modulation differed between groups by analyzing emotion contrast scores (neutral–positive, neutral–negative, positive–negative). A multivariate analysis of variance (MANOVA) indicated a significant overall difference across groups [Wilks' λ = .863, F (4, 160) = 3.07, p = .018]. Follow‑up univariate tests showed significant group differences for the neutral–positive contrast [ F (2, 81) = 4.77, p = .011, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.11] and the neutral-negative contrast [ F (2, 81) = 4.99, p = .010, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.11], but not for the positive-negative contrast [ F (2, 81) = 0.82, p = .444]. Bonferroni‑corrected post‑hoc comparisons revealed that, for both the neutral–positive and neutral–negative contrasts, HC showed a significantly larger emotional modulation than the RMDD group (neutral–positive: p = .008; neutral–negative: p = .009). The HR group did not differ significantly from either group (all p > .05). In summary, while all groups exhibited the same directional pattern of emotional modulation, the significant group × condition interaction was driven by differences in the magnitude of this modulation, with HC showing larger emotional contrasts than the RMDD group. 3.3 Brain Activation The ANCOVA analysis did not reveal a significant group × condition interaction [ F (3.34, 135.10) = 0.725, p = 0.552, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.018] or main effect of emotional condition [ F (1.67, 135.10) = 1.115, p = 0.323 \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.014] (Fig. 3 c). However, the analysis yielded a significant main effect of group [ F (2, 81) = 3.696, p = 0.029, \(\:{\eta}_{\text{P}}^{\text{2}}\) = 0.084]. Post-hoc pairwise comparisons with Bonferroni-corrected showed that the HC group exhibited significantly higher ∆HbO activation compared to the RMDD group ( t (81) = 2.705, p = 0.024). No significant difference was observed between the HC and HR groups ( t (81) = 1.203, p = 0.700), or between the HR and RMDD groups ( t (81) = 0.868, p = 1.000) (Fig. 3 d). 3.4 Brain activation and behavioral correlation Partial Pearson correlation analyses were performed to examine the relationship between the mean total VFT accuracy score and the mean total ∆HbO concentration change in the bilateral DLPFC within each group, controlling for age, gender, and education level (Fig. 3 e). None of the correlations reached statistical significance ( p < 0.05) in any of the three groups (HC group: r = 0.059, p = 0.739; HR group: r = -0.557, p = 0.060; RMDD group: r = 0.240, p = 0.186). 3.5 Functional connectivity patterns and graph-theoretical analysis Group-level resting-state functional connectivity (FC) matrices are presented in Fig. 4 a. To visualize the topological characteristics across the full range of network densities, E glob and E loc are displayed as a function of sparsity threshold (0.10–0.50, step = 0.01) in Fig. 4 b, with the area under the curve (AUC) computed for each metric and each participant to provide an integrated summary measure independent of a single threshold selection. One-way ANOVA comparing the AUC of E glob and E loc across the three groups revealed no significant group differences [E glob : F (2, 51) = 0.151, p = 0.860, η p 2 = 0.007; E loc : F (2, 51) = 0.893, p = 0.417, η p 2 = 0.038] (Fig. 4 c). Similarly, exploratory analyses conducted at individual sparsity thresholds (0.10–0.50) yielded no significant differences in either metric after correction for multiple comparisons across thresholds. 4. Discussion To the best of our knowledge, this is the first study to jointly examine rMDD, HR, and HC cohorts to investigate prefrontal activity during an emotional VFT using fNIRS. The rMDD group exhibited reduced prefrontal activation relative to HC, whereas the HR group displayed an intermediate level of activation that did not significantly differ either from HC or rMDD. Behaviorally, while all groups consistently performed best in the neutral condition, followed by the negative and then the positive condition, a critical group × condition interaction was observed. This interaction was driven by the fact that the HC group exhibited significantly larger emotional modulation magnitudes compared to the rMDD group. Graph-theoretic analyses revealed no group differences in E glob and E loc . By integrating these cohorts within a single analytical framework, we sought to examine whether prefrontal dysfunction represents a heritable trait marker for depression. Our results showed that prefrontal activation was significantly reduced in the rMDD group compared to HCs, whereas the HR group exhibited an intermediate profile that did not statistically differ from either group. This gradient suggests that prefrontal hypoactivation is likely driven by a combination of familial vulnerability and disease-related scarring, with the latter playing a more prominent role. Considered alongside prior evidence of prefrontal hypoactivation during acute episodes [ 26 , 60 – 62 ], our findings indicate that the profound dysfunction observed in rMDD is likely a persistence of state-related alterations, representing a 'neural scar' of the illness. In contrast, the HR group, who possess familial vulnerability but have not endured the neurobiological impact of acute depressive episodes, displayed only mild, non-significant attenuation. This distinction supports a cumulative model: while genetic liability may confer a subtle baseline deficit, the clinical experience of depression itself appears to impose an additional, substantial burden on prefrontal function. Thus, reduced prefrontal recruitment during verbal fluency may be better conceptualized as a marker of cumulative disease burden rather than a pure pre-morbid trait. Despite comparable overall accuracy, the significant group × condition interaction highlights a fundamental divergence in how emotional cues influence cognition. In healthy controls, the pronounced decline in verbal fluency during emotional conditions reflects a competition for cognitive resources [ 63 ]. Emotional category labels appear to automatically capture attention, generating a strong affective signal that diverts processing power away from the primary goal of word generation [ 64 ]. This interference suggests that, in a healthy neurocognitive system, emotional salience naturally competes with cognitive demands. In contrast, the rMDD group displayed a markedly flatter behavioral profile, characterized by minimal performance shifts across conditions. Rather than indicating superior cognitive control, this stability likely points to reduced emotional reactivity. For these individuals, emotional cues appear to generate a weaker initial signal, thereby failing to compete effectively for attentional resources or interfere with the task. This pattern aligns with the concept of emotional blunting, representing a dampened sensitivity to emotional stimuli [ 65 ]. Thus, the absence of typical emotional modulation suggests a persistent alteration in the automatic interplay between emotion and cognition, potentially serving as a residual scar of prior depressive episodes. We anticipated that prefrontal activation would correlate with VFT performance but did not detect a significant association. Several considerations may account for this null result. Our PFC measure aggregated signals across a relatively broad region, which may obscure subregional or network-specific relationships, given the functional heterogeneity of the PFC [ 66 ]. Prior work suggests that distinct cognitive operations and symptom dimensions map more closely onto specific prefrontal circuits and connectivity patterns [ 67 ]. It is also possible that the task demands were insufficient to elicit the range of neural resource engagement needed to reveal behavior–brain coupling in a young, high-performing sample [ 68 ]. These possibilities point to the value of regionally specific metrics, connectivity-based analyses, and more challenging paradigms in future research. Furthermore, we found no group differences in fNIRS-derived resting-state network metrics (Eg, Eloc). This pattern may reflect a normalization of disease-related network topology during remission [ 69 ], although it contrasts with other reports of reduced global efficiency in regions implicated in cognitive control [ 70 ]. These inconsistencies may stem from differences in neuroimaging modalities (e.g., the depth sensitivity and spatial resolution of fMRI versus fNIRS), and sample characteristics. It is plausible that while task-evoked activation remains impaired, the resting topological organization of the network may recover. Our findings should be interpreted in light of several limitations. First, the absence of a concurrently assessed acute MDD cohort precludes direct comparisons across the full disease trajectory; however, this specific design focusing on remitted and high-risk groups was sufficient to successfully distinguish stable trait markers from disease-related scars. Regarding data acquisition, our system lacked short-separation channels due to hardware constraints. To mitigate this, we rigorously applied standardized preprocessing protocols in conjunction with a control-task subtraction method, effectively minimizing systemic physiological interference to isolate task-specific cortical activation. Finally, regarding cohort characteristics, the predominance of university students and the relatively modest sample size restricted broad generalizability and precluded stratification by clinical subtypes. Future investigations utilizing larger, more diverse populations are warranted to validate these findings and explore subtype-specific neural patterns. In conclusion, our findings show that prefrontal hypoactivation during emotional processing is not a simple heritable trait. The graded reduction from healthy controls to high-risk relatives and then to remitted patients suggests a deficit shaped by both familial vulnerability and illness burden. This deficit, alongside blunted behavioral sensitivity to emotional cues, shifts the focus of these markers from predicting initial onset to tracking the persistent impact of depression. Thus, fNIRS-measured prefrontal activity holds greater potential for monitoring residual disease effects and guiding targeted interventions in recovery Declarations 5. Acknowledgement This study was supported in part by grants from the Department of Rehabilitation Sciences (Grant numbers: P0045136 and P0043155) of the Hong Kong Polytechnic University, and by the General Research Fund (number 15106222) under the University Grants Committee of the HKSAR. The funders had no role in the study design; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. We appreciate the facilities support provided by the University Research Facility in Behavioral and Systems Neurosciences at The Hong Kong Polytechnic University. We also thank all participants for their time and effort. In addition, we are grateful to the master’s and bachelor’s students for their assistance with data collection. 6. Disclosure Statement GK received honoraria from Storz medical and the Academy of Brain Stimulation, Healthlink Holdings. BBBZ, RLDK, MXJ, and AWLX received honoraria from the Academy of Brain Stimulation. BBBZ received the Eurasia-Pacific Ernst Mach Scholarship from Austria’s Agency for Education and Internationalisation. PPQ received honoraria from Storz Medical. All other authors reported no potential conflicts of interest. 7. Author Contributions Conceptualization: BBBZ, SXW, TFY, GSK; Methodology: BBBZ, SXW, RLDK, VWML, NMXYS; Investigation: BBBZ, SXW, VWML, NMXYS, RLDK, TTZL, PPQ, JMX, AWLX; Data curation: BBBZ, SXW, VWML, RLDK, NMXYS; Software: BBBZ, SXW, VWML, RLDK, TTZL, PPQ; Analysis: BBBZ, SXW, VWML, RLDK; Visualization: BBBZ, SXW, VWML, RLDK; Validation: SXW, TTZL, PPQ, JMX, AWLX; Writing-original draft: BBBZ, SXW, GSK; Writing-review & editing: BBBZ, SXW, VWML, NMXYS, RLDK, TTZL, PPQ, JMX, AWLX, TFY, GSK; Resources, Supervision, Project administration: GSK. 8. Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Malhi, G.S. and J.J. Mann, Depression. Lancet, 2018. 392(10161): p. 2299–2312. Proudman, D., P. Greenberg, and D. Nellesen, The Growing Burden of Major Depressive Disorders (MDD): Implications for Researchers and Policy Makers . Pharmacoeconomics, 2021. 39(6): p. 619–625. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet Psychiatry, 2022. 9(2): p. 137–150. Keller, M.B., et al., Time to recovery, chronicity, and levels of psychopathology in major depression. A 5-year prospective follow-up of 431 subjects . Arch Gen Psychiatry, 1992. 49(10): p. 809–16. Sullivan, P.F., et al., Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo . Mol Psychiatry, 2009. 14(4): p. 359–75. Flint, J. and K.S. Kendler, The genetics of major depression . Neuron, 2014. 81(3): p. 484–503. American Psychiatric, A. and D.S.M.T.F. American Psychiatric Association, Diagnostic and statistical manual of mental disorders: DSM-5 . 5th ed. DSM-5. 2013, Arlington, VA: American Psychiatric Association. WHO, ICD-11 for for mortality and morbidity statistics, 2018 version . Geneva: World Health Organization. https://icd.who.int/browse11/l-m/en , 2018. Hamilton, J.P., et al., Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data . Am J Psychiatry, 2012. 169(7): p. 693–703. Wang, X., et al., Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function . JAMA Network Open, 2024. 7(2): p. e2356787-e2356787. Lynch, C.J., et al., Frontostriatal salience network expansion in individuals in depression . Nature, 2024. 633(8030): p. 624–633. Williams, L.M., Precision psychiatry: a neural circuit taxonomy for depression and anxiety . Lancet Psychiatry, 2016. 3(5): p. 472–80. Bench, C.J., et al., The anatomy of melancholia – focal abnormalities of cerebral blood flow in major depression . Psychological Medicine, 1992. 22(3): p. 607–615. Fu, C., et al., Functional assessment of prefrontal lobes in patients with major depression disorder using a dual-mode technique of 3D-arterial spin labeling and (18)F-fluorodeoxyglucose positron emission tomography/computed tomography . Exp Ther Med, 2017. 14(2): p. 1058–1064. Zhong, X., W. Pu, and S. Yao, Functional alterations of fronto-limbic circuit and default mode network systems in first-episode, drug-naïve patients with major depressive disorder: A meta-analysis of resting-state fMRI data . Journal of Affective Disorders, 2016. 206: p. 280–286. Siegle, G.J., et al., Increased Amygdala and Decreased Dorsolateral Prefrontal BOLD Responses in Unipolar Depression: Related and Independent Features . Biological Psychiatry, 2007. 61(2): p. 198–209. Pinti, P., et al., The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience . Annals of the New York Academy of Sciences, 2020. 1464(1): p. 5–29. Kim, H.Y., et al., Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models . Mol Cells, 2017. 40(8): p. 523–532. Yeung, M.K. and J. Lin, Probing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: A systematic review and meta-analysis . J Psychiatr Res, 2021. 140: p. 416–435. Ehlis, A.C., et al., Application of functional near-infrared spectroscopy in psychiatry. Neuroimage, 2014. 85 Pt 1: p. 478 – 88. Amunts, J., et al., Executive functions predict verbal fluency scores in healthy participants . Scientific Reports, 2020. 10(1): p. 11141. Troyer, A.K., M. Moscovitch, and G. Winocur, Clustering and switching as two components of verbal fluency: evidence from younger and older healthy adults . Neuropsychology, 1997. 11(1): p. 138–46. Shao, Z., et al., What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults . Frontiers in Psychology, 2014. Volume 5–2014. Abeare, C.A., et al., The Emotion Word Fluency Test (EWFT): Initial psychometric, validation, and physiological evidence in young adults . J Clin Exp Neuropsychol, 2017. 39(8): p. 738–752. Yeung, M.K., Effects of age, gender, and education on task performance and prefrontal cortex processing during emotional and non-emotional verbal fluency tests . Brain and Language, 2023. 245: p. 105325. Takizawa, R., et al., Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage, 2014. 85 Pt 1: p. 498–507. Husain, S.F., et al., Validating a functional near-infrared spectroscopy diagnostic paradigm for Major Depressive Disorder . Sci Rep, 2020. 10(1): p. 9740. Chao, J., et al., fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls . IEEE Trans Neural Syst Rehabil Eng, 2021. 29: p. 2211–2221. Yang, T., et al., The fNIRS evaluation of frontal and temporal lobe cortical activation in Chinese first-episode medication-naïve and recurrent depression during a verbal fluency task . Front Psychiatry, 2023. 14: p. 1132666. Akashi, H., et al., Prefrontal cortex activation is associated with a discrepancy between self- and observer-rated depression severities of major depressive disorder: A multichannel near-infrared spectroscopy study . Journal of Affective Disorders, 2015. 174: p. 165–172. Ho, C.S.H., et al., Diagnostic and Predictive Applications of Functional Near-Infrared Spectroscopy for Major Depressive Disorder: A Systematic Review . Frontiers in Psychiatry, 2020. Volume 11–2020. Nixon, N.L., et al., Prefrontal cortex function in remitted major depressive disorder . Psychological Medicine, 2013. 43(6): p. 1219–1230. Schiller, C.E., et al., Remitted major depression is characterized by reduced prefrontal cortex reactivity to reward loss . Journal of Affective Disorders, 2013. 151(2): p. 756–762. Ming, Q., et al., State-Independent and Dependent Neural Responses to Psychosocial Stress in Current and Remitted Depression . American Journal of Psychiatry, 2017. 174(10): p. 971–979. Watters, A.J., et al., Characterizing neurocognitive markers of familial risk for depression using multi-modal imaging, behavioral and self-report measures . Journal of Affective Disorders, 2019. 253: p. 336–342. Cl, N., et al., The neural basis of hot and cold cognition in depressed patients, unaffected relatives, and low-risk healthy controls: An fMRI investigation . Journal of Affective Disorders, 2020. 274: p. 389–398. Oldfield, R.C., The assessment and analysis of handedness: the Edinburgh inventory . Neuropsychologia, 1971. 9(1): p. 97–113. Yang, N., et al., Translation, cultural adaption, and test-retest reliability of Chinese versions of the Edinburgh Handedness Inventory and Waterloo Footedness Questionnaire . Laterality, 2018. 23(3): p. 255–273. Kroenke, K., R.L. Spitzer, and J.B. Williams, The PHQ-9: validity of a brief depression severity measure . J Gen Intern Med, 2001. 16(9): p. 606–13. Si, T.-M., et al., Evaluation of the reliability and validity of Chinese version of the Mini-International Neuropsychiatric Interview in patients with mental disorders . Chinese Mental Health Journal, 2009. 23(7): p. 493–503. Chen, M.-H., et al., Risk and coaggregation of major psychiatric disorders among first-degree relatives of patients with bipolar disorder: a nationwide population-based study . Psychological Medicine, 2019. 49(14): p. 2397–2404. Yeung, M.K., et al., Altered Frontal Lateralization Underlies the Category Fluency Deficits in Older Adults with Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study . Frontiers in Aging Neuroscience, 2016. 8. Zohdi, H., F. Scholkmann, and U. Wolf, Individual Differences in Hemodynamic Responses Measured on the Head Due to a Long-Term Stimulation Involving Colored Light Exposure and a Cognitive Task: A SPA-fNIRS Study . Brain Sci, 2021. 11(1). Holper, L., et al., Brain correlates of verbal fluency in subthreshold psychosis assessed by functional near-infrared spectroscopy . Schizophrenia Research, 2015. 168(1): p. 23–29. Yeung, M.K., Frontal cortical activation during emotional and non-emotional verbal fluency tests . Scientific Reports, 2022. 12(1): p. 8497. Ye, J.C., et al., NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy . NeuroImage, 2009. 44(2): p. 428–447. Huppert, T.J., et al., HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain . Appl Opt, 2009. 48(10): p. D280-98. Li, X., et al., Brain correlates of motor complexity during observed and executed actions . Scientific Reports, 2020. 10(1): p. 10965. Jahani, S., et al., Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering . Neurophotonics, 2018. 5(1): p. 015003. Chiarelli, A.M., et al., A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data . Neuroimage, 2015. 112: p. 128–137. Kirilina, E., et al., Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex . Front Hum Neurosci, 2013. 7: p. 864. Delpy, D.T., et al., Estimation of optical pathlength through tissue from direct time of flight measurement . Phys Med Biol, 1988. 33(12): p. 1433–42. Kocsis, L., P. Herman, and A. Eke, The modified Beer-Lambert law revisited . Phys Med Biol, 2006. 51(5): p. N91-8. Yeung, M.K. and J. Lin, Probing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: A systematic review and meta-analysis . Journal of Psychiatric Research, 2021. 140: p. 416–435. Cui, X., et al., A quantitative comparison of NIRS and fMRI across multiple cognitive tasks . Neuroimage, 2011. 54(4): p. 2808–2821. Xu, J., et al., FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data. Biomed Res Int, 2015. 2015: p. 248724. Liu, G., et al., Developmental alterations in brain network asymmetry in 3- to 9-month infants with congenital sensorineural hearing loss . Science Advances, 2025. 11(42): p. eadx1327. Rubinov, M. and O. Sporns, Complex network measures of brain connectivity: uses and interpretations . Neuroimage, 2010. 52(3): p. 1059–69. Lin, C.E., et al., Functional near-infrared spectroscopy as a biomarker of TMS efficacy in treatment-resistant depression . Psychiatry Clin Neurosci, 2025. 79(11): p. 765–775. Shen, Y., et al., Functional near-infrared spectroscopy (fNIRS) in patients with major depressive disorder, generalized anxiety disorder and their comorbidity: Comparison with healthy controls . Asian Journal of Psychiatry, 2025. 105: p. 104382. Pizzagalli, D.A. and A.C. Roberts, Prefrontal cortex and depression . Neuropsychopharmacology, 2022. 47(1): p. 225–246. Pu, S., et al., Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder . Journal of Affective Disorders, 2015. 181: p. 9–17. Pessoa, L., The cognitive-emotional brain: From interactions to integration . 2013: MIT press. Okon-Singer, H., et al., The neurobiology of emotion-cognition interactions: fundamental questions and strategies for future research . Front Hum Neurosci, 2015. 9: p. 58. Brosch, T., et al., Additive effects of emotional, endogenous, and exogenous attention: Behavioral and electrophysiological evidence . Neuropsychologia, 2011. 49(7): p. 1779–1787. Friedman, N.P. and T.W. Robbins, The role of prefrontal cortex in cognitive control and executive function . Neuropsychopharmacology, 2022. 47(1): p. 72–89. Williams, L.M., Precision psychiatry: a neural circuit taxonomy for depression and anxiety . The Lancet Psychiatry, 2016. 3(5): p. 472–480. Agbangla, N.F., et al., Load-Dependent Prefrontal Cortex Activation Assessed by Continuous-Wave Near-Infrared Spectroscopy during Two Executive Tasks with Three Cognitive Loads in Young Adults . Brain Sciences, 2022. 12(11): p. 1462. Servaas, M.N., et al., Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder . Neuropsychopharmacology, 2017. 42(13): p. 2583–2592. Liu, G., et al., Changes of functional brain network topology associated with nutritional indicator of patients with recurrent major depressive disorder . Front Nutr, 2025. 12: p. 1615978. Additional Declarations Yes GK received honoraria from Storz medical and the Academy of Brain Stimulation, Healthlink Holdings. BBBZ, RLDK, MXJ, and AWLX received honoraria from the Academy of Brain Stimulation. BBBZ received the Eurasia-Pacific Ernst Mach Scholarship from Austria’s Agency for Education and Internationalisation. PPQ received honoraria from Storz Medical. All other authors reported no potential conflicts of interest. Supplementary Files Supplementary.docx Supplementary Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 19 Mar, 2026 Review # 2 received at journal 19 Feb, 2026 Review # 1 received at journal 09 Feb, 2026 Reviewer # 2 agreed at journal 06 Feb, 2026 Reviewer # 1 agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 07 Jan, 2026 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-8537776","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572120769,"identity":"8d87f63f-1d19-49cf-a94b-d701abacb1c0","order_by":0,"name":"Georg Kranz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYPCCBBDB+ICBwQLMlWBgOGBAjBZmA7BiUrSwSRClRd69+djHLwxp+fyz259VF1RIJG44wHzwNg/DHWNcWgzPHEueLcOQYznjzoG02zPOgLSwJVvzMDwzw6llRo4xswRDhQHDjYRjt3nbQFp4zKR5GA7b4NQy/w1Ei/yNxLZiiBb+b3i1yEvwGDN+YMgxMLiRzMYMtYUNpAWnwwx40pKZGQzSDAxvpDFLA/1iPPMwm7HlHIPDOL0v3374MOOPimQDuRvpDz8XVNjI9h1vfnjjTcVhwwZcthwARiEPNA6YgdixgRksjssOoC1Asxh/MCC02ONWOwpGwSgYBSMVAAASqVPFMsHuXQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3892-1804","institution":"The Hong Kong Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Georg","middleName":"","lastName":"Kranz","suffix":""},{"id":572120770,"identity":"34f4a215-1ad0-41fb-a5f0-7f10a4b29df0","order_by":1,"name":"Bella Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bella","middleName":"","lastName":"Zhang","suffix":""},{"id":572120771,"identity":"09807203-783a-4f0c-aead-dcca0b1e8cc3","order_by":2,"name":"Sharie Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sharie","middleName":"","lastName":"Wang","suffix":""},{"id":572120772,"identity":"fffa2914-4552-4a25-bf26-815c191a852e","order_by":3,"name":"Vera Wai Man Lam","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"Wai Man","lastName":"Lam","suffix":""},{"id":572120773,"identity":"2b3b7d5c-6aa3-4e33-b6e4-dac5eb2b922a","order_by":4,"name":"Rebecca Kan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Kan","suffix":""},{"id":572120774,"identity":"c3ae349f-ea37-49a8-a138-83bfcaad09c5","order_by":5,"name":"Nancy Manxinyu Shi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"Manxinyu","lastName":"Shi","suffix":""},{"id":572120775,"identity":"74d3c99b-a815-4281-aae9-faebfe1bbc0f","order_by":6,"name":"Tim Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Lin","suffix":""},{"id":572120776,"identity":"42e54dd1-5905-410e-bd0c-e80f0cf38559","order_by":7,"name":"Minxia Jin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Minxia","middleName":"","lastName":"Jin","suffix":""},{"id":572120777,"identity":"8ffd952b-b84f-4498-913a-c6d29ecd0cbf","order_by":8,"name":"Adam Xia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Xia","suffix":""},{"id":572120778,"identity":"e25d1cbc-6142-4ff8-99ff-20bb78b2ad29","order_by":9,"name":"Penny Qin","email":"","orcid":"https://orcid.org/0000-0002-9517-1579","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Penny","middleName":"","lastName":"Qin","suffix":""},{"id":572120779,"identity":"df589104-5d5d-40de-9c73-82d26f33bbd9","order_by":10,"name":"Ti-fei Yuan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ti-fei","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-01-07 06:55:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8537776/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8537776/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101075103,"identity":"469a1e52-9a1b-4d57-80ca-61a092c63f7c","added_by":"auto","created_at":"2026-01-25 10:24:59","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5208476,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/d7050129b2a9113248b67071.tif"},{"id":101075247,"identity":"b7b15966-8fb8-4618-88f6-c0f2ffdf1980","added_by":"auto","created_at":"2026-01-25 10:25:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/93d7e59c3614805907c3a13d.docx"},{"id":101075180,"identity":"a2a23e64-1540-42a3-b179-982991597337","added_by":"auto","created_at":"2026-01-25 10:25:09","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4143536,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/bf2ecf8a3db658333427f56c.tif"},{"id":101205296,"identity":"d71dd3c7-0c18-45fc-9b87-84086f3f3cf2","added_by":"auto","created_at":"2026-01-27 09:48:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22291,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/da4587fd0615fa636935f30d.docx"},{"id":101075213,"identity":"be7d7c4b-7612-46c4-adfe-e981f6ebd08e","added_by":"auto","created_at":"2026-01-25 10:25:12","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4889598,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/a7864248e646d388b336447d.tif"},{"id":101207394,"identity":"aeeab022-e050-43c9-961d-feed97426ed8","added_by":"auto","created_at":"2026-01-27 10:02:56","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4659584,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/2034b41d018006340437e8e8.tif"},{"id":101075147,"identity":"8e4076bd-f447-4901-a70d-3f37b3c07183","added_by":"auto","created_at":"2026-01-25 10:25:03","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11597,"visible":true,"origin":"","legend":"","description":"","filename":"2026TP000044.json","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/4e2ee9ec1bfc149c85bc8d09.json"},{"id":101075134,"identity":"d86a57ea-6bd7-4633-ad89-32eba4d5aa2b","added_by":"auto","created_at":"2026-01-25 10:25:02","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42690,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/11a411be51edcef49f2e7c6e.docx"},{"id":101075148,"identity":"1d1d58c2-6d91-4161-824c-2171aa211598","added_by":"auto","created_at":"2026-01-25 10:25:03","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141073,"visible":true,"origin":"","legend":"","description":"","filename":"2026TP0000440enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/0eba82cc0dd3d18b0c4f3ca5.xml"},{"id":101075316,"identity":"a8d86082-75a5-445d-8bed-9d066a2d1af6","added_by":"auto","created_at":"2026-01-25 10:25:26","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5208476,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/dd8ce19268a93125a1792463.tif"},{"id":101075105,"identity":"abd5d652-074a-49d3-84f3-854cb78f8147","added_by":"auto","created_at":"2026-01-25 10:24:59","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4143536,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/869731780ef8c06e81e625db.tif"},{"id":101075226,"identity":"42164d4a-a29d-46e8-bd51-51bdeb35ffc5","added_by":"auto","created_at":"2026-01-25 10:25:14","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4889598,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/f79d10751f257d1ce880c0c2.tif"},{"id":101075130,"identity":"a5b84e9c-7919-4600-a8fd-e564b871f8eb","added_by":"auto","created_at":"2026-01-25 10:25:01","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4659584,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/441baf0f0e0025b2b399f6eb.tif"},{"id":101075205,"identity":"7603a2b8-9d33-4cd8-a37a-159c1648bffd","added_by":"auto","created_at":"2026-01-25 10:25:11","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72919,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/f1222cd05940cee20460a16a.jpeg"},{"id":101075131,"identity":"75784f04-669b-45ef-8f5d-c4234f8835ec","added_by":"auto","created_at":"2026-01-25 10:25:01","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49119,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/4e82c6ac7d5fd7deeb05bac6.png"},{"id":101075149,"identity":"322b6497-3c60-4aaa-848f-d9290cbcb917","added_by":"auto","created_at":"2026-01-25 10:25:03","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112666,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/b684745a70b3cfce5f4583e9.png"},{"id":101075215,"identity":"c926ead7-d571-4996-8ace-a9a3cbe0ab5e","added_by":"auto","created_at":"2026-01-25 10:25:12","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49804,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/a7b49e7b602e8d218813e6a4.png"},{"id":101075209,"identity":"fc02cd43-7eb6-4b0e-bcf7-70c9faadf041","added_by":"auto","created_at":"2026-01-25 10:25:11","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75710,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/75c025b60d1dc3974dd26bfe.png"},{"id":101075244,"identity":"146e99cb-39fc-4088-9d86-dcb9b2726391","added_by":"auto","created_at":"2026-01-25 10:25:16","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12509,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/0ce1636cb01a68f930336dd9.png"},{"id":101075153,"identity":"8b9d9f25-d14d-44df-854d-677671084575","added_by":"auto","created_at":"2026-01-25 10:25:03","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137550,"visible":true,"origin":"","legend":"","description":"","filename":"2026TP0000440structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/4ef8d872db2f0e238954b468.xml"},{"id":101075106,"identity":"d995fe9a-66f2-44a5-a037-14223453befc","added_by":"auto","created_at":"2026-01-25 10:24:59","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155666,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/4336dd8465f97058cb485d78.html"},{"id":101075078,"identity":"0c1bb958-043f-46ee-ab55-6b403d2845ec","added_by":"auto","created_at":"2026-01-25 10:24:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":743607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. Flowchart of participant enrollment, group allocation (RMDD, HR, HC), and exclusions leading to the final analytic sample.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/db60d69d37c12fc755db49d1.png"},{"id":101075214,"identity":"538fa81b-ed23-4976-9692-746dafbae73f","added_by":"auto","created_at":"2026-01-25 10:25:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1992948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic illustration of fNIRS optode placement and channel configuration. \u003c/strong\u003e(a-c) Locations of fNIRS sources (red circles) and detectors (blue circles) on the head as seen from the left (a), top-anterior (b), and right (c) perspectives. (d) The probe layout configuration, where yellow squares represent the measuring channels between adjacent source-detector pairs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/5ee6f49f42dacd51f882a6ca.png"},{"id":101075233,"identity":"b13efdfa-8bf5-4093-86bc-65826e986b46","added_by":"auto","created_at":"2026-01-25 10:25:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":915686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVFT behavioral performance, bilateral DLPFC activation and correlation results.\u003c/strong\u003e \u0026nbsp;VFT accuracy refers to the total number of correct words generated during the task. Mean of Δ HbO refers to the average concentration of oxygenated hemoglobin changes. \u003cstrong\u003e(a)\u003c/strong\u003e Interaction effect of group and emotional condition on VFT accuracy. \u003cstrong\u003e(b)\u003c/strong\u003e Simple effects of emotional condition within each group, illustrating differences in VFT accuracy across the three emotional conditions. \u003cstrong\u003e(c) \u003c/strong\u003eMean\u003cstrong\u003e \u003c/strong\u003eΔ HbO changes in the bilateral DLPFC across groups and emotional conditions. \u003cstrong\u003e(d) \u003c/strong\u003eMain group effect of mean Δ HbO. \u003cstrong\u003e(e)\u003c/strong\u003e Partial correlations between VFT accuracy and mean Δ HbO changes within each group. In line plots \u003cstrong\u003e(a) \u003c/strong\u003eand \u003cstrong\u003e(c)\u003c/strong\u003e, data points and bars represent estimated marginal means ± standard error (SE), adjusted for age, gender, and education level. In bar plots \u003cstrong\u003e(b) \u003c/strong\u003eand\u003cstrong\u003e (d)\u003c/strong\u003e, bar represent estimated marginal means ± standard error (SE), adjusted for age, gender, and education level. Individual dots in \u003cstrong\u003e(b)\u003c/strong\u003e and scatter points \u003cstrong\u003e(e)\u003c/strong\u003erepresent raw data points. In panel \u003cstrong\u003e(e)\u003c/strong\u003e, solid lines represent the linear regression fit, and shaded areas indicate the 95% confidence interval. \u003cem\u003ep\u003c/em\u003evalues were corrected for multiple comparisons using the Bonferroni method. Significance levels: * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, **** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/0310eafbfd14fa1a7b0088f4.png"},{"id":101075250,"identity":"4e4e85f8-a2b7-4a57-a230-c65d55ef3bd0","added_by":"auto","created_at":"2026-01-25 10:25:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2339868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResting-state HbO connectivity and graph-theoretical efficiencies across groups. \u003c/strong\u003e(a) Group-averaged channel-by-channel correlation matrices for RMDD, HR, and HC. Warmer colors indicate stronger positive correlations. (b) Global efficiency (E_glob, left panel) and local efficiency (E_loc, right panel) plotted as a function of network sparsity. The analysis spans sparsity levels from 0.10 to 0.50 in increments of 0.01. Error bars indicate SE. (c) Area under the curve (AUC) of graph metrics across sparsity, summarized as mean ± SE for each group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/cd73666fc23a8003a2b82daf.png"},{"id":101209035,"identity":"c387326f-eb46-4506-8e43-b21f8959856b","added_by":"auto","created_at":"2026-01-27 10:13:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6890678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/be07da92-b2e3-48bb-898c-443ff144a13c.pdf"},{"id":101075101,"identity":"b55a0e56-f0f1-4ec4-bb94-f36733034730","added_by":"auto","created_at":"2026-01-25 10:24:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42690,"visible":true,"origin":"","legend":"Supplementary","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8537776/v1/a0c3e06adf25b09c617df735.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e\nGK received honoraria from Storz medical and the Academy of Brain Stimulation, Healthlink Holdings. BBBZ, RLDK, MXJ, and AWLX received honoraria from the Academy of Brain Stimulation. BBBZ received the Eurasia-Pacific Ernst Mach Scholarship from Austria’s Agency for Education and Internationalisation. PPQ received honoraria from Storz Medical. All other authors reported no potential conflicts of interest.","formattedTitle":"Familial risk and illness burden jointly shape prefrontal hypoactivation in depression: An fNIRS study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a prevalent and disabling condition that imposes a substantial disease burden [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite the availability of various therapeutic strategies, a substantial proportion of patients exhibit treatment resistance or high recurrence rates, resulting in chronic disability and severe psychosocial impairment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given the heritable component of MDD risk [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], discerning the predispositions that confer vulnerability to both disease onset and recurrence is paramount. Yet current diagnostic frameworks rely primarily on symptom-based interviewing and subjective clinical judgment, which limits biological specificity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, the identification of objective trait markers that persist independently of the depressive state via scalable clinical tools is central to biomarker guided prevention and care.\u003c/p\u003e \u003cp\u003eConverging evidence implicates dysregulated brain function in MDD [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies utilizing positron emission tomography (PET) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and functional magnetic resonance imaging (fMRI) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] have frequently reported hypoactivation and dysfunction in the prefrontal cortex (PFC) among depressed patients. While these modalities have been instrumental in mapping brain abnormalities, their application in routine clinical practice is often restricted by high cost and environmental constraints. In this context, functional near-infrared spectroscopy (fNIRS) has emerged as a promising alternative, offering a non-invasive, cost-effective, and ecologically valid neuroimaging method for monitoring brain activity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Based on the principle of neurovascular coupling, fNIRS measures the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) within the near-infrared spectrum (700-900nm). Unlike the restrictive environments of fMRI or PET, fNIRS allows patients to be comfortably seated in well-lit, ordinary clinical settings, thereby facilitating the measurement of spatiotemporal brain characteristics in a naturalistic environment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past two decades, fNIRS has been increasingly adopted to assess brain function and screen for psychiatric disorders, most commonly in conjunction with the verbal fluency task (VFT) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By requiring word generation under time constraints, the VFT engages executive functions like working memory and cognitive flexibility, reliably recruiting the PFC [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, as MDD involves fundamental emotional dysregulation beyond general cognitive deficits, standard VFT may overlook these affective dimensions. To address this, emotional VFT, which requires word generation based on emotional categories, was developed [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This paradigm probes the interaction between executive control and emotional processing, capturing the emotion-cognition integration deficits central to depression.\u003c/p\u003e \u003cp\u003eExtensive research using the fNIRS-VFT paradigm has consistently reported attenuated task-evoked HbO increases in the PFC among depressed patients [\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While this hypoactivation is a robust signature of the acute state, its status as a stable neurobiological trait remains debated due to inconsistent findings in remitted MDD (rMDD) [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and high-risk (HR) populations. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This implies that localized task-evoked activation alone may be insufficient to fully capture the vulnerability to depression. Given that depression is increasingly conceptualized as a disconnection syndrome [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], incorporating the characterization of intrinsic brain organization via resting state graph theory as a complementary dimension may provide a more comprehensive framework to understand neural dysregulation and identify potential endophenotypes of depression.\u003c/p\u003e \u003cp\u003eIsolating the mechanisms of risk across the clinical spectrum requires a strategic group comparison. Remitted patients provide a unique opportunity to identify trait markers in a symptom free state, yet their neural profiles remain difficult to interpret as they may reflect either preexisting vulnerability or the cumulative impact of prior episodes. The inclusion of unaffected HR individuals is therefore essential to isolate premorbid vulnerability from such confounding history. Therefore, by comparing these distinct cohorts with healthy controls (HC), the present study aims to investigate whether prefrontal disorganization constitutes stable trait markers of depression. We hypothesized that both rMDD and HR groups would exhibit attenuated activation and disrupted topological efficiency, thereby reflecting a core endophenotypic feature of MDD risk.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and Participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional, observational study involved three distinct groups: rMDD, HR, and healthy controls (HC). Participants were recruited from The Hong Kong Polytechnic University between July 2021 and November 2024. The study was approved by the Institutional Review Board of The Hong Kong Polytechnic University (HSEARS2021032300) and was registered at clinicaltrials.gov (NCT05427578). All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eProspective participants first completed a telephone or online screening to assess their eligibility. Those who met the criteria were then invited to the laboratory, where they provided written informed consent before participating. To be included, participants were required to be aged 18\u0026ndash;65 years, right-handed, have completed at least six years of formal education, possess normal or corrected-to-normal vision, and be capable of understanding verbal instructions. Right-handedness was confirmed using the Edinburgh Handedness Inventory (laterality quotient\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;40) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Clinical characteristics were rigorously assessed by a trained therapist on the day of testing. Current depressive symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], with a score of \u0026le;\u0026thinsp;4 required for all participants to ensure the absence of significant current depressive symptoms. Lifetime and current psychiatric diagnoses were evaluated using the Chinese version of Mini-International Neuropsychiatry Interview [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Detailed family psychiatric history was collected via a customized questionnaire to verify the presence of first-degree biological relatives with MDD. Specific inclusion criteria were defined for each study group. The rMDD group comprised individuals with a history of at least one major depressive episode according to DSM/ICD criteria, who had been in remission for a minimum of two months prior to enrollment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The HR group consisted of individuals with no personal lifetime history of any psychiatric disorder but who reported having at least one first-degree biological relative with a confirmed lifetime diagnosis of MDD. Finally, the HC group included individuals with neither a personal lifetime history of any psychiatric disorder nor a known first-degree family history of MDD [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. General exclusion criteria for all groups included a history of neurological disorders, unstable medical conditions requiring treatment, or an inability to tolerate the experimental procedure. The participant screening workflow is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Demographic characteristics and clinical details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;87\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMDD \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;35\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.98 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.94 (10.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.20 (3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.19 (10.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2; = 5.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary (0-6y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School (6-12y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates (13-14y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor (15-16y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster (17-18y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor (\u0026gt;\u0026thinsp;19y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePHQ-9 Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.79 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 (3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60 (2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.22 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDD Episodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06 (2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality Quotient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.25 (13.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.57 (13.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.67 (15.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.00 (12.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2; = 2.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eMean (SD); n (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003eOne-way analysis of means; Exact tests with Monte Carlo simulation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental Procedure\u003c/h2\u003e \u003cp\u003e Upon arrival at the laboratory, participants provided informed consent and completed the confirmation assessments for handedness and clinical status. Following eligibility confirmation, participants were seated comfortably in a quiet, dimly lit room, positioned approximately 50 cm in front of a computer screen. The fNIRS probe was then positioned and calibrated. Participants were instructed to keep their head and body as motionless as possible throughout the entire recording session to reduce motion artifacts. Data collection commenced once the fNIRS signals stabilized. The session began with a 2min resting-state measurement, during which participants were instructed to fixate on a central crosshair \u0026ldquo;+\u0026rdquo; displayed on the screen, remain in a state of relaxed wakefulness, and avoid mind wandering. This was followed by the VFT, where participants were instructed to follow the specific visual cues presented on the screen during the task.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Verbal fluency task\u003c/h2\u003e \u003cp\u003eWe used an emotion semantic version of VFT with a block design [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The paradigm consisted of four experimental blocks and four control blocks, with each block lasting 60 seconds. The categories used for the experimental blocks were Animals, Positive Emotions, Supermarket Items, and Negative Emotions. The task sequence commenced with a control block. A 20-second resting period was inserted between the control block and the experimental block. During this resting period, participants were instructed to fixate on a central crosshair (\u0026ldquo;+\u0026rdquo;) displayed on the screen and remain motionless. During the experimental condition, participants were instructed to generate as many unique words as possible belonging to the specific semantic category displayed on the screen without repetition. During the control block, participants were required to repeat the numbers \u0026ldquo;1, 2, 3, 4, 1, 2, 3, 4\u0026hellip;\u0026rdquo; at a steady pace. This control condition served as a baseline to account for hemodynamic changes induced by the motor act of articulation. Prior to the start of the task, a practice trial (using the category \"Flowers\" and the number sequence) was conducted to ensure participants fully understood the task requirements. The overall duration of the VFT session was 740 seconds. All stimuli were presented using E-Prime 2 (Psychology Software Tools, Pittsburgh, PA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 fNIRS measurement\u003c/h2\u003e \u003cp\u003eHemodynamic responses were measured using a 52-channel continuous wave fNIRS system (ETG-4000, Hitachi Medical Co., Tokyo, Japan) with dual-wavelength emitters (695 and 830 nm) and a sampling rate of 10 Hz. The optode montage consisted of a 3\u0026times;11 array with 17 sources and 16 detectors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A fixed source-detector separation of 3.0 cm was maintained to ensure the detection of hemodynamic signals from the cerebral cortex. Each pair of adjacent source and detector was defined as a channel (Ch). The probe array was positioned on the forehead with its lower edge aligned with the T4-Fpz-T3 line of the International 10\u0026ndash;20 system, and the sixth column centered on the midsagittal line. During measurement, the probe holder was securely fastened to the participant's head to ensure stable optode-scalp coupling. Following the fNIRS recording, a Polhemus 3D digitizer (Patriot\u0026trade;, Polhemus, USA) was used to record the 3D positions of all sources and detectors for every participant. NIRS_SPM was used to perform probabilistic anatomical localization, mapping each channel to MNI coordinates and corresponding Brodmann areas (BA) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This spatial registration confirmed that the probe covered the bilateral dorsolateral prefrontal cortex (DLPFC; BA 9, 46), frontopolar area (BA 10), anterior superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21). The MNI coordinates for the midpoint of each channel and their estimated BAs, derived from a representative participant, are detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 fNIRS data analysis\u003c/h2\u003e \u003cp\u003efNIRS data analysis was performed using the HOMER 2 toolbox and custom scripts developed in MATLAB 2013b (The MathWorks, Inc., Natick, MA) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For preprocessing, channels with SNR lower than 2 were excluded (38). The raw fNIRS data was then converted to optical density (OD) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Motion artifacts were corrected using spline interpolation and wavelet-based algorithms [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. A bandpass filter (0.006\u0026ndash;0.05 Hz) was then employed to low-frequency drift and high-frequency physiological noise (e.g., heartbeat and respiration) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These preprocessed signals were converted to the concentration change of HbO (ΔHbO) based on the modified Beer-Lambert Law with a differential pathlength factor of six [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. For the activation analysis, block-averaged hemodynamic responses (0\u0026ndash;60 s) were calculated for each block. Baseline correction was applied to each block by subtracting the mean signal of the 10 seconds preceding the block onset. Task-related activation was determined by calculating the contrast between conditions (i.e., Experimental Blocks minus Control Blocks). We defined bilateral prefrontal cortex (PFC) as our region of interest (ROI) which including channels 25, 26, 27, 28, 36, 38, 46, 47, 48, and 49 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. We focused primarily on HbO signals as an indicator of hemodynamic response due to its greater signal-to-noise ratio [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The mean of HbO concentration change for different groups were used for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Graph-Theoretical Network Analysis\u003c/h2\u003e \u003cp\u003eGraph-theoretical network analysis was performed using the MATLAB-based FC-NIRS toolbox [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Data preprocessing proceeded in several steps. First, channels with a SNR lower than 2 were identified as low-quality and excluded from further analysis. Second, motion artifacts were detected and corrected using a spline interpolation method combined with a moving standard deviation (MSD), applying a sliding window of 2 s and a threshold of 5 standard deviations (SD). Third, a principal component analysis (PCA) was applied to filter out the first principal component, thereby reducing global noise. Finally, a band-pass filter (0.01\u0026ndash;0.1 Hz) was applied to remove physiological noise and high-frequency interference. The concentration changes of HbO and HbR were calculated based on the modified Beer-Lambert Law.\u003c/p\u003e \u003cp\u003eThe HbO signals were subsequently used to construct functional networks and calculate topological characteristics. Resting-state functional connectivity was estimated using Pearson correlation analysis between the time series of all pairs of measurement channels (nodes), resulting in a correlation matrix for each participant per session. Given the ambiguous biological basis for negative correlations in functional connectivity analyses, we set all negative correlations to zero and restricted our analysis to positive correlations. To improve the normality distribution of the correlations in the matrix, we converted correlation coefficients into z values via Fisher\u0026rsquo;s r-to-z transformation. To define the topological organization of the functional networks, these correlation matrices were thresholded into binary matrices. To mitigate potential bias induced by a single threshold, the networks were binarized across a range of sparsity thresholds (0.1 to 0.5) with an increment of 0.01[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Network topology was characterized using global efficiency (E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e) and local efficiency (E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e). E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e reflects the average inverse shortest path length between all node pairs, representing the capacity for overall network integration and global information transfer. Conversely, E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e assesses network segregation and resilience; it is defined as the average efficiency of the subgraphs formed by the neighbors of a given node, capturing the efficiency of local communication in the event of node failure [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Both metrics were computed across the entire range of sparsity thresholds, and the area under the curve (AUC) was calculated for statistical analysis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA). The statistical significance threshold was set at \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 (two-tailed). Descriptive statistics for continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while model-adjusted data are presented as estimated marginal means (EMMs)\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE). One-way analysis of variance (ANOVA) was used to compare continuous demographic and clinical variables (age, PHQ-9 scores, and Laterality Quotient) among the three groups (HC, HR, and RMDD). For categorical variables (gender and education level), exact tests with Monte Carlo simulation were employed to account for small cell counts in some categories. To investigate group differences in behavioral performance (VFT accuracy) and hemodynamic responses (mean ∆HbO in the bilateral DLPFC) across emotional contexts, a 3 \u0026times; 3 repeated measures analysis of covariance (ANCOVA) was conducted. The model included group (HC, HR, RMDD) as the between-subjects factor, emotional condition (neutral, positive, negative) as the within-subjects factor, and age, gender, and education level as covariates to control for potential confounding effects. We utilized Type III Sum of Squares to account for the unbalanced design. Effect sizes for main effects and interactions were estimated using partial eta squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e).The assumption of sphericity was assessed using Mauchly\u0026rsquo;s test; if violated, degrees of freedom were adjusted using the Greenhouse-Geisser correction. To further decompose significant group \u0026times; condition interactions, we calculated emotion contrast scores (i.e., neutral-positive, neutral-negative, and positive\u0026ndash;negative) to quantify the magnitude of emotional modulation. These contrast scores were entered into a one-way multivariate analysis of covariance (MANOVA) controlling age, gender and education level. This was followed by covariate-adjusted univariate \u003cem\u003eF\u003c/em\u003e-tests and Bonferroni-corrected post-hoc pairwise comparisons. Finally, to explore the relationship between brain activation and behavioral performance while accounting for confounders, partial Pearson correlation analyses were performed. We examined the associations between mean ∆HbO and VFT accuracy within each group, controlling for age, gender, and education level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 87 participants were included in the final data analysis (HC: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37; HR: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15; RMDD: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35). Prior to the main analysis, baseline demographic and clinical variables were compared among the three groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ANOVA indicated no significant differences in age [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 84)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.779, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.462, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.018], laterality quotient (LQ) [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 84)\u003c/sub\u003e = 0.355, \u003cem\u003ep\u003c/em\u003e = 0.703, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.008], and depressive symptom severity (PHQ-9 scores) [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 84)\u003c/sub\u003e = 1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.143, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.045]. Education level [χ\u0026sup2;\u003csub\u003e(10)\u003c/sub\u003e = 5.384, df = 10, Monte Carlo \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.910] and gender distribution [χ\u0026sup2;\u003csub\u003e(2)\u003c/sub\u003e = 2.244, df = 2, Monte Carlo \u003cem\u003ep\u003c/em\u003e = 0.354] were also comparable among the three groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 VFT Performance\u003c/h2\u003e \u003cp\u003eANCOVA analysis revealed a significant main effect of emotional condition on VFT accuracy [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1.77, 143.59)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.462, \u003cem\u003ep\u003c/em\u003e ˂ 0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.095], and critically, a significant group \u0026times; condition interaction was observed [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(3.55, 143.59)\u003c/sub\u003e = 4.013, \u003cem\u003ep\u003c/em\u003e = 0.006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.090], although the main effect of group was non-significant [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 81)\u003c/sub\u003e = 0.594, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.554, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.014] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). To decompose the significant interaction, Bonferroni-corrected pairwise comparisons were performed within each group, confirmed that VFT accuracy was significantly modulated by emotional condition. Specifically, all groups consistently showed highest accuracy in the neutral condition, followed by the negative and then positive condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Given this consistent ordinal pattern, we further tested whether the magnitude of emotional modulation differed between groups by analyzing emotion contrast scores (neutral\u0026ndash;positive, neutral\u0026ndash;negative, positive\u0026ndash;negative). A multivariate analysis of variance (MANOVA) indicated a significant overall difference across groups [Wilks' λ\u0026thinsp;=\u0026thinsp;.863, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(4, 160)\u003c/sub\u003e = 3.07, \u003cem\u003ep\u003c/em\u003e = .018]. Follow‑up univariate tests showed significant group differences for the neutral\u0026ndash;positive contrast [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 81)\u003c/sub\u003e = 4.77, \u003cem\u003ep\u003c/em\u003e = .011, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.11] and the neutral-negative contrast [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 81)\u003c/sub\u003e = 4.99, \u003cem\u003ep\u003c/em\u003e = .010, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.11], but not for the positive-negative contrast [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 81)\u003c/sub\u003e = 0.82, \u003cem\u003ep\u003c/em\u003e = .444]. Bonferroni‑corrected post‑hoc comparisons revealed that, for both the neutral\u0026ndash;positive and neutral\u0026ndash;negative contrasts, HC showed a significantly larger emotional modulation than the RMDD group (neutral\u0026ndash;positive: \u003cem\u003ep\u003c/em\u003e = .008; neutral\u0026ndash;negative: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009). The HR group did not differ significantly from either group (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .05). In summary, while all groups exhibited the same directional pattern of emotional modulation, the significant group \u0026times; condition interaction was driven by differences in the magnitude of this modulation, with HC showing larger emotional contrasts than the RMDD group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Brain Activation\u003c/h2\u003e \u003cp\u003eThe ANCOVA analysis did not reveal a significant group \u0026times; condition interaction [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(3.34, 135.10)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.725, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.552, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.018] or main effect of emotional condition [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1.67, 135.10)\u003c/sub\u003e = 1.115, \u003cem\u003ep\u003c/em\u003e = 0.323 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.014] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). However, the analysis yielded a significant main effect of group [\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 81)\u003c/sub\u003e = 3.696, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta}_{\\text{P}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e = 0.084]. Post-hoc pairwise comparisons with Bonferroni-corrected showed that the HC group exhibited significantly higher ∆HbO activation compared to the RMDD group (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(81)\u003c/sub\u003e = 2.705, \u003cem\u003ep\u003c/em\u003e = 0.024). No significant difference was observed between the HC and HR groups (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(81)\u003c/sub\u003e = 1.203, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.700), or between the HR and RMDD groups (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(81)\u003c/sub\u003e = 0.868, \u003cem\u003ep\u003c/em\u003e = 1.000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Brain activation and behavioral correlation\u003c/h2\u003e \u003cp\u003ePartial Pearson correlation analyses were performed to examine the relationship between the mean total VFT accuracy score and the mean total ∆HbO concentration change in the bilateral DLPFC within each group, controlling for age, gender, and education level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). None of the correlations reached statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in any of the three groups (HC group: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.739; HR group: \u003cem\u003er\u003c/em\u003e = -0.557, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.060; RMDD group: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.240, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.186).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional connectivity patterns and graph-theoretical analysis\u003c/h2\u003e \u003cp\u003eGroup-level resting-state functional connectivity (FC) matrices are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. To visualize the topological characteristics across the full range of network densities, E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e and E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e are displayed as a function of sparsity threshold (0.10\u0026ndash;0.50, step\u0026thinsp;=\u0026thinsp;0.01) in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, with the area under the curve (AUC) computed for each metric and each participant to provide an integrated summary measure independent of a single threshold selection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOne-way ANOVA comparing the AUC of E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e and E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e across the three groups revealed no significant group differences [E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 51)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.151, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.860, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.007; E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 51)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.893, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.417, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.038] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Similarly, exploratory analyses conducted at individual sparsity thresholds (0.10\u0026ndash;0.50) yielded no significant differences in either metric after correction for multiple comparisons across thresholds.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to jointly examine rMDD, HR, and HC cohorts to investigate prefrontal activity during an emotional VFT using fNIRS. The rMDD group exhibited reduced prefrontal activation relative to HC, whereas the HR group displayed an intermediate level of activation that did not significantly differ either from HC or rMDD. Behaviorally, while all groups consistently performed best in the neutral condition, followed by the negative and then the positive condition, a critical group \u0026times; condition interaction was observed. This interaction was driven by the fact that the HC group exhibited significantly larger emotional modulation magnitudes compared to the rMDD group. Graph-theoretic analyses revealed no group differences in E\u003csub\u003e\u003cem\u003eglob\u003c/em\u003e\u003c/sub\u003e and E\u003csub\u003e\u003cem\u003eloc\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eBy integrating these cohorts within a single analytical framework, we sought to examine whether prefrontal dysfunction represents a heritable trait marker for depression. Our results showed that prefrontal activation was significantly reduced in the rMDD group compared to HCs, whereas the HR group exhibited an intermediate profile that did not statistically differ from either group. This gradient suggests that prefrontal hypoactivation is likely driven by a combination of familial vulnerability and disease-related scarring, with the latter playing a more prominent role. Considered alongside prior evidence of prefrontal hypoactivation during acute episodes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], our findings indicate that the profound dysfunction observed in rMDD is likely a persistence of state-related alterations, representing a 'neural scar' of the illness. In contrast, the HR group, who possess familial vulnerability but have not endured the neurobiological impact of acute depressive episodes, displayed only mild, non-significant attenuation. This distinction supports a cumulative model: while genetic liability may confer a subtle baseline deficit, the clinical experience of depression itself appears to impose an additional, substantial burden on prefrontal function. Thus, reduced prefrontal recruitment during verbal fluency may be better conceptualized as a marker of cumulative disease burden rather than a pure pre-morbid trait.\u003c/p\u003e \u003cp\u003eDespite comparable overall accuracy, the significant group \u0026times; condition interaction highlights a fundamental divergence in how emotional cues influence cognition. In healthy controls, the pronounced decline in verbal fluency during emotional conditions reflects a competition for cognitive resources [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Emotional category labels appear to automatically capture attention, generating a strong affective signal that diverts processing power away from the primary goal of word generation [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This interference suggests that, in a healthy neurocognitive system, emotional salience naturally competes with cognitive demands. In contrast, the rMDD group displayed a markedly flatter behavioral profile, characterized by minimal performance shifts across conditions. Rather than indicating superior cognitive control, this stability likely points to reduced emotional reactivity. For these individuals, emotional cues appear to generate a weaker initial signal, thereby failing to compete effectively for attentional resources or interfere with the task. This pattern aligns with the concept of emotional blunting, representing a dampened sensitivity to emotional stimuli [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Thus, the absence of typical emotional modulation suggests a persistent alteration in the automatic interplay between emotion and cognition, potentially serving as a residual scar of prior depressive episodes.\u003c/p\u003e \u003cp\u003eWe anticipated that prefrontal activation would correlate with VFT performance but did not detect a significant association. Several considerations may account for this null result. Our PFC measure aggregated signals across a relatively broad region, which may obscure subregional or network-specific relationships, given the functional heterogeneity of the PFC [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Prior work suggests that distinct cognitive operations and symptom dimensions map more closely onto specific prefrontal circuits and connectivity patterns [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. It is also possible that the task demands were insufficient to elicit the range of neural resource engagement needed to reveal behavior\u0026ndash;brain coupling in a young, high-performing sample [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. These possibilities point to the value of regionally specific metrics, connectivity-based analyses, and more challenging paradigms in future research.\u003c/p\u003e \u003cp\u003eFurthermore, we found no group differences in fNIRS-derived resting-state network metrics (Eg, Eloc). This pattern may reflect a normalization of disease-related network topology during remission [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], although it contrasts with other reports of reduced global efficiency in regions implicated in cognitive control [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. These inconsistencies may stem from differences in neuroimaging modalities (e.g., the depth sensitivity and spatial resolution of fMRI versus fNIRS), and sample characteristics. It is plausible that while task-evoked activation remains impaired, the resting topological organization of the network may recover.\u003c/p\u003e \u003cp\u003eOur findings should be interpreted in light of several limitations. First, the absence of a concurrently assessed acute MDD cohort precludes direct comparisons across the full disease trajectory; however, this specific design focusing on remitted and high-risk groups was sufficient to successfully distinguish stable trait markers from disease-related scars. Regarding data acquisition, our system lacked short-separation channels due to hardware constraints. To mitigate this, we rigorously applied standardized preprocessing protocols in conjunction with a control-task subtraction method, effectively minimizing systemic physiological interference to isolate task-specific cortical activation. Finally, regarding cohort characteristics, the predominance of university students and the relatively modest sample size restricted broad generalizability and precluded stratification by clinical subtypes. Future investigations utilizing larger, more diverse populations are warranted to validate these findings and explore subtype-specific neural patterns.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings show that prefrontal hypoactivation during emotional processing is not a simple heritable trait. The graded reduction from healthy controls to high-risk relatives and then to remitted patients suggests a deficit shaped by both familial vulnerability and illness burden. This deficit, alongside blunted behavioral sensitivity to emotional cues, shifts the focus of these markers from predicting initial onset to tracking the persistent impact of depression. Thus, fNIRS-measured prefrontal activity holds greater potential for monitoring residual disease effects and guiding targeted interventions in recovery\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by grants from the Department of Rehabilitation Sciences (Grant numbers: P0045136 and P0043155) of the Hong Kong Polytechnic University, and by the General Research Fund (number 15106222) under the University Grants Committee of the HKSAR. The funders had no role in the study design; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. We appreciate the facilities support provided by the University Research Facility in Behavioral and Systems Neurosciences at The Hong Kong Polytechnic University. We also thank all participants for their time and effort. In addition, we are grateful to the master’s and bachelor’s students for their assistance with data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; \u0026nbsp;Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGK received honoraria from Storz medical and the Academy of Brain Stimulation, Healthlink Holdings. BBBZ, RLDK, MXJ, and AWLX received honoraria from the Academy of Brain Stimulation. BBBZ received the Eurasia-Pacific Ernst Mach Scholarship from Austria’s Agency for Education and Internationalisation. PPQ received honoraria from Storz Medical. All other authors reported no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.\u0026nbsp; \u0026nbsp;Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: BBBZ, SXW, TFY, GSK; Methodology: BBBZ, SXW, RLDK, VWML, NMXYS; Investigation: BBBZ, SXW, VWML, NMXYS, RLDK, TTZL, PPQ, JMX, AWLX; Data curation: BBBZ, SXW, VWML, RLDK, NMXYS; Software: BBBZ, SXW, VWML, RLDK, TTZL, PPQ; Analysis: BBBZ, SXW, VWML, RLDK; Visualization: BBBZ, SXW, VWML, RLDK; Validation: SXW, TTZL, PPQ, JMX, AWLX; Writing-original draft: BBBZ, SXW, GSK; Writing-review \u0026amp; editing: BBBZ, SXW, VWML, NMXYS, RLDK, TTZL, PPQ, JMX, AWLX, TFY, GSK; Resources, Supervision, Project administration: GSK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u0026nbsp; \u0026nbsp;Data availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMalhi, G.S. and J.J. Mann, \u003cem\u003eDepression.\u003c/em\u003e Lancet, 2018. 392(10161): p. 2299\u0026ndash;2312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProudman, D., P. Greenberg, and D. Nellesen, \u003cem\u003eThe Growing Burden of Major Depressive Disorders (MDD): Implications for Researchers and Policy Makers\u003c/em\u003e. Pharmacoeconomics, 2021. 39(6): p. 619\u0026ndash;625.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal, \u003cem\u003eregional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019\u003c/em\u003e. Lancet Psychiatry, 2022. 9(2): p. 137\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller, M.B., et al., \u003cem\u003eTime to recovery, chronicity, and levels of psychopathology in major depression. A 5-year prospective follow-up of 431 subjects\u003c/em\u003e. Arch Gen Psychiatry, 1992. 49(10): p. 809\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan, P.F., et al., \u003cem\u003eGenome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo\u003c/em\u003e. Mol Psychiatry, 2009. 14(4): p. 359\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlint, J. and K.S. Kendler, \u003cem\u003eThe genetics of major depression\u003c/em\u003e. Neuron, 2014. 81(3): p. 484\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric, A. and D.S.M.T.F. American Psychiatric Association, \u003cem\u003eDiagnostic and statistical manual of mental disorders: DSM-5\u003c/em\u003e. 5th ed. DSM-5. 2013, Arlington, VA: American Psychiatric Association.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, \u003cem\u003eICD-11 for for mortality and morbidity statistics, 2018 version\u003c/em\u003e. Geneva: World Health Organization. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icd.who.int/browse11/l-m/en\u003c/span\u003e\u003cspan address=\"https://icd.who.int/browse11/l-m/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton, J.P., et al., \u003cem\u003eFunctional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data\u003c/em\u003e. Am J Psychiatry, 2012. 169(7): p. 693\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., et al., \u003cem\u003eLifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function\u003c/em\u003e. JAMA Network Open, 2024. 7(2): p. e2356787-e2356787.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynch, C.J., et al., \u003cem\u003eFrontostriatal salience network expansion in individuals in depression\u003c/em\u003e. Nature, 2024. 633(8030): p. 624\u0026ndash;633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams, L.M., \u003cem\u003ePrecision psychiatry: a neural circuit taxonomy for depression and anxiety\u003c/em\u003e. Lancet Psychiatry, 2016. 3(5): p. 472\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBench, C.J., et al., \u003cem\u003eThe anatomy of melancholia \u0026ndash; focal abnormalities of cerebral blood flow in major depression\u003c/em\u003e. Psychological Medicine, 1992. 22(3): p. 607\u0026ndash;615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, C., et al., \u003cem\u003eFunctional assessment of prefrontal lobes in patients with major depression disorder using a dual-mode technique of 3D-arterial spin labeling and (18)F-fluorodeoxyglucose positron emission tomography/computed tomography\u003c/em\u003e. Exp Ther Med, 2017. 14(2): p. 1058\u0026ndash;1064.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong, X., W. Pu, and S. Yao, \u003cem\u003eFunctional alterations of fronto-limbic circuit and default mode network systems in first-episode, drug-na\u0026iuml;ve patients with major depressive disorder: A meta-analysis of resting-state fMRI data\u003c/em\u003e. Journal of Affective Disorders, 2016. 206: p. 280\u0026ndash;286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegle, G.J., et al., \u003cem\u003eIncreased Amygdala and Decreased Dorsolateral Prefrontal BOLD Responses in Unipolar Depression: Related and Independent Features\u003c/em\u003e. Biological Psychiatry, 2007. 61(2): p. 198\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinti, P., et al., \u003cem\u003eThe present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience\u003c/em\u003e. Annals of the New York Academy of Sciences, 2020. 1464(1): p. 5\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H.Y., et al., \u003cem\u003eApplication of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models\u003c/em\u003e. Mol Cells, 2017. 40(8): p. 523\u0026ndash;532.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, M.K. and J. Lin, \u003cem\u003eProbing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: A systematic review and meta-analysis\u003c/em\u003e. J Psychiatr Res, 2021. 140: p. 416\u0026ndash;435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhlis, A.C., et al., \u003cem\u003eApplication of functional near-infrared spectroscopy in psychiatry.\u003c/em\u003e Neuroimage, 2014. 85 Pt 1: p. 478\u0026thinsp;\u0026ndash;\u0026thinsp;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmunts, J., et al., \u003cem\u003eExecutive functions predict verbal fluency scores in healthy participants\u003c/em\u003e. Scientific Reports, 2020. 10(1): p. 11141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroyer, A.K., M. Moscovitch, and G. Winocur, \u003cem\u003eClustering and switching as two components of verbal fluency: evidence from younger and older healthy adults\u003c/em\u003e. Neuropsychology, 1997. 11(1): p. 138\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, Z., et al., \u003cem\u003eWhat do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults\u003c/em\u003e. Frontiers in Psychology, 2014. Volume 5\u0026ndash;2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbeare, C.A., et al., \u003cem\u003eThe Emotion Word Fluency Test (EWFT): Initial psychometric, validation, and physiological evidence in young adults\u003c/em\u003e. J Clin Exp Neuropsychol, 2017. 39(8): p. 738\u0026ndash;752.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, M.K., \u003cem\u003eEffects of age, gender, and education on task performance and prefrontal cortex processing during emotional and non-emotional verbal fluency tests\u003c/em\u003e. Brain and Language, 2023. 245: p. 105325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakizawa, R., et al., \u003cem\u003eNeuroimaging-aided differential diagnosis of the depressive state.\u003c/em\u003e Neuroimage, 2014. 85 Pt 1: p. 498\u0026ndash;507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHusain, S.F., et al., \u003cem\u003eValidating a functional near-infrared spectroscopy diagnostic paradigm for Major Depressive Disorder\u003c/em\u003e. Sci Rep, 2020. 10(1): p. 9740.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao, J., et al., \u003cem\u003efNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls\u003c/em\u003e. IEEE Trans Neural Syst Rehabil Eng, 2021. 29: p. 2211\u0026ndash;2221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, T., et al., \u003cem\u003eThe fNIRS evaluation of frontal and temporal lobe cortical activation in Chinese first-episode medication-na\u0026iuml;ve and recurrent depression during a verbal fluency task\u003c/em\u003e. Front Psychiatry, 2023. 14: p. 1132666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkashi, H., et al., \u003cem\u003ePrefrontal cortex activation is associated with a discrepancy between self- and observer-rated depression severities of major depressive disorder: A multichannel near-infrared spectroscopy study\u003c/em\u003e. Journal of Affective Disorders, 2015. 174: p. 165\u0026ndash;172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo, C.S.H., et al., \u003cem\u003eDiagnostic and Predictive Applications of Functional Near-Infrared Spectroscopy for Major Depressive Disorder: A Systematic Review\u003c/em\u003e. Frontiers in Psychiatry, 2020. Volume 11\u0026ndash;2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNixon, N.L., et al., \u003cem\u003ePrefrontal cortex function in remitted major depressive disorder\u003c/em\u003e. Psychological Medicine, 2013. 43(6): p. 1219\u0026ndash;1230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiller, C.E., et al., \u003cem\u003eRemitted major depression is characterized by reduced prefrontal cortex reactivity to reward loss\u003c/em\u003e. Journal of Affective Disorders, 2013. 151(2): p. 756\u0026ndash;762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMing, Q., et al., \u003cem\u003eState-Independent and Dependent Neural Responses to Psychosocial Stress in Current and Remitted Depression\u003c/em\u003e. American Journal of Psychiatry, 2017. 174(10): p. 971\u0026ndash;979.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatters, A.J., et al., \u003cem\u003eCharacterizing neurocognitive markers of familial risk for depression using multi-modal imaging, behavioral and self-report measures\u003c/em\u003e. Journal of Affective Disorders, 2019. 253: p. 336\u0026ndash;342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCl, N., et al., \u003cem\u003eThe neural basis of hot and cold cognition in depressed patients, unaffected relatives, and low-risk healthy controls: An fMRI investigation\u003c/em\u003e. Journal of Affective Disorders, 2020. 274: p. 389\u0026ndash;398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOldfield, R.C., \u003cem\u003eThe assessment and analysis of handedness: the Edinburgh inventory\u003c/em\u003e. Neuropsychologia, 1971. 9(1): p. 97\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, N., et al., \u003cem\u003eTranslation, cultural adaption, and test-retest reliability of Chinese versions of the Edinburgh Handedness Inventory and Waterloo Footedness Questionnaire\u003c/em\u003e. Laterality, 2018. 23(3): p. 255\u0026ndash;273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke, K., R.L. Spitzer, and J.B. Williams, \u003cem\u003eThe PHQ-9: validity of a brief depression severity measure\u003c/em\u003e. J Gen Intern Med, 2001. 16(9): p. 606\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSi, T.-M., et al., \u003cem\u003eEvaluation of the reliability and validity of Chinese version of the Mini-International Neuropsychiatric Interview in patients with mental disorders\u003c/em\u003e. Chinese Mental Health Journal, 2009. 23(7): p. 493\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, M.-H., et al., \u003cem\u003eRisk and coaggregation of major psychiatric disorders among first-degree relatives of patients with bipolar disorder: a nationwide population-based study\u003c/em\u003e. Psychological Medicine, 2019. 49(14): p. 2397\u0026ndash;2404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, M.K., et al., \u003cem\u003eAltered Frontal Lateralization Underlies the Category Fluency Deficits in Older Adults with Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study\u003c/em\u003e. Frontiers in Aging Neuroscience, 2016. 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZohdi, H., F. Scholkmann, and U. Wolf, \u003cem\u003eIndividual Differences in Hemodynamic Responses Measured on the Head Due to a Long-Term Stimulation Involving Colored Light Exposure and a Cognitive Task: A SPA-fNIRS Study\u003c/em\u003e. Brain Sci, 2021. 11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolper, L., et al., \u003cem\u003eBrain correlates of verbal fluency in subthreshold psychosis assessed by functional near-infrared spectroscopy\u003c/em\u003e. Schizophrenia Research, 2015. 168(1): p. 23\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, M.K., \u003cem\u003eFrontal cortical activation during emotional and non-emotional verbal fluency tests\u003c/em\u003e. Scientific Reports, 2022. 12(1): p. 8497.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe, J.C., et al., \u003cem\u003eNIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy\u003c/em\u003e. NeuroImage, 2009. 44(2): p. 428\u0026ndash;447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuppert, T.J., et al., \u003cem\u003eHomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain\u003c/em\u003e. Appl Opt, 2009. 48(10): p. D280-98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X., et al., \u003cem\u003eBrain correlates of motor complexity during observed and executed actions\u003c/em\u003e. Scientific Reports, 2020. 10(1): p. 10965.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahani, S., et al., \u003cem\u003eMotion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering\u003c/em\u003e. Neurophotonics, 2018. 5(1): p. 015003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiarelli, A.M., et al., \u003cem\u003eA kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data\u003c/em\u003e. Neuroimage, 2015. 112: p. 128\u0026ndash;137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirilina, E., et al., \u003cem\u003eIdentifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex\u003c/em\u003e. Front Hum Neurosci, 2013. 7: p. 864.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelpy, D.T., et al., \u003cem\u003eEstimation of optical pathlength through tissue from direct time of flight measurement\u003c/em\u003e. Phys Med Biol, 1988. 33(12): p. 1433\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKocsis, L., P. Herman, and A. Eke, \u003cem\u003eThe modified Beer-Lambert law revisited\u003c/em\u003e. Phys Med Biol, 2006. 51(5): p. N91-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, M.K. and J. Lin, \u003cem\u003eProbing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: A systematic review and meta-analysis\u003c/em\u003e. Journal of Psychiatric Research, 2021. 140: p. 416\u0026ndash;435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, X., et al., \u003cem\u003eA quantitative comparison of NIRS and fMRI across multiple cognitive tasks\u003c/em\u003e. Neuroimage, 2011. 54(4): p. 2808\u0026ndash;2821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, J., et al., \u003cem\u003eFC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data.\u003c/em\u003e Biomed Res Int, 2015. 2015: p. 248724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, G., et al., \u003cem\u003eDevelopmental alterations in brain network asymmetry in 3- to 9-month infants with congenital sensorineural hearing loss\u003c/em\u003e. Science Advances, 2025. 11(42): p. eadx1327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubinov, M. and O. Sporns, \u003cem\u003eComplex network measures of brain connectivity: uses and interpretations\u003c/em\u003e. Neuroimage, 2010. 52(3): p. 1059\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, C.E., et al., \u003cem\u003eFunctional near-infrared spectroscopy as a biomarker of TMS efficacy in treatment-resistant depression\u003c/em\u003e. Psychiatry Clin Neurosci, 2025. 79(11): p. 765\u0026ndash;775.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, Y., et al., \u003cem\u003eFunctional near-infrared spectroscopy (fNIRS) in patients with major depressive disorder, generalized anxiety disorder and their comorbidity: Comparison with healthy controls\u003c/em\u003e. Asian Journal of Psychiatry, 2025. 105: p. 104382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePizzagalli, D.A. and A.C. Roberts, \u003cem\u003ePrefrontal cortex and depression\u003c/em\u003e. Neuropsychopharmacology, 2022. 47(1): p. 225\u0026ndash;246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu, S., et al., \u003cem\u003eSuicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder\u003c/em\u003e. Journal of Affective Disorders, 2015. 181: p. 9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePessoa, L., \u003cem\u003eThe cognitive-emotional brain: From interactions to integration\u003c/em\u003e. 2013: MIT press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkon-Singer, H., et al., \u003cem\u003eThe neurobiology of emotion-cognition interactions: fundamental questions and strategies for future research\u003c/em\u003e. Front Hum Neurosci, 2015. 9: p. 58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrosch, T., et al., \u003cem\u003eAdditive effects of emotional, endogenous, and exogenous attention: Behavioral and electrophysiological evidence\u003c/em\u003e. Neuropsychologia, 2011. 49(7): p. 1779\u0026ndash;1787.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman, N.P. and T.W. Robbins, \u003cem\u003eThe role of prefrontal cortex in cognitive control and executive function\u003c/em\u003e. Neuropsychopharmacology, 2022. 47(1): p. 72\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams, L.M., \u003cem\u003ePrecision psychiatry: a neural circuit taxonomy for depression and anxiety\u003c/em\u003e. The Lancet Psychiatry, 2016. 3(5): p. 472\u0026ndash;480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgbangla, N.F., et al., \u003cem\u003eLoad-Dependent Prefrontal Cortex Activation Assessed by Continuous-Wave Near-Infrared Spectroscopy during Two Executive Tasks with Three Cognitive Loads in Young Adults\u003c/em\u003e. Brain Sciences, 2022. 12(11): p. 1462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eServaas, M.N., et al., \u003cem\u003eAssociations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder\u003c/em\u003e. Neuropsychopharmacology, 2017. 42(13): p. 2583\u0026ndash;2592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, G., et al., \u003cem\u003eChanges of functional brain network topology associated with nutritional indicator of patients with recurrent major depressive disorder\u003c/em\u003e. Front Nutr, 2025. 12: p. 1615978.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression, prefrontal cortex, functional near-infrared spectroscopy, trait marker, verbal fluency task","lastPublishedDoi":"10.21203/rs.3.rs-8537776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8537776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Prefrontal dysfunction is a well-documented deficit in major depressive disorder (MDD), yet it remains unclear whether this represents a genetic endophenotype or a persistent \"scar\" of the illness. This study aimed to disentangle familial risk from disease chronicity by simultaneously examining remitted MDD (rMDD) patients, unaffected high-risk (HR) individuals, and healthy controls (HC). We recruited 87 participants: 35 with rMDD, 15 unaffected HR individuals, and 37 HCs. Hemodynamic responses in the bilateral prefrontal cortex (PFC) were recorded using functional near-infrared spectroscopy (fNIRS) during an emotional Verbal Fluency Task (VFT). Resting-state network topology (global/local efficiency) was analyzed via graph theory. The rMDD group exhibited significantly attenuated PFC activation compared to HCs (p = 0.024) during VFT. The HR group displayed an intermediate level of activation that did not statistically differ from either HCs (p = 0.700) or the rMDD group (p = 1.000). Behaviorally, rMDD patients showed blunted emotional modulation of performance compared to HCs (p \u003c 0.05). No significant group differences were found in resting-state network topology. In conclusion, prefrontal hypoactivation during emotional processing appears to be a graded deficit shaped by both familial vulnerability and illness burden, rather than by a simple heritable trait. Combined with blunted behavioral reactivity to emotional cues in remission, these measures shift in their utility from predicting onset to tracking illness impact. Thus, fNIRS-derived prefrontal activation may serve as a biomarker for monitoring incomplete neural recovery rather than predicting initial disease onset.","manuscriptTitle":"Familial risk and illness burden jointly shape prefrontal hypoactivation in depression: An fNIRS study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-25 10:23:21","doi":"10.21203/rs.3.rs-8537776/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-03-19T11:27:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-19T16:01:45+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-09T11:53:37+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-06T09:03:46+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-21T15:21:07+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-21T13:51:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T14:32:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T14:32:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2026-01-07T06:52:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a5cdeef7-979a-4803-afe3-7af510744c18","owner":[],"postedDate":"January 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60885775,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":60885776,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2026-04-27T08:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-25 10:23:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8537776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8537776","identity":"rs-8537776","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}