Abnormal cerebral-limbic functional connectivity between bipolar mania and bipolar depression under resting state

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This study was designed to compare the cerebral-limbic FC characteristics of BD-M, BD-D patients and healthy control (HC) subjects. Method Resting-state functional magnetic resonance imaging (fMRI) was performed on 30 BD-M patients, 31 BD-D patients and 30 HC subjects. Interregional cerebral FC values were calculated for group-wise comparisons, and the correlation between abnormal FC and depressive symptom severity was further explored. Results Abnormal cerebral-limbic FC in the default mode network (DMN), attention network and limbic areas was observed in both BD-M and BD-D groups. Specifically, BD-D patients showed elevated FC mainly in the DMN [posterior cingulate gyrus (PCG), precuneus (PCUN)], attention network [superior parietal gyrus (SPG), inferior parietal gyrus (IPG)] and limbic regions [hippocampus (HIP), parahippocampus (PHG)], while BD-M patients displayed reduced cerebral-limbic FC in the DMN and limbic areas. Conclusions BD-M and BD-D patients exhibit characteristic and divergent cerebral-limbic FC abnormalities—DMN FC reduction in BD-M and FC elevation in BD-D. These specific FC patterns may act as potential resting-state biomarkers for distinguishing between the two mood episodes of bipolar disorder. Bipolar Mania Bipolar Depression Cerebral-limbic Functional Connectivity Resting-state fMRI Figures Figure 1 Introduction Bipolar disorder (BD) is a chronic and recurrent psychiatric disorder characterized by alternating episodes of mania and depression, affecting approximately 1–2% of the global population [ 1 ]. A core clinical feature of BD is the profound impairment of cognitive functions—including associative memory, attention, and executive function—across both mood episodes [ 2 , 3 ]. Among these, associative memory (a key component of verbal declarative memory) is consistently reported to be defective in BD patients [ 4 – 6 ], with evidence linking this impairment to dysregulation of the cerebral-limbic circuit [ 7 , 8 ]. The cerebral-limbic system, encompassing the hippocampus (HIP), parahippocampus (PHG), posterior cingulate gyrus (PCG), precuneus (PCUN), and parietal cortices, is critical for emotion processing, memory formation, and self-referential cognition [ 9 – 11 ]. Notably, BD mania (BD-M) and BD depression (BD-D) represent two opposite poles of mood states: BD-M is marked by elevated mood, distractibility, and flight of ideas, while BD-D is characterized by anhedonia, pessimistic rumination, and diminished concentration [ 12 ]. DSM-5 criteria further highlight these opposing neurocognitive profiles, implying distinct underlying neuropathological mechanisms [ 13 ]. However, the extent to which cerebral-limbic functional connectivity (FC) differs between BD-M and BD-D remains unclear, limiting the development of targeted diagnostic and therapeutic strategies. Existing research on BD’s neurofunctional basis has failed to address key questions due to three interrelated limitations. First, most studies focus on either BD-M or BD-D in isolation, with few directly comparing cerebral-limbic FC across the two episodes [ 14 – 16 ]. Those that include both mood states often report generalized FC abnormalities rather than isolating episode-specific differences, leaving the unique neurofunctional signatures of each state undefined [ 17 , 18 ]. Second, while hippocampal dysfunction is widely implicated in BD-related memory impairments, findings are contradictory: structural imaging studies have reported preserved, increased, or decreased hippocampal volumes [ 19 – 21 ], and functional studies have primarily focused on task-related activation rather than resting-state intrinsic connectivity [ 22 , 23 ]. Resting-state fMRI is uniquely suited to capture inherent brain network dynamics, yet comprehensive whole-brain analyses exploring cerebral-limbic connectivity across both BD episodes are scarce. Third, the clinical utility of FC as a biomarker for differentiating BD-M and BD-D remains unproven—an critical gap given the high rate of misdiagnosis between these mood states and unipolar depression [ 26 ], which delays effective treatment. Collectively, these limitations create a clear need for a study that directly compares whole-brain cerebral-limbic FC across BD-M, BD-D, and healthy controls (HCs) to clarify episode-specific mechanisms. To address these gaps, the present study conducted a whole-brain resting-state fMRI analysis with three core objectives: (1) compare cerebral-limbic FC patterns among BD-M patients, BD-D patients, and HCs; (2) explore the relationship between abnormal FC and clinical symptom severity; and (3) investigate whether FC patterns can differentiate the two BD mood states. We hypothesized that BD-M and BD-D would exhibit distinct cerebral-limbic FC abnormalities, reflecting their opposing neurocognitive and clinical profiles. The innovation of this study lies in its integration of a comprehensive whole-brain approach with direct comparison of two opposite BD mood states, filling a critical void in existing research by testing the potential of FC as a diagnostic biomarker—findings that could advance our understanding of BD’s neurobiology and improve clinical differentiation of mood episodes. Methods Study Participants and Recruitment A total of 31 BD-D patients and 30 BD-M patients were enrolled from the inpatient and outpatient psychiatric clinics of the Second People’s Hospital of Hunan Province. All diagnoses were verified using the Structured Clinical Interview for DSM-IV-Patient Edition (SCID-P). The inclusion criteria were as follows: aged 18 to 45 years, Han Chinese, at least 9 years of formal education, and intact communication skills; for BD-D patients, a score of ≥ 17 on the 17-item Hamilton Depression Rating Scale (HAMD) and ≤ 6 on the Young Mania Rating Scale (YMRS); for BD-M patients, a score of ≥ 12 on the YMRS and ≤ 7 on the HAMD. Exclusion criteria included severe learning disabilities, current substance-induced psychosis, alcohol intake within 24 hours before interview and fMRI scanning, a history of traumatic brain injury or neurological diseases, left-handedness, previous electroconvulsive therapy, and other medical conditions precluding MRI scans. All patients were on antipsychotic medication during the study, and benzodiazepine administration (if applicable) was suspended 24 hours before fMRI acquisition. Thirty HC subjects were recruited from the local community in Changsha, with the same inclusion and exclusion criteria as the patient groups except for the absence of DSM-IV Axis-I psychiatric disorders. Demographic matching showed no significant differences in gender (χ²=2.442, p = 0.295) and years of education (t = 0.712, p = 0.146) between HCs and the two patient groups. All subjects signed informed consent to participate in the study. The study was approved by the Ethics Committee of the Second People’s Hospital of Hunan Province. Assessments and Procedures All subjects were assessed for cognitive function using the Information and Digit Symbol Coding subsets of the Wechsler Adult Intelligence Scale (WAIS) [ 23 ]. Demographic data (age, sex, years of education) were recorded. Clinical information (diagnosis, duration of illness) was collected for patients. The 17-item Hamilton Depression Rating Scale (HAMD) [ 24 ] was used to assess depressive symptom severity, and the Young Mania Rating Scale (YMRS) [ 25 ] was employed to evaluate manic symptoms. fMRI Data Acquisition fMRI data were acquired using a 3.0-T Philips Achieva whole-body MRI scanner (Philips Healthcare, Netherlands), with a total scanning session of 8 min 26 s and 250 functional volumes obtained. The imaging sequence was gradient-echo echo-planar imaging (EPI), with the following parameters: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle = 90°, matrix size = 64×64, slice thickness = 4 mm, inter-slice gap = 0 mm, and 36 axial slices covering the whole brain. Image Processing The initial 10 functional volumes were discarded to eliminate signal instability caused by scanner warm-up and subject acclimatization. Functional image preprocessing was conducted using SPM8 (University College London, UK) and DPARSF (Data Processing Assistant for Resting-State fMRI). The remaining volumes were first subjected to slice timing correction to correct for inter-slice acquisition delays, then realigned to the middle volume to correct for head motion artifacts. Functional images were normalized to the Montreal Neurological Institute (MNI) EPI template via DARTEL algorithm, with a resampled voxel size of 3×3×3 mm³. After normalization, the blood oxygen level-dependent (BOLD) signal of each voxel was detrended and band-pass filtered (0.01–0.08 Hz) to mitigate low-frequency drift and high-frequency physiological noise (e.g., cardiac and respiratory signals). Nuisance variables including head motion parameters, global mean BOLD signal, white matter signal and cerebrospinal fluid signal were further regressed out from the preprocessed BOLD data. Head motion was quantified by frame-wise displacement (FD) according to the method of Power et al. [ 27 ]; volumes with FD > 0.5 mm were removed and interpolated linearly to minimize motion-related artifacts. Data Analysis Demographic, clinical, and behavioral data Continuous demographic and clinical variables were compared across the three groups using one-way analysis of variance (ANOVA) or independent-samples t-tests, while categorical variables were analyzed with Pearson’s Chi-square test. Cognitive performance across groups was compared using analysis of covariance (ANCOVA), with age as a covariate to control for its potential influence. Imaging data Prior to FC calculation, subject-specific motion and physiological noise components (e.g., cardiac and respiratory fluctuations) were regressed out to reduce data variability. The whole brain was parcellated into 90 cerebral regions (excluding the cerebellum) based on the Automated Anatomical Labeling (AAL) template [ 28 ]. For each of the 90 regions, the average BOLD time series was extracted after artifact removal, and the pairwise Pearson correlation coefficients between regional time series were defined as interregional FC values. Two-sample t-tests with false discovery rate (FDR) correction (p < 0.05) were used to identify group differences in FC. Pearson correlation analysis was further performed to examine the relationship between group-differentiated FC values and clinical scores (HAMD/YMRS). Results Demographics, Clinical, and Behavioral Data No significant differences in age, gender, or years of education were observed across the three groups (Table 2). The patient groups did not differ significantly in illness duration or medication dosage. However, BD-M patients showed significantly lower WAIS-Digit Symbol scores than BD-D patients. HAMD and YMRS scores differed significantly between the two patient groups (all p < 0.001). Table 1 Names and abbreviations of the regions used in this study Regions Abbr. Regions Abbr. Amygdala AMYG Hippocampus HIP Thalamus THA Angular gyrus ANG Inferior temporal gyrus ITG Medial superior frontal gyrus SFGmed Superior temporal gyrus STG Inferior occipital gyrus IOG Calcarine cortex CAL Superior occipital gyrus SOG Supramarginal gyrus SMG Superior orbitofrontal cortex ORBsup Putamen PUT Pallidum PAL Middle temporal gyrus MTG Posterior cingulate gyrus PCG Precuneus PCUN Superior parietal gyrus SPG Hippocampus HIP Inferior parietal gyrus IPG ParaHippocampal PHG Posterior cingulate gyrus PCG Precuneus PCUN Table2 Demographic and clinical characteristics of bipolar manic patients, bipolar depressive patients, and healthy controls Characteristics (Mean ± SD) BD-D (n=31) BD-M (n=30) HC (n=30) Analysis F/ χ^2 P Age (year) 25.81±5.86 26.53±7.62 25.23±6.28 0.227 0.797 a Education (year) 10.52±2.79 10.67±2.58 10.78±2.89 1.645 0.148 a Sex (Male/Female) 19/12 18/12 17/13 2.442 0.295 a Duration of illness (months) 55.60±9.92 60.8±6.52 - 3.325 0.327 b Chlorpromazine equivalents (mg) HAMD YMRS WAIS-Digit symbol 257.02 ±215.46 258.00±219.23 - 0.315 0.735 b 21.42±4.27 4.2±2.1 - 14.69 <.001 b 1.84±1.7 21.27 ±7.95 - -19.28 <.001 b 63.20 ±12.57 54.26 ±11.08 72.08±10.45 -25.014 <.001 b Abbreviations: HC healthy controls; BD-D Bipolar Depressive patients; BD-M Bipolar Manic patients; HAMD Hamilton Rating Scale for Depression; YMRS Young Mania Rating Scale. a analysis of variance b Two-sample T Tests Note: p<0.05 Functional Connectivity No significant differences in cerebral-limbic FC were detected between either patient group and HCs after FDR correction (p<0.05). At an uncorrected statistical threshold of p<0.001, both BD-M and BD-D groups presented abnormal cerebral-limbic FC in the DMN, attention network and limbic regions (Table 3, Fig. 1). BD-D patients exhibited elevated interregional FC in the DMN (PCG, PCUN), attention network (SPG, IPG) and limbic regions (HIP, PHG), whereas BD-M patients displayed reduced FC in the DMN and limbic areas compared with HCs. Table 3. Differences in functional connectivity among patients with BD-M or BD-D, and healthy subjects. Connections t p Connections t p BD-D> HC PCUN.L-SPG.R 3.423 0.000179 SPG.L-PCG.L 4.337 0.000303 PCUN.L-IPG.R 4.912 0.000347 SPG.L-PCG.R 3.384 0.000162 PCUN.R-SPG.L 4.102 0.000269 SPG.R-PCG.L 4.016 0.000103 PCUN.R-SPG.R 3.056 0.000144 SPG.R-PCG.R 5.048 0.000124 HIP.L-IPG.R 4.015 0.000388 IPG.R- PCG.L 3.042 0.000465 HIP.R-PCG.L 3.018 0.000452 BD-M< HC PCUN.L- PHG.L 4.523 0.000136 PCUN.R- PHG.L 3.126 0.000074 PCUN.L- PHG.R 3.466 0.000334 PCUN.R- PHG.R 4.134 0.000015 PCUN.L- HIP.L 4.515 0.000123 PCUN.R- HIP.L 5.254 0.000032 PCUN.L- HIP.R 5.437 0.000191 PCUN.R- HIP.R 4.113 0.000402 PCG.L- PHG.L 4.389 0.000175 PHG.R- SPG.L 3.156 0.000104 PCG.R- PHG.L 3.365 0.000711 PHG.R- SPG.R 4.307 0.000282 PCG.L- HIP.L 3.651 0.000643 PHG.R- IPG.L 4.307 0.000282 PHG.R- IPG.R 4.307 0.000282 Abbreviations:PCUN=precuneus; SPG=superior parietal gyrus; IPG=inferior parietal gyrus; HIP=hippocampus; PCG=posterior cingulate gyrus; PHG=parahippocampus; L=left; R=right. Clinical correlations In BD-D patients, HAMD scores were positively correlated with PCUN.L-IPG.R (r=0.532, p=0.004), PCUN.R-SPG.R (r=0.547, p=0.014), and PCG.R-SPG.R (r=0.601, p=0.002) connectivity (Table 4). Table 4. Correlations of abnormal functional connectivity with HAMD scores in BD-D Functional connectivity Clinical variables r p BD-D PCUN.L-IPG.R HAMD scores 0.532 0.004 PCUN.R-SPG.R HAMD scores 0.547 0.014 PCG.R-SPG.R HAMD scores 0.601 0.002 Abbreviations: See Tables 2 and 3. Correlations are uncorrected (p<0.05). Discussion This study compared cerebral-limbic FC patterns across BD-M, BD-D, and HCs using a whole-brain resting-state fMRI approach, finding abnormal FC in the DMN, attention network, and limbic regions in both patient groups. Notably, BD-D patients showed increased FC within the DMN, attention network, and DMN-limbic connections, while BD-M patients exhibited decreased FC in DMN and limbic regions. These findings highlight distinct neurofunctional features between the two BD mood states, aligning with our hypothesis and addressing key gaps in existing research. The cerebral-limbic circuit is a key neural substrate for emotional processing and regulation [29-31], and persistent structural and functional abnormalities in this circuit have been well documented in bipolar disorder [32-35]. Our findings are consistent with previous research that reported elevated DMN activity in BD-D [36,37] and impaired cerebral-limbic connectivity in BD-M [38,39]. The positive correlation between elevated FC and HAMD scores in BD-D patients further confirms a direct association between aberrant cerebral-limbic connectivity and depressive symptom severity, indicating that DMN-limbic circuit dysfunction is a common neural mechanism underlying mood instability in both BD-M and BD-D episodes. The divergent FC patterns in the cerebral-limbic circuit may mirror the distinct clinical manifestations of the two mood states: BD-D patients exhibit a strong internal cognitive focus (e.g., pessimistic rumination) [41-43], while BD-M patients show an external cognitive orientation (e.g., irritability and distractibility) [40]. As the DMN is a core network mediating internal self-awareness and spontaneous cognitive activity [44,45], the elevated FC in BD-D may underpin excessive self-referential negative thinking [46], whereas the reduced DMN-limbic FC in BD-M may lead to impaired internal cognitive processing and deficient filtering of external sensory stimuli. These episode-specific patterns fill the gap left by prior studies that failed to directly compare FC across BD-M and BD-D, providing clarity on the distinct neurobiological mechanisms of each mood state. Our whole-brain approach also addresses the limitation of prior studies that focused on task-related activation or targeted networks, allowing for a comprehensive exploration of cerebral-limbic connectivity. The potential of these FC patterns as diagnostic biomarkers is particularly valuable given the high rate of misdiagnosis between BD episodes and unipolar depression [26]. By identifying distinct FC signatures, our findings offer a promising tool for improving diagnostic accuracy and guiding targeted treatments. This study has several limitations. First, all patients were receiving antipsychotic medication during the study, and medication effects may have potential impacts on cerebral-limbic FC patterns; future research should enroll drug-naïve BD patients to eliminate this confounding factor. Second, the current study only used univariate analysis to identify group differences in FC; multivariate pattern analysis (MPA) could be applied in subsequent studies to further verify the diagnostic value of cerebral-limbic FC patterns for distinguishing BD-M and BD-D. Conclusion BD-M and BD-D exhibit distinct abnormal cerebral-limbic FC patterns (decreased in BD-M, increased in BD-D) when analyzed via whole-brain resting-state fMRI. These patterns may serve as resting-state biomarkers for differentiating the two mood states, supporting distinct neurobiological mechanisms underlying BD mania and depression. Our findings advance understanding of BD’s neurobiology and have implications for clinical diagnosis and personalized treatment. Abbreviations BD : bipolar disorder ; BD-D : bipolar disorder depression; BD-M : bipolar disorder mania; fMRI : Functional magnetic resonance; FC : functional connectivity; ANOVA : analysis of variance; HAMD : Hamilton Depression Rating Scale; DMN : default mode network; FDR : false discovery rate; YMRS : Young Mania Rating Scale; EPI : echo echo-planar imaging; PCUN: Precuneus; SPG : Superior parietal gyrus; IPG : Inferior parietal gyrus; HIP : Hippocampus; PCG : posterior cingulated gyrus; PHIP : paraHippocampal; WAIS : Wechsler Adult Intelligence Scale; AAL : Automated Anatomical Labeling; BO L D : Blood Oxygenation Level Department; FD : frame-wise displacement; RSN : resting-state networks Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province). All subjects provided written informed consent after understanding the study procedures. For patients with severe suicidal thoughts or behaviors, or those unable to comprehend the informed consent information, written informed consent was obtained from their legal guardians. This study was conducted in compliance with the Declaration of Helsinki. Ethical approval was obtained from the relevant institutional review board, and informed consent was secured from all participants. Consent to publish Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article. All of the raw data are stored in Department of Psychiatry, the Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests All authors declare that they have no conflicts of interests. Funding This research was supported by Provincial technology innovation guide program funded by the Hunan Department of Science and Technology (2017SK50312 to Dr. C Liu). The design of the study, collection and analysis were supported by the Hunan Provincial Department of Education Excellent Young Scholars Project (18B254 to Dr. C Liu) and China Postdoctoral Science Foundation General Program (2018M642990 to Dr. C Liu). The interpretation of data and writing the manuscript was provided by a grant of the Provincial technology innovation guide program, funded by the Hunan Department of Science and Technology (2017SK50312). Authors’ contributions LC and WGW designed the study. LC and WGW managed the data collection. LC and LXC undertook the statistical analysis. YSH and LC wrote the first draft of the manuscript. We all contributed to and approved the final manuscript. All authors read and approved the final manuscript. 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Spatiotemporal approach and the history of psychopathology. J Affect Disord. 2016;190:867–879. Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. Mason MF, Norton M, Van Horn JD, et al. Wandering Minds: The Default Network and Stimulus-Independent Thought. Science. 2007;315:393–395. Sheri J, Tanya T. Bipolar disorder: what can psychotherapists learn from the cognitive research? J Clin Psychol. 2007;63:425–432. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8692854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607424342,"identity":"f1ea4de0-2256-4995-8b02-7f5f4db4f9d3","order_by":0,"name":"Shanghao Yang","email":"","orcid":"","institution":"The Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province)","correspondingAuthor":false,"prefix":"","firstName":"Shanghao","middleName":"","lastName":"Yang","suffix":""},{"id":607424343,"identity":"cd096716-8790-4043-bd1e-d13213d0600d","order_by":1,"name":"Chang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3QsUrEQBCA4ZGFjcUkwW7lRH2EkUCqcHkQm5HA2pxwYJviIJDrtPU1fAOPgbS+gMWKYH1wIBaiJuIVFkksBfevtpgPdgbA5/uDRQpAdQ8dVCvHlGGslLghorckwqYgN7cH+0ttaZDANzk0s3TPrSWjezw2gyTAk828fJjWxlpgUpgIAkGZnfZ/DJPJTfNc1PjUtLtoTCW8c9DYi0U/SRVqKXRgz4kJWxIx7SxkhLy3BGapYTKYVEhmlIS1TPXuFyFs1xkj+nISXgnr7shMjEbaI/PALnEstxt8kfxoWa0eX98+8vhaxK3LrJdsO/s5wCPjXfkvZnw+n++/9gkp8VAqK4faUQAAAABJRU5ErkJggg==","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":607424344,"identity":"c027b228-89c4-48f8-9adc-6975a8699e08","order_by":2,"name":"Guowei Wu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Wu","suffix":""},{"id":607424345,"identity":"3420b17a-2546-44c8-91a3-be4d139abfcb","order_by":3,"name":"Xinchun Li","email":"","orcid":"","institution":"The Second People’s Hospital of Hunan Province (Brain Hospital of Hunan Province)","correspondingAuthor":false,"prefix":"","firstName":"Xinchun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-25 13:54:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8692854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8692854/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104888051,"identity":"e30de333-a54b-448e-88d0-f83b75733e98","added_by":"auto","created_at":"2026-03-18 10:13:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69323,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional network structure with different links of the healthy control template, bipolar disorder depression(BD-D) and bipolar disorder mania(BD-M).The red lines indicate links that were strengthened in networks of the BD-D, while blue lines are links that were strengthened in BD-M. The widths of the lines are proportional to the odds ratio scores.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8692854/v1/5b99eff01a0fa5ba957acbca.jpeg"},{"id":107915947,"identity":"7f60b82e-c59a-4286-a383-deed7ce4fb09","added_by":"auto","created_at":"2026-04-27 14:12:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":404379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8692854/v1/3160c8ad-8f3a-4116-90e1-630ee576051c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal cerebral-limbic functional connectivity between bipolar mania and bipolar depression under resting state","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBipolar disorder (BD) is a chronic and recurrent psychiatric disorder characterized by alternating episodes of mania and depression, affecting approximately 1\u0026ndash;2% of the global population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A core clinical feature of BD is the profound impairment of cognitive functions\u0026mdash;including associative memory, attention, and executive function\u0026mdash;across both mood episodes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among these, associative memory (a key component of verbal declarative memory) is consistently reported to be defective in BD patients [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with evidence linking this impairment to dysregulation of the cerebral-limbic circuit [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The cerebral-limbic system, encompassing the hippocampus (HIP), parahippocampus (PHG), posterior cingulate gyrus (PCG), precuneus (PCUN), and parietal cortices, is critical for emotion processing, memory formation, and self-referential cognition [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, BD mania (BD-M) and BD depression (BD-D) represent two opposite poles of mood states: BD-M is marked by elevated mood, distractibility, and flight of ideas, while BD-D is characterized by anhedonia, pessimistic rumination, and diminished concentration [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. DSM-5 criteria further highlight these opposing neurocognitive profiles, implying distinct underlying neuropathological mechanisms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the extent to which cerebral-limbic functional connectivity (FC) differs between BD-M and BD-D remains unclear, limiting the development of targeted diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eExisting research on BD\u0026rsquo;s neurofunctional basis has failed to address key questions due to three interrelated limitations. First, most studies focus on either BD-M or BD-D in isolation, with few directly comparing cerebral-limbic FC across the two episodes [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Those that include both mood states often report generalized FC abnormalities rather than isolating episode-specific differences, leaving the unique neurofunctional signatures of each state undefined [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Second, while hippocampal dysfunction is widely implicated in BD-related memory impairments, findings are contradictory: structural imaging studies have reported preserved, increased, or decreased hippocampal volumes [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and functional studies have primarily focused on task-related activation rather than resting-state intrinsic connectivity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Resting-state fMRI is uniquely suited to capture inherent brain network dynamics, yet comprehensive whole-brain analyses exploring cerebral-limbic connectivity across both BD episodes are scarce. Third, the clinical utility of FC as a biomarker for differentiating BD-M and BD-D remains unproven\u0026mdash;an critical gap given the high rate of misdiagnosis between these mood states and unipolar depression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which delays effective treatment. Collectively, these limitations create a clear need for a study that directly compares whole-brain cerebral-limbic FC across BD-M, BD-D, and healthy controls (HCs) to clarify episode-specific mechanisms.\u003c/p\u003e \u003cp\u003eTo address these gaps, the present study conducted a whole-brain resting-state fMRI analysis with three core objectives: (1) compare cerebral-limbic FC patterns among BD-M patients, BD-D patients, and HCs; (2) explore the relationship between abnormal FC and clinical symptom severity; and (3) investigate whether FC patterns can differentiate the two BD mood states. We hypothesized that BD-M and BD-D would exhibit distinct cerebral-limbic FC abnormalities, reflecting their opposing neurocognitive and clinical profiles. The innovation of this study lies in its integration of a comprehensive whole-brain approach with direct comparison of two opposite BD mood states, filling a critical void in existing research by testing the potential of FC as a diagnostic biomarker\u0026mdash;findings that could advance our understanding of BD\u0026rsquo;s neurobiology and improve clinical differentiation of mood episodes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants and Recruitment\u003c/h2\u003e \u003cp\u003eA total of 31 BD-D patients and 30 BD-M patients were enrolled from the inpatient and outpatient psychiatric clinics of the Second People\u0026rsquo;s Hospital of Hunan Province. All diagnoses were verified using the Structured Clinical Interview for DSM-IV-Patient Edition (SCID-P). The inclusion criteria were as follows: aged 18 to 45 years, Han Chinese, at least 9 years of formal education, and intact communication skills; for BD-D patients, a score of \u0026ge;\u0026thinsp;17 on the 17-item Hamilton Depression Rating Scale (HAMD) and \u0026le;\u0026thinsp;6 on the Young Mania Rating Scale (YMRS); for BD-M patients, a score of \u0026ge;\u0026thinsp;12 on the YMRS and \u0026le;\u0026thinsp;7 on the HAMD. Exclusion criteria included severe learning disabilities, current substance-induced psychosis, alcohol intake within 24 hours before interview and fMRI scanning, a history of traumatic brain injury or neurological diseases, left-handedness, previous electroconvulsive therapy, and other medical conditions precluding MRI scans. All patients were on antipsychotic medication during the study, and benzodiazepine administration (if applicable) was suspended 24 hours before fMRI acquisition. Thirty HC subjects were recruited from the local community in Changsha, with the same inclusion and exclusion criteria as the patient groups except for the absence of DSM-IV Axis-I psychiatric disorders. Demographic matching showed no significant differences in gender (χ\u0026sup2;=2.442, p\u0026thinsp;=\u0026thinsp;0.295) and years of education (t\u0026thinsp;=\u0026thinsp;0.712, p\u0026thinsp;=\u0026thinsp;0.146) between HCs and the two patient groups. All subjects signed informed consent to participate in the study. The study was approved by the Ethics Committee of the Second People\u0026rsquo;s Hospital of Hunan Province.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessments and Procedures\u003c/h3\u003e\n\u003cp\u003eAll subjects were assessed for cognitive function using the Information and Digit Symbol Coding subsets of the Wechsler Adult Intelligence Scale (WAIS) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Demographic data (age, sex, years of education) were recorded. Clinical information (diagnosis, duration of illness) was collected for patients. The 17-item Hamilton Depression Rating Scale (HAMD) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to assess depressive symptom severity, and the Young Mania Rating Scale (YMRS) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was employed to evaluate manic symptoms.\u003c/p\u003e\n\u003ch3\u003efMRI Data Acquisition\u003c/h3\u003e\n\u003cp\u003efMRI data were acquired using a 3.0-T Philips Achieva whole-body MRI scanner (Philips Healthcare, Netherlands), with a total scanning session of 8 min 26 s and 250 functional volumes obtained. The imaging sequence was gradient-echo echo-planar imaging (EPI), with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, matrix size\u0026thinsp;=\u0026thinsp;64\u0026times;64, slice thickness\u0026thinsp;=\u0026thinsp;4 mm, inter-slice gap\u0026thinsp;=\u0026thinsp;0 mm, and 36 axial slices covering the whole brain.\u003c/p\u003e\n\u003ch3\u003eImage Processing\u003c/h3\u003e\n\u003cp\u003eThe initial 10 functional volumes were discarded to eliminate signal instability caused by scanner warm-up and subject acclimatization. Functional image preprocessing was conducted using SPM8 (University College London, UK) and DPARSF (Data Processing Assistant for Resting-State fMRI). The remaining volumes were first subjected to slice timing correction to correct for inter-slice acquisition delays, then realigned to the middle volume to correct for head motion artifacts. Functional images were normalized to the Montreal Neurological Institute (MNI) EPI template via DARTEL algorithm, with a resampled voxel size of 3\u0026times;3\u0026times;3 mm\u0026sup3;. After normalization, the blood oxygen level-dependent (BOLD) signal of each voxel was detrended and band-pass filtered (0.01\u0026ndash;0.08 Hz) to mitigate low-frequency drift and high-frequency physiological noise (e.g., cardiac and respiratory signals). Nuisance variables including head motion parameters, global mean BOLD signal, white matter signal and cerebrospinal fluid signal were further regressed out from the preprocessed BOLD data. Head motion was quantified by frame-wise displacement (FD) according to the method of Power et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; volumes with FD\u0026thinsp;\u0026gt;\u0026thinsp;0.5 mm were removed and interpolated linearly to minimize motion-related artifacts.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDemographic, clinical, and behavioral data\u003c/strong\u003e \u003cp\u003eContinuous demographic and clinical variables were compared across the three groups using one-way analysis of variance (ANOVA) or independent-samples t-tests, while categorical variables were analyzed with Pearson\u0026rsquo;s Chi-square test. Cognitive performance across groups was compared using analysis of covariance (ANCOVA), with age as a covariate to control for its potential influence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImaging data\u003c/strong\u003e \u003cp\u003ePrior to FC calculation, subject-specific motion and physiological noise components (e.g., cardiac and respiratory fluctuations) were regressed out to reduce data variability. The whole brain was parcellated into 90 cerebral regions (excluding the cerebellum) based on the Automated Anatomical Labeling (AAL) template [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For each of the 90 regions, the average BOLD time series was extracted after artifact removal, and the pairwise Pearson correlation coefficients between regional time series were defined as interregional FC values. Two-sample t-tests with false discovery rate (FDR) correction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were used to identify group differences in FC. Pearson correlation analysis was further performed to examine the relationship between group-differentiated FC values and clinical scores (HAMD/YMRS).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics, Clinical, and Behavioral Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant differences in age, gender, or years of education were observed across the three groups (Table 2). The patient groups did not differ significantly in illness duration or medication dosage. However, BD-M patients showed significantly lower WAIS-Digit Symbol scores than BD-D patients. HAMD and YMRS scores differed significantly between the two patient groups (all p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eNames and abbreviations of the regions used in this study\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eAbbr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eAbbr.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eAmygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eAMYG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eHippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eHIP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eThalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eTHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eAngular gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eANG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eInferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eITG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eMedial superior frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eSFGmed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eSuperior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eSTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eInferior occipital gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eIOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eCalcarine cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eCAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eSuperior occipital gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eSOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eSupramarginal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eSMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eSuperior orbitofrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eORBsup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003ePutamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003ePUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003ePallidum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003ePAL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eMiddle temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eMTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003ePosterior cingulate gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003ePCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003ePCUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eSuperior parietal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eSPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eHippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003eHIP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003eInferior parietal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003eIPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003eParaHippocampal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003ePHG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.0435%;\"\u003e\n \u003cp\u003ePosterior cingulate gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.587%;\"\u003e\n \u003cp\u003ePCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6014%;\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7681%;\"\u003e\n \u003cp\u003ePCUN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2\u003c/strong\u003e Demographic and clinical characteristics of bipolar manic patients, bipolar depressive patients, and healthy controls\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBD-D\u003c/p\u003e\n \u003cp\u003e(n=31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eBD-M\u003c/p\u003e\n \u003cp\u003e(n=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003cp\u003e(n=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 121px;\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eF/ \u0026chi;^2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e25.81\u0026plusmn;5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e26.53\u0026plusmn;7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e25.23\u0026plusmn;6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.797\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eEducation (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e10.52\u0026plusmn;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e10.67\u0026plusmn;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10.78\u0026plusmn;2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.148\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eSex (Male/Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e19/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e18/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e17/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.295\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eDuration of illness (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e55.60\u0026plusmn;9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e60.8\u0026plusmn;6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.327\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 158px;\"\u003e\n \u003cp\u003eChlorpromazine equivalents (mg)\u003c/p\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003cp\u003eYMRS \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWAIS-Digit symbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e257.02 \u0026plusmn;215.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e258.00\u0026plusmn;219.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.735\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e21.42\u0026plusmn;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e4.2\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1.84\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e21.27 \u0026plusmn;7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-19.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e63.20 \u0026plusmn;12.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e54.26 \u0026plusmn;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e72.08\u0026plusmn;10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-25.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003eHC\u003c/em\u003e healthy controls; \u003cem\u003eBD-D\u0026nbsp;\u003c/em\u003eBipolar Depressive patients; \u003cem\u003eBD-M\u003c/em\u003e Bipolar Manic patients; \u003cem\u003eHAMD\u0026nbsp;\u003c/em\u003eHamilton Rating Scale for Depression; \u003cem\u003eYMRS\u0026nbsp;\u003c/em\u003eYoung Mania Rating Scale.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eanalysis of variance \u003csup\u003eb\u0026nbsp;\u003c/sup\u003eTwo-sample T Tests\u003c/p\u003e\n\u003cp\u003eNote: p\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant differences in cerebral-limbic FC were detected between either patient group and HCs after FDR correction (p\u0026lt;0.05). At an uncorrected statistical threshold of p\u0026lt;0.001, both BD-M and BD-D groups presented abnormal cerebral-limbic FC in the DMN, attention network and limbic regions (Table 3, Fig. 1). BD-D patients exhibited elevated interregional FC in the DMN (PCG, PCUN), attention network (SPG, IPG) and limbic regions (HIP, PHG), whereas BD-M patients displayed reduced FC in the DMN and limbic areas compared with HCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eDifferences in functional connectivity among patients with BD-M or BD-D, and healthy subjects.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eConnections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eConnections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD-D\u0026gt; HC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L-SPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSPG.L-PCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L-IPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSPG.L-PCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.R-SPG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSPG.R-PCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.R-SPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSPG.R-PCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHIP.L-IPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eIPG.R- PCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHIP.R-PCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD-M\u0026lt; HC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L- PHG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePCUN.R- PHG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L- PHG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePCUN.R- PHG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L- HIP.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePCUN.R- HIP.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCUN.L- HIP.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e5.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePCUN.R- HIP.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCG.L- PHG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePHG.R- SPG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCG.R- PHG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePHG.R- SPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePCG.L- HIP.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePHG.R- IPG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePHG.R- IPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:PCUN=precuneus; SPG=superior parietal gyrus; IPG=inferior parietal gyrus; HIP=hippocampus; PCG=posterior cingulate gyrus; PHG=parahippocampus; L=left; R=right.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn BD-D patients, HAMD scores were positively correlated with PCUN.L-IPG.R (r=0.532, p=0.004), PCUN.R-SPG.R (r=0.547, p=0.014), and PCG.R-SPG.R (r=0.601, p=0.002) connectivity (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e Correlations of abnormal functional connectivity with HAMD scores in BD-D\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eFunctional connectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003eClinical variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD-D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePCUN.L-IPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHAMD scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.532\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003ePCUN.R-SPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003eHAMD scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003ePCG.R-SPG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003eHAMD scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: See Tables 2 and 3. Correlations are uncorrected (p\u0026lt;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared cerebral-limbic FC patterns across BD-M, BD-D, and HCs using a whole-brain resting-state fMRI approach, finding abnormal FC in the DMN, attention network, and limbic regions in both patient groups. Notably, BD-D patients showed increased FC within the DMN, attention network, and DMN-limbic connections, while BD-M patients exhibited decreased FC in DMN and limbic regions. These findings highlight distinct neurofunctional features between the two BD mood states, aligning with our hypothesis and addressing key gaps in existing research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cerebral-limbic circuit is a key neural substrate for emotional processing and regulation [29-31], and persistent structural and functional abnormalities in this circuit have been well documented in bipolar disorder [32-35]. Our findings are consistent with previous research that reported elevated DMN activity in BD-D [36,37] and impaired cerebral-limbic connectivity in BD-M [38,39]. The positive correlation between elevated FC and HAMD scores in BD-D patients further confirms a direct association between aberrant cerebral-limbic connectivity and depressive symptom severity, indicating that DMN-limbic circuit dysfunction is a common neural mechanism underlying mood instability in both BD-M and BD-D episodes.\u003c/p\u003e\n\u003cp\u003eThe divergent FC patterns in the cerebral-limbic circuit may mirror the distinct clinical manifestations of the two mood states: BD-D patients exhibit a strong internal cognitive focus (e.g., pessimistic rumination) [41-43], while BD-M patients show an external cognitive orientation (e.g., irritability and distractibility) [40]. As the DMN is a core network mediating internal self-awareness and spontaneous cognitive activity [44,45], the elevated FC in BD-D may underpin excessive self-referential negative thinking [46], whereas the reduced DMN-limbic FC in BD-M may lead to impaired internal cognitive processing and deficient filtering of external sensory stimuli. These episode-specific patterns fill the gap left by prior studies that failed to directly compare FC across BD-M and BD-D, providing clarity on the distinct neurobiological mechanisms of each mood state.\u003c/p\u003e\n\u003cp\u003eOur whole-brain approach also addresses the limitation of prior studies that focused on task-related activation or targeted networks, allowing for a comprehensive exploration of cerebral-limbic connectivity. The potential of these FC patterns as diagnostic biomarkers is particularly valuable given the high rate of misdiagnosis between BD episodes and unipolar depression [26]. By identifying distinct FC signatures, our findings offer a promising tool for improving diagnostic accuracy and guiding targeted treatments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study has several limitations. First, all patients were receiving antipsychotic medication during the study, and medication effects may have potential impacts on cerebral-limbic FC patterns; future research should enroll drug-na\u0026iuml;ve BD patients to eliminate this confounding factor. Second, the current study only used univariate analysis to identify group differences in FC; multivariate pattern analysis (MPA) could be applied in subsequent studies to further verify the diagnostic value of cerebral-limbic FC patterns for distinguishing BD-M and BD-D.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBD-M and BD-D exhibit distinct abnormal cerebral-limbic FC patterns (decreased in BD-M, increased in BD-D) when analyzed via whole-brain resting-state fMRI. These patterns may serve as resting-state biomarkers for differentiating the two mood states, supporting distinct neurobiological mechanisms underlying BD mania and depression. Our findings advance understanding of BD’s neurobiology and have implications for clinical diagnosis and personalized treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBD\u003c/strong\u003e: bipolar disorder ; \u003cstrong\u003eBD-D\u003c/strong\u003e: bipolar disorder depression; \u003cstrong\u003eBD-M\u003c/strong\u003e: bipolar disorder mania;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efMRI\u003c/strong\u003e: Functional magnetic resonance;\u0026nbsp;\u003cstrong\u003eFC\u003c/strong\u003e: functional connectivity;\u003cstrong\u003e\u0026nbsp;ANOVA\u003c/strong\u003e: analysis of variance;\u0026nbsp;\u003cstrong\u003eHAMD\u003c/strong\u003e: Hamilton Depression Rating Scale; \u003cstrong\u003eDMN\u003c/strong\u003e: default mode network;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e: false discovery rate; \u003cstrong\u003eYMRS\u003c/strong\u003e: Young Mania Rating Scale;\u0026nbsp;\u003cstrong\u003eEPI\u003c/strong\u003e:\u0026nbsp;echo echo-planar imaging;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCUN:\u0026nbsp;\u003c/strong\u003ePrecuneus;\u0026nbsp;\u003cstrong\u003eSPG\u003c/strong\u003e: Superior parietal gyrus; \u003cstrong\u003eIPG\u003c/strong\u003e: Inferior parietal gyrus;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIP\u003c/strong\u003e: Hippocampus;\u0026nbsp;\u003cstrong\u003ePCG\u003c/strong\u003e: posterior cingulated gyrus; \u003cstrong\u003ePHIP\u003c/strong\u003e: paraHippocampal;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWAIS\u003c/strong\u003e: Wechsler Adult Intelligence Scale; \u003cstrong\u003eAAL\u003c/strong\u003e: Automated Anatomical Labeling;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBO\u003c/strong\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e: Blood Oxygenation Level Department;\u0026nbsp;\u003cstrong\u003eFD\u003c/strong\u003e:\u0026nbsp;frame-wise displacement;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRSN\u003c/strong\u003e: resting-state networks\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThis study was approved by the Ethics Committee of the Second People\u0026rsquo;s Hospital of Hunan Province (Brain Hospital of Hunan Province). All subjects provided written informed consent after understanding the study procedures. For patients with severe suicidal thoughts or behaviors, or those unable to comprehend the informed consent information, written informed consent was obtained from their legal guardians. This study was conducted in compliance with the Declaration of Helsinki. Ethical approval was obtained from the relevant institutional review board, and informed consent was secured from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. All of the raw data are stored in Department of Psychiatry, the Second People\u0026rsquo;s Hospital of Hunan Province (Brain Hospital of Hunan Province). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Provincial technology innovation guide program funded by the Hunan Department of Science and Technology (2017SK50312 to Dr. C Liu). The design of the study, collection and analysis were supported by the Hunan Provincial Department of Education Excellent Young Scholars Project (18B254 to Dr. C Liu) and China Postdoctoral Science Foundation General Program (2018M642990 to Dr. C Liu). The interpretation of data and writing the manuscript was provided by a grant of the Provincial technology innovation guide program, funded by the Hunan Department of Science and Technology (2017SK50312).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC and WGW designed the study. LC and WGW managed the data collection. LC and LXC undertook the statistical analysis. YSH and LC wrote the first draft of the manuscript. We all contributed to and approved the final manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Psychiatry, The Second People\u0026rsquo;s Hospital of Hunan Province (Brain Hospital of Hunan Province), Changsha, Hunan, China\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eMental Health Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan, People\u0026rsquo;s Republic of China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBearden CE, Hoffman KM, Cannon TD. The neuropsychology and neuroanatomy of bipolar affective disorder: a critical review. Bipolar Disord. 2001;3:106\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Aran A, et al. 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Spatiotemporal approach and the history of psychopathology. J Affect Disord. 2016;190:867\u0026ndash;879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676\u0026ndash;682.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMason MF, Norton M, Van Horn JD, et al. Wandering Minds: The Default Network and Stimulus-Independent Thought. Science. 2007;315:393\u0026ndash;395.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheri J, Tanya T. Bipolar disorder: what can psychotherapists learn from the cognitive research? J Clin Psychol. 2007;63:425\u0026ndash;432.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bipolar Mania, Bipolar Depression, Cerebral-limbic, Functional Connectivity, Resting-state fMRI","lastPublishedDoi":"10.21203/rs.3.rs-8692854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8692854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003ePrevious research has identified aberrant functional connectivity (FC) in the neural circuits of patients with bipolar mania (BD-M) and bipolar depression (BD-D), yet the specificity of these FC patterns to each mood state remains unelucidated. This study was designed to compare the cerebral-limbic FC characteristics of BD-M, BD-D patients and healthy control (HC) subjects.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eResting-state functional magnetic resonance imaging (fMRI) was performed on 30 BD-M patients, 31 BD-D patients and 30 HC subjects. Interregional cerebral FC values were calculated for group-wise comparisons, and the correlation between abnormal FC and depressive symptom severity was further explored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAbnormal cerebral-limbic FC in the default mode network (DMN), attention network and limbic areas was observed in both BD-M and BD-D groups. Specifically, BD-D patients showed elevated FC mainly in the DMN [posterior cingulate gyrus (PCG), precuneus (PCUN)], attention network [superior parietal gyrus (SPG), inferior parietal gyrus (IPG)] and limbic regions [hippocampus (HIP), parahippocampus (PHG)], while BD-M patients displayed reduced cerebral-limbic FC in the DMN and limbic areas.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBD-M and BD-D patients exhibit characteristic and divergent cerebral-limbic FC abnormalities\u0026mdash;DMN FC reduction in BD-M and FC elevation in BD-D. These specific FC patterns may act as potential resting-state biomarkers for distinguishing between the two mood episodes of bipolar disorder.\u003c/p\u003e","manuscriptTitle":"Abnormal cerebral-limbic functional connectivity between bipolar mania and bipolar depression under resting state","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:11:34","doi":"10.21203/rs.3.rs-8692854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5aa4f636-d9a7-43f6-9f2d-c31e11add4f6","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T14:11:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:11:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8692854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8692854","identity":"rs-8692854","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}