White Matter Microstructural Alterations and their Association with Decision-Making Deficits in Suicide Attempters

White Matter Microstructural Alterations and their Association with Decision-Making Deficits in Suicide Attempters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article White Matter Microstructural Alterations and their Association with Decision-Making Deficits in Suicide Attempters Gerd Wagner, Ani Zerekidze, Meng Li, Lydia Bahlmann, Martin Walter, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8194143/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Decision-making and inhibition deficits are well documented among individuals with a history of suicide attempt (SAs), but the underlying neurobiological mechanisms remain unclear. This study investigated white matter (WM) tracts associated with intermediate phenotypes, namely, decision-making and inhibition, among SAs (n=48), patient controls (PCs; n=34), and healthy controls (HCs; n=49) using diffusion tensor imaging and tractometry, a novel method allowing pointwise investigation of WM microstructure. We hypothesized alterations in the striato-fronto-orbital tract (ST-FO), superior longitudinal fasciculus II (SLF-II), and cingulum bundle in SAs and subgroups, as well as associations between diffusion metrics and cognitive performance on the Iowa Gambling (IGT) and Go/No-Go tasks. As hypothesized, compared with the PCs and HCs, the SAs presented significant alterations in the right ST-FO, with elevated fractional anisotropy (FA) in the central segment and lower FA in the anterior segment; similar effects were observed for radial diffusivity (RD). RD values bilaterally and FA values in the left ST-FO correlated significantly with IGT performance. Additional RD and FA alterations were detected in the SLF-II, although no differences emerged between patient groups. No significant group differences were found in the cingulum bundle, although SAs made more Go/No-Go commission errors than both control groups. In addition, SAs who used violent suicide methods differed from nonviolent SAs in the central segments of the ST-FO tract in the RD. This is the first study to apply tractometry in SAs, providing novel evidence that WM alterations, mainly in orbitofronto-striatal pathways, are linked to cognitive deficits relevant to suicidal behavior. Health sciences/Biomarkers/Diagnostic markers Biological sciences/Neuroscience Health sciences/Diseases/Psychiatric disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Specific cognitive deficits in suicide attempters (SAs) have been consistently reported in previous studies and are considered a diathesis for suicidal behaviors ( 1 , 2 ). Systematic reviews have shown that SAs mainly differ from nonattempters in cognitive domains related to decision-making (DM) and cognitive and motor inhibition ( 3 , 4 ). DM is a complex process involving subprocesses such as evaluating options and assessing risk and rewards, which requires the integration of multiple brain regions ( 5 ). Individuals with a history of suicide attempt(s) have difficulties incorporating prior experiences ( 6 ) and contextually relevant information ( 7 ) into their decision-making processes. They tend to make choices that are disadvantageous in the long term, as demonstrated by poor performance on the Iowa Gambling Task (IGT) ( 8 ). IGT deficits represent one of the most consistently reported cognitive impairments in SA ( 9 ), notably in those who use violent means (vSAs) ( 8 , 10 ). As vSAs are at higher risk of suicide death in comparison to SAs that use only nonviolent means (nvSAs), they represent the population closer to suicide victims. Moreover, impaired inhibitory processes foster decisional impulsivity, leading to premature and risky behavior ( 11 ). On a behavioral level, motor inhibition comprises the capacity to stop motor impulses (contextually inappropriate or non–goal-directed behaviors), whereas cognitive inhibition is related to the suppression of irrelevant stimuli that could interfere with task performance ( 12 ). Alterations in cognitive inhibition ( 13 , 14 ) and motor inhibition ( 15 – 17 ) have been repeatedly reported in SA. Thus, altered inhibitory processes may facilitate transition to suicidal behavior following impaired value-based DM. Furthermore, these cognitive deficits may reflect dysfunction in neural networks, particularly disruptions in structural and functional connectivity among brain regions supporting decision-making and inhibitory subprocesses ( 18 ). Such associations between cognitive test performance and white matter (WM) integrity have been reported, particularly in domains such as attention and cognitive flexibility ( 19 ), cognitive control ( 20 ) and reward processing ( 21 ). Additionally, prior studies have demonstrated a relationship between structural and functional connectivity within specific brain networks in healthy controls ( 22 ) and have reported links between structural abnormalities, altered functional connectivity and cognitive deficits in patients with schizophrenia ( 23 ) or MDD ( 24 ). Diffusion tensor imaging (DTI) is an approach for investigating white matter connectivity on the basis of the diffusion direction of water molecules ( 25 ). The broadly investigated parameter via DTI is fractional anisotropy (FA), a scalar measure that quantifies the directionality of water diffusion within a voxel. Findings from previous DTI studies in the SAs have been largely inconsistent and heterogeneous ( 26 ). Nevertheless, several studies have relatively consistently reported reduced FA in SAs compared with patients with an affective disorder and no history of suicide attempts, particularly in the prefrontal ( 27 – 29 ), orbitofrontal ( 30 , 31 ) and striatal regions ( 32 , 33 ). The WM tract linking the dorsal striatum to the orbitofrontal cortex is the striato-fronto-orbital (ST-FO) tract, comprising fibers that originate from the caudate nucleus and putamen and project to the orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC) ( 34 ). Abnormalities in frontostriatal tracts have also been observed in SAs via further DTI-derived metrics, including radial diffusivity (RD), a measure of the rate of water diffusion perpendicular to the primary diffusion direction ( 35 ). In addition, compared with those in patients with treatment-resistant depression and suicidal ideation, elevated axial diffusivity (AD), a diffusion coefficient along the principal axis of the fiber tract, and extracellular free water in fronto-thalamo-limbic white matter tracts were found in SAs ( 36 ). Furthermore, reduced fiber projections—a reduced percentage of passing fibers—from the anterior limb of the internal capsule (ALIC) to the left OFC and thalamus were found in SAs ( 30 ). The variability in the result might be due to the heterogenity and small size of the samples as well as the methods applied for preprocessing DTI data and extracting the white matter tracts. The most frequently used methods in those studies are voxel-based analysis (VBA) ( 37 ) and tract based spatial statistics (TBSS)( 38 ). These methods are, however, subject to ongoing discussion regarding their limitations and the interpretation of results. Challenges include issues with data preprocessing, such as coregistration to a template image ( 39 ), spatial smoothing prior to voxelwise statistical analysis ( 38 , 40 , 41 ), poor segmentation quality ( 42 ), and concerns about reliability ( 43 ). The skeletonization process in TBSS, while enhancing alignment across subjects, may exclude peripheral or less common white matter tracts and miss pathology outside the core skeleton, leading to partial information loss. Additionally, because TBSS averages and projects diffusion metrics onto a mean skeleton, subtle or spatially localized microstructural alterations may be diluted or entirely overlooked. To investigate white matter microstructure with greater anatomical precision, we employed TractSeg, a deep-learning-based tool that performs fast and accurate subject-specific white matter tract segmentation in individual native space ( 42 , 44 , 45 ). Following tract segmentation, we applied tractometry, which samples diffusion metrics (e.g., FA) along the length of each tract—typically divided into 100 equidistant segments—enabling the detection and localization of microstructural alterations at specific tract locations, rather than relying solely on mean values across the entire tract ( 46 , 47 ). The goal of the present study is, therefore, to investigate WM integrity in individuals with a history of suicide attempt(s) via tractometry, which allows more precise localization of structural connectivity changes than traditional group-level approaches do. To assess the specificity of potential alterations, we compared SA patients to two control groups: psychiatric patients with an affective disorder and without any history of suicidal act and healthy controls. We focused on neural pathways linked to decision-making and inhibition. Specifically, we aimed to examine structural connectivity between brain regions associated with IGT performance. Original research by Damasio and colleagues has linked impaired IGT performance primarily to lesions in the VMPFC, OFC and amygdala ( 48 ). Consistently, neuroimaging studies have shown IGT-related activations in the VMPFC, OFC, and striatum ( 49 – 51 ). Moreover, altered OFC activation during the IGT has been observed in SAs ( 52 ) as well as in healthy first-degree biological relatives of people who died from suicide ( 53 ). Therefore, we selected the ST-FO as the primary tract of interest. In addition, we examined alterations in structural connectivity along the superior longitudinal fasciculus II (SLF II), which connects parieto-occipital regions with the dorsolateral prefrontal cortex (DLPFC), and along the cingulum bundle, which links the cingulate gyrus with the DLPFC, OFC, dorsal anterior cingulate cortex, and premotor areas ( 54 ). The DLPFC has been associated with the IGT ( 55 ) and Stroop task performance ( 23 , 56 ), playing crucial roles in cognitive control and executive functions. Connectivity in the anterior and middle parts of the cingulum bundle was investigated in connection with motor inhibition. The cingulum bundle is involved in executive functions, especially in tasks assessing inhibition ( 18 ). On the basis of previous findings, we hypothesized that the SA patients would show altered structural integrity in the ST-FO, the anterior part of the SLF-II, and the anterior and middle parts of the cingulum bundle compared with control patients without any personal history of suicide and healthy controls. As vSAs show greater impairment in the IGT ( 10 ), we expect greater structural alterations in these individuals than in nvSAs. Second, we expect that FA in the ST-FO tract and the SLF-II would correlate with IGT performance, whereas FA in the cingulum bundle is expected to correlate with Go/No-Go performance. In an exploratory analysis, we also examined differences in RD, which provides insights into axon organization and alignment within these three tracts, complementing the FA measurement. 2. Methods 2.1. Study sample The study (named SUICIDE_DECIDE) was approved by the ethics committee of Friedrich Schiller University, Jena, Germany, and the “Comité de Protection des Personnes SUD-EST IV” in France. It was conducted from 2021 to 2023 at two different cooperating sites: Jena Academic Hospital, Germany, and Academic Hospital of Nimes, France. Patients who had attempted suicide were admitted to university hospitals, where they were informed about the study. All participants gave their informed consent prior to the study and were compensated with €100 for participating. Individual data were then saved in accordance with the European data protection guidelines (GDRP). The study was declared on ClinicalTrial.gov (NCT05230043). A total of 131 participants were recruited (92 in Jena and 39 in Nîmes): 48 inpatients who had attempted suicide (47 with MDD and 1 with BD), 34 nonattempter MDD patient controls (PC), and 49 healthy controls (HC). The control participants had neither a personal history of suicide attempts nor a family history of suicide attempt or suicide in first- or second-degree relatives. Owing to scanner-related issues, DTI acquisitions from the Nimes site were excluded from further analysis. Additionally, four participants from the Jena sample did not complete the DTI protocol and were excluded. Consequently, diffusion-weighted images from 88 participants at the Jena site were included in the final preprocessing pipeline, comprising 40 SA, 20 PC, and 28 HC. The inclusion criteria for all the subjects were being right-handed, German and French-speaking men and women between 18 and 60 years of age who agreed to participate and signed the consent form. For patients, an additional inclusion criterion was having a current depressive episode of MDD or BD according to the DSM-5. The exclusion criteria were as follows: loss of consciousness due to the suicidal act; intellectual disability assessed via the Multiple Choice Vocabulary Intelligence Test (MWT-B; ( 57 )); left-handedness assessed via the Annett Hand Preference Questionnaire ( 58 ); current psychotic disorders; current manic, hypomanic, mixed, or rapid cycling episode; severe alcohol or substance dependence within the last 3 months, as defined by the DSM-5 criteria; previous brain trauma; electroconvulsive therapy within the last 3 months; and contraindications for magnetic resonance imaging (MRI). We further divided SA into two subgroups (vSAs and nvSAs) on the basis of the suicidal means they used. Violent suicide methods were categorized according to the ICD-10 codes X66–X82. Methods such as hanging, firearm use, jumping from heights, deep cuts, car crashes, severe burning, gas poisoning, drowning, electrocution, and jumping in front of a train are classified as violent methods, whereas drug overdose and superficial cutting are classified as nonviolent methods. 2.2. Clinical and neuropsychological assessments A psychiatric diagnosis was established was established using the M.I.N.I. interview ( 59 ) for major psychiatric disorders on the basis of DSM-5 criteria, which were administered by trained psychologists with master’s degrees. Suicide ideations, intentions and behaviors were assessed via the Columbia-Suicide Severity Rating Scale (C-SSRS)( 60 ). The Young-Mania Rating Scale (YMRS-D)( 61 ), Beck Depression Inventory (BDI-II)( 62 ), and Montgomery-Asberg Depression Scale (MADRS)( 63 ) were used to assess depressive and manic symptoms. To investigate decision-making in all participants, we adopted a computerized version of the IGT ( 50 , 64 ). For details, see the supplementary materials . The net score was used for the statistical analysis, which was determined by subtracting the number of disadvantageous from advantageous choices from the overall 100 choices. Response inhibition was assessed with a computerized Go/No-Go task ( 65 ) and incorrect responses to nontarget stimuli, i.e., commission errors, which were used for group comparisons and correlational analysis. 2.3. MRI acquisition parameters All imaging data were collected on a 3T whole-body system equipped with a 64-element head matrix coil (MAGNETOM PRISMA FIT, Siemens Healthineers, Erlangen, Germany). First, a structural T1 image was acquired, followed by diffusion tensor imaging to investigate putatively altered structures. DTI data were acquired with both AP (anterior‒posterior) and PA (posterior‒anterior) phase-encoding directions to correct for susceptibility artifacts. A total of 96 contiguous transverse slices with 1.5 mm thickness were obtained (TR = 3318 ms, TE = 87.40 ms, flip angle = 78°, multiband factor = 4) that covered the entire brain, including the lower brainstem. The matrix size was 140 × 140 pixels with an in-plane resolution of 1.5 × 1.5 mm 2 (FOV (field of view) = 210 × 210 mm 2 , b value = 2500 s/mm 2 , and 103 collinear directions with 8 non-weighted b0 images: two at the beginning, one at the end, and 5 interleaved throughout the sequence, following every 16 diffusion-weighted images. The DTI scan time was approximately 12 minutes (2 × 6:03 minutes). 2.4. Diffusion MRI Preprocessing Diffusion-weighted images were preprocessed via an integrated pipeline to ensure high-quality data for diffusion tensor estimation and tract-based quantification. The preprocessing steps included denoising using MRtrix3’s dwidenoise, removal of Gibbs ringing artifacts with mrdegibbs, and correction for eddy currents and head motion using FSL’s eddy implemented via dwifslpreproc ( 66 ). To correct for spatial intensity inhomogeneities, B1 bias field correction was performed using ANTs through dwibiascorrect ( 67 ). At this stage, two patients from the SAs group were excluded from further analysis because of poor image quality. From the preprocessed diffusion data, diffusion tensor–derived scalar maps were computed using TractSeg’s built-in tools. The tensor model was fitted to generate maps of RD and FA. These scalar maps were then registered to the TractSeg MNI FA template using rigid-body transformation with FSL FLIRT to achieve alignment in standard space. Tract-specific diffusion metrics were subsequently extracted using TractSeg’s Tractometry framework. Fiber orientation distributions (FODs) were then computed via multishell multitissue constrained spherical deconvolution. White matter tracts were identified and segmented in subject space through TractSeg’s convolutional neural network–based approach, and corresponding tract orientation maps (TOMs) and endpoint segmentations were generated. The MNI-registered diffusion scalar maps (e.g., RD, FA) were then projected onto these tract masks to compute the diffusion values along each tract. For further analysis, bundle-specific streamlines were generated via TractSeg's probabilistic tracking algorithm. The resulting tractograms were filtered using a combination of anatomical constraints and quantitative quality metrics to ensure biological plausibility. Then, tractometry (Chandio et al., 2020) was used to resample all streamlines to ensure equal segment numbers across tracts. The FA and RD profiles were then evaluated at 100 equidistant points along each tract's trajectory ( https://github.com/MIC-DKFZ/TractSeg/blob/master/resources/Tractometry_documentation.md ). The values from the first and last segments are automatically removed. This approach enables pointwise statistical comparisons along the entire tract length while accounting for positional correspondence across subjects. 2.5. Statistical analysis All the statistical analyses were conducted using RStudio (Version: 2024.12.1 + 563). To analyze the DTI data, we extracted 98 FA and RD values from each tract and used them in an analysis of covariance (ANCOVA) to examine group differences. ANCOVA was conducted separately for each tract segment, with FA or RD as the dependent variable, group (SA vs. PC vs. HC) as the independent variable, and age and sex as covariates. The FDR method was applied to correct for multiple comparisons. Spearman’s rank correlation coefficient was used to assess correlations between FA/RD values and relevant neurocognitive and clinical measures. Finally, linear regression analyses were conducted in the SA group with the IGT net score as the dependent variable. Each FA and RD value in the right ST-FO tract that was significant in the ANCOVA was entered separately as an independent variable. In the statistical analysis of IGT performance, four subjects in the IGT were excluded for noncompliance with the task instructions. ANCOVA was conducted with group as the independent variable and net score as the dependent variable. Age, sex , and study site were included as covariates. The commission error rate in the Go/No-Go task showed a nonnormal distribution. Linear regression with age , sex , and study site as independent variables was used to obtain residualized values, which were analyzed for overall group differences via the Kruskal‒Wallis test. Post hoc Mann‒Whitney U tests with FDR correction were applied for multiple comparisons. 3. Results 3.1 Sociodemographic and neurocognitive measures Age and verbal intelligence did not differ between the groups. Significantly more female individuals were recruited in both patient groups compared to heathy controls. SAs made more Go/No-Go commission errors than both control groups. They also showed lower IGT scores compared to healthy controls, but not compared to psychiatric controls (Table 1 ). Table 1 Sociodemographic and clinical characteristics of the study sample Suicide attempters (SA) (N = 48) Patient controls (PC) (N = 34) Healthy controls (HC) (N = 49) χ 2 /ANO(C)VA p Post hoc (Mann‒Whitney U) Age (Mean ± SD) 31.3 ± 11.9 33.6 ± 11.6 32.7 ± 9.9 n.s. Female (%) 67.3 67.6 44.9 = .024 χ 2 : SA, PC > HC* Recurrent MDD (%) 55.1 52.9 n.a n.s. Previous depressive episodes ( Mean ± SD) 4.1 ± 5.3 2.5 ± 3.5 n.a. n.s. MDD/BD onset age in years ( Mean ± SD) 20.7 ± 10 22.8 ± 9 n.a. n.s. Lifetime N of suicide attempts (Median, range) 2 ( 1 – 25 ) 0 0 Multiple attempts (%) 75.5 n.a. n.a. History of violent suicidal act N , (%) 44.9% n.a. n.a. SIS (last attempt; Mean ± SD) 18.3 ± 5.3 n.a. n.a. Suicidal Ideation (item “wish to die” in C-SSRS in last 3 months in %) 95 50 0 Suicidal act in first degree relatives (%) 16.3 0 0 Suicidal act in second degree relatives (%) 14.3 0 0 Current psychiatric medication (%) 83.7 70.6 0 Comorbid diagnosis (%) Anxiety disorders 16.3 11.8 0 PTSD 8.2 0 0 OCD 6.1 0 0 Substance use disorder 36.7 14.7 8.2 Eating disorders 6.1 2.9 0 MWT-B (IQ) 107.5 ± 14.7 107.6 ± 12.9 105.1 ± 11.9 n.s. NART (raw value) 24.6 ± 3.7 24.0 ± 3.50 26.0 ± 3.7 n.s. BDI-II 31.8 ± 14 22.1 ± 12.8 2.2 ± 3.6 PC***>HC*** MADRS 22.8 ± 10 18.6 ± 10.6 1.5 ± 2.2 PC*>HC*** Iowa Gambling Task (IGT) Total net score 1.5 ± 30.9 -0.5 ± 30.6 16.8 ± 29.5 0.013 SA < HC ** + PC PC * SA > HC ** + Abbreviations: SD, standard deviation; MDD, major depressive disorder; N, number; SB, suicide behavior; SIS, Beck’s suicide intent scale; CSSRS, Columbia-Suicide Severity Rating scale; PTSD, posttraumatic stress disorder; OCD, obsessive‒compulsive disorder; MWT-B, vocabulary intelligence test; NART, National Adult Reading Test; BDI-II, Beck Depression Inventory; MADRS, Montgomery Asberg Depression Scale; n.s., nonsignificant; n.a., not applicable * p < 0.05, ** p < 0.01, *** p < 0.001 , + survives FDR correction 3.3 Tractometry results: Main group effects A significant main effect of group was observed in fractional anisotropy within the right ST-FO, specifically in the central (segments 44–52) and anterior portions (segments 65–84) (Fig. 1 ). Post hoc tests indicated that suicide attempters presented significantly greater FA in the central segments and lower FA in the more anterior segments than both control groups. RD analyses corroborated these anterior frontal connectivity alterations, revealing significantly elevated RD in the SA compared with the healthy controls across anterior frontal segments 59–94, especially in the OFC. In contrast, no significant group differences in FA or RD were detected in the left ST-FO, as illustrated in Fig. 1 . A significant group effect was also observed in the left SLF-II tract (Fig. 2 ). FA differences were localized to the posterior (segments 1–6 and 15–17) and anterior portions of the tract (segments 90–92). A post hoc test revealed lower FA in segments 1–6 and 90–92 in the SA and PC groups than in the HC group, with no significant difference between the SA and PC groups. Compared with HCs, patients in both groups also presented elevated RDs in the posterior (segments 1–9) and central (segments 41–44) portions of the tract. In contrast, no significant group differences in FA or RD were detected in the cingulum bundle (Fig. 2 ). 3.4. Subgroup analysis in SA Compared with the nvSA, the vSA presented a significantly lower RD in the central portion of the right ST-FO tract (segments 37–41) (Fig. 3 ). FA was also elevated in these segments in the VSA group, although this difference did not remain significant after FDR correction. No significant subgroup differences were found in the SLF-II or the cingulum bundle. 3.5. Correlations in the whole study sample The means of FA values in the left ST-FO (r = .34, p = .001) and RD values in the left (r = − .38, p < .001) and right ST-FO (r = − .30, p = .006) significantly correlated with IGT performance. Only the correlations with the left ST-FO metrics survived FDR correction. No significant association was found between the IGT net score and FA values in the right ST-FO (Fig. 4 ). Moreover, a significant correlation was found between the IGT net score and FA values in the left SLF-II (r = .227, p = .036) as well as between RD values in the left (r = − .237, p = .029) and right SLF-II (r = − .238, p = .028), which were not significant after FDR correction. RD values in both the left (r = .24, p = .026) and right cingulum bundles (r = .258, p = .017) were positively associated with the commission error rate. These correlations did not survive the FDR correction. 3.6. Regression analysis in SAs To investigate the relationship between altered WM integrity and task performance in SAs, we conducted a regression analysis with the IGT net score as the dependent variable and only significant (based on ANCOVA) FA and RD values from the right ST-FO as independent variables. RD values in the central (segments 57–62) and anterior (segments 88–90) portions significantly predicted the IGT net score (p < .05), explaining approximately 11–13% of the variance in IGT performance. 4. Discussion This study aimed to examine WM alterations in SAs and their relationships with decision-making and cognitive inhibition performance, two intermediate phenotypes of suicidal behavior. As hypothesized, compared with HCs, SAs presented altered structural integrity in terms of lower FA values and higher RD values in the anterior segments of the right ST-FO and left SLF-II, a pattern compatible with reduced myelin integrity. No group differences were found in the cingulate bundle. In the whole group, mostly left-sided (unaffected) ST-FO and SLF-II DTI measures were significantly correlated with IGT performance (decision-making), whereas cingulum WM integrity was associated with Go/No-Go performance (cognitive inhibition). In SA only, RD values in the central and anterior segments of the right ST-FO tract were significant predictors of IGT performance. To our knowledge, this is the first study to investigate WM tracts within the frontostriatal network in SA using a pointwise analysis methodology, allowing for more precise localization of WM alterations. While some of our findings in ST-FO patients are consistent with those in previous studies ( 26 , 68 ), we also report a novel observation: structural alterations in the right ST-FO tract affect specific portions of the tract and vary along its course. The comparable FA and RD values in the left ST-FO tract across groups further support the robustness of our right-sided findings. The presence of tract-specific alterations exclusively in the right ST-FO suggests hemispheric asymmetry in the structural organization of the frontostriatal network ( 69 ). This asymmetry may indicate that functional specialization of the right tract renders it more susceptible to pathological neurocognitive processes underlying suicidal behavior, notably value-based decision-making or inhibitory processes. Greater FA values in the SA group than in the control group were observed in the central segments of the ST-FO tract passing through the subgenual anterior cingulate cortex (sgACC), followed by profoundly lower FA values in the anterior segments located in the OFC/vmPFC. FA values are typically greater in well-myelinated white matter tracts, since the myelin sheath restricts water diffusion in all directions except along the length of the fibers, resulting in strongly anisotropic diffusion ( 70 ). Conversely, in tissues where water molecules move randomly and uniformly in all directions, the FA values are close to zero. Higher FA was previously associated with better performance on cognitive functioning tasks in individuals at ultrahigh risk for psychosis but not in healthy individuals ( 71 ). Thus, the interpretation of elevated FA values remains uncertain. Elevated anisotropy has been hypothesized to reflect region specific, compensatory responses to cortical thinning ( 72 ) or local FA alterations ( 71 ). Therefore, we speculate that higher FA in the central tract segments of ST-FO patients might compensate for the reduced FA in the anterior segments. Furthermore, we found a similar trend in RD analysis: a lower RD in the central, but a greater RD in the anterior right ST-FO in SAs than in the HCs. As RD describes water diffusion perpendicular to a fiber tract ( 73 ), it provides complementary measures of FA for the WM microstructure. A higher RD is generally associated with a lower FA since greater cross-tract diffusion reduces anisotropy. The results of RD analysis further validate our primary findings in FA. Altered WM integrity in the central ST-FO might influence the specific functions of this region. The sgACC acts as a bridge between limbic structures and the frontal lobe and integrates cognitive activity with affective experience ( 74 ). However, the impact of WM alterations on cognitive–affective integration is not yet well understood. Moreover, we found reduced FA in the OFC/VMPFC, which is in line with previous reports in the SA patients ( 29 – 31 , 35 ). As this region has previously been identified as a neural correlate of IGT performance in both lesion ( 48 , 64 ) and fMRI studies ( 50 , 52 , 75 ), our findings suggest that structural connectivity may contribute to decision-making deficits in SAs, as further supported by regression analysis and significant correlations between IGT net scores and mean FA and RD values in the ST-FO tract (Fig. 4 ). Higher FA and lower RD values were associated with better performance on the IGT in the whole group. However, in the right ST-FO tract (Fig. 4 ), FA was not significantly associated with the IGT net score, in contrast to RD. This dissociation, especially in light of the comparable measures in the left ST-FO tract, raises the possibility that potential alterations on the right side may also modulate the association with task performance. Taken together, these patterns suggest that the underlying relationship is nonlinear and more complex than can be adequately captured by simple linear correlational analyses. Furthermore, RD in the bilateral cingulum bundle was positively correlated with performance in the Go/No-Go task, with higher RD values associated with more commission errors. This finding indicates the association between motor inhibition and cingulum bundle WM connectivity not only at the functional level but also at the structural level. Behaviorally, the SA group made significantly more commission errors than both the patient and healthy control groups did, indicating deficits in motor inhibition. Thus, this finding is unlikely to be related to MDD and may instead represent a stable vulnerability factor for suicidal behavior. Reduced motor inhibition may be critical in the transition from suicidal ideation to action, as impaired inhibition could increase susceptibility to act on suicidal impulses. However, no group differences in cingulum bundle WM integrity were found, suggesting that the behavioral deficits may stem from functional rather than structural alterations. In addition, the analysis revealed subgroup differences in WM integrity among SAs. As expected, individuals who attempted suicide using violent means exhibited more pronounced alterations in ST-FO white matter integrity compared to those who used nonviolent methods. Specifically, the vSA subgroup displayed significantly lower RD and a nonsignificant trend toward higher FA in central segments of the right ST-FO than the nvSA, PC and HC. These findings indicate that subgroups defined by the chosen method to attempt suicide differ not only in decision-making but also in WM structure. Significant subgroup differences have been previously reported at the behavioral level in IGT task performance ( 10 ). In particular, vSAs made significantly more disadvantageous choices ( 10 ). This subgroup also differed in trait impulsivity from nvSAs, reporting higher levels of sensation seeking ( 76 ). However, WM alterations among SA subgroups have rarely been investigated. To date, only one study has explored subgroup differences based on suicide attempt methods (drugs vs. other), but it reported no significant differences in FA ( 27 ). Importantly, that analysis relied on the mean FA per tract, which may have limited sensitivity to subtle or subregion-specific alterations, as observed in the present study. Given the clinical heterogeneity of SAs, it is plausible to assume that distinct subgroups exhibit different neural correlates. Our study provides initial evidence of subgroup-specific WM alterations, underscoring the need for future research to systematically implement subgroup analyses to replicate and extend these findings. The findings of this study have important clinical implications. Detecting WM alterations in SA may serve as an additional cornerstone for developing targeted treatment strategies for individuals with suicidal behavior. Identifying specific neural markers of suicide risk—both at the group and subgroup levels—may support the development of tailored interventions. For example, deep brain stimulation (DBS) of the sgACC can substantially reduce symptoms in patients with treatment-resistant depression ( 77 ), a major risk factor for suicidal behavior. Clarifying the specific neural correlates of SB may help refine DBS treatment strategies to more effectively target both depressive symptoms and suicide risk. Some limitations of the study must be mentioned. Several subjects had to be excluded from the DTI analysis because of scanner-related issues. Nevertheless, the remaining sample size was sufficient to detect not only group differences but also subgroup differences among SAs at an FDR-corrected level. This study included only patients with a current depressive episode, primarily MDD. Since SAs are also prevalent in other mental disorders, such as borderline personality and psychotic disorders, future studies should examine white matter integrity across a broader range of clinical populations. In conclusion, this study demonstrated altered WM microstructure within the frontostriatal network in SA, a tract critically involved in cognitive and affective processes. WM integrity of the ST-FO tract was linked to performance on the IGT and Go/No-Go tasks. Such alterations may contribute to deficits in decision-making and motor inhibition in SA, with pronounced changes in those who used violent means. Future research should replicate these findings and explore subgroup-specific differences in greater depth. Declarations Acknowledgement Nîmes site: We would like to thank Dr. Pascale Fabbro-Peray as a methodologist, Mrs Bérangère Gomaere, Mr. Antoine Giron and Mr. Thibault Gibert for their active participation in recruitment; Mrs. Léonie Gazel for the administrative coordination of the project; Mrs. Lorrie Lafuente and Mr. Rabah Tamimou for their help in managing the project; and Mrs. Camille Briand for data management. Jena site: We thank Ms. Johanna Walther, Ms. Anna Karoline Seiffert and Ms. Anna Bollmann for their contributions to data acquisition. We acknowledge support from the Open Access Publication Fund of the Thüringen Universitates- und Landesbibliothek Jena. Author Contributions A.Z.: investigation; data collection and analysis, writing - original draft. M.L.: data preprocessing and analysis, manuscript reviewing/editing, approval of the final article. L.B.: data collection, manuscript reviewing/editing, approval of the final article. M.W.: administrative support; supervision, reviewing/editing, approval of the final article. F.P.: conceptualization; Writing - Review & Editing, approval of the final article. D.G.: data curation and analysis, Writing - Review & Editing, approval of the final article. F.J., G.W.: concept and design; supervision; critical revision and editing of the manuscript; administrative, technical, or material support; supervision, Writing - Review & Editing. All authors have read and agreed to the published version of the manuscript. Financial support FJ and GW obtained a grant from the American Foundation for Suicide Prevention (AFSP, grant number LSRG-1-086-19). The sponsors had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. Conflicts of interest MW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All the other authors have no conflicts of interest to disclose. 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16:38:20","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91846,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/38bd1c4c3e1b1fdb0bfce1e7.png"},{"id":99274298,"identity":"c8666f35-0ad2-4195-be23-f90252542c20","added_by":"auto","created_at":"2025-12-31 06:40:14","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44750,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/36ec7d4c6fbfd3a3118622cc.png"},{"id":99321164,"identity":"063c420e-a9d3-4cc7-b077-c7c42a252777","added_by":"auto","created_at":"2025-12-31 16:39:14","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56298,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/2c9e4f0f7c20b3357a038c59.png"},{"id":99274302,"identity":"bebae434-7057-41a2-a4c8-1fe4ae4d1cbe","added_by":"auto","created_at":"2025-12-31 06:40:14","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173390,"visible":true,"origin":"","legend":"","description":"","filename":"2025MP0030610structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/f8f5fca3cd8f2b309a55b93f.xml"},{"id":99274303,"identity":"180cd3d9-c464-4846-ac86-63fdb216006e","added_by":"auto","created_at":"2025-12-31 06:40:14","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":188225,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/483afbc96b5a3c2b80b35bd8.html"},{"id":99274287,"identity":"5c2b1f0a-c0d0-4fa6-91b0-ea378a88f419","added_by":"auto","created_at":"2025-12-31 06:40:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":914962,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented striato-fronto-orbital tract and tractometry results in suicide attempters, patients and healthy controls for fractional anisotropy (FA) and radial diffusivity (RD), corrected for sex and age.\u003c/p\u003e\n\u003cp\u003eFor tractometry, the tract was divided into 98 segments, and the mean fractional anisotropy (FA) and mean diffusivity (RD) were calculated for each segment. These values were analyzed viaANCOVA for between-group comparisons. Sex and age were used as covariates. False Discovery Rate (FDR) correction was applied to correct for multiple comparisons. a) FA with standard deviation (SD) in the left striato-fronto-orbital tract, nonsignificant ANCOVA results; b) FA with SD in the right striato-fronto-orbital tract, significant GROUP effect in the central and anterior segments of the tract; significant differences between SA and PC in segments 43-52 and 71; c) RD with SD in the left striato-fronto-orbital tract, nonsignificant ANCOVA results; d) RD with SD in the right striato-fronto-orbital tract, significant GROUP effect in the anterior segments of the tract, with no differences between SA and PC.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: SA, suicide attempters; PC, patient controls; HC, healthy controls; FA, fractional anisotropy; RD, radial diffusivity.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* p \u0026lt; 0.05 after FDR correction\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/ae27a44cb9d8bc60e63b598b.png"},{"id":99318901,"identity":"c8df66b4-51e5-4f79-9460-fa8318dbd794","added_by":"auto","created_at":"2025-12-31 16:35:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":545011,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented superior longitudinal fasciculus II and cingulum bundle and tractometry results in suicide attempters, patientsand healthy controls for fractional anisotropy (FA) and radial diffusivity (RD), corrected for sex and age.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: SLF, superior longitudinal fasciculus; CB, cingulum bundle; SA, suicide attempters; PC, patient controls; HC, healthy controls; FA, fractional anisotropy; RD, radial diffusivity.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* p \u0026lt; 0.05 after FDR correction\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/d2303aea428f5cc96a8ba3ab.png"},{"id":99319114,"identity":"8fc7866e-93e2-4526-b954-5930674e8884","added_by":"auto","created_at":"2025-12-31 16:36:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":275249,"visible":true,"origin":"","legend":"\u003cp\u003eTractometry results of the right striato-fronto-orbital tract in suicide attempters who used violent or nonviolent suicidemeans and in patientsand healthy controls.\u003c/p\u003e\n\u003cp\u003ea) Fractional anisotropy across the tract in suicide attempter subgroups and control groups and significant tract segments in ANCOVA. There were no significant post hoc differences between SA and PC; b) Radial diffusivity across the tract in the SA subgroups and control groups. Significant post hoc differences between SA and PC in segments 37-41. Significant tract segments in ANCOVA that survived FDR correction for multiple comparisons are marked with *.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: VSA, suicide attempterswho used violent suicidemeans; nVSA, suicideattempters who used nonviolent suicide means; PC, patient control; HC, healthy control; FA, fractional anisotropy; RD, radial diffusivity.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* p \u0026lt; 0.05 after FDR correction\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/6fa5526894cda5ecbac0c254.png"},{"id":99321174,"identity":"7bfd959f-40ad-4367-8d0f-b9b5eecf15cf","added_by":"auto","created_at":"2025-12-31 16:39:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":244715,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of fractional anisotropy and radial diffusivity in the striato-fronto-orbital tract with the IGT net score for the whole sample\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: FA, fractional anisotropy; RD, radial diffusivity; IGT, Iowa gambling task; ST-FO, striato-fronto-orbital tract; SA, suicide attempters; PC, patient controls; HC, healthy controls.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/32a43cbbd9f984e736998419.jpeg"},{"id":99788169,"identity":"e28b92e7-9047-4248-9f03-b56712f15bdc","added_by":"auto","created_at":"2026-01-08 12:45:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2902143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/2910f9bf-ac16-4b50-ae8f-d7de0ed8838f.pdf"},{"id":99274284,"identity":"7c505479-b45d-49a3-8607-267875528e85","added_by":"auto","created_at":"2025-12-31 06:40:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23810,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SupplementarymaterialsDTIAZ.docx","url":"https://assets-eu.researchsquare.com/files/rs-8194143/v1/bfe5d614aae9e747b665e99c.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"White Matter Microstructural Alterations and their Association with Decision-Making Deficits in Suicide Attempters","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSpecific cognitive deficits in suicide attempters (SAs) have been consistently reported in previous studies and are considered a diathesis for suicidal behaviors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Systematic reviews have shown that SAs mainly differ from nonattempters in cognitive domains related to decision-making (DM) and cognitive and motor inhibition (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDM is a complex process involving subprocesses such as evaluating options and assessing risk and rewards, which requires the integration of multiple brain regions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Individuals with a history of suicide attempt(s) have difficulties incorporating prior experiences (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and contextually relevant information (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) into their decision-making processes. They tend to make choices that are disadvantageous in the long term, as demonstrated by poor performance on the Iowa Gambling Task (IGT) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). IGT deficits represent one of the most consistently reported cognitive impairments in SA (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), notably in those who use violent means (vSAs) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). As vSAs are at higher risk of suicide death in comparison to SAs that use only nonviolent means (nvSAs), they represent the population closer to suicide victims.\u003c/p\u003e\u003cp\u003eMoreover, impaired inhibitory processes foster decisional impulsivity, leading to premature and risky behavior (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). On a behavioral level, motor inhibition comprises the capacity to stop motor impulses (contextually inappropriate or non\u0026ndash;goal-directed behaviors), whereas cognitive inhibition is related to the suppression of irrelevant stimuli that could interfere with task performance (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Alterations in cognitive inhibition (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and motor inhibition (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) have been repeatedly reported in SA. Thus, altered inhibitory processes may facilitate transition to suicidal behavior following impaired value-based DM.\u003c/p\u003e\u003cp\u003eFurthermore, these cognitive deficits may reflect dysfunction in neural networks, particularly disruptions in structural and functional connectivity among brain regions supporting decision-making and inhibitory subprocesses (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Such associations between cognitive test performance and white matter (WM) integrity have been reported, particularly in domains such as attention and cognitive flexibility (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), cognitive control (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and reward processing (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, prior studies have demonstrated a relationship between structural and functional connectivity within specific brain networks in healthy controls (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and have reported links between structural abnormalities, altered functional connectivity and cognitive deficits in patients with schizophrenia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) or MDD (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDiffusion tensor imaging (DTI) is an approach for investigating white matter connectivity on the basis of the diffusion direction of water molecules (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The broadly investigated parameter via DTI is fractional anisotropy (FA), a scalar measure that quantifies the directionality of water diffusion within a voxel.\u003c/p\u003e\u003cp\u003eFindings from previous DTI studies in the SAs have been largely inconsistent and heterogeneous (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Nevertheless, several studies have relatively consistently reported reduced FA in SAs compared with patients with an affective disorder and no history of suicide attempts, particularly in the prefrontal (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), orbitofrontal (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and striatal regions (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The WM tract linking the dorsal striatum to the orbitofrontal cortex is the striato-fronto-orbital (ST-FO) tract, comprising fibers that originate from the caudate nucleus and putamen and project to the orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAbnormalities in frontostriatal tracts have also been observed in SAs via further DTI-derived metrics, including radial diffusivity (RD), a measure of the rate of water diffusion perpendicular to the primary diffusion direction (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In addition, compared with those in patients with treatment-resistant depression and suicidal ideation, elevated axial diffusivity (AD), a diffusion coefficient along the principal axis of the fiber tract, and extracellular free water in fronto-thalamo-limbic white matter tracts were found in SAs (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, reduced fiber projections\u0026mdash;a reduced percentage of passing fibers\u0026mdash;from the anterior limb of the internal capsule (ALIC) to the left OFC and thalamus were found in SAs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe variability in the result might be due to the heterogenity and small size of the samples as well as the methods applied for preprocessing DTI data and extracting the white matter tracts. The most frequently used methods in those studies are voxel-based analysis (VBA) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and tract based spatial statistics (TBSS)(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These methods are, however, subject to ongoing discussion regarding their limitations and the interpretation of results. Challenges include issues with data preprocessing, such as coregistration to a template image (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), spatial smoothing prior to voxelwise statistical analysis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), poor segmentation quality (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), and concerns about reliability (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The skeletonization process in TBSS, while enhancing alignment across subjects, may exclude peripheral or less common white matter tracts and miss pathology outside the core skeleton, leading to partial information loss. Additionally, because TBSS averages and projects diffusion metrics onto a mean skeleton, subtle or spatially localized microstructural alterations may be diluted or entirely overlooked.\u003c/p\u003e\u003cp\u003eTo investigate white matter microstructure with greater anatomical precision, we employed TractSeg, a deep-learning-based tool that performs fast and accurate subject-specific white matter tract segmentation in individual native space (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Following tract segmentation, we applied tractometry, which samples diffusion metrics (e.g., FA) along the length of each tract\u0026mdash;typically divided into 100 equidistant segments\u0026mdash;enabling the detection and localization of microstructural alterations at specific tract locations, rather than relying solely on mean values across the entire tract (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe goal of the present study is, therefore, to investigate WM integrity in individuals with a history of suicide attempt(s) via tractometry, which allows more precise localization of structural connectivity changes than traditional group-level approaches do. To assess the specificity of potential alterations, we compared SA patients to two control groups: psychiatric patients with an affective disorder and without any history of suicidal act and healthy controls.\u003c/p\u003e\u003cp\u003eWe focused on neural pathways linked to decision-making and inhibition. Specifically, we aimed to examine structural connectivity between brain regions associated with IGT performance. Original research by Damasio and colleagues has linked impaired IGT performance primarily to lesions in the VMPFC, OFC and amygdala (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Consistently, neuroimaging studies have shown IGT-related activations in the VMPFC, OFC, and striatum (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Moreover, altered OFC activation during the IGT has been observed in SAs (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) as well as in healthy first-degree biological relatives of people who died from suicide (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, we selected the ST-FO as the primary tract of interest.\u003c/p\u003e\u003cp\u003eIn addition, we examined alterations in structural connectivity along the superior longitudinal fasciculus II (SLF II), which connects parieto-occipital regions with the dorsolateral prefrontal cortex (DLPFC), and along the cingulum bundle, which links the cingulate gyrus with the DLPFC, OFC, dorsal anterior cingulate cortex, and premotor areas (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The DLPFC has been associated with the IGT (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) and Stroop task performance (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), playing crucial roles in cognitive control and executive functions. Connectivity in the anterior and middle parts of the cingulum bundle was investigated in connection with motor inhibition. The cingulum bundle is involved in executive functions, especially in tasks assessing inhibition (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the basis of previous findings, we hypothesized that the SA patients would show altered structural integrity in the ST-FO, the anterior part of the SLF-II, and the anterior and middle parts of the cingulum bundle compared with control patients without any personal history of suicide and healthy controls. As vSAs show greater impairment in the IGT (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), we expect greater structural alterations in these individuals than in nvSAs. Second, we expect that FA in the ST-FO tract and the SLF-II would correlate with IGT performance, whereas FA in the cingulum bundle is expected to correlate with Go/No-Go performance.\u003c/p\u003e\u003cp\u003eIn an exploratory analysis, we also examined differences in RD, which provides insights into axon organization and alignment within these three tracts, complementing the FA measurement.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study sample\u003c/h2\u003e\u003cp\u003e The study (named SUICIDE_DECIDE) was approved by the ethics committee of Friedrich Schiller University, Jena, Germany, and the \u0026ldquo;Comit\u0026eacute; de Protection des Personnes SUD-EST IV\u0026rdquo; in France. It was conducted from 2021 to 2023 at two different cooperating sites: Jena Academic Hospital, Germany, and Academic Hospital of Nimes, France. Patients who had attempted suicide were admitted to university hospitals, where they were informed about the study. All participants gave their informed consent prior to the study and were compensated with \u0026euro;100 for participating. Individual data were then saved in accordance with the European data protection guidelines (GDRP). The study was declared on ClinicalTrial.gov (NCT05230043).\u003c/p\u003e\u003cp\u003eA total of 131 participants were recruited (92 in Jena and 39 in N\u0026icirc;mes): 48 inpatients who had attempted suicide (47 with MDD and 1 with BD), 34 nonattempter MDD patient controls (PC), and 49 healthy controls (HC). The control participants had neither a personal history of suicide attempts nor a family history of suicide attempt or suicide in first- or second-degree relatives. Owing to scanner-related issues, DTI acquisitions from the Nimes site were excluded from further analysis. Additionally, four participants from the Jena sample did not complete the DTI protocol and were excluded. Consequently, diffusion-weighted images from 88 participants at the Jena site were included in the final preprocessing pipeline, comprising 40 SA, 20 PC, and 28 HC.\u003c/p\u003e\u003cp\u003e The inclusion criteria for all the subjects were being right-handed, German and French-speaking men and women between 18 and 60 years of age who agreed to participate and signed the consent form. For patients, an additional inclusion criterion was having a current depressive episode of MDD or BD according to the DSM-5. The exclusion criteria were as follows: loss of consciousness due to the suicidal act; intellectual disability assessed via the Multiple Choice Vocabulary Intelligence Test (MWT-B; (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)); left-handedness assessed via the Annett Hand Preference Questionnaire (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e); current psychotic disorders; current manic, hypomanic, mixed, or rapid cycling episode; severe alcohol or substance dependence within the last 3 months, as defined by the DSM-5 criteria; previous brain trauma; electroconvulsive therapy within the last 3 months; and contraindications for magnetic resonance imaging (MRI).\u003c/p\u003e\u003cp\u003eWe further divided SA into two subgroups (vSAs and nvSAs) on the basis of the suicidal means they used. Violent suicide methods were categorized according to the ICD-10 codes X66\u0026ndash;X82. Methods such as hanging, firearm use, jumping from heights, deep cuts, car crashes, severe burning, gas poisoning, drowning, electrocution, and jumping in front of a train are classified as violent methods, whereas drug overdose and superficial cutting are classified as nonviolent methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Clinical and neuropsychological assessments\u003c/h2\u003e\u003cp\u003eA psychiatric diagnosis was established was established using the M.I.N.I. interview (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) for major psychiatric disorders on the basis of DSM-5 criteria, which were administered by trained psychologists with master\u0026rsquo;s degrees. Suicide ideations, intentions and behaviors were assessed via the Columbia-Suicide Severity Rating Scale (C-SSRS)(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). The Young-Mania Rating Scale (YMRS-D)(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), Beck Depression Inventory (BDI-II)(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), and Montgomery-Asberg Depression Scale (MADRS)(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) were used to assess depressive and manic symptoms.\u003c/p\u003e\u003cp\u003eTo investigate decision-making in all participants, we adopted a computerized version of the IGT (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). For details, see the \u003cb\u003esupplementary materials\u003c/b\u003e. The net score was used for the statistical analysis, which was determined by subtracting the number of disadvantageous from advantageous choices from the overall 100 choices. Response inhibition was assessed with a computerized Go/No-Go task (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) and incorrect responses to nontarget stimuli, i.e., commission errors, which were used for group comparisons and correlational analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. MRI acquisition parameters\u003c/h2\u003e\u003cp\u003eAll imaging data were collected on a 3T whole-body system equipped with a 64-element head matrix coil (MAGNETOM PRISMA FIT, Siemens Healthineers, Erlangen, Germany). First, a structural T1 image was acquired, followed by diffusion tensor imaging to investigate putatively altered structures. DTI data were acquired with both AP (anterior‒posterior) and PA (posterior‒anterior) phase-encoding directions to correct for susceptibility artifacts. A total of 96 contiguous transverse slices with 1.5 mm thickness were obtained (TR\u0026thinsp;=\u0026thinsp;3318 ms, TE\u0026thinsp;=\u0026thinsp;87.40 ms, flip angle\u0026thinsp;=\u0026thinsp;78\u0026deg;, multiband factor\u0026thinsp;=\u0026thinsp;4) that covered the entire brain, including the lower brainstem. The matrix size was 140 \u0026times; 140 pixels with an in-plane resolution of 1.5 \u0026times; 1.5 mm\u003csup\u003e2\u003c/sup\u003e (FOV (field of view)\u0026thinsp;=\u0026thinsp;210 \u0026times; 210 mm\u003csup\u003e2\u003c/sup\u003e, b value\u0026thinsp;=\u0026thinsp;2500 s/mm\u003csup\u003e2\u003c/sup\u003e, and 103 collinear directions with 8 non-weighted b0 images: two at the beginning, one at the end, and 5 interleaved throughout the sequence, following every 16 diffusion-weighted images. The DTI scan time was approximately 12 minutes (2 \u0026times; 6:03 minutes).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Diffusion MRI Preprocessing\u003c/h2\u003e\u003cp\u003eDiffusion-weighted images were preprocessed via an integrated pipeline to ensure high-quality data for diffusion tensor estimation and tract-based quantification. The preprocessing steps included denoising using MRtrix3\u0026rsquo;s dwidenoise, removal of Gibbs ringing artifacts with mrdegibbs, and correction for eddy currents and head motion using FSL\u0026rsquo;s eddy implemented via dwifslpreproc (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). To correct for spatial intensity inhomogeneities, B1 bias field correction was performed using ANTs through dwibiascorrect (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). At this stage, two patients from the SAs group were excluded from further analysis because of poor image quality.\u003c/p\u003e\u003cp\u003eFrom the preprocessed diffusion data, diffusion tensor\u0026ndash;derived scalar maps were computed using TractSeg\u0026rsquo;s built-in tools. The tensor model was fitted to generate maps of RD and FA. These scalar maps were then registered to the TractSeg MNI FA template using rigid-body transformation with FSL FLIRT to achieve alignment in standard space. Tract-specific diffusion metrics were subsequently extracted using TractSeg\u0026rsquo;s Tractometry framework. Fiber orientation distributions (FODs) were then computed via multishell multitissue constrained spherical deconvolution. White matter tracts were identified and segmented in subject space through TractSeg\u0026rsquo;s convolutional neural network\u0026ndash;based approach, and corresponding tract orientation maps (TOMs) and endpoint segmentations were generated. The MNI-registered diffusion scalar maps (e.g., RD, FA) were then projected onto these tract masks to compute the diffusion values along each tract.\u003c/p\u003e\u003cp\u003eFor further analysis, bundle-specific streamlines were generated via TractSeg's probabilistic tracking algorithm. The resulting tractograms were filtered using a combination of anatomical constraints and quantitative quality metrics to ensure biological plausibility. Then, tractometry (Chandio et al., 2020) was used to resample all streamlines to ensure equal segment numbers across tracts. The FA and RD profiles were then evaluated at 100 equidistant points along each tract's trajectory (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MIC-DKFZ/TractSeg/blob/master/resources/Tractometry_documentation.md\u003c/span\u003e\u003cspan address=\"https://github.com/MIC-DKFZ/TractSeg/blob/master/resources/Tractometry_documentation.md\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The values from the first and last segments are automatically removed. This approach enables pointwise statistical comparisons along the entire tract length while accounting for positional correspondence across subjects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll the statistical analyses were conducted using RStudio (Version: 2024.12.1\u0026thinsp;+\u0026thinsp;563).\u003c/p\u003e\u003cp\u003eTo analyze the DTI data, we extracted 98 FA and RD values from each tract and used them in an analysis of covariance (ANCOVA) to examine group differences. ANCOVA was conducted separately for each tract segment, with FA or RD as the dependent variable, \u003cem\u003egroup\u003c/em\u003e (SA vs. PC vs. HC) as the independent variable, and \u003cem\u003eage\u003c/em\u003e and \u003cem\u003esex\u003c/em\u003e as covariates. The FDR method was applied to correct for multiple comparisons. Spearman\u0026rsquo;s rank correlation coefficient was used to assess correlations between FA/RD values and relevant neurocognitive and clinical measures.\u003c/p\u003e\u003cp\u003eFinally, linear regression analyses were conducted in the SA group with the IGT net score as the dependent variable. Each FA and RD value in the right ST-FO tract that was significant in the ANCOVA was entered separately as an independent variable.\u003c/p\u003e\u003cp\u003eIn the statistical analysis of IGT performance, four subjects in the IGT were excluded for noncompliance with the task instructions. ANCOVA was conducted with \u003cem\u003egroup\u003c/em\u003e as the independent variable and net score as the dependent variable. \u003cem\u003eAge, sex\u003c/em\u003e, and \u003cem\u003estudy site\u003c/em\u003e were included as covariates. The commission error rate in the Go/No-Go task showed a nonnormal distribution. Linear regression with \u003cem\u003eage\u003c/em\u003e, \u003cem\u003esex\u003c/em\u003e, and \u003cem\u003estudy site\u003c/em\u003e as independent variables was used to obtain residualized values, which were analyzed for overall group differences via the Kruskal‒Wallis test. Post hoc Mann‒Whitney U tests with FDR correction were applied for multiple comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Sociodemographic and neurocognitive measures\u003c/h2\u003e\n \u003cp\u003eAge and verbal intelligence did not differ between the groups. Significantly more female individuals were recruited in both patient groups compared to heathy controls. SAs made more Go/No-Go commission errors than both control groups. They also showed lower IGT scores compared to healthy controls, but not compared to psychiatric controls (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic and clinical characteristics of the study sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSuicide attempters (SA)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient controls\u003c/p\u003e\n \u003cp\u003e(PC)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy controls (HC)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/ANO(C)VA\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost hoc (Mann‒Whitney U)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e \u003cem\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u0026thinsp;.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e: SA, PC\u0026thinsp;\u0026gt;\u0026thinsp;HC*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrent MDD\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious depressive episodes (\u003c/strong\u003e\u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDD/BD onset age in years (\u003c/strong\u003e\u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifetime N of suicide attempts\u003c/strong\u003e \u003cem\u003e(Median, range)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple attempts\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of violent suicidal act N\u003c/strong\u003e, \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIS\u003c/strong\u003e \u003cem\u003e(last attempt; Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuicidal Ideation\u003c/strong\u003e \u003cem\u003e(item \u0026ldquo;wish to die\u0026rdquo; in C-SSRS in last 3 months in %)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuicidal act in first degree relatives\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuicidal act in second degree relatives\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent psychiatric medication\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbid diagnosis\u003c/strong\u003e \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAnxiety disorders\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePTSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOCD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSubstance use disorder\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEating disorders\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMWT-B (IQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNART (raw value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBDI-II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u0026thinsp;\u0026gt;\u0026thinsp;PC***\u0026gt;HC***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMADRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u0026thinsp;\u0026gt;\u0026thinsp;PC*\u0026gt;HC***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIowa Gambling Task (IGT)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTotal net score\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003csup\u003e**\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003ePC\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003csup\u003e*\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGo/No-Go\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCommission errors (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\n \u003cp\u003e(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\n \u003cp\u003e(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003cp\u003e(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u0026thinsp;\u0026gt;\u0026thinsp;PC\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eSA\u0026thinsp;\u0026gt;\u0026thinsp;HC\u003csup\u003e**\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: SD, standard deviation; MDD, major depressive disorder; N, number; SB, suicide behavior; SIS, Beck\u0026rsquo;s suicide intent scale; CSSRS, Columbia-Suicide Severity Rating scale; PTSD, posttraumatic stress disorder; OCD, obsessive‒compulsive disorder; MWT-B, vocabulary intelligence test; NART, National Adult Reading Test; BDI-II, Beck Depression Inventory; MADRS, Montgomery Asberg Depression Scale; n.s., nonsignificant; n.a., not applicable\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e, \u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e \u003cem\u003esurvives FDR correction\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Tractometry results: Main group effects\u003c/h2\u003e\n \u003cp\u003eA significant main effect of group was observed in fractional anisotropy within the \u003cem\u003eright\u003c/em\u003e ST-FO, specifically in the central (segments 44\u0026ndash;52) and anterior portions (segments 65\u0026ndash;84) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Post hoc tests indicated that suicide attempters presented significantly greater FA in the central segments and lower FA in the more anterior segments than both control groups. RD analyses corroborated these anterior frontal connectivity alterations, revealing significantly elevated RD in the SA compared with the healthy controls across anterior frontal segments 59\u0026ndash;94, especially in the OFC.\u003c/p\u003e\n \u003cp\u003eIn contrast, no significant group differences in FA or RD were detected in the \u003cem\u003eleft\u003c/em\u003e ST-FO, as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eA significant group effect was also observed in the left SLF-II tract (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). FA differences were localized to the posterior (segments 1\u0026ndash;6 and 15\u0026ndash;17) and anterior portions of the tract (segments 90\u0026ndash;92). A post hoc test revealed lower FA in segments 1\u0026ndash;6 and 90\u0026ndash;92 in the SA and PC groups than in the HC group, with no significant difference between the SA and PC groups. Compared with HCs, patients in both groups also presented elevated RDs in the posterior (segments 1\u0026ndash;9) and central (segments 41\u0026ndash;44) portions of the tract.\u003c/p\u003e\n \u003cp\u003eIn contrast, no significant group differences in FA or RD were detected in the cingulum bundle (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Subgroup analysis in SA\u003c/h2\u003e\n \u003cp\u003eCompared with the nvSA, the vSA presented a significantly lower RD in the central portion of the right ST-FO tract (segments 37\u0026ndash;41) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). FA was also elevated in these segments in the VSA group, although this difference did not remain significant after FDR correction.\u003c/p\u003e\n \u003cp\u003eNo significant subgroup differences were found in the SLF-II or the cingulum bundle.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Correlations in the whole study sample\u003c/h2\u003e\n \u003cp\u003eThe means of FA values in the left ST-FO (r\u0026thinsp;=\u0026thinsp;.34, p\u0026thinsp;=\u0026thinsp;.001) and RD values in the left (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and right ST-FO (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.30, p\u0026thinsp;=\u0026thinsp;.006) significantly correlated with IGT performance. Only the correlations with the left ST-FO metrics survived FDR correction. No significant association was found between the IGT net score and FA values in the right ST-FO (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, a significant correlation was found between the IGT net score and FA values in the left SLF-II (r\u0026thinsp;=\u0026thinsp;.227, p\u0026thinsp;=\u0026thinsp;.036) as well as between RD values in the left (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.237, p\u0026thinsp;=\u0026thinsp;.029) and right SLF-II (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.238, p\u0026thinsp;=\u0026thinsp;.028), which were not significant after FDR correction.\u003c/p\u003e\n \u003cp\u003eRD values in both the left (r\u0026thinsp;=\u0026thinsp;.24, p\u0026thinsp;=\u0026thinsp;.026) and right cingulum bundles (r\u0026thinsp;=\u0026thinsp;.258, p\u0026thinsp;=\u0026thinsp;.017) were positively associated with the commission error rate. These correlations did not survive the FDR correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Regression analysis in SAs\u003c/h2\u003e\n \u003cp\u003eTo investigate the relationship between altered WM integrity and task performance in SAs, we conducted a regression analysis with the IGT net score as the dependent variable and only significant (based on ANCOVA) FA and RD values from the right ST-FO as independent variables. RD values in the central (segments 57\u0026ndash;62) and anterior (segments 88\u0026ndash;90) portions significantly predicted the IGT net score (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), explaining approximately 11\u0026ndash;13% of the variance in IGT performance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to examine WM alterations in SAs and their relationships with decision-making and cognitive inhibition performance, two intermediate phenotypes of suicidal behavior. As hypothesized, compared with HCs, SAs presented altered structural integrity in terms of lower FA values and higher RD values in the anterior segments of the right ST-FO and left SLF-II, a pattern compatible with reduced myelin integrity. No group differences were found in the cingulate bundle. In the whole group, mostly left-sided (unaffected) ST-FO and SLF-II DTI measures were significantly correlated with IGT performance (decision-making), whereas cingulum WM integrity was associated with Go/No-Go performance (cognitive inhibition). In SA only, RD values in the central and anterior segments of the right ST-FO tract were significant predictors of IGT performance.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first study to investigate WM tracts within the frontostriatal network in SA using a pointwise analysis methodology, allowing for more precise localization of WM alterations. While some of our findings in ST-FO patients are consistent with those in previous studies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), we also report a novel observation: structural alterations in the right ST-FO tract affect specific portions of the tract and vary along its course.\u003c/p\u003e\u003cp\u003eThe comparable FA and RD values in the left ST-FO tract across groups further support the robustness of our right-sided findings. The presence of tract-specific alterations exclusively in the right ST-FO suggests hemispheric asymmetry in the structural organization of the frontostriatal network (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). This asymmetry may indicate that functional specialization of the right tract renders it more susceptible to pathological neurocognitive processes underlying suicidal behavior, notably value-based decision-making or inhibitory processes.\u003c/p\u003e\u003cp\u003eGreater FA values in the SA group than in the control group were observed in the central segments of the ST-FO tract passing through the subgenual anterior cingulate cortex (sgACC), followed by profoundly lower FA values in the anterior segments located in the OFC/vmPFC. FA values are typically greater in well-myelinated white matter tracts, since the myelin sheath restricts water diffusion in all directions except along the length of the fibers, resulting in strongly anisotropic diffusion (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Conversely, in tissues where water molecules move randomly and uniformly in all directions, the FA values are close to zero. Higher FA was previously associated with better performance on cognitive functioning tasks in individuals at ultrahigh risk for psychosis but not in healthy individuals (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Thus, the interpretation of elevated FA values remains uncertain. Elevated anisotropy has been hypothesized to reflect region specific, compensatory responses to cortical thinning (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) or local FA alterations (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Therefore, we speculate that higher FA in the central tract segments of ST-FO patients might compensate for the reduced FA in the anterior segments.\u003c/p\u003e\u003cp\u003eFurthermore, we found a similar trend in RD analysis: a lower RD in the central, but a greater RD in the anterior right ST-FO in SAs than in the HCs. As RD describes water diffusion perpendicular to a fiber tract (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), it provides complementary measures of FA for the WM microstructure. A higher RD is generally associated with a lower FA since greater cross-tract diffusion reduces anisotropy. The results of RD analysis further validate our primary findings in FA. Altered WM integrity in the central ST-FO might influence the specific functions of this region. The sgACC acts as a bridge between limbic structures and the frontal lobe and integrates cognitive activity with affective experience (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). However, the impact of WM alterations on cognitive\u0026ndash;affective integration is not yet well understood.\u003c/p\u003e\u003cp\u003eMoreover, we found reduced FA in the OFC/VMPFC, which is in line with previous reports in the SA patients (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). As this region has previously been identified as a neural correlate of IGT performance in both lesion (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) and fMRI studies (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), our findings suggest that structural connectivity may contribute to decision-making deficits in SAs, as further supported by regression analysis and significant correlations between IGT net scores and mean FA and RD values in the ST-FO tract (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher FA and lower RD values were associated with better performance on the IGT in the whole group.\u003c/p\u003e\u003cp\u003eHowever, in the right ST-FO tract (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), FA was not significantly associated with the IGT net score, in contrast to RD. This dissociation, especially in light of the comparable measures in the left ST-FO tract, raises the possibility that potential alterations on the right side may also modulate the association with task performance. Taken together, these patterns suggest that the underlying relationship is nonlinear and more complex than can be adequately captured by simple linear correlational analyses.\u003c/p\u003e\u003cp\u003eFurthermore, RD in the bilateral cingulum bundle was positively correlated with performance in the Go/No-Go task, with higher RD values associated with more commission errors. This finding indicates the association between motor inhibition and cingulum bundle WM connectivity not only at the functional level but also at the structural level. Behaviorally, the SA group made significantly more commission errors than both the patient and healthy control groups did, indicating deficits in motor inhibition. Thus, this finding is unlikely to be related to MDD and may instead represent a stable vulnerability factor for suicidal behavior. Reduced motor inhibition may be critical in the transition from suicidal ideation to action, as impaired inhibition could increase susceptibility to act on suicidal impulses. However, no group differences in cingulum bundle WM integrity were found, suggesting that the behavioral deficits may stem from functional rather than structural alterations.\u003c/p\u003e\u003cp\u003eIn addition, the analysis revealed subgroup differences in WM integrity among SAs. As expected, individuals who attempted suicide using violent means exhibited more pronounced alterations in ST-FO white matter integrity compared to those who used nonviolent methods. Specifically, the vSA subgroup displayed significantly lower RD and a nonsignificant trend toward higher FA in central segments of the right ST-FO than the nvSA, PC and HC. These findings indicate that subgroups defined by the chosen method to attempt suicide differ not only in decision-making but also in WM structure.\u003c/p\u003e\u003cp\u003eSignificant subgroup differences have been previously reported at the behavioral level in IGT task performance (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In particular, vSAs made significantly more disadvantageous choices (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This subgroup also differed in trait impulsivity from nvSAs, reporting higher levels of sensation seeking (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). However, WM alterations among SA subgroups have rarely been investigated. To date, only one study has explored subgroup differences based on suicide attempt methods (drugs vs. other), but it reported no significant differences in FA (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Importantly, that analysis relied on the mean FA per tract, which may have limited sensitivity to subtle or subregion-specific alterations, as observed in the present study. Given the clinical heterogeneity of SAs, it is plausible to assume that distinct subgroups exhibit different neural correlates. Our study provides initial evidence of subgroup-specific WM alterations, underscoring the need for future research to systematically implement subgroup analyses to replicate and extend these findings.\u003c/p\u003e\u003cp\u003eThe findings of this study have important clinical implications. Detecting WM alterations in SA may serve as an additional cornerstone for developing targeted treatment strategies for individuals with suicidal behavior. Identifying specific neural markers of suicide risk\u0026mdash;both at the group and subgroup levels\u0026mdash;may support the development of tailored interventions. For example, deep brain stimulation (DBS) of the sgACC can substantially reduce symptoms in patients with treatment-resistant depression (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), a major risk factor for suicidal behavior. Clarifying the specific neural correlates of SB may help refine DBS treatment strategies to more effectively target both depressive symptoms and suicide risk.\u003c/p\u003e\u003cp\u003eSome limitations of the study must be mentioned. Several subjects had to be excluded from the DTI analysis because of scanner-related issues. Nevertheless, the remaining sample size was sufficient to detect not only group differences but also subgroup differences among SAs at an FDR-corrected level. This study included only patients with a current depressive episode, primarily MDD. Since SAs are also prevalent in other mental disorders, such as borderline personality and psychotic disorders, future studies should examine white matter integrity across a broader range of clinical populations.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrated altered WM microstructure within the frontostriatal network in SA, a tract critically involved in cognitive and affective processes. WM integrity of the ST-FO tract was linked to performance on the IGT and Go/No-Go tasks. Such alterations may contribute to deficits in decision-making and motor inhibition in SA, with pronounced changes in those who used violent means. Future research should replicate these findings and explore subgroup-specific differences in greater depth.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNîmes site: We would like to thank Dr. Pascale Fabbro-Peray as a methodologist, Mrs Bérangère Gomaere, Mr. Antoine Giron and Mr. Thibault Gibert for their active participation in recruitment; Mrs. Léonie Gazel for the administrative coordination of the project; Mrs. Lorrie Lafuente and Mr. Rabah Tamimou for their help in managing the project; and Mrs. Camille Briand for data management.\u003c/p\u003e\n\u003cp\u003eJena site: We thank Ms. Johanna Walther, Ms. Anna Karoline Seiffert and Ms. Anna Bollmann for their contributions to data acquisition. We acknowledge support\u0026nbsp;from\u0026nbsp;the Open Access Publication Fund of the Thüringen Universitates- und Landesbibliothek Jena.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.Z.: investigation; data collection and analysis, writing - original draft.\u003c/p\u003e\n\u003cp\u003eM.L.: data preprocessing and analysis, manuscript reviewing/editing, approval of the final article.\u003c/p\u003e\n\u003cp\u003eL.B.: data collection, manuscript reviewing/editing, approval of the final article.\u003c/p\u003e\n\u003cp\u003eM.W.: administrative support; supervision, reviewing/editing, approval of the final article.\u003c/p\u003e\n\u003cp\u003eF.P.: conceptualization; Writing - Review \u0026amp; Editing,\u0026nbsp;approval of the final article.\u003c/p\u003e\n\u003cp\u003eD.G.: data curation and analysis, Writing - Review \u0026amp; Editing,\u0026nbsp;approval of the final article.\u003c/p\u003e\n\u003cp\u003eF.J., G.W.: concept and design; supervision; critical revision and editing of the manuscript; administrative, technical, or material support; supervision,\u0026nbsp;Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFJ and GW obtained a grant from the American Foundation for Suicide Prevention (AFSP, grant number LSRG-1-086-19).\u003c/p\u003e\n\u003cp\u003eThe sponsors had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All the other authors have no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Sevillano J, Gonz\u0026aacute;lez-Pinto A, Rodr\u0026iacute;guez-Revuelta J, Alberich S, G\u0026oacute;nzalez-Blanco L, Zorrilla I, et al. Suicidal behaviour and cognition: A systematic review with special focus on prefrontal deficits. Journal of affective disorders. 2021;278:488\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMann JJ, Rizk MM. A Brain-Centric Model of Suicidal Behavior. 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Neurotherapeutics. 2022;19(4):1229\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8194143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8194143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Decision-making and inhibition deficits are well documented among individuals with a history of suicide attempt (SAs), but the underlying neurobiological mechanisms remain unclear. This study investigated white matter (WM) tracts associated with intermediate phenotypes, namely, decision-making and inhibition, among SAs (n=48), patient controls (PCs; n=34), and healthy controls (HCs; n=49) using diffusion tensor imaging and tractometry, a novel method allowing pointwise investigation of WM microstructure. We hypothesized alterations in the striato-fronto-orbital tract (ST-FO), superior longitudinal fasciculus II (SLF-II), and cingulum bundle in SAs and subgroups, as well as associations between diffusion metrics and cognitive performance on the Iowa Gambling (IGT) and Go/No-Go tasks. As hypothesized, compared with the PCs and HCs, the SAs presented significant alterations in the right ST-FO, with elevated fractional anisotropy (FA) in the central segment and lower FA in the anterior segment; similar effects were observed for radial diffusivity (RD). RD values bilaterally and FA values in the left ST-FO correlated significantly with IGT performance. Additional RD and FA alterations were detected in the SLF-II, although no differences emerged between patient groups. No significant group differences were found in the cingulum bundle, although SAs made more Go/No-Go commission errors than both control groups. In addition, SAs who used violent suicide methods differed from nonviolent SAs in the central segments of the ST-FO tract in the RD. This is the first study to apply tractometry in SAs, providing novel evidence that WM alterations, mainly in orbitofronto-striatal pathways, are linked to cognitive deficits relevant to suicidal behavior.","manuscriptTitle":"White Matter Microstructural Alterations and their Association with Decision-Making Deficits in Suicide Attempters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 06:40:08","doi":"10.21203/rs.3.rs-8194143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-03T08:56:25+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-19T07:40:25+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-02T12:47:33+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-12-29T13:20:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T13:11:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T13:11:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-11-27T10:36:30+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-11-26T16:05:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4cda0e83-b624-4907-9d37-b6e5274293b9","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60334826,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":60334827,"name":"Biological sciences/Neuroscience"},{"id":60334828,"name":"Health sciences/Diseases/Psychiatric disorders"}],"tags":[],"updatedAt":"2025-12-31T06:40:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 06:40:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8194143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8194143","identity":"rs-8194143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}