Investigating Working Memory and Brain Activation in Major Depressive Disorder with and without Insomnia: Insights from Functional Near-Infrared Spectroscopy (fNIRS)

Investigating Working Memory and Brain Activation in Major Depressive Disorder with and without Insomnia: Insights from Functional Near-Infrared Spectroscopy (fNIRS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating Working Memory and Brain Activation in Major Depressive Disorder with and without Insomnia: Insights from Functional Near-Infrared Spectroscopy (fNIRS) Yanli Li, Chao Zheng, Yanni Li, Yubo Wang, Zichen Ding, Miaomiao Xu, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9434079/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Major depressive disorder (MDD) frequently coexists with insomnia, a comorbidity that exacerbates cognitive deficits, particularly in working memory (WM). While previous research has established links between insomnia and impaired brain function, the specific neurofunctional mechanisms underlying WM deficits in MDD patients with insomnia remain unclear. This study explores cortical activation patterns in MDD patients with and without insomnia using functional near-infrared spectroscopy (fNIRS). Methods A total of 55 MDD patients with insomnia and 67 without insomnia were recruited. Cortical activation during WM tasks was assessed using fNIRS, focusing on oxygenated hemoglobin (Oxy-Hb) concentration changes. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), depression severity was assessed using the Hamilton Depression Scale (HAMD), and cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Results MDD patients with insomnia exhibited lower RBANS scores and reduced accuracy in medium-load WM tasks compared to non-insomnia patients. Additionally, fNIRS analysis revealed diminished Oxy-Hb concentrations in the bilateral dorsolateral prefrontal cortex (DLPFC) during medium-load tasks and in the left DLPFC during high-load tasks. Correlation analyses indicated that immediate memory scores positively correlated with left DLPFC and bilateral medial prefrontal cortex (mPFC) activation during medium-load tasks, while task accuracy negatively correlated with bilateral mPFC activation (p < 0.05). Conclusion The presence of insomnia in MDD is associated with exacerbated WM deficits and altered prefrontal cortical activation, particularly in tasks with increased cognitive demand. These findings highlight potential neurofunctional biomarkers that could inform personalized interventions for MDD patients with insomnia. Moreover, the sensitivity of fNIRS in detecting these neural activation differences suggests its potential as a diagnostic tool for identifying MDD subtypes and guiding targeted therapeutic strategies. major depressive disorder insomnia functional near-infrared spectroscopy (fNIRS) working memory prefrontal cortex Figures Figure 1 Figure 2 1.Introduction Major Depressive Disorder (MDD) is a leading global psychiatric disorder, affecting over 322 million people worldwide. It is projected to become the primary cause of disease burden within the next two decades. Core symptoms of MDD include persistent low mood, anhedonia, fatigue, cognitive impairments (e.g., reduced attention and low self-esteem), and sleep disturbances (Anderson et al., 2024 ; McCarron et al., 2021 ). Notably, insomnia affects 67–84% of MDD patients during depressive episodes, exacerbating symptom severity, impairing recovery, and increasing risks of cardiovascular and autoimmune comorbidities (Boland et al., 2023 ; Sivertsen et al., 2014 ; Sofi et al., 2014 ). While insomnia’s role in emotional dysregulation is well-documented, its contribution to cognitive dysfunction—particularly working memory deficits—remains poorly understood, despite evidence linking insomnia severity to poorer task accuracy and slower processing speeds in MDD patients(Maramis et al., 2021 ; Songco et al., 2023 ). Cognitive impairments, especially working memory deficits, are a hallmark of MDD and critically predict functional disability and relapse risk (Chen et al., 2023 ). These deficits may represent endophenotypes with distinct pathophysiology, yet current clinical practices prioritize emotional symptom management, overlooking cognitive dysfunction as a secondary concern (Brownlow et al., 2020 ). To address this gap, functional near-infrared spectroscopy (fNIRS) offers a unique tool for mapping cortical hemodynamics in clinical settings. Unlike fMRI, fNIRS combines portability with tolerance to movement artifacts, enabling real-time monitoring of prefrontal activation during ecologically valid tasks (Pinti et al., 2020 ). Prior fNIRS studies have revealed reduced dorsolateral prefrontal cortex (DLPFC) activation in MDD patients during working memory tasks (Liu et al., 2024 ; Xu et al., 2023 ). However, no studies have compared neural activation patterns between MDD patients with and without insomnia—a critical omission given insomnia’s potential to affect prefrontal function. This study hypothesizes that MDD patients with insomnia will exhibit (1) poorer working memory performance, (2) reduced DLPFC activation during medium/high-load tasks, and (3) a stronger correlation between DLPFC hypoactivation and cognitive deficits in the insomnia subgroup. By clarifying these neurofunctional distinctions, our findings aim to advance diagnostic precision and guide targeted therapies for MDD patients with comorbid insomnia. 2.Methods 2.1 Subjects This study was conducted at Kangning Hospital in Ningbo, Zhejiang Province, from July 2021 to December 2023 and included 122 eligible patients diagnosed with MDD. Participants were recruited through physician referrals, with doctors introducing the research project to potential candidates. Inclusion criteria required patients to meet the diagnostic criteria for MDD as defined by the (Inclusion criteria for subjects included:) 1) Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), 2) age between 16 and 65 years, and have a Hamilton Depression Rating Scale-24 (HAMD-24) total score of ≥ 20, indicating moderate to severe depression. Exclusion criteria included a history of psychosis, significant physical or organic brain diseases, substance abuse or dependence (except for cigarette smoking), and pregnancy or lactation. Prior to the study's commencement, ethical approval was obtained from the Ethics Committee of Ningbo Kangning Hospital (NBKNYY-2023-LC-31), ensuring compliance with the ethical standards and regulations of the Declaration of Helsinki on Human Research. All participants were provided with a comprehensive explanation of the study's purpose, procedures, and potential risks. Those who agreed to participate voluntarily provided written informed consent, and minor participants have voluntarily provided written informed consent through their legal guardians. These steps ensured that the study adhered to ethical standards and that participants were fully informed about their involvement in the research. 2.2. Measures In this study, depressive symptoms in patients with MDD were assessed using the HAMD-24. The total HAMD-24 score reflects the severity of depression, with scores interpreted as follows: ≤7 indicates no depressive symptoms, 8–20 suggests probable depression, 20–35 indicates definite depression, and > 35 signifies severe depression. Higher scores correspond to more severe depressive symptoms(Maier, 1990 ). Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a seven-component scale that assesses sleep quality, latency, duration, efficiency, disturbances, use of hypnotics, and daytime dysfunction over the past month. Each component is scored from 0 to 3, and the cumulative score ranges from 0 to 21, with higher scores indicating poorer sleep quality (Buysse et al., 1989 ). In this study, insomnia was defined as a total PSQI score of ≥ 11. Immediate Memory (IM) was assessed using items 1 (word learning) and 2 (story retelling) from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Raw scores were converted to scaled scores based on American norms, and the two scores were combined to generate an immediate memory score, with higher scores indicating better memory function (Faust et al., 2017 ). WM was evaluated using a task that included low-, middle-, and high-load conditions. Each session began with a 5-second welcome screen and a 5-second relaxation period, followed by four task sets (WM + Relax + Non-WM + Relax). The task involved random combinations of four shapes (circles, triangles, hearts, stars) and four colors (red, yellow, blue, green). In the low-load condition, Stage 1 required participants to remember the colors, shapes, and order of two sequentially displayed shapes (3 seconds each). Stage 2 involved recalling and clicking the shapes in the memorized order within 10 seconds, while Stage 3 was a non-WM task where participants clicked graphical cues without memorization for 15 seconds. The middle- and high-load conditions involved 3- and 4-stimuli memory tasks, respectively (Sun et al., 2019 ) (Fig. 1 ). Participants practiced once before the formal test to ensure understanding. During testing, they remained seated and avoided large movements to minimize interference. Prior to the study, two experienced psychiatrists were trained to administer the assessments. Their inter-rater reliability, measured by correlation coefficients for HAMD-24, PSQI, and RBANS, exceeded 0.8 after repeated measures, ensuring consistency in evaluations. This rigorous training and validation process helped maintain the accuracy and reliability of the data collected throughout the study. 2.3. fNIRS Measurements A 48-channel fNIRS system (NirScan, Danyang Huichuang Medical Equipment Co., Ltd., China) was used to record hemodynamic responses in the cerebral cortex of patients with MDD during the WM task. The system operated at a sampling frequency of 11 Hz and utilized near-infrared light at wavelengths of 730, 808, and 850 nm to measure changes in oxyhemoglobin (Oxy-Hb), deoxyhemoglobin (Deoxy-Hb), and total hemoglobin (Total-Hb) concentrations. The fNIRS probes, comprising 15 light sources and 16 detectors, were positioned over the frontal and temporal regions according to the 10–20 international electroencephalogram (EEG) system. The inter-probe distance was fixed at 3 cm, with the bottom probe aligned along the Fp1-Fp2 line. The 48 channels were grouped into six regions of interest (ROIs) based on device coordinates: the right dorsolateral prefrontal cortex (R-DLPFC), left dorsolateral prefrontal cortex (L-DLPFC), right medial prefrontal cortex (R-mPFC), left medial prefrontal cortex (L-mPFC), right temporal lobe (R-TL), and left temporal lobe (L-TL). The specific channel-to-region mapping is illustrated in Supplementary materials 1. This setup allowed for precise localization and analysis of cortical hemodynamic activity during the WM task. 2.4. Data Processing and Analysis The fNIRS data were processed using NirSpark analysis software. Motion artifacts and environmental noise were first corrected, followed by the removal of physiological noise using a 0.01–0.20 Hz bandpass filter. The Beer-Lambert law was then applied to convert the optical data into blood oxygen concentration values. Oxy-Hb changes were prioritized for analysis due to their stronger correlation with cortical activation and higher signal-to-noise ratio compared to Deoxy-Hb (Y. Li et al., 2024 ). A 2-second pre-task interval was used as the baseline for normalization. The average Oxy-Hb activation changes were calculated for the WM task, with durations of 10 seconds, 10 seconds, and 15 seconds for the low-, medium-, and high-load conditions, respectively, as well as for the non-working memory (NWM) task (15 seconds) in each channel. Within each ROI, the Oxy-Hb concentration changes were averaged across all channels to derive the mean values for the WM task. This approach ensured a robust and standardized analysis of cortical hemodynamic responses during the task. 2.5. Statistical Analysis The sociodemographic characteristics, clinical variables, accuracy rates, and Oxy-Hb activation during the WM task were first described for all subjects. The normality of all variables was confirmed using the Kolmogorov-Smirnov one-sample test. Group differences between MDD patients with and without insomnia were analyzed using one-way ANOVA for continuous variables and the chi-square test (χ² test) for categorical variables, with Bonferroni correction applied for multiple comparisons. Pearson’s correlation coefficient was used to evaluate the relationship between working memory function and Oxy-Hb activation during the WM task in both groups. Additionally, multiple linear regression analyses were conducted in MDD patients with insomnia to identify brain regions with the strongest association between Oxy-Hb activation and immediate memory. Statistical analyses were performed using SPSS 25.0, with a two-tailed p < 0.05 considered statistically significant. 3.Results 3.1. Demographic and basic descriptive data No significant differences were observed between MDD patients with and without insomnia in years of depressive symptoms, age, education, gender, BMI, or and course of illness (all p > 0.05), with significant differences in HAMD-24 scores (F = 4.661, p = 0.033), PSQI scores (F = 257.626, p 0.05), the two groups showed statistically significant differences in ACC for the middle-load WM task (F = 5.531, p = 0.021) (Table 1 ). Table 1 Demographic and clinical characteristics of MDD patients with or without insomnia Age(years) Insomnia(N = 67) Non-insomnia (N = 55) F\x 2 p-Value 30.39 ± 15.16 29.15 ± 12.55 0.063 0.943 Episode duration (m) 65.43 ± 65.43 63.39 ± 57.42 0.185 0.305 Education(years) 11.86 ± 3.22 11.98 ± 2.40 0.051 0.822 BMI (kg/m²) 21.20 ± 3.92 21.86 ± 3.94 0.803 0.372 Gender(male/female) 16/52 21/33 3.173 0.075 Suicide(yes/no) 15/44 20/30 2.638 0.104 HAMD-24 25.87 ± 5.39 23.87 ± 4.50 4.661 0.033* PSQI 14.49 ± 2.70 7.11 ± 2.22 257.626 <0.001*** Immediate Memory 79.015 ± 16.797 86.315 ± 17.250 5.435 0.021* WM task ACC (low-load) 0.913 ± 0.172 0.954 ± 0.111 2.000 0.160 ACC (middle -load) 0.783 ± 0.238 0.881 ± 0.177 5.531 0.021* ACC (high-load) 0.650 ± 0.259 0.663 ± 0.249 0.006 0.939 Note: BMI=body mass index; HAMD-24 = 24-item Hamilton Depression Scale; PSQI= Pittsburgh sleep quality index; ACC=Accuracy rate; WM= working memory *P < 0.05; **P < 0.01; ***P < 0.001. 3.2. Oxy-Hb Values During WM Task in MDD Patients with and without Insomnia This study presents a comparison of the mean Oxy-Hb activation changes between MDD patients with and without insomnia. Patients with insomnia demonstrated significantly lower Oxy-Hb activation changes in the R-DLPFC (F = 6.195, p = 0.014) and L-DLPFC (F = 5.070, p = 0.026) during the middle-load WM task, as well as in the L-DLPFC (F = 4.368, p = 0.039) during the high-load WM task, compared to patients without insomnia. However, no significant differences in Oxy-Hb activation changes were observed between the two groups during the low-load WM task across all brain regions (all p > 0.05). These findings suggest that insomnia in MDD patients is associated with reduced prefrontal cortical activation, particularly under higher cognitive load conditions. (Table 2 and Fig. 2 ) Table 2 Oxy-Hb values during the WM task between MDD patients with or without insomnia Insomnia(N = 67) Non-insomnia (N = 55) F p-Value Oxy-Hb activation of low-load WM task R-DLPFC -0.0004 ± 0.024 0.004 ± 0.027 1.018 0.315 L-DLPFC -0.002 ± 0.025 0.005 ± 0.028 1.879 0.173 R-TL 0.004 ± 0.037 0.010 ± 0.035 0.859 0.356 L-TL 0.008 ± 0.040 0.005 ± 0.038 0.189 0.664 R-mPFC 0.001 ± 0.029 0.056 ± 0.034 0.705 0.403 L-mPFC -0.002 ± 0.032 0.004 ± 0.035 0.875 0.352 Oxy-Hb activation of middle-load WM task R-DLPFC -0.006 ± 0.026 0.006 ± 0.027 6.195 0.014* L-DLPFC -0.005 ± 0.021 0.007 ± 0.032 5.070 0.026* R-TL 0.007 ± 0.048 -0.004 ± 0.049 1.558 0.215 L-TL 0.009 ± 0.054 -0.001 ± 0.045 1.228 0.270 R-mPFC -0.0003 ± 0.034 0.005 ± 0.033 0.714 0.400 L-mPFC -0.002 ± 0.042 -0.003 ± 0.040 0.007 0.934 Oxy-Hb activation of high-load WM task R-DLPFC -0.009 ± 0.033 -0.004 ± 0.043 0.546 0.461 L-DLPFC -0.012 ± 0.030 0.0002 ± 0.034 4.368 0.039* R-TL 0.006 ± 0.066 -0.001 ± 0.056 0.348 0.557 L-TL 0.017 ± 0.059 0.007 ± 0.063 0.789 0.376 R-mPFC -0.008 ± 0.050 0.005 ± 0.051 1.785 0.184 L-mPFC -0.012 ± 0.049 -0.004 ± 0.050 0.748 0.389 Note: RBANS= Repeatable Battery for the Assessment of Neuropsychological Status; WM=work memory; DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left *P < 0.05; **P < 0.01; ***P < 0.001. 3.3. Relationship between Working Memory and Oxy-Hb Values During the WM Task in MDD Patients with and without Insomnia Table 3 presents the correlation between working memory function and mean changes in Oxy-Hb activation in MDD patients with insomnia. Correlation analysis revealed that, during the middle-load WM task, the immediate memory subscale score was negatively correlated with Oxy-Hb activation in the L-DLPFC (r = -0.323, df = 63, p = 0.009), as well as the left (r = -0.261, df = 63, p = 0.036) and right (r = -0.256, df = 63, p = 0.039) medial prefrontal cortex (mPFC). Additionally, the ACC rate was negatively correlated with Oxy-Hb activation in the left (r = -0.303, df = 63, p = 0.023) and right (r = -0.346, df = 63, p = 0.009) mPFC. However, no significant correlations were observed between working memory performance and brain activation during the low- and high-load WM tasks. Further multiple linear regression analysis indicated that mean changes in Oxy-Hb activation in the L-DLPFC (B = -88.977, t = -2.7146, p = 0.009, R² change = 0.324) were independently associated with the immediate memory subscale score during the middle-load WM task. Table 3 Relationship between WM and Oxy-Hb values during the task in MDD patients with insomnia Immediate Memory ACC (low-load) ACC (middle -load) ACC (high-load) Oxy-Hb activation of low-load WM task R-DLPFC 0.080 -0.015 --- --- L-DLPFC -0.013 -0.075 --- --- R-TL -0.195 -0.119 --- --- L-TL -0.116 0.057 --- --- R-mPFC -0.095 -0.194 --- --- L-mPFC -0.163 -0.130 --- --- Oxy-Hb activation of middle-load WM task R-DLPFC -0.145 --- -0.237 --- L-DLPFC -0.323 ** --- -0.250 --- R-TL -0.132 --- -0.132 --- L-TL -0.057 --- -0.097 --- R-mPFC -0.256 * --- -0.346 ** --- L-mPFC -0.261 * --- -0.303 * --- Oxy-Hb activation of high-load WM task R-DLPFC -0.089 --- --- -0.108 L-DLPFC -0.041 --- --- -0.038 R-TL -0.059 --- --- -0.120 L-TL -0.039 --- --- -0.020 R-mPFC -0.225 --- --- -0.217 L-mPFC -0.190 --- --- -0.169 Note: DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left; ACC=Accuracy rate, WM= working memory *P < 0.05; **P < 0.01; ***P < 0.001. In contrast, for MDD patients without insomnia, mean changes in Oxy-Hb activation across all brain regions showed no significant correlations with the immediate memory subscale score or accuracy rate during the low-, middle-, or high-load WM tasks (all p > 0.05) (Table 4 ). These findings suggest that, in MDD patients with insomnia, reduced prefrontal cortical activation during cognitive tasks is associated with poorer working memory performance, particularly under moderate cognitive load conditions. Table 4 Relationship between WM and Oxy-Hb values during the task in MDD patients with non-insomnia Immediate Memory ACC (low-load) ACC (middle -load) ACC (high-load) Oxy-Hb activation of low-load WM task R-DLPFC -0.198 -0.019 --- --- L-DLPFC -0.164 -0.059 --- --- R-TL -0.138 -0.087 --- --- L-TL -0.198 -0.045 --- --- R-mPFC -0.266 -0.016 --- --- L-mPFC -0.266 -0.099 --- --- Oxy-Hb activation of middle-load WM task R-DLPFC -0.071 --- 0.070 --- L-DLPFC 0.157 --- 0.041 --- R-TL 0.042 --- -0.137 --- L-TL 0.230 --- 0.076 --- R-mPFC -0.142 --- -0.235 --- L-mPFC -0.077 --- -0.176 --- Oxy-Hb activation of high-load WM task R-DLPFC 0.111 --- --- -0.055 L-DLPFC -0.057 --- --- -0.007 R-TL -0.102 --- --- -0.111 L-TL 0.054 --- --- -0.018 R-mPFC -0.224 --- --- -0.163 L-mPFC -0.244 --- --- -0.015 Note: DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left; ACC=Accuracy rate,WM= working memory *P < 0.05; **P < 0.01; ***P < 0.001. 4.Discussion This study represents the first investigation into WM function and prefrontal cortical activation patterns in MDD patients with and without insomnia using fNIRS during WM tasks. Our findings reveal three key insights: (1) MDD patients with insomnia exhibit greater WM impairment compared to those without insomnia; (2) reduced prefrontal Oxy-Hb activation, particularly in the DLPFC, is observed in the insomnia group during middle- and high-load WM tasks; and (3) WM performance is correlated with prefrontal Oxy-Hb activation specifically in MDD patients with insomnia. These results provide novel neurofunctional evidence linking insomnia to cognitive deficits in MDD and highlight the potential of fNIRS as a tool for identifying distinct MDD subtypes. The observed WM deficits in MDD patients with insomnia align with prior research demonstrating the detrimental effects of sleep disturbances on cognitive function, particularly memory processes (Pearson et al., 2023 ). Sleep disturbances, such as insomnia, are known to disrupt memory consolidation and retrieval, which are critical for effective WM performance. In healthy populations, sleep deprivation has been shown to impair memory encoding and retrieval, leading to fragmented memory processes. In MDD patients, insomnia may exacerbate these effects by impairing emotional regulation and increasing negative emotional states, further disrupting cognitive functions (Hutka et al., 2021 ; Palmer & Alfano, 2017 ). Additionally, altered sleep architecture in MDD—characterized by increased rapid eye movement (REM) sleep and reduced slow-wave sleep (SWS)—may contribute to WM deficits (Della Monica et al., 2018 ; Riemann et al., 2020 ). SWS plays a crucial role in memory consolidation and processing speed, and its reduction in MDD patients with insomnia may underlie the observed WM dysfunction. These findings underscore the importance of addressing insomnia in MDD patients as a modifiable factor that could improve cognitive outcomes. Our study also revealed distinct prefrontal activation patterns between MDD patients with and without insomnia during WM tasks. During the middle-load WM task, MDD patients with insomnia exhibited inhibitory activation in the bilateral DLPFC, while those without insomnia showed excitatory activation in the same regions. A similar pattern was observed during the high-load WM task, with significantly weaker L-DLPFC activation in the insomnia group. However, no group differences in frontotemporal activation were observed during the low-load WM task, likely due to the task's simplicity masking performance disparities. These findings suggest that insomnia-related cognitive deficits are more pronounced during complex WM tasks. The underlying mechanism may involve insomnia-related dysregulation of prefrontal cortical function, as sleep disturbances have been shown to reduce the connection between prefrontal and gray matter volume and impair neurophysiological processes critical for cognitive load management (Xie et al., 2020 ). Moreover, heightened arousal—a core feature of insomnia—has been linked to altered functional connectivity between the salience network (SN) and the DLPFC, with stronger SN-DLPFC coupling correlating with greater insomnia severity (measured by PSQI scores) (Cheng et al., 2022 ). These neurofunctional alterations likely contribute to the observed deficits in complex WM performance. Another significant finding of this study is the negative correlation between Oxy-Hb concentrations in the left DLPFC and bilateral medial prefrontal cortex (mPFC) and immediate memory function in MDD patients with insomnia. Specifically, the Oxy-Hb concentration in the L-DLPFC was most strongly associated with immediate memory function. Additionally, during the middle-load WM task, the Oxy-Hb concentrations in the bilateral mPFC were negatively correlated with the accuracy rate in MDD patients with insomnia. These results suggest that impairments in working memory are closely related to reduced frontal cortex activation in MDD patients with insomnia. These findings are consistent with previous studies using VFT, which also reported negative correlations between memory function and Oxy-Hb concentrations in the L-DLPFC and left mPFC in MDD patients with insomnia. Mechanistically, this may reflect structural and functional alterations in the prefrontal cortex, such as reduced gray matter volume observed in insomnia-related MDD, which could impair synaptic efficiency and neural resource allocation during cognitive tasks (M. Li et al., 2018 ; Zhu et al., 2021 ). Additionally, diminished brain-derived neurotrophic factor (BDNF) levels—a key mediator of synaptic plasticity and neurogenesis—may exacerbate these deficits by disrupting neural circuits critical for memory encoding and retrieval (Feng et al., 2019 ). To our knowledge, this is the first study to utilize fNIRS during a WM task paradigm to directly associate prefrontal hypoactivation with working memory impairments in MDD patients with insomnia, providing a neurofunctional framework for understanding cognitive decline in this comorbid population. Despite these significant findings, our study has several limitations. Firstly, as a cross-sectional study, it is difficult to establish causality between insomnia, prefrontal activation, and WM deficits in MDD patients. Future longitudinal studies are needed to explore these relationships further. Secondly, the study was conducted in a single psychiatric hospital in Zhejiang Province, southern China, which may limit the generalizability of the findings to other regions or cultural backgrounds. Expanding the sample size and including participants from diverse geographical and cultural settings would enhance the external validity of the results. Finally, our study did not control for medication use in MDD patients, which may influence brain activation patterns. Future research should account for the effects of antidepressants and other medications to better understand their impact on cognitive function and brain activation in this population. 5.Conclusion This study provides novel insights into the neurofunctional correlates of working memory deficits in MDD patients with insomnia. The reduced prefrontal activation observed in the insomnia group, particularly during complex WM tasks, highlights the potential of fNIRS as a diagnostic tool for identifying distinct MDD subtypes. With the increasing availability of neuroimaging data such as fNIRS, artificial intelligence (AI) technologies hold promise in enhancing brain health promotion. Future work may incorporate machine learning algorithms to classify MDD subtypes (e.g., insomnia vs non-insomnia), predict cognitive outcomes based on prefrontal hemodynamic patterns, or assist in individualized treatment planning. By leveraging AI to decode complex brain-behavior relationships, we can move toward precision psychiatry that enables early detection and targeted intervention for cognitive impairment in MDD patients with insomnia. Abbreviations Major depressive disorder (MDD), working memory (WM), functional near-infrared spectroscopy (fNIRS), Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Scale (HAMD), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Immediate Memory (IM), oxyhemoglobin (Oxy-Hb), deoxyhemoglobin (Deoxy-Hb), total hemoglobin (Total-Hb), electroencephalogram (EEG), regions of interest (ROIs), right dorsolateral prefrontal cortex (R-DLPFC), left dorsolateral prefrontal cortex (L-DLPFC), right medial prefrontal cortex (R-mPFC), left medial prefrontal cortex (L-mPFC), right temporal lobe (R-TL), and left temporal lobe (L-TL), non-working memory (NWM), rapid eye movement (REM), reduced slow-wave sleep (SWS), salience network (SN), brain-derived neurotrophic factor (BDNF), artificial intelligence (AI). Declarations ● Ethics approval and consent to participate Prior to the study's commencement, ethical approval was obtained from the Ethics Committee of Ningbo Kangning Hospital (NBKNYY-2023-LC-31), ensuring compliance with the ethical standards and regulations of the Declaration of Helsinki on Human Research. All participants were provided with a comprehensive explanation of the study's purpose, procedures, and potential risks. Those who agreed to participate voluntarily provided written informed consent, and minor participants have voluntarily provided written informed consent through their legal guardians. ● Consent for publication We have anonymized all patient information, and all participate s agreed to the publication. ● Availability of data and materials Data available on request from the authors. ● Competing Interests No conflict of interest was disclosed for each author. ● Funding This research was supported by The National Natural Science Foundation of China‌ (82501842); Humanities and Social Sciences Project of the Ministry of Education (No. 24YJCZH129); Ningbo Medical and Health Brand Discipline (No. PPXK20182024-0708); Ningbo Clinical Medical Research Centre for Mental Health (No. 2022L002); Ningbo Top Medical and Health Research Program (No. 2022030410); Zhejiang Province Medical and Health Project (No. 2025KY276). ● Authors' contributions YLL and CZ were responsible for the study concept and design. YNL, MMX, YBW, YYZ, TTL, LW, ZCD, ZWL, LJL, and KYJ contributed to the data collection and experimental procedures. YLL, CZ and YMX assisted with data analysis and interpretation of findings. YLL and HHY drafted the manuscript. XXL and DSZ provided critical revision of the manuscript for important intellectual content. All authors critically reviewed the content and approved the final version for publication. We anonymized all patient information, and all patients consented to publication. ● Acknowledgements We sincerely thank all the patients who participated in this study. We are also grateful to the clinical and research staff, including physicians, nurses, and research coordinators, for their invaluable contributions to patient recruitment, assessments. we extend our gratitude to the technical team for providing assistance with the fNIRS equipment and software used in this trial. References Anderson, E., et al. (2024). Depression - Understanding, Identifying, and Diagnosing. 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Brain Imaging Behav, 12 (6), 1759-1767. doi:10.1007/s11682-018-9844-x Li, Y., et al. (2024). Relationship between cognitive function and brain activation in major depressive disorder patients with and without insomnia: A functional near-infrared spectroscopy (fNIRS) study. J Psychiatr Res, 169 , 134-141. doi:10.1016/j.jpsychires.2023.11.002 Liu, X., et al. (2024). The abnormal brain activation pattern of adolescents with major depressive disorder based on working memory tasks: A fNIRS study. J Psychiatr Res, 169 , 31-37. doi:10.1016/j.jpsychires.2023.10.054 Maier, W. (1990). The Hamilton Depression Scale and its alternatives: a comparison of their reliability and validity. Psychopharmacol Ser, 9 , 64-71. doi:10.1007/978-3-642-75373-2_8 Maramis, M. M., et al. (2021). Impaired Cognitive Flexibility and Working Memory Precedes Depression: A Rat Model to Study Depression. Neuropsychobiology, 80 (3), 225-233. doi:10.1159/000508682 McCarron, R. M., et al. (2021). Depression. Ann Intern Med, 174 (5), Itc65-itc80. doi:10.7326/aitc202105180 Palmer, C. A., & Alfano, C. A. (2017). Sleep and emotion regulation: An organizing, integrative review. Sleep Med Rev, 31 , 6-16. doi:10.1016/j.smrv.2015.12.006 Pearson, O., et al. (2023). The relationship between sleep disturbance and cognitive impairment in mood disorders: A systematic review. J Affect Disord, 327 , 207-216. doi:10.1016/j.jad.2023.01.114 Pinti, P., et al. (2020). The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci, 1464 (1), 5-29. doi:10.1111/nyas.13948 Riemann, D., et al. (2020). Sleep, insomnia, and depression. Neuropsychopharmacology, 45 (1), 74-89. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/31071719. doi:10.1038/s41386-019-0411-y Sivertsen, B., et al. (2014). Insomnia as a risk factor for ill health: results from the large population-based prospective HUNT Study in Norway. J Sleep Res, 23 (2), 124-132. doi:10.1111/jsr.12102 Sofi, F., et al. (2014). Insomnia and risk of cardiovascular disease: a meta-analysis. Eur J Prev Cardiol, 21 (1), 57-64. doi:10.1177/2047487312460020 Songco, A., et al. (2023). Affective working memory in depression. Emotion, 23 (6), 1802-1807. doi:10.1037/emo0001130 Sun, J., et al. (2019). Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study. J Biomed Opt, 24 (5), 1-7. doi:10.1117/1.Jbo.24.5.051410 Xie, D., et al. (2020). Functional Connectivity Abnormalities of Brain Regions With Structural Deficits in Primary Insomnia Patients. Front Neurosci, 14 , 566. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/32670005. doi:10.3389/fnins.2020.00566 Xu, H., et al. (2023). Insomniacs show greater prefrontal activation during verbal fluency task compared to non-insomniacs: a functional near-infrared spectroscopy investigation of depression in patients. BMC Psychiatry, 23 (1), 217. doi:10.1186/s12888-023-04694-z Zhu, Y., et al. (2021). Functional connectivity density abnormalities and anxiety in primary insomnia patients. Brain Imaging Behav, 15 (1), 114-121. doi:10.1007/s11682-019-00238-w Additional Declarations No competing interests reported. Supplementary Files figROI.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Editor invited by journal 22 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 21 Apr, 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. 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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-9434079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633016675,"identity":"c9cd8fa3-f665-464a-8a88-e20e349aa3d4","order_by":0,"name":"Yanli Li","email":"","orcid":"","institution":"Kangning Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Li","suffix":""},{"id":633016676,"identity":"1bc45f6f-2ce8-4c4a-8658-8250e24159b7","order_by":1,"name":"Chao Zheng","email":"","orcid":"","institution":"Kangning Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zheng","suffix":""},{"id":633016677,"identity":"92eae2ab-84f9-4277-b944-deb03270ad43","order_by":2,"name":"Yanni Li","email":"","orcid":"","institution":"Kangning Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yanni","middleName":"","lastName":"Li","suffix":""},{"id":633016678,"identity":"7bf20eb7-0319-46c9-8610-918a36d11856","order_by":3,"name":"Yubo Wang","email":"","orcid":"","institution":"Kangning Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Wang","suffix":""},{"id":633016679,"identity":"89881e11-26b7-4259-ac38-4cc31a0ee64a","order_by":4,"name":"Zichen Ding","email":"","orcid":"","institution":"Kangning Hospital of Ningbo 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University","correspondingAuthor":true,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-04-16 06:40:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9434079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9434079/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108947478,"identity":"580c59d5-b55f-440a-b821-2cc79b493bc0","added_by":"auto","created_at":"2026-05-11 06:29:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175965,"visible":true,"origin":"","legend":"\u003cp\u003eThe protocol of the three WM-load tasks.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e• low-load WM task: WM task total 19s = task cue 3s + Stage 1 (WM memory, one graph 3s, total 6s) + Stage 2 (WM search 10s), NWM task total 15s = Stage 3 (NWM search 15s)\u003c/p\u003e\n\u003cp\u003e• middle-load WM task: WM task total 22s = task cue 3s + Stage 1 (WM memory, one graph 3s, total 6s) + Stage 2 (WM search 10s), NWM task total 15s = Stage 3 (NWM search 15s)\u003c/p\u003e\n\u003cp\u003e• high-load WM task: WM task total 30s = task cue 3s + Stage 1 (WM memory, one graph 3s, total 6s) + Stage 2 (WM search 15s), NWM task total 15s = Stage 3 (NWM search 15s)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9434079/v1/6976294ca3d5cb04b30968ec.png"},{"id":108947489,"identity":"c5384464-d95a-4aca-b1bb-82647011c360","added_by":"auto","created_at":"2026-05-11 06:29:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":623947,"visible":true,"origin":"","legend":"\u003cp\u003eA: Differences in Oxy-Hb values during low-load WM task\u003c/p\u003e\n\u003cp\u003eB: Differences in Oxy-Hb values during middle -load WM task\u003c/p\u003e\n\u003cp\u003eC: Differences in Oxy-Hb values during high -load WM task\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9434079/v1/df14430ed7077652fc1a92ab.png"},{"id":108947577,"identity":"f83a8413-75ec-4d75-b829-69e5cd09b2ca","added_by":"auto","created_at":"2026-05-11 06:29:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9434079/v1/8f98ed72-d4af-430e-8a2a-435b62a0a906.pdf"},{"id":108947559,"identity":"033c8204-2b23-45b9-ac82-e59b32a06053","added_by":"auto","created_at":"2026-05-11 06:29:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":129491,"visible":true,"origin":"","legend":"","description":"","filename":"figROI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9434079/v1/ce44e3377c75e51c4b86c8e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Working Memory and Brain Activation in Major Depressive Disorder with and without Insomnia: Insights from Functional Near-Infrared Spectroscopy (fNIRS)","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eMajor Depressive Disorder (MDD) is a leading global psychiatric disorder, affecting over 322\u0026nbsp;million people worldwide. It is projected to become the primary cause of disease burden within the next two decades. Core symptoms of MDD include persistent low mood, anhedonia, fatigue, cognitive impairments (e.g., reduced attention and low self-esteem), and sleep disturbances (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; McCarron et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, insomnia affects 67\u0026ndash;84% of MDD patients during depressive episodes, exacerbating symptom severity, impairing recovery, and increasing risks of cardiovascular and autoimmune comorbidities (Boland et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sivertsen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sofi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While insomnia\u0026rsquo;s role in emotional dysregulation is well-documented, its contribution to cognitive dysfunction\u0026mdash;particularly working memory deficits\u0026mdash;remains poorly understood, despite evidence linking insomnia severity to poorer task accuracy and slower processing speeds in MDD patients(Maramis et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Songco et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCognitive impairments, especially working memory deficits, are a hallmark of MDD and critically predict functional disability and relapse risk (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These deficits may represent endophenotypes with distinct pathophysiology, yet current clinical practices prioritize emotional symptom management, overlooking cognitive dysfunction as a secondary concern (Brownlow et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address this gap, functional near-infrared spectroscopy (fNIRS) offers a unique tool for mapping cortical hemodynamics in clinical settings. Unlike fMRI, fNIRS combines portability with tolerance to movement artifacts, enabling real-time monitoring of prefrontal activation during ecologically valid tasks (Pinti et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Prior fNIRS studies have revealed reduced dorsolateral prefrontal cortex (DLPFC) activation in MDD patients during working memory tasks (Liu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, no studies have compared neural activation patterns between MDD patients with and without insomnia\u0026mdash;a critical omission given insomnia\u0026rsquo;s potential to affect prefrontal function.\u003c/p\u003e \u003cp\u003eThis study hypothesizes that MDD patients with insomnia will exhibit (1) poorer working memory performance, (2) reduced DLPFC activation during medium/high-load tasks, and (3) a stronger correlation between DLPFC hypoactivation and cognitive deficits in the insomnia subgroup. By clarifying these neurofunctional distinctions, our findings aim to advance diagnostic precision and guide targeted therapies for MDD patients with comorbid insomnia.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eThis study was conducted at Kangning Hospital in Ningbo, Zhejiang Province, from July 2021 to December 2023 and included 122 eligible patients diagnosed with MDD. Participants were recruited through physician referrals, with doctors introducing the research project to potential candidates. Inclusion criteria required patients to meet the diagnostic criteria for MDD as defined by the (Inclusion criteria for subjects included:) 1) Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), 2) age between 16 and 65 years, and have a Hamilton Depression Rating Scale-24 (HAMD-24) total score of \u0026ge;\u0026thinsp;20, indicating moderate to severe depression. Exclusion criteria included a history of psychosis, significant physical or organic brain diseases, substance abuse or dependence (except for cigarette smoking), and pregnancy or lactation.\u003c/p\u003e \u003cp\u003e Prior to the study's commencement, ethical approval was obtained from the Ethics Committee of Ningbo Kangning Hospital (NBKNYY-2023-LC-31), ensuring compliance with the ethical standards and regulations of the Declaration of Helsinki on Human Research. All participants were provided with a comprehensive explanation of the study's purpose, procedures, and potential risks. Those who agreed to participate voluntarily provided written informed consent, and minor participants have voluntarily provided written informed consent through their legal guardians. These steps ensured that the study adhered to ethical standards and that participants were fully informed about their involvement in the research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cp\u003eIn this study, depressive symptoms in patients with MDD were assessed using the HAMD-24. The total HAMD-24 score reflects the severity of depression, with scores interpreted as follows: \u0026le;7 indicates no depressive symptoms, 8\u0026ndash;20 suggests probable depression, 20\u0026ndash;35 indicates definite depression, and \u0026gt;\u0026thinsp;35 signifies severe depression. Higher scores correspond to more severe depressive symptoms(Maier, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a seven-component scale that assesses sleep quality, latency, duration, efficiency, disturbances, use of hypnotics, and daytime dysfunction over the past month. Each component is scored from 0 to 3, and the cumulative score ranges from 0 to 21, with higher scores indicating poorer sleep quality (Buysse et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In this study, insomnia was defined as a total PSQI score of \u0026ge;\u0026thinsp;11.\u003c/p\u003e \u003cp\u003eImmediate Memory (IM) was assessed using items 1 (word learning) and 2 (story retelling) from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Raw scores were converted to scaled scores based on American norms, and the two scores were combined to generate an immediate memory score, with higher scores indicating better memory function (Faust et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWM was evaluated using a task that included low-, middle-, and high-load conditions. Each session began with a 5-second welcome screen and a 5-second relaxation period, followed by four task sets (WM\u0026thinsp;+\u0026thinsp;Relax\u0026thinsp;+\u0026thinsp;Non-WM\u0026thinsp;+\u0026thinsp;Relax). The task involved random combinations of four shapes (circles, triangles, hearts, stars) and four colors (red, yellow, blue, green). In the low-load condition, Stage 1 required participants to remember the colors, shapes, and order of two sequentially displayed shapes (3 seconds each). Stage 2 involved recalling and clicking the shapes in the memorized order within 10 seconds, while Stage 3 was a non-WM task where participants clicked graphical cues without memorization for 15 seconds. The middle- and high-load conditions involved 3- and 4-stimuli memory tasks, respectively (Sun et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants practiced once before the formal test to ensure understanding. During testing, they remained seated and avoided large movements to minimize interference.\u003c/p\u003e \u003cp\u003ePrior to the study, two experienced psychiatrists were trained to administer the assessments. Their inter-rater reliability, measured by correlation coefficients for HAMD-24, PSQI, and RBANS, exceeded 0.8 after repeated measures, ensuring consistency in evaluations. This rigorous training and validation process helped maintain the accuracy and reliability of the data collected throughout the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. fNIRS Measurements\u003c/h2\u003e \u003cp\u003eA 48-channel fNIRS system (NirScan, Danyang Huichuang Medical Equipment Co., Ltd., China) was used to record hemodynamic responses in the cerebral cortex of patients with MDD during the WM task. The system operated at a sampling frequency of 11 Hz and utilized near-infrared light at wavelengths of 730, 808, and 850 nm to measure changes in oxyhemoglobin (Oxy-Hb), deoxyhemoglobin (Deoxy-Hb), and total hemoglobin (Total-Hb) concentrations.\u003c/p\u003e \u003cp\u003eThe fNIRS probes, comprising 15 light sources and 16 detectors, were positioned over the frontal and temporal regions according to the 10\u0026ndash;20 international electroencephalogram (EEG) system. The inter-probe distance was fixed at 3 cm, with the bottom probe aligned along the Fp1-Fp2 line. The 48 channels were grouped into six regions of interest (ROIs) based on device coordinates: the right dorsolateral prefrontal cortex (R-DLPFC), left dorsolateral prefrontal cortex (L-DLPFC), right medial prefrontal cortex (R-mPFC), left medial prefrontal cortex (L-mPFC), right temporal lobe (R-TL), and left temporal lobe (L-TL). The specific channel-to-region mapping is illustrated in Supplementary materials 1. This setup allowed for precise localization and analysis of cortical hemodynamic activity during the WM task.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Processing and Analysis\u003c/h2\u003e \u003cp\u003eThe fNIRS data were processed using NirSpark analysis software. Motion artifacts and environmental noise were first corrected, followed by the removal of physiological noise using a 0.01\u0026ndash;0.20 Hz bandpass filter. The Beer-Lambert law was then applied to convert the optical data into blood oxygen concentration values. Oxy-Hb changes were prioritized for analysis due to their stronger correlation with cortical activation and higher signal-to-noise ratio compared to Deoxy-Hb (Y. Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA 2-second pre-task interval was used as the baseline for normalization. The average Oxy-Hb activation changes were calculated for the WM task, with durations of 10 seconds, 10 seconds, and 15 seconds for the low-, medium-, and high-load conditions, respectively, as well as for the non-working memory (NWM) task (15 seconds) in each channel. Within each ROI, the Oxy-Hb concentration changes were averaged across all channels to derive the mean values for the WM task. This approach ensured a robust and standardized analysis of cortical hemodynamic responses during the task.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe sociodemographic characteristics, clinical variables, accuracy rates, and Oxy-Hb activation during the WM task were first described for all subjects. The normality of all variables was confirmed using the Kolmogorov-Smirnov one-sample test. Group differences between MDD patients with and without insomnia were analyzed using one-way ANOVA for continuous variables and the chi-square test (χ\u0026sup2; test) for categorical variables, with Bonferroni correction applied for multiple comparisons. Pearson\u0026rsquo;s correlation coefficient was used to evaluate the relationship between working memory function and Oxy-Hb activation during the WM task in both groups. Additionally, multiple linear regression analyses were conducted in MDD patients with insomnia to identify brain regions with the strongest association between Oxy-Hb activation and immediate memory. Statistical analyses were performed using SPSS 25.0, with a two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Demographic and basic descriptive data\u003c/h2\u003e \u003cp\u003eNo significant differences were observed between MDD patients with and without insomnia in years of depressive symptoms, age, education, gender, BMI, or and course of illness (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with significant differences in HAMD-24 scores (F\u0026thinsp;=\u0026thinsp;4.661, p\u0026thinsp;=\u0026thinsp;0.033), PSQI scores (F\u0026thinsp;=\u0026thinsp;257.626, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Immediate Memory (F\u0026thinsp;=\u0026thinsp;5.453, p\u0026thinsp;=\u0026thinsp;0.021). Additionally, while no significant differences were found in ACC for low- or high-load WM tasks (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the two groups showed statistically significant differences in ACC for the middle-load WM task (F\u0026thinsp;=\u0026thinsp;5.531, p\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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 MDD patients with or without insomnia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsomnia(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-insomnia\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\\x\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.39\u0026thinsp;\u0026plusmn;\u0026thinsp;15.16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisode duration (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.43\u0026thinsp;\u0026plusmn;\u0026thinsp;65.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.39\u0026thinsp;\u0026plusmn;\u0026thinsp;57.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21/33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide(yes/no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmediate Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.015\u0026thinsp;\u0026plusmn;\u0026thinsp;16.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.315\u0026thinsp;\u0026plusmn;\u0026thinsp;17.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWM task\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC (low-load)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.913\u0026thinsp;\u0026plusmn;\u0026thinsp;0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u0026thinsp;\u0026plusmn;\u0026thinsp;0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC (middle -load)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u0026thinsp;\u0026plusmn;\u0026thinsp;0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881\u0026thinsp;\u0026plusmn;\u0026thinsp;0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC (high-load)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.650\u0026thinsp;\u0026plusmn;\u0026thinsp;0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.663\u0026thinsp;\u0026plusmn;\u0026thinsp;0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: BMI=body mass index; HAMD-24\u0026thinsp;=\u0026thinsp;24-item Hamilton Depression Scale; PSQI= Pittsburgh sleep quality index; ACC=Accuracy rate; WM= working memory\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Oxy-Hb Values During WM Task in MDD Patients with and without Insomnia\u003c/h2\u003e \u003cp\u003eThis study presents a comparison of the mean Oxy-Hb activation changes between MDD patients with and without insomnia. Patients with insomnia demonstrated significantly lower Oxy-Hb activation changes in the R-DLPFC (F\u0026thinsp;=\u0026thinsp;6.195, p\u0026thinsp;=\u0026thinsp;0.014) and L-DLPFC (F\u0026thinsp;=\u0026thinsp;5.070, p\u0026thinsp;=\u0026thinsp;0.026) during the middle-load WM task, as well as in the L-DLPFC (F\u0026thinsp;=\u0026thinsp;4.368, p\u0026thinsp;=\u0026thinsp;0.039) during the high-load WM task, compared to patients without insomnia. However, no significant differences in Oxy-Hb activation changes were observed between the two groups during the low-load WM task across all brain regions (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings suggest that insomnia in MDD patients is associated with reduced prefrontal cortical activation, particularly under higher cognitive load conditions. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOxy-Hb values during the WM task between MDD patients with or without insomnia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsomnia(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-insomnia\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of low-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of middle-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of high-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: RBANS= Repeatable Battery for the Assessment of Neuropsychological Status; WM=work memory; DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3. Relationship between Working Memory and Oxy-Hb Values During the WM Task in MDD Patients with and without Insomnia\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the correlation between working memory function and mean changes in Oxy-Hb activation in MDD patients with insomnia. Correlation analysis revealed that, during the middle-load WM task, the immediate memory subscale score was negatively correlated with Oxy-Hb activation in the L-DLPFC (r = -0.323, df\u0026thinsp;=\u0026thinsp;63, p\u0026thinsp;=\u0026thinsp;0.009), as well as the left (r = -0.261, df\u0026thinsp;=\u0026thinsp;63, p\u0026thinsp;=\u0026thinsp;0.036) and right (r = -0.256, df\u0026thinsp;=\u0026thinsp;63, p\u0026thinsp;=\u0026thinsp;0.039) medial prefrontal cortex (mPFC). Additionally, the ACC rate was negatively correlated with Oxy-Hb activation in the left (r = -0.303, df\u0026thinsp;=\u0026thinsp;63, p\u0026thinsp;=\u0026thinsp;0.023) and right (r = -0.346, df\u0026thinsp;=\u0026thinsp;63, p\u0026thinsp;=\u0026thinsp;0.009) mPFC. However, no significant correlations were observed between working memory performance and brain activation during the low- and high-load WM tasks. Further multiple linear regression analysis indicated that mean changes in Oxy-Hb activation in the L-DLPFC (B = -88.977, t = -2.7146, p\u0026thinsp;=\u0026thinsp;0.009, R\u0026sup2; change\u0026thinsp;=\u0026thinsp;0.324) were independently associated with the immediate memory subscale score during the middle-load WM task.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between WM and Oxy-Hb values during the task in MDD patients with insomnia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate Memory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(low-load)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(middle -load)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(high-load)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of low-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of middle-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.323\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.346\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.261\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.303\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of high-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left; ACC=Accuracy rate, WM= working memory\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, for MDD patients without insomnia, mean changes in Oxy-Hb activation across all brain regions showed no significant correlations with the immediate memory subscale score or accuracy rate during the low-, middle-, or high-load WM tasks (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings suggest that, in MDD patients with insomnia, reduced prefrontal cortical activation during cognitive tasks is associated with poorer working memory performance, particularly under moderate cognitive load conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between WM and Oxy-Hb values during the task in MDD patients with non-insomnia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate Memory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(low-load)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(middle -load)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003cp\u003e(high-load)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of low-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of middle-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxy-Hb activation of high-load WM task\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-TL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-mPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: DLPFC=dorsolateral prefrontal cortex; mPFC=medial prefrontal cortex; TL=temporal lobe; R=right; L=left; ACC=Accuracy rate,WM= working memory\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eThis study represents the first investigation into WM function and prefrontal cortical activation patterns in MDD patients with and without insomnia using fNIRS during WM tasks. Our findings reveal three key insights: (1) MDD patients with insomnia exhibit greater WM impairment compared to those without insomnia; (2) reduced prefrontal Oxy-Hb activation, particularly in the DLPFC, is observed in the insomnia group during middle- and high-load WM tasks; and (3) WM performance is correlated with prefrontal Oxy-Hb activation specifically in MDD patients with insomnia. These results provide novel neurofunctional evidence linking insomnia to cognitive deficits in MDD and highlight the potential of fNIRS as a tool for identifying distinct MDD subtypes.\u003c/p\u003e \u003cp\u003eThe observed WM deficits in MDD patients with insomnia align with prior research demonstrating the detrimental effects of sleep disturbances on cognitive function, particularly memory processes (Pearson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sleep disturbances, such as insomnia, are known to disrupt memory consolidation and retrieval, which are critical for effective WM performance. In healthy populations, sleep deprivation has been shown to impair memory encoding and retrieval, leading to fragmented memory processes. In MDD patients, insomnia may exacerbate these effects by impairing emotional regulation and increasing negative emotional states, further disrupting cognitive functions (Hutka et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Palmer \u0026amp; Alfano, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, altered sleep architecture in MDD\u0026mdash;characterized by increased rapid eye movement (REM) sleep and reduced slow-wave sleep (SWS)\u0026mdash;may contribute to WM deficits (Della Monica et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Riemann et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SWS plays a crucial role in memory consolidation and processing speed, and its reduction in MDD patients with insomnia may underlie the observed WM dysfunction. These findings underscore the importance of addressing insomnia in MDD patients as a modifiable factor that could improve cognitive outcomes.\u003c/p\u003e \u003cp\u003eOur study also revealed distinct prefrontal activation patterns between MDD patients with and without insomnia during WM tasks. During the middle-load WM task, MDD patients with insomnia exhibited inhibitory activation in the bilateral DLPFC, while those without insomnia showed excitatory activation in the same regions. A similar pattern was observed during the high-load WM task, with significantly weaker L-DLPFC activation in the insomnia group. However, no group differences in frontotemporal activation were observed during the low-load WM task, likely due to the task's simplicity masking performance disparities. These findings suggest that insomnia-related cognitive deficits are more pronounced during complex WM tasks. The underlying mechanism may involve insomnia-related dysregulation of prefrontal cortical function, as sleep disturbances have been shown to reduce the connection between prefrontal and gray matter volume and impair neurophysiological processes critical for cognitive load management (Xie et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, heightened arousal\u0026mdash;a core feature of insomnia\u0026mdash;has been linked to altered functional connectivity between the salience network (SN) and the DLPFC, with stronger SN-DLPFC coupling correlating with greater insomnia severity (measured by PSQI scores) (Cheng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These neurofunctional alterations likely contribute to the observed deficits in complex WM performance.\u003c/p\u003e \u003cp\u003eAnother significant finding of this study is the negative correlation between Oxy-Hb concentrations in the left DLPFC and bilateral medial prefrontal cortex (mPFC) and immediate memory function in MDD patients with insomnia. Specifically, the Oxy-Hb concentration in the L-DLPFC was most strongly associated with immediate memory function. Additionally, during the middle-load WM task, the Oxy-Hb concentrations in the bilateral mPFC were negatively correlated with the accuracy rate in MDD patients with insomnia. These results suggest that impairments in working memory are closely related to reduced frontal cortex activation in MDD patients with insomnia. These findings are consistent with previous studies using VFT, which also reported negative correlations between memory function and Oxy-Hb concentrations in the L-DLPFC and left mPFC in MDD patients with insomnia. Mechanistically, this may reflect structural and functional alterations in the prefrontal cortex, such as reduced gray matter volume observed in insomnia-related MDD, which could impair synaptic efficiency and neural resource allocation during cognitive tasks (M. Li et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, diminished brain-derived neurotrophic factor (BDNF) levels\u0026mdash;a key mediator of synaptic plasticity and neurogenesis\u0026mdash;may exacerbate these deficits by disrupting neural circuits critical for memory encoding and retrieval (Feng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, this is the first study to utilize fNIRS during a WM task paradigm to directly associate prefrontal hypoactivation with working memory impairments in MDD patients with insomnia, providing a neurofunctional framework for understanding cognitive decline in this comorbid population.\u003c/p\u003e \u003cp\u003eDespite these significant findings, our study has several limitations. Firstly, as a cross-sectional study, it is difficult to establish causality between insomnia, prefrontal activation, and WM deficits in MDD patients. Future longitudinal studies are needed to explore these relationships further. Secondly, the study was conducted in a single psychiatric hospital in Zhejiang Province, southern China, which may limit the generalizability of the findings to other regions or cultural backgrounds. Expanding the sample size and including participants from diverse geographical and cultural settings would enhance the external validity of the results. Finally, our study did not control for medication use in MDD patients, which may influence brain activation patterns. Future research should account for the effects of antidepressants and other medications to better understand their impact on cognitive function and brain activation in this population.\u003c/p\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eThis study provides novel insights into the neurofunctional correlates of working memory deficits in MDD patients with insomnia. The reduced prefrontal activation observed in the insomnia group, particularly during complex WM tasks, highlights the potential of fNIRS as a diagnostic tool for identifying distinct MDD subtypes. With the increasing availability of neuroimaging data such as fNIRS, artificial intelligence (AI) technologies hold promise in enhancing brain health promotion. Future work may incorporate machine learning algorithms to classify MDD subtypes (e.g., insomnia vs non-insomnia), predict cognitive outcomes based on prefrontal hemodynamic patterns, or assist in individualized treatment planning. By leveraging AI to decode complex brain-behavior relationships, we can move toward precision psychiatry that enables early detection and targeted intervention for cognitive impairment in MDD patients with insomnia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMajor depressive disorder (MDD), working memory (WM), functional near-infrared spectroscopy (fNIRS), Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Scale (HAMD), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Immediate Memory (IM), oxyhemoglobin (Oxy-Hb), deoxyhemoglobin (Deoxy-Hb), total hemoglobin (Total-Hb), electroencephalogram (EEG), regions of interest (ROIs), right dorsolateral prefrontal cortex (R-DLPFC), left dorsolateral prefrontal cortex (L-DLPFC), right medial prefrontal cortex (R-mPFC), left medial prefrontal cortex (L-mPFC), right temporal lobe (R-TL), and left temporal lobe (L-TL), non-working memory (NWM), rapid eye movement (REM), reduced slow-wave sleep (SWS), salience network (SN), brain-derived neurotrophic factor (BDNF), artificial intelligence (AI).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e● Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003ePrior to the study\u0026apos;s commencement, ethical approval was obtained from the Ethics Committee of Ningbo Kangning Hospital (NBKNYY-2023-LC-31), ensuring compliance with the ethical standards and regulations of the Declaration of Helsinki on Human Research. All participants were provided with a comprehensive explanation of the study\u0026apos;s purpose, procedures, and potential risks. Those who agreed to participate voluntarily provided written informed consent, and minor participants have voluntarily provided written informed consent through their legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● Consent for publication\u003c/p\u003e\n\u003cp\u003eWe have anonymized all patient information, and all participate s agreed to the publication.\u003c/p\u003e\n\u003cp\u003e● Availability of data and materials\u003c/p\u003e\n\u003cp\u003eData available on request from the authors.\u003c/p\u003e\n\u003cp\u003e● Competing Interests\u003c/p\u003e\n\u003cp\u003eNo conflict of interest was disclosed for each author.\u003c/p\u003e\n\u003cp\u003e● Funding\u003c/p\u003e\n\u003cp\u003eThis research was supported by The National Natural Science Foundation of China\u0026zwnj; (82501842); Humanities and Social Sciences Project of the Ministry of Education (No. 24YJCZH129); Ningbo Medical and Health Brand Discipline (No. PPXK20182024-0708); Ningbo Clinical Medical Research Centre for Mental Health (No. 2022L002); Ningbo Top Medical and Health Research Program (No. 2022030410); Zhejiang Province Medical and Health Project (No. 2025KY276).\u003c/p\u003e\n\u003cp\u003e● Authors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eYLL and CZ were responsible for the study concept and design. YNL, MMX, YBW, YYZ, TTL, LW, ZCD, ZWL, LJL, and KYJ contributed to the data collection and experimental procedures. YLL, CZ and YMX assisted with data analysis and interpretation of findings. YLL and HHY drafted the manuscript. XXL and DSZ provided critical revision of the manuscript for important intellectual content. All authors critically reviewed the content and approved the final version for publication. We anonymized all patient information, and all patients consented to publication.\u003c/p\u003e\n\u003cp\u003e● Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the patients who participated in this study. We are also grateful to the clinical and research staff, including physicians, nurses, and research coordinators, for their invaluable contributions to patient recruitment, assessments. we extend our gratitude to the technical team for providing assistance with the fNIRS equipment and software used in this trial.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, E., et al. (2024). Depression - Understanding, Identifying, and Diagnosing. \u003cem\u003eN Engl J Med, 390\u003c/em\u003e(17), e41. doi:10.1056/NEJMp2310179\u003c/li\u003e\n\u003cli\u003eBoland, E. M., et al. (2023). Does insomnia treatment prevent depression? \u003cem\u003eSleep, 46\u003c/em\u003e(6). doi:10.1093/sleep/zsad104\u003c/li\u003e\n\u003cli\u003eBrownlow, J. A., et al. (2020). 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The abnormal brain activation pattern of adolescents with major depressive disorder based on working memory tasks: A fNIRS study. \u003cem\u003eJ Psychiatr Res, 169\u003c/em\u003e, 31-37. doi:10.1016/j.jpsychires.2023.10.054\u003c/li\u003e\n\u003cli\u003eMaier, W. (1990). The Hamilton Depression Scale and its alternatives: a comparison of their reliability and validity. \u003cem\u003ePsychopharmacol Ser, 9\u003c/em\u003e, 64-71. doi:10.1007/978-3-642-75373-2_8\u003c/li\u003e\n\u003cli\u003eMaramis, M. M., et al. (2021). Impaired Cognitive Flexibility and Working Memory Precedes Depression: A Rat Model to Study Depression. \u003cem\u003eNeuropsychobiology, 80\u003c/em\u003e(3), 225-233. doi:10.1159/000508682\u003c/li\u003e\n\u003cli\u003eMcCarron, R. M., et al. (2021). Depression. \u003cem\u003eAnn Intern Med, 174\u003c/em\u003e(5), Itc65-itc80. doi:10.7326/aitc202105180\u003c/li\u003e\n\u003cli\u003ePalmer, C. A., \u0026amp; Alfano, C. A. (2017). 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Insomnia as a risk factor for ill health: results from the large population-based prospective HUNT Study in Norway. \u003cem\u003eJ Sleep Res, 23\u003c/em\u003e(2), 124-132. doi:10.1111/jsr.12102\u003c/li\u003e\n\u003cli\u003eSofi, F., et al. (2014). Insomnia and risk of cardiovascular disease: a meta-analysis. \u003cem\u003eEur J Prev Cardiol, 21\u003c/em\u003e(1), 57-64. doi:10.1177/2047487312460020\u003c/li\u003e\n\u003cli\u003eSongco, A., et al. (2023). Affective working memory in depression. \u003cem\u003eEmotion, 23\u003c/em\u003e(6), 1802-1807. doi:10.1037/emo0001130\u003c/li\u003e\n\u003cli\u003eSun, J., et al. (2019). Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study. \u003cem\u003eJ Biomed Opt, 24\u003c/em\u003e(5), 1-7. doi:10.1117/1.Jbo.24.5.051410\u003c/li\u003e\n\u003cli\u003eXie, D., et al. (2020). Functional Connectivity Abnormalities of Brain Regions With Structural Deficits in Primary Insomnia Patients. \u003cem\u003eFront Neurosci, 14\u003c/em\u003e, 566. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/32670005. doi:10.3389/fnins.2020.00566\u003c/li\u003e\n\u003cli\u003eXu, H., et al. (2023). Insomniacs show greater prefrontal activation during verbal fluency task compared to non-insomniacs: a functional near-infrared spectroscopy investigation of depression in patients. \u003cem\u003eBMC Psychiatry, 23\u003c/em\u003e(1), 217. doi:10.1186/s12888-023-04694-z\u003c/li\u003e\n\u003cli\u003eZhu, Y., et al. (2021). Functional connectivity density abnormalities and anxiety in primary insomnia patients. \u003cem\u003eBrain Imaging Behav, 15\u003c/em\u003e(1), 114-121. doi:10.1007/s11682-019-00238-w\u003c/li\u003e\n\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":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"major depressive disorder, insomnia, functional near-infrared spectroscopy (fNIRS), working memory, prefrontal cortex","lastPublishedDoi":"10.21203/rs.3.rs-9434079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9434079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMajor depressive disorder (MDD) frequently coexists with insomnia, a comorbidity that exacerbates cognitive deficits, particularly in working memory (WM). While previous research has established links between insomnia and impaired brain function, the specific neurofunctional mechanisms underlying WM deficits in MDD patients with insomnia remain unclear. This study explores cortical activation patterns in MDD patients with and without insomnia using functional near-infrared spectroscopy (fNIRS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 55 MDD patients with insomnia and 67 without insomnia were recruited. Cortical activation during WM tasks was assessed using fNIRS, focusing on oxygenated hemoglobin (Oxy-Hb) concentration changes. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), depression severity was assessed using the Hamilton Depression Scale (HAMD), and cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMDD patients with insomnia exhibited lower RBANS scores and reduced accuracy in medium-load WM tasks compared to non-insomnia patients. Additionally, fNIRS analysis revealed diminished Oxy-Hb concentrations in the bilateral dorsolateral prefrontal cortex (DLPFC) during medium-load tasks and in the left DLPFC during high-load tasks. Correlation analyses indicated that immediate memory scores positively correlated with left DLPFC and bilateral medial prefrontal cortex (mPFC) activation during medium-load tasks, while task accuracy negatively correlated with bilateral mPFC activation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe presence of insomnia in MDD is associated with exacerbated WM deficits and altered prefrontal cortical activation, particularly in tasks with increased cognitive demand. These findings highlight potential neurofunctional biomarkers that could inform personalized interventions for MDD patients with insomnia. Moreover, the sensitivity of fNIRS in detecting these neural activation differences suggests its potential as a diagnostic tool for identifying MDD subtypes and guiding targeted therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Investigating Working Memory and Brain Activation in Major Depressive Disorder with and without Insomnia: Insights from Functional Near-Infrared Spectroscopy (fNIRS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:27:16","doi":"10.21203/rs.3.rs-9434079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T15:36:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T01:32:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228381874903899649611048554874898857993","date":"2026-05-01T14:44:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T01:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84713890715093056466981364490977925339","date":"2026-04-30T00:07:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T14:35:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T14:15:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-22T12:10:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T16:07:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-04-21T15:07:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f413cc8-59af-43cf-bf66-6d62af7c86b1","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T15:36:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T01:32:12+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"228381874903899649611048554874898857993","date":"2026-05-01T14:44:56+00:00","index":18,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T01:13:11+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"84713890715093056466981364490977925339","date":"2026-04-30T00:07:07+00:00","index":16,"fulltext":""},{"type":"reviewersInvited","content":"2","date":"2026-04-29T14:35:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T15:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:27:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9434079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9434079","identity":"rs-9434079","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}