Multimodal neural evidence on the corticostriatal underpinning of suicidality in Late-Life Depression

  • Robin Shao
    Affiliations
    State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, 000000

    Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong, 000000
    Search for articles by this author
  • Mengxia Gao
    Affiliations
    State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, 000000

    Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong, 000000
    Search for articles by this author
  • Chemin Lin
    Affiliations
    Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, Taiwan, 20401

    College of Medicine, Chang Gung University, Taoyuan County, Taiwan, 33305

    Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan, 20401
    Search for articles by this author
  • Chih-Mao Huang
    Affiliations
    Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan, 30010

    Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Taipei, Taiwan, 30010
    Search for articles by this author
  • Ho-Ling Liu
    Affiliations
    Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, 77030
    Search for articles by this author
  • Cheng-Hong Toh
    Affiliations
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan County, Taiwan, 33305
    Search for articles by this author
  • Changwei Wu
    Affiliations
    Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan, 23561

    Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan, 110122
    Search for articles by this author
  • Yun-Fang Tsai
    Affiliations
    School of Nursing, College of Medicine, Chang Gung University, Taoyuan City, Taiwan, 33305

    Department of Nursing, Chang Gung University of Science and Technology, Taoyuan City, Taiwan, 10650
    Search for articles by this author
  • Di Qi
    Affiliations
    State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, 000000

    Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong, 000000
    Search for articles by this author
  • Shwu-Hua Lee
    Correspondence
    Correspondence to: Shwu-Hua Lee, MD Address: No.5, Fuxing St., Guishan Dist., Taoyuan City 333, Taiwan Tel.: +886-3-3281200
    Affiliations
    College of Medicine, Chang Gung University, Taoyuan County, Taiwan, 33305

    Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan County, Taiwan, 33305
    Search for articles by this author
  • Tatia M.C. Lee
    Correspondence
    Correspondence to: Tatia M.C. Lee, Ph.D. Address: Room 656, Laboratory of Neuropsychology & Human Neuroscience, The Jockey Club Tower, The University of Hong Kong, Pokfulam Road, Hong Kong Tel.: 852-39178394
    Affiliations
    State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, 000000

    Laboratory of Neuropsychology & Human Neuroscience, The University of Hong Kong, Hong Kong, 000000

    Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China, 510000
    Search for articles by this author
Open AccessPublished:November 29, 2021DOI:https://doi.org/10.1016/j.bpsc.2021.11.011

      Abstract

      Background

      Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death. To improve management and prevention of suicidality, it is essential to understand the key neural mechanisms underlying suicidal thoughts and actions. Following empirically informed neural framework, we hypothesized that suicidal thoughts would be primarily characterized by alterations in the default mode network (DMN) indicating disrupted self-related processing, whereas suicidal actions would be characterized by changes in the lateral prefrontal cortico-striatal circuitries implicating compromised action control.

      Methods

      We analysed the grey matter volume and resting-state functional connectivity (RSFC) of 113 individuals with late-life depression (LLD), including 45 non-suicidal patients, 33 with suicidal thoughts but no action, and 35 with past suicidal action. Between-group analyses revealed key neural features associated with suicidality. The functional directionality of the identified RSFC was examined using dynamic causal modelling to further elucidate its mechanistic nature. Post-hoc classification analysis examined the contribution of the neural measures to suicide classification.

      Results

      As expected, reduced grey matter volumes in DMN and lateral prefrontal regions characterized patients with suicidal thought and those with past suicidal actions compared to non-suicidal patients. Furthermore, region-of-interest analyses revealed the directionality and strength of the ventrolateral prefrontal cortex-caudate RSFC were related to suicidal thoughts and actions. The neural features significantly improved classification of suicidal thoughts and actions, over that based on clinical and suicide questionnaire variables.

      Conclusions

      Grey matter reductions in the DMN and lateral prefrontal regions, and the ventrolateral prefrontal cortex-caudate connectivity alterations, characterised suicidal thoughts and actions in LLD patients.

      Key words

      Introduction

      Suicidality is a serious health issue worldwide, accounting for almost 1 million deaths annually (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      ). Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death (
      • Nock M.K.
      • Borges G.
      • Bromet E.J.
      • Alonso J.
      • Angermeyer M.
      • Beautrais A.
      • et al.
      Cross-national prevalence and risk factors for suicidal ideation, plans and attempts.
      ). Individuals with past or current suicidal thoughts are particularly vulnerable to future suicidal actions (
      • Conwell Y.
      • Duberstein P.R.
      • Caine E.D.
      Risk factors for suicide in later life.
      ), and those with past suicidal actions are particularly likely to attempt suicide again (
      • Hawton K.
      • i Comabella C.C.
      • Haw C.
      • Saunders K.
      Risk factors for suicide in individuals with depression: a systematic review.
      ). To improve management and prevention of suicidality, it is essential to understand the key brain mechanisms underlying suicidal thoughts and actions. Notably, with advancing knowledge about principal brain networks involved in internal self-related mental processing and in adaptive action selection, it is now possible to examine the key neural processes related to suicidality adopting empirically informed neural frameworks.
      In most countries, old age (≥ 60 years) is associated with the highest rates of attempted and completed suicides (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      ,
      • Conwell Y.
      • Duberstein P.R.
      • Caine E.D.
      Risk factors for suicide in later life.
      ,
      • Conwell Y.
      Suicide in later life: a review and recommendations for prevention.
      ,
      • Phillips M.R.
      • Li X.
      • Zhang Y.
      Suicide rates in China, 1995–99.
      ), and late-life depression (LLD) is a strong correlate with suicidality (
      • Hawton K.
      • i Comabella C.C.
      • Haw C.
      • Saunders K.
      Risk factors for suicide in individuals with depression: a systematic review.
      ). However, key features that distinguish non-suicidal LLD individuals, and those with suicidal thoughts and actions, are still yet to be found. Common factors such as poor physical and mental health, impaired functionality, low social support and financial status bear only limited relationship with suicide risk (
      • Hawton K.
      • i Comabella C.C.
      • Haw C.
      • Saunders K.
      Risk factors for suicide in individuals with depression: a systematic review.
      ,
      • Richard-Devantoy S.
      • Turecki G.
      • Jollant F.
      Neurobiology of elderly suicide.
      ,
      • May A.M.
      • Klonsky E.D.
      • Klein D.N.
      Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study.
      ). Furthermore, questionnaires assessing suicidality uniformly show equivocal prediction for suicidality (
      • Cochrane-Brink K.A.
      • Lofchy J.S.
      • Sakinofsky I.
      Clinical rating scales in suicide risk assessment.
      ). Thus, identifying neural patterns that help pinpoint suicidality among LLD individuals may confer important clinical values for early identification and intervention strategies. Notably, alterations in the grey matter volume and resting-state patterns may be particularly implicated in late-life suicidality, since LLD is associated with pronounced grey matter atrophy in frontoparietal cortical regions (
      • Mackin R.S.
      • Tosun D.
      • Mueller S.G.
      • Lee J.Y.
      • Insel P.
      • Schuff N.
      • et al.
      Patterns of reduced cortical thickness in late-life depression and relationship to psychotherapeutic response.
      ), and suicidality involves excessively negative and death-related self-referential associations indicating disrupted resting-state functioning (
      • Nock M.K.
      • Park J.M.
      • Finn C.T.
      • Deliberto T.L.
      • Dour H.J.
      • Banaji M.R.
      Measuring the suicidal mind: Implicit cognition predicts suicidal behavior.
      ).
      Suicidal actions and thoughts may be respectively linked to aberrations in the lateral prefrontal-striatal-limbic network, and in the default mode network (DMN) (Figure 1). Specifically, the VLPFC/OFC performs crucial top-down regulatory functions on lower-level affective, cognitive and behavioural processes, and is among the most critical structures for emotion regulation and action control (
      • Kim J.U.
      • Weisenbach S.L.
      • Zald D.H.
      Ventral prefrontal cortex and emotion regulation in aging: A case for utilizing transcranial magnetic stimulation.
      ,
      • Ochsner K.N.
      • Silvers J.A.
      • Buhle J.T.
      Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion.
      ). Notably, the lateral OFC and VLPFC might be crucially involved in the signalling and regulation of ‘non-reward’ and punishment (
      • McTeague L.M.
      • Rosenberg B.M.
      • Lopez J.W.
      • Carreon D.M.
      • Huemer J.
      • Jiang Y.
      • et al.
      Identification of common neural circuit disruptions in emotional processing across psychiatric disorders.
      ), abnormality of which was proposed to underlie anhedonia and negative-biased processing in major depression (
      • Rolls E.T.
      A non-reward attractor theory of depression.
      ). Through extensive connection with the caudate (
      • Jarbo K.
      • Verstynen T.D.
      Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum.
      ,
      • Jaspers E.
      • Balsters J.H.
      • Kassraian Fard P.
      • Mantini D.
      • Wenderoth N.
      Corticostriatal connectivity fingerprints: Probability maps based on resting‐state functional connectivity.
      ), the VLPFC/OFC has been consistently demonstrated to implement flexible actions adjusted on changing environmental contingencies (
      • Rudebeck P.H.
      • Murray E.A.
      The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.
      ,
      • Shao R.
      • Lee T.M.C.
      Aging and risk taking: toward an integration of cognitive, emotional, and neurobiological perspectives.
      ), a crucial function that is deficient in older suicide attempters (
      • Clark L.
      • Dombrovski A.Y.
      • Siegle G.J.
      • Butters M.A.
      • Shollenberger C.L.
      • Sahakian B.J.
      • Szanto K.
      Impairment in risk-sensitive decision-making in older suicide attempters with depression.
      ). The DMN is comprised of the core components of the dorsomedial prefrontal cortex (DMPFC), inferior parietal cortex (IPC) and the posterior cingulate cortex (PCC) with the adjacent precuneus, and is principally involved in self-referential and self-related processes during resting state (
      • Sridharan D.
      • Levitin D.J.
      • Menon V.
      A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.
      ). Among suicidal patients, the thoughts of killing oneself may be closely related to disruptions in these processes (
      • Nock M.K.
      • Park J.M.
      • Finn C.T.
      • Deliberto T.L.
      • Dour H.J.
      • Banaji M.R.
      Measuring the suicidal mind: Implicit cognition predicts suicidal behavior.
      ).
      Figure thumbnail gr1
      Figure 1The roles of the lateral prefrontal-striatum-amygdala pathway and the DMN in suicidal behaviour and thoughts. The former pathway is considered to perform crucial behavioural control and emotion regulatory functions, disruption of which is hypothesized to cause elevated suicidal behavioural tendency and actions. The DMN is considered to be principally engaged in self-referential affective and cognitive processes, disruption of which is anticipated to lead to excessive negative self-related bias and death-related self-concept. DLPFC: Dorsolateral prefrontal cortex; DMPFC: Dorsomedial prefrontal cortex; VLPFC: Ventrolateral/Orbitofrontal cortex; PCC: Posterior cingulate cortex/Precuneus; IPC: Inferior parietal cortex. The pinpointing of each region and the interregional connectivity are for illustration only and not related to the current results.
      Existing evidence indicates that suicide attempt in LLD and younger depressed patients is associated with volumetric reductions in the lateral prefrontal executive control network. Specifically, the ventrolateral/orbitofrontal prefrontal cortex (VLPFC/OFC) along with the neighbouring anterior insular cortex (
      • Benedetti F.
      • Radaelli D.
      • Poletti S.
      • Locatelli C.
      • Falini A.
      • Colombo C.
      • Smeraldi E.
      Opposite effects of suicidality and lithium on gray matter volumes in bipolar depression.
      ,
      • Hwang J.P.
      • Lee T.W.
      • Tsai S.J.
      • Chen T.J.
      • Yang C.H.
      • Lirng J.F.
      • Tsai C.F.
      Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry.
      ,
      • Monkul E.S.
      • Hatch J.P.
      • Nicoletti M.A.
      • Spence S.
      • Brambilla P.
      • Lacerda A.D.
      • et al.
      Fronto-limbic brain structures in suicidal and non-suicidal female patients with major depressive disorder.
      ,
      • Schmaal L.
      • van Harmelen A.L.
      • Chatzi V.
      • Lippard E.T.
      • Toenders Y.J.
      • Averill L.A.
      • et al.
      Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.
      ), and the dorsolateral prefrontal cortex (DLPFC) (
      • Hwang J.P.
      • Lee T.W.
      • Tsai S.J.
      • Chen T.J.
      • Yang C.H.
      • Lirng J.F.
      • Tsai C.F.
      Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry.
      ,
      • Monkul E.S.
      • Hatch J.P.
      • Nicoletti M.A.
      • Spence S.
      • Brambilla P.
      • Lacerda A.D.
      • et al.
      Fronto-limbic brain structures in suicidal and non-suicidal female patients with major depressive disorder.
      ,
      • Schmaal L.
      • van Harmelen A.L.
      • Chatzi V.
      • Lippard E.T.
      • Toenders Y.J.
      • Averill L.A.
      • et al.
      Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.
      ), showed decreased grey matter volumes in suicide attempters. Altered OFC, insular and amygdala resting-state functional connectivity (RSFC) was also found in suicidal versus non-suicidal depressed patients (
      • Cao J.
      • Chen X.
      • Chen J.
      • Ai M.
      • Gan Y.
      • Wang W.
      • et al.
      Resting-state functional MRI of abnormal baseline brain activity in young depressed patients with and without suicidal behavior.
      ,
      • Kang S.G.
      • Na K.S.
      • Choi J.W.
      • Kim J.H.
      • Son Y.D.
      • Lee Y.J.
      Resting-state functional connectivity of the amygdala in suicide attempters with major depressive disorder.
      ). Changes in striatal resting-state connectivity strength were also observed in depressed patients with suicide history (
      • Qiu H.
      • Cao B.
      • Cao J.
      • Li X.
      • Chen J.
      • Wang W.
      • et al.
      Resting-state functional connectivity of the anterior cingulate cortex in young adults depressed patients with and without suicidal behavior.
      ), and in Bipolar type II patients with suicide attempt (
      • Wang H.
      • Zhu R.
      • Dai Z.
      • Tian S.
      • Shao J.
      • Wang X.
      • et al.
      Aberrant functional connectivity and graph properties in bipolar II disorder with suicide attempts.
      ). Nevertheless, only one of the above studies was conducted in LLD patients (
      • Hwang J.P.
      • Lee T.W.
      • Tsai S.J.
      • Chen T.J.
      • Yang C.H.
      • Lirng J.F.
      • Tsai C.F.
      Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry.
      ).
      Relatively few studies have examined the grey matter or resting-state correlates of suicidal thoughts (
      • Schmaal L.
      • van Harmelen A.L.
      • Chatzi V.
      • Lippard E.T.
      • Toenders Y.J.
      • Averill L.A.
      • et al.
      Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.
      ). Functional activation studies employing cognitive or motor tasks generally pointed to altered recruitment of the DMN linked with suicidal thoughts among depressed individuals, including the dorsomedial prefrontal cortex (DMPFC), inferior parietal cortex (IPC) and posterior cingulate cortex (PCC)/precuneus (
      • Marchand W.R.
      • Lee J.N.
      • Johnson S.
      • Thatcher J.
      • Gale P.
      • Wood N.
      • Jeong E.K.
      Striatal and cortical midline circuits in major depression: implications for suicide and symptom expression.
      ). The involvement of these regions in suicidal ideation was also observed in schizophrenia (
      • Minzenberg M.J.
      • Lesh T.A.
      • Niendam T.A.
      • Yoon J.H.
      • Rhoades R.N.
      • Carter C.S.
      Frontal cortex control dysfunction related to long-term suicide risk in recent-onset schizophrenia.
      ) and other mood disorders (
      • Matthews S.C.
      • Spadoni A.D.
      • Lohr J.B.
      • Strigo I.A.
      • Simmons A.N.
      Combat-exposed war veterans at risk for suicide show hyperactivation of prefrontal cortex and anterior cingulate during error processing.
      ,
      • van Heeringen K.
      • Bijttebier S.
      • Desmyter S.
      • Vervaet M.
      • Baeken C.
      Is there a neuroanatomical basis of the vulnerability to suicidal behavior? A coordinate-based meta-analysis of structural and functional MRI studies.
      ). The DMPFC was also recruited when people recalled mental pain associated with suicidality (
      • Reisch T.
      • Seifritz E.
      • Esposito F.
      • Wiest R.
      • Valach L.
      • Michel K.
      An fMRI study on mental pain and suicidal behavior.
      ). Notably, one study used deep-learning method to classify suicidal ideators from healthy controls, and found the most discriminating brain regions were the DMPFC and the left IPC (
      • Just M.A.
      • Pan L.
      • Cherkassky V.L.
      • McMakin D.L.
      • Cha C.
      • Nock M.K.
      • Brent D.
      Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.
      ). Moreover, repetitive transcranial magnetic stimulation on bilateral DMPFC effectively reduced suicidal impulse in borderline personality disorder (
      • Feffer K.
      • Peters S.K.
      • Bhui K.
      • Downar J.
      • Giacobbe P.
      Successful dorsomedial prefrontal rTMS for major depression in borderline personality disorder: three cases.
      ). Nevertheless, to our awareness, no study has examined the neural correlates of suicidal thoughts in older adults.
      Therefore, we aimed to identify key grey matter and RSFC measures associated with suicidal thoughts and actions among LLD individuals. To elucidate the network mechanistic nature of the key RSFC, we conducted follow-up dynamic causal modelling (DCM) analysis to examine its functional directionality. Finally, we performed complementary post-hoc support vector machine (SVM) analysis to quantify whether the neural measures significantly improved classification of suicidal thoughts and actions. We hypothesized that suicidal action would be associated with grey matter reductions in the VLPFC and DLPFC, whereas suicidal thoughts would be associated with volumetric reductions in DMN regions including the DMPFC, IPC and PCC/precuneus. Also, suicidal patients would exhibit altered directed connectivity from the VLPFC/OFC to the subcortical striatal and amygdala regions.

      Methods and Materials

       Participant and Instruments

      This study was approved by the institutional review board of Chang Gung Memorial Hospital of Taiwan, and complied with the Declaration of Helsinki. In total, 113 older adults (60-79 years) formally diagnosed with LLD by board certified psychiatrists were included. Written informed consent was obtained from all participants. Patients’ depressive and anxiety symptom levels were respectively assessed with the Hamilton Depression/Anxiety Rating Scale (HAMD/A). Patients’ cognitive function was assessed with the Mini-Mental State Exam (MMSE). Individual patients’ medication load was calculated as an aggravated score over all psychotropic drugs that the patient was taking at the time of study (for each patient, the same drug treatment protocol had been maintained for at least 2 months preceding the study), which was recorded using the Antidepressant Treatment History Form (
      • Sackeim H.A.
      • Aaronson S.T.
      • Bunker M.T.
      • Conway C.R.
      • Demitrack M.A.
      • George M.S.
      • et al.
      The assessment of resistance to antidepressant treatment: rationale for the antidepressant treatment history form: short form (ATHF-SF).
      ). Details of the participants’ demographic, clinical and suicide-related information are included in Table 1.
      Table 1The demographic, LLD- and suicidality-related information of the participants.
      NS (N=45)I/P (N=33)SA (N=35)Between-Group Effectsb
      Age (years)67.73 (5.68)

      60-79 years
      67.48 (5.64)

      60-79 years
      64.74 (5.22)

      60-79 years
      Χ22 = 7.037, p=.03c*
      SA – NS: t=-2.477, p=.013, pcorr=.04*

      SA – I/P: t=-2.104, p=.035, pcorr=.106#

      I/P – NS: t=-.208, p=.835, pcorr=1
      Sex (M/F)13/326/272/33Pearson Χ22 = 6.993, p=.03d*
      SA – NS: X21 = 6.453, p=.011, pcorr=.033*

      SA – I/P: X21 =2.543, p=.111, pcorr=.333

      I/P – NS: X21 = 1.185, p=.276, pcorr=.828
      Education (years)7.93 (3.00)8.76 (4.05)8.34 (3.47)X22 = 1.03, p=.598c
      MMSE27.09 (2.05)27.24 (1.85)27.11 (2.21)X22 = .265, p=.876c
      LLD Onset Age (years)a58.25 (8.22)55.66 (12.32)52.29 (10.44)F2,107 = 3.248, p=.043*
      SA – NS: t76=-2.549, p=.012, pcorr=.037*

      SA – I/P: t64=-1.334, p=.185, pcorr=.555

      I/P – NS: t74=-1.091, p=.278, pcorr=.833
      LLD Number of Episodea1.98 (1.58)3.00 (2.18)3.15 (3.23)Χ22 = 11.728, p=.003c*
      SA – NS: t76=3.049, p=.002, pcorr=.007**

      SA – I/P: t64=.267, p=.79, pcorr=1

      I/P – NS: t74=2.715, p=.007, pcorr=.02*
      LLD Duration (months)a112.91 (80.95)141.00 (118.56)147.88 (107.72)Χ22 = 1.702, p=.427c
      Medication Loada3.56 (1.12)3.79 (1.02)3.67 (1.05)Χ22 = .638, p=.727c
      HAMD8.44 (6.42)9.82 (6.81)9.57 (6.36)Χ22 = .941, p=.625c
      HAMA11.27 (8.84)13.30 (7.75)12.03 (9.55)Χ22 = 2.239, p=.326c
      TSIIa2.26 (1.77)3.62 (2.08)2.59 (1.94)Χ22 = 7.045, p=.03c*
      SA – NS: t71=.644, p=.52, pcorr=1

      SA – I/P: t61=-1.917, p=.055, pcorr=.166

      I/P – NS: t66=2.592, p=.01, pcorr=.029*
      BSSa2.62 (4.14)4.14 (5.08)6.94 (5.41)Χ22 = 17.516, p<.001c***
      SA – NS: t69=4.109, p<.001, pcorr<.001***

      SA – I/P: t58=2.764, p=.006, pcorr=.017*

      I/P – NS: t65=1.07, p=.285, pcorr=.854
      SAD PERSONSa3.36 (1.01)4.55 (1.24)5.32 (1.12)Χ22 = 39.084, p<.001c***
      SA – NS: t71=6.186, p<.001, pcorr<.001***

      SA – I/P: t61=2.282, p=.022, pcorr=.067#

      I/P – NS: t66=3.567, p<.001, pcorr=.001***
      Medication Type
      SSRI191211Χ22 = 2.009, p=.366d
      SNRI4611Χ22 = 5.425, p=.066d
      Agomelatine877Χ22 = 0.158, p=.924d
      Others (NaSSA, NDRI, TCA)775Χ22 = .938, p=.626d
      Non-Antidepressant (Benzodiazepine, Antipsychotics, Zolpidem)352728Χ22 = 1.881, p=.39d
      The participants were divided into the LLD group without suicidal ideation, plan or action (‘NS’), the LLD group with suicidal thoughts (ideation and/or plan, ‘I/P’), and the LLD group with suicidal thoughts as well as previous suicide action (‘SA’). The Mean (SD) is shown for each variable. Rows under ‘Medication Type’ denote the number of patients in each group who were taking the medication at the time of study. */**/***: Significant effects at p<0.05/0.01/0.001 (2-tailed) after post-hoc correction. #: Marginal effects that did not survive post-hoc correction. a: A small number of cases for these variables had missing data. LLD Onset Age, Episode Number and Duration were based on 44 NS, 32 I/P, and 34 SA. Medication load was based on 45 NS, 33 I/P, and 33 SA. TSII and SAD PERSONS scores were based on 39 NS, 29 I/P, and 34 SA. BSS scores were based on 39 NS, 28 I/P, and SA. b: All post-hoc pairwise comparisons employed Bonferroni correction. c: Non-parametric independent-samples Kruskal-Wallis Test was employed, and the associated X2 statistics were reported, for variables that had non-normal distribution (Kolmogorov-Smirnov Test p<.05). d: Chi-square Test was employed to analyse the binary variable of Sex and Medication use. SSRI: Selective Serotonin Reuptake Inhibitor. SNRI: Serotonin-Norepinephrine Reuptake Inhibitor. NASSA: Noradrenergic and Specific Serotonergic Antidepressant. NDRI: Norepinephrine-Dopamine Reuptake Inhibitors. TCA: Tricyclic Antidepressant.
      The LLD sample consisted of 45 patients (32 females) without suicidal thoughts (ideation or plan) or action (non-suicidal, NS), 33 patients (27 females) reporting past and/or present suicidal thoughts (ideation and/or plan, I/P) but no action, and 35 (33 females) patients with both suicidal thoughts and previous suicidal action (SA). All patients completed the Beck Scale for Suicidal Ideation (BSS) (

      Beck AT, Steer RA. (1991): Manual for the Beck Scale for Suicide Ideation. (TX: Psychological Corporation, San Antonio).

      ), the SAD PERSONS Scale that assesses suicide potential (
      • Hockberger R.S.
      • Rothstein R.J.
      Assessment of suicide potential by nonpsychiatrists using the SAD PERSONS score.
      ), and the Triggers of Suicidal Ideation Inventory (TSII) (
      • Lee S.H.
      • Tsai Y.F.
      • Wang Y.W.
      • Chen Y.J.
      • Tsai H.H.
      Development and psychometric testing of the triggers of Suicidal Ideation Inventory for assessing older outpatients in primary care settings.
      ).
      No participant was diagnosed with bipolar, psychotic or substance use disorders or major physical or neurological condition. However, 3 patients had comorbidity of Generalized Anxiety Disorder (GAD). All participants were taking psychotropic medication at the time of study (Table 1). Further details about participants are included in Supplement.

       Imaging Acquisition and Preprocessing

      Both structural T1-weighted and resting-state functional T2*-weighted MRI images were acquired on a clinical 3-Tesla scanner equipped with an 8-channel head coil (GE Healthcare). For structural images, a total of 160 sagittal slices were acquired with TR=8.2 milliseconds, TE=3.2 milliseconds, flip angle=12°, field-of-view=250×250×160 mm3, voxel size=0.98×0.98×1mm3. For the resting-state fMRI data, a total of 180 volumes were acquired with slice number=36, TR=2000 milliseconds, TE=30 milliseconds, flip angle=90°, field-of-view=220×220×144 mm3, voxel size=3.44×3.44×4mm3. During scanning, participants were instructed to stay awake with eyes closed.
      Structural data processing and analyses were performed using the Computational Anatomy Toolbox (CAT12) (http://dbm.neuro.uni-jena.de/cat/), and embedded functions of Statistical Parametric Mapping (SPM12) (Wellcome Department of Cognitive Neurology, UK). The bias-corrected T1 images were segmented and normalized following the Diffeomorphic-Anatomical-Registration-Using-Exponentiated-Lie-Algebra (DARTEL) procedure (
      • Ashburner J.
      A fast diffeomorphic image registration algorithm.
      ), using customized normalized DARTEL template. The resulted modulated normalized grey matter images were smoothed using an 8mm-FHWM Gaussian Kernel. Two participants’ structural data were absent due to acquisition failure, resulting in 111 LLD patients (43 NS, 33 I/P, 35 SA) in structural data analyses.
      The resting-state data preprocessing was conducted using DPARSFA v.4.3 (
      • Yan C.G.
      • Wang X.D.
      • Zuo X.N.
      • Zang Y.F.
      DPABI: data processing & analysis for (resting-state) brain imaging.
      ) and SPM12. The first 5 volumes were discarded for MR signal equilibrium. The data were corrected for slice timing and head motion. Procedures for minimizing confounding motion effect are detailed in Supplement. Eight participants’ resting-state data were absent due to excessive motion (see Supplement) or acquisition failure, resulting in 105 patients (43 NS, 29 I/P, 33 SA) in resting-state analyses. The Friston-24 motion parameters, the white matter and cerebrospinal fluid signals were regressed out. The data were then detrended, normalized to the MNI space via the DARTEL procedure and resampled to 3×3×3mm3, smoothed using 6-mm FWHM Gaussian Kernel, and temporally band-pass filtered at 0.01-0.1 Hz.
      Further details about pre-processing steps are included in Supplement.

       Behavioural Analysis

      Data processing was conducted using SPSS v.26 (IBM Corp.). Data normality was assessed using the Kolmogorov-Smirnov Test. The demographic, clinical and suicidal characteristics were tested for between-group effects, using either univariate ANOVA or the non-parametric independent-samples Kruskal-Wallis Test. Significant between-group effects were further delineated with post-hoc pairwise comparisons, employing Bonferroni multiple-testing correction. The statistical threshold was p<.05, two-tailed.

       Imaging Data Analysis

      The grey matter volume (GMV) data were analysed using a general linear model (GLM) including the NS, I/P and SA group variables, and controlling for the total intracranial volume (TIV), age and sex. We specifically conducted two t-tests to examine the effects of a priori interest: first, the contrast NS-(I/P+SA)/2 assessed the effect of general suicidality on GMV; second, the contrast (NS+I/P)/2-SA assessed the specific effect of suicidal action on GMV. We focused on the a priori regions of interest (ROI), including the DMPFC (bilateral medial superior frontal gyri, BA 8/9), lateral parietal cortex (bilateral superior and inferior parietal cortices, BA 7/39/40) and the PCC/precuneus (BA 7/31) for the general suicidality effect, and the DLPFC (bilateral superior and middle frontal gyri, BA 9/46) and VLPFC/OFC (bilateral inferior and orbitofrontal gyri, including the adjacent insular cortex, BA 11/13/44/45/47) for the suicidal action effect. All ROIs were extracted and constructed using the WFU-Pickatlas toolbox based on the Automatic Anatomical Labelling (AAL) templates. Additional whole-brain analyses were performed for completeness. Statistical thresholds were determined using the threshold-free-cluster-enhancement (TFCE) method with 5000 permutations (
      • Smith S.M.
      • Nichols T.E.
      Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.
      ), as ROI-based or whole-brain familywise-error (FWE)-corrected p<.05. To correct for the number of ROIs, additional Holm-Bonferroni corrections were performed on the pFWE values. The mean GMV values of the ROI clusters showing significant effects were extracted and subjected to further linear regression analyses in SPSS to delineate the precise between-group differences in those clusters, and supplementary analyses were conducted to test whether the effects persisted after controlling for the demographic and clinical variables.
      Resting-state analyses examined the functional connectivity of the VLPFC/OFC. The seed region was constructed as a 6-mm sphere centred at the peak coordinate within the VLPFC/OFC mask that showed significant effects in the structural analysis, in order to locate key VLPFC/OFC region which showed suicidality-related alterations in both structural and functional connectivity patterns. Timeseries signals of the seed were extracted, and the correlations between the rest of the brain and the seed were computed for each participant. These correlations were forwarded to a second-level GLM assessing between-group effect, controlling for age and sex. We focused on the RSFC between the VLPFC/OFC seed and two ROIs: the bilateral amygdala and striatum, both generated using the Wick_Pickatlas toolbox. Statistical thresholding and the follow-up regression analyses on extracted RSFC values were the same as in the grey matter analyses.

       Spectral Dynamic Causal Modelling (spDCM) analyses

      We conducted spDCM analysis to establish the causal interactions and directionality of the RSFC patterns (
      • Razi A.
      • Kahan J.
      • Rees G.
      • Friston K.J.
      Construct validation of a DCM for resting state fMRI.
      ). The following steps were conducted: 1) selecting the seed regions to be the VLPFC/OFC, and the region(s) of which the RSFC with the VLPFC/OFC showed significant suicide group effect; 2) drawing the voxels of interest (VOI) from 6-mm (for the VLPFC/OFC) or 3-mm (for the amygdala/striatum) spheres centred at the peak coordinates; 3) extracting the first eigenvariate of the timeseries of the VOIs, which had been corrected for mean, motion, white matter and cerebrospinal fluid signals; 4) specifying 3 spDCM models, namely VLPFC/OFC-to-target, target-to-VLPFC/OFC, and bidirectional; 5) Performing a family-level Bayesian Model Selection (BMS) (
      • Penny W.D.
      • Stephan K.E.
      • Daunizeau J.
      • Rosa M.J.
      • Friston K.J.
      • Schofield T.M.
      • Leff A.P.
      Comparing families of dynamic causal models.
      ) to identify the winning model for each suicide group; 6) Estimating the amplitudes of intrinsic connections using Bayesian Model Average (BMA) which weighted each model according to its evidence; 7) Conducting linear regression analyses to test whether the connectivity strengths showed significant group effect, controlling for demographic and clinical variables.

       Post-hoc Classification Analyses

      We formally tested the classification utility of both the structural and RSFC features on the suicide groups. The support vector machine (SVM) is a common machine-learning approach to compute the classification strengths of both models and individual features, through permutation testing (10000 times) and internal validation (leave-one-out-cross-validation, LOOCV). The LOOCV procedure was performed to avoid the circularity issue that weight vectors that were generated by a given data were applied to classify that same data. The SVM analyses were conducted using the NumPy library (www.numpy.org) built for Python. For each group classification, we first tested the model including only the significant clinical and suicide characteristics, then the model additionally incorporating the significant neural features. Comparison between these models revealed the additional classification value of the neural features, and respective classification strengths of individual features were calculated based on the comprehensive model. All features were z-transformed for standardization of strength. As the features included in the models were already found to show significant group difference, the SVM analyses were post-hoc in nature, similar to the method employed previously (
      • Lin K.
      • Shao R.
      • Geng X.
      • Chen K.
      • Lu R.
      • Gao Y.
      • et al.
      Illness, at-risk and resilience neural markers of early-stage bipolar disorder.
      ). Additional details about the SVM are included in Supplement.

       Supplementary Analyses

      Given our patients were predominantly female (71%, 82% and 94% for the NS, I/P and SA groups respectively), we replicated the main analyses using only female samples only. Additional analyses controlling for medication type and comorbidity with GAD are presented in Supplement.

      Results

       Participant Characteristics

      Table 1 presents between-group statistical comparisons on the demographic, clinical and suicide questionnaire measures. No significant group difference was observed for the ratio of patients taking each type of medication around the time of study, the average number of medication type received by patients, or the medication load (all p>.05).

       GMV Analyses

      The two planned contrasts respectively generated significant GMV results in the a priori ROIs (Table 2, Figure 2). As hypothesized, general suicidality effect represented by the contrast NS-(IP+SA)/2 revealed GMV differences in the bilateral DMPFC (pFWE<.05, Holm-Bonferroni corrected p<.06) and the right IPC (pFWE<.05, Holm-Bonferroni corrected p<.06), whereas suicidal action effect represented by the contrast (NS+IP)/2-SA revealed GMV differences in the right VLPFC/OFC (pFWE<.05 at both ROI and whole-brain levels, Holm-Bonferroni corrected p<.05). Contrary to the hypotheses, the contrast NS-(IP+SA)/2 revealed no significant PCC/precuneus cluster, but exploratory analysis revealed significant bilateral precuneus regions to the (NS+IP)/2-SA contrast (pFWE<.05). Conversely, while no significant DLPFC cluster was observed to the (NS+IP)/2-SA contrast, we found areas in this ROI showing marginal effect to the NS-(IP+SA)/2 contrast (pFWE=.077) (Table 2). Whole-brain analysis revealed no additional significant effect.
      Table 2Regional grey matter volume (GMV) which showed significant between-group effect.
      Contrast/EffectRegionaBALocus of MaximaKeTFCEPFWE
      NS-(I/P+SA)/2Bilateral DMPFC8[0 48 41]123342.64.026
      Right Inferior Parietal Lobule40[42 -39 53]52388.15.039
      Right DLPFC10[26 48 30]80341.26.077
      (NS+IP)/2-SARight VLPFC/OFC47[41 20 -14]231574.4.009
      Bilateral Precuneus7[3 -45 44]79410.68.032
      The contrast NS-(IP+SA)/2 assessed general suicidality effect, and the contrast (NS+IP)/2-SA assessed suicidal action effect. NS: Non-suicidal LLD patients; I/P: LLD patients with past and/or present suicidal thoughts (ideation and/or plan); SA: LLD patients with both suicidal thoughts and previous suicidal action. DLPFC: Dorsolateral Prefrontal Cortex; DMPFC: Dorsomedial Prefrontal Cortex; VLPFC/OFC: Ventrolateral Prefrontal Cortex/Orbitofrontal Cortex; BA: Brodmann Area; Ke: Cluster size; a: Results computed within a priori Regions of Interest (ROI).
      Figure thumbnail gr2
      Figure 2The grey matter volume (GMV) of regions of interest (ROIs) that showed significant differences between suicide groups. The results are overlain on the normalized DARTEL grey matter template of the total participant sample. The GMVs were corrected for age, sex and TIV. Both the means and the ±1 standard errors of the mean (SE) are displayed. DMPFC: Dorsomedial prefrontal cortex; PCC: Posterior Cingulate Cortex; VLPFC/OFC: Ventrolateral prefrontal cortex/Orbitofrontal cortex; NS: Non-suicidal LLD patients; I/P: LLD patients with past and/or present suicidal thoughts (ideation and/or plan); SA: LLD patients with previous suicidal action; **: p<.01; *: p<.05. All p values were corrected for multiple-testing using the Holm-Bonferroni method.
      Follow-up regression analyses controlling for age, sex and TIV confirmed that for the bilateral DMPFC, the right IPC and right DLPFC, both the I/P and the SA groups showed significantly less GMV than the NS group (t105<-2.86, pcorrected<.01), while no significant difference was found between the I/P and SA groups (p>.67) (Figure 2b,c). On the other hand, for the right VLPFC/OFC and bilateral precuneus, the SA group showed reduced grey matter volume than both the NS and the I/P groups (t105<-3.07, pcorrected<.01), which did not differ from each other (p>.2) (Figure 2a,d).
      The results did not change after additionally controlling for education, LLD onset, duration and episode number, medication load, HAMD/A and MMSE scores. In the DMPFC, IPC and DLPFC regions, Both the I/P and SA groups showed significantly less GMV than the NS group (t94<-2.628, pcorrected<.01), while the I/P and SA groups did not differ (p>.72). In the VLPFC/OFC and precuneus regions, the SA group showed reduced grey matter volume than both the NS and the I/P groups (t105<-3.013, pcorrected<.01), which did not differ from each other (p>.55).
      All the above results were replicated using female and non-GAD samples only, and after controlling for medication type (see Supplement).

       RSFC Analyses

      Following our a priori hypothesis and the structural results that the right VLPFC/OFC grey matter reduction was the most prominent feature of the SA group, we specifically examined the RSFC of this region with the striatum and amygdala. Controlling for age and sex, we found that the RSFC between the VLPFC/OFC and the bilateral caudate nucleus (locus-of-maxima = [9,6,15], 47 voxels, TFCE=78.95, pFWE=.012) was significant to the NS-(I/P+SA)/2 contrast (Figure 3a). While the RSFC was significantly negative in the NS group (t42=-4.221, p<.001), it was insignificantly positive in the I/P group (t28=1.591, p=.12) and insignificantly negative in the SA group (t32=-.512, p=.61). Follow-up regression analyses confirmed that the VLPFC/OFC-caudate RSFC was more positive in both the SA (t100=2.298, p=.024) and the IP groups (t100=3.999, p<.001) compared to the NS group (Figure 3b). The SA and I/P groups were not significantly different (p=.129). The effects did not change after additionally controlling for the nuisance factors as outlined above (NS vs. I/P: t87=3.733, p<.001; NS vs. SA: t87=2.029, p=.046; I/P vs. SA: t87=1.584, p=.117). All the above results were replicated using female and non-GAD samples only, and after controlling for medication type (see Supplement).
      Figure thumbnail gr3
      Figure 3Functional and effective connectivity between the VLPFC/OFC seed ([41, 20, -14]) and the caudate ([9, 6, 15]). (a) The location of the seed regions overlain on a glass brain. (b) the RSFC between the VLPFC/OFC seed and a caudate region which was significantly more positive in the I/P and SA groups compared to the NS group. (c-e) Bayesian Model Selection procedure revealed the best model in each group regarding the directionality of the VLPFC/OFC-caudate connectivity. The numbers above bars represent model posterior probability. Models with probability ≥90% are typically considered as receiving ‘significant’ support (Razi et al., 2015). Both mean and ±1 standard error of the mean (SE) are presented. *: p<.05 after Holm-Bonferroni correction; **: p<.01 after Holm-Bonferroni correction.
      No significant effect was found for the RSFC with the amygdala to either the NS-(I/P+SA)/2 or the (NS+I/P)/2-SA contrast. No other whole-brain results were found.

       spDCM analyses

      Initial model diagnostic confirmed that the DCM models explained reasonable variance of participants’ seed timeseries (all >17%). Moreover, all participants showed largest connectivity well above the baseline of 1/8 Hz, supporting that model-fitting was satisfactory. The best model for the NS and SA groups was characterised by the VLPFC/OFC-to-caudate connectivity, whereas for the I/P group it was defined by a caudate-to-VLPFC/OFC connectivity (Figure 3c-e). In all cases, the best model received greater than or almost 95% posterior probability, and Bayes Factors >16, indicating high confidence over model selection (
      • Penny W.D.
      • Stephan K.E.
      • Daunizeau J.
      • Rosa M.J.
      • Friston K.J.
      • Schofield T.M.
      • Leff A.P.
      Comparing families of dynamic causal models.
      ). The VLPFC/OFC-to-caudate connectivity was significantly negative in the NS group (t42=-21.85, p<.001) but not in the SA group (t32=-1.15, p=.273). The caudate-to-VLPFC/OFC connectivity was marginally positive in the I/P group (t28=1.61, p=.1).
      After controlling for age and sex, the NS group showed more negative VLPFC/OFC-to-caudate connection than the SA group (t72=-11.883, p<.001), even after controlling for all the nuisance factors (t60=-10.778, p<.001). All the above results were replicated using female and non-GAD samples only, and after controlling for medication type (see Supplement).

       Post-hoc SVM analyses

      All the significant clinical, suicide questionnaire and neural measures were fed into SVM models to quantify their classification strength, utilizing LOOCV. Model 1 included only non-neural variables, while Model 2 additionally incorporated neural (GMV and RSFC) features (Table 3). For the NS vs. SA classification, the GMV measures improved classification sensitivity by 6.25%, thereby increasing the final accuracy to 85.64%. While SAD PERSONS score was the highest feature, parietal and VLPFC/OFC GMV were the second and third highest features. For the NS vs. I/P classification, the neural features improved sensitivity by 20% and specificity by 8.57%, resulting in the final accuracy of 85%. SAD PERSONS score and LLD episode number were the top features, followed by the VLPFC/OFC-caudate RSFC as the third highest feature. For the I/P vs. SA classification, the GMV features improved sensitivity by 37.5%, resulting in the final accuracy of 68.33%. The VLPFC and precuneus GMV were the top highest features.
      Table 3The SVM results for classifying NS vs. SA, NS vs. I/P, and I/P vs. SA groups.
      Group LabelsModelFeature WeightsAccuracy (%)Sensitivity (%)Specificity (%)Model Performance
      NS (0) vs. SA (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      )
      1SAD PERSONS (1.28)

      BSS (0.41)

      LLD Onset (-0.26)

      LLD Episode (0.13)
      82.5281.2583.78p<.001
      2SAD PERSONS (2.13)

      Parietal GMV (-0.77)

      VLPFC/OFC GMV (-0.62)

      DMPFC GMV (-0.43)

      Precuneus GMV (0.32)

      DLPFC GMV (-0.26)

      LLD Onset (-0.22)

      LLD Episode (-0.17)

      BSS (0.15)

      TIV (0.05)
      85.6487.583.78p<.001
      NS (0) vs. I/P (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      )
      1SAD PERSONS (1.36)

      LLD Episode (1.26)

      TSII (-0.01)
      69.435682.86p=.002
      2SAD PERSONS (1.20)

      LLD Episode (0.90)

      VLPFC/OFC-caudate RSFC (0.73)

      DLPFC GMV (-0.44)

      DMPFC GMV (0.37)

      Parietal GMV (-0.29)

      TIV (-0.22)

      TSII (0.08)
      857691.43p<.001
      I/P (0) vs. SA (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      )
      1BSS (0.87)51.6740.6364.29p=.65
      2VLPFC GMV (-0.85)

      Precuneus GMV (-0.72)

      TIV (0.47)

      BSS (0.38)
      68.3378.1357.14p=.003
      For each classification, two models were tested, with model 1 including only the significant clinical and suicidality-related variables, and model 2 additionally including the GMV and RSFC measures. For NS vs. SA, the clinical variables were LLD onset age and LLD episode number; the suicidality-related variables were BSS and SAD PERSONS scores. For NS vs. I/P, the clinical variables were LLD episode number; the suicidality-related variables were TSII and SAD PERSONS scores. For I/P vs. SA, no clinical variable was included; the suicidality-related variable was BSS score. Model classification accuracy, as well as sensitivity and specificity, are presented, along with the significance of model classification based on 10000-bootstrapping permutation testing. Feature weights are ordered from largest to smallest based on absolute weight. Larger absolute weights indicate stronger contribution to the classification.

      Discussion

      In this study, we demonstrated that first, lateral prefrontal and DMN regional grey matter volumetric reductions characterised suicidal thoughts and behaviours; Second, the directed resting-state connectivity between the VLPFC/OFC and the caudate showed differential alterations in LLD patients with suicidal thoughts, and in those with past suicidal actions, compared to non-suicidal patients. The neural measures improved classification accuracy of both non-suicidal vs. ideators/planners, and ideators/planners vs. attempters, by over 15% compared to that based on clinical and suicide questionnaire measures alone, suggesting that the neural measures could be potentially utilized to identify LLD patients with high suicide risk. However, it should also be noted that our findings were obtained on predominantly female samples, and their generalization to male LLD patients is not warranted. This is an important consideration given although females may constitute the majority of LLD patients, males may indeed carry higher risk for suicide (
      • Hawton K.
      • i Comabella C.C.
      • Haw C.
      • Saunders K.
      Risk factors for suicide in individuals with depression: a systematic review.
      ).
      The VLPFC/OFC is among the most implicated neural structures in suicidality (
      • Benedetti F.
      • Radaelli D.
      • Poletti S.
      • Locatelli C.
      • Falini A.
      • Colombo C.
      • Smeraldi E.
      Opposite effects of suicidality and lithium on gray matter volumes in bipolar depression.
      ,
      • Hwang J.P.
      • Lee T.W.
      • Tsai S.J.
      • Chen T.J.
      • Yang C.H.
      • Lirng J.F.
      • Tsai C.F.
      Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry.
      ,
      • Monkul E.S.
      • Hatch J.P.
      • Nicoletti M.A.
      • Spence S.
      • Brambilla P.
      • Lacerda A.D.
      • et al.
      Fronto-limbic brain structures in suicidal and non-suicidal female patients with major depressive disorder.
      ,
      • Schmaal L.
      • van Harmelen A.L.
      • Chatzi V.
      • Lippard E.T.
      • Toenders Y.J.
      • Averill L.A.
      • et al.
      Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.
      ). We found as expected, the VLPFC/OFC grey matter reduction was specifically observed in suicide attempters, while no significant difference was evident between the non-suicidal patients and suicide ideators. VLPFC/OFC volume was also the most important feature in distinguishing suicidal ideators from attempters. Along with other grey matter features, the VLPFC/OFC volume boosted the classification sensitivity by 37%, representing a substantial increase in the power to detect LLD individuals who acted on their suicidal thoughts. The VLPFC/OFC plays a crucial role in implementing goal-directed response selection, particularly in volatile environments where actions need to be flexibly adjusted based on joint consideration of immediate and distant reinforcement histories (
      • Rudebeck P.H.
      • Murray E.A.
      The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.
      ,
      • Shao R.
      • Lee T.M.C.
      Aging and risk taking: toward an integration of cognitive, emotional, and neurobiological perspectives.
      ). Notably, the grey matter loss in the suicide attempters was mostly located on the pars orbitalis portion of the inferior frontal gyrus and the OFC. These regions are heavily involved in making goal-directed decisions based on adaptive reinforcement processing (
      • Ridderinkhof K.R.
      • Van Den Wildenberg W.P.
      • Segalowitz S.J.
      • Carter C.S.
      Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning.
      ,
      • Eshel N.
      • Nelson E.E.
      • Blair R.J.
      • Pine D.S.
      • Ernst M.
      Neural substrates of choice selection in adults and adolescents: development of the ventrolateral prefrontal and anterior cingulate cortices.
      ,
      • Ostlund S.B.
      • Balleine B.W.
      The contribution of orbitofrontal cortex to action selection.
      ,
      • Young J.J.
      • Shapiro M.L.
      The orbitofrontal cortex and response selection.
      ,
      • Yang L.
      • Masmanidis S.C.
      Differential encoding of action selection by orbitofrontal and striatal population dynamics.
      ). Further, the VLPFC/OFC connects extensively with the caudate nucleus (
      • Jarbo K.
      • Verstynen T.D.
      Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum.
      ,
      • Jaspers E.
      • Balsters J.H.
      • Kassraian Fard P.
      • Mantini D.
      • Wenderoth N.
      Corticostriatal connectivity fingerprints: Probability maps based on resting‐state functional connectivity.
      ), which is also particularly involved in implementing instrumental processing and goal-directed behaviours (
      • Yang L.
      • Masmanidis S.C.
      Differential encoding of action selection by orbitofrontal and striatal population dynamics.
      ,
      • Tricomi E.M.
      • Delgado M.R.
      • Fiez J.A.
      Modulation of caudate activity by action contingency.
      ,
      • Cooper J.C.
      • Dunne S.
      • Furey T.
      • O'Doherty J.P.
      Human dorsal striatum encodes prediction errors during observational learning of instrumental actions.
      ,
      • Peak J.
      • Hart G.
      • Balleine B.W.
      From learning to action: the integration of dorsal striatal input and output pathways in instrumental conditioning.
      ). Thus, our results supported the notion that taking the critical step towards suicide results from failure in selecting the more beneficial (less detrimental) action under situations where hardships may seem to overwhelm the positive prospects. This notion is consistent with the proposed specialized role of the lateral OFC and VLPFC-insular systems in signalling and regulating ‘non-reward’ or aversive processing (
      • Rolls E.T.
      A non-reward attractor theory of depression.
      ), which is an integral component of the extended VLPFC/OFC functioning in top-down inhibitory control of emotion, cognition and actions (
      • McTeague L.M.
      • Rosenberg B.M.
      • Lopez J.W.
      • Carreon D.M.
      • Huemer J.
      • Jiang Y.
      • et al.
      Identification of common neural circuit disruptions in emotional processing across psychiatric disorders.
      ).
      We found little DLPFC volumetric difference between the ideators and attempters. The DLPFC may be principally engaged in high-level executive control processes such as attention and working memory (
      • Taylor S.F.
      • Liberzon I.
      Neural correlates of emotion regulation in psychopathology.
      ), and may also be recruited during deliberate reasoning and reframing of cognitive schemata (
      • Ochsner K.N.
      • Gross J.J.
      The cognitive control of emotion.
      ). In this regard, our results suggest that rather than being chiefly involved in action selection and control, the role of the DLPFC might be more related to initiating, reappraising and regulating thoughts related to death and suicide, which are prevalent in both ideators and attempters (
      • Clark L.
      • Dombrovski A.Y.
      • Siegle G.J.
      • Butters M.A.
      • Shollenberger C.L.
      • Sahakian B.J.
      • Szanto K.
      Impairment in risk-sensitive decision-making in older suicide attempters with depression.
      ).
      As expected, both the anterior DMPFC and the IPC volumes showed comparable reductions in the ideators and attempters compared to the non-suicidal patients, indicating their involvements in general suicidality. More specifically, based on the role of these regions in social cognition, episodic memory and automatic attention (
      • Eickhoff S.B.
      • Laird A.R.
      • Fox P.T.
      • Bzdok D.
      • Hensel L.
      Functional segregation of the human dorsomedial prefrontal cortex.
      ,
      • Humphreys G.F.
      • Lambon Ralph M.A.
      Fusion and fission of cognitive functions in the human parietal cortex.
      ), their structural deficiency may lead to reduced responsiveness to other people’s concern (
      • May A.M.
      • Klonsky E.D.
      • Klein D.N.
      Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study.
      ), failure to recall positive autobiographic memory (
      • Richard-Devantoy S.
      • Berlim M.T.
      • Jollant F.
      Suicidal behaviour and memory: A systematic review and meta-analysis.
      ), and a tendency to attend to death and suicide-related thoughts about oneself (
      • Nock M.K.
      • Park J.M.
      • Finn C.T.
      • Deliberto T.L.
      • Dour H.J.
      • Banaji M.R.
      Measuring the suicidal mind: Implicit cognition predicts suicidal behavior.
      ), a combination of which may drive an individual towards suicidality. Owing to the cross-sectional nature of the current study, it was not known with certainty which ideators would commit suicide in future years. However, based on the close association between suicide thoughts and action (
      • Nock M.K.
      • Borges G.
      • Bromet E.J.
      • Alonso J.
      • Angermeyer M.
      • Beautrais A.
      • et al.
      Cross-national prevalence and risk factors for suicidal ideation, plans and attempts.
      ,
      • Hawton K.
      • i Comabella C.C.
      • Haw C.
      • Saunders K.
      Risk factors for suicide in individuals with depression: a systematic review.
      ), it could be that the deficient DMPFC and IPC structures are among the shared brain mechanisms underlying the prevalent transition from ideators to attempters.
      The lack of PCC/precuneus volumetric difference between non-suicidal patients and ideators was somewhat unexpected. Rather, the PCC/precuneus volume distinguished suicide attempters from both non-suicidal patients and ideators. The PCC/precuneus plays a prominent role in self-referential processing, self-consciousness and first-person perspective taking (
      • Cavanna A.E.
      • Trimble M.R.
      The precuneus: a review of its functional anatomy and behavioural correlates.
      ). Our previous work identified this region as central to a network crucial for self-referential emotional regulation, and attachment/detachment from the affective self-viewpoint (
      • Shao R.
      • Keuper K.
      • Geng X.
      • Lee T.M.
      Pons to posterior cingulate functional projections predict affective processing changes in the elderly following eight weeks of meditation training.
      ). The structural deficit of the PCC/precuneus could result in disregarding of the affective wellbeing of oneself, which in the long run may predispose the individual to engage in suicidal acts (
      • Conwell Y.
      • Duberstein P.R.
      • Caine E.D.
      Risk factors for suicide in later life.
      ).
      On the functional network level, we discovered that the RSFC between the VLPFC/OFC and the caudate was less negative in the suicide ideators compared to that in the non-suicidal patients. A similar result was observed when comparing attempters and non-suicidal patients, albeit to lesser extent. Critically, while the RSFC was directed from the VLPFC-OFC to caudate in the non-suicidal patients and attempters, the reverse direction was found for the ideators. Existing evidence suggests that the head and body of the caudate are respectively implicated in cognitive-emotional and perceptual-motor processing (
      • Robinson J.L.
      • Laird A.R.
      • Glahn D.C.
      • Blangero J.
      • Sanghera M.K.
      • Pessoa L.
      • et al.
      The functional connectivity of the human caudate: an application of meta-analytic connectivity modeling with behavioral filtering.
      ). Via connectivity with the VLPFC/OFC, the caudate may act as an interface mediating cognitive and affective control of behaviours (
      • Graff-Radford J.
      • Williams L.
      • Jones D.T.
      • Benarroch E.E.
      Caudate nucleus as a component of networks controlling behavior.
      ). In particular, the OFC (inhibiting) and the caudate (facilitating) are considered to play opposing roles in initiation and control of compulsive behaviour, namely habitual, ritualistic behaviours aimed to prevent perceived negative consequences (
      • Fineberg N.A.
      • Potenza M.N.
      • Chamberlain S.R.
      • Berlin H.A.
      • Menzies L.
      • Bechara A.
      • et al.
      Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review.
      ). Therefore, the markedly more negative connectivity directed from the OFC to the caudate in the non-suicidal group may indicate better capacity to regulate the compulsive suicidal behaviour, which is deficient in the attempters.
      Contrary to our hypothesis, we found no suicidality-related difference in the VLPFC/OFC- amygdala RSFC, which was previously found to be increased in depressed suicide attempters (
      • Kang S.G.
      • Na K.S.
      • Choi J.W.
      • Kim J.H.
      • Son Y.D.
      • Lee Y.J.
      Resting-state functional connectivity of the amygdala in suicide attempters with major depressive disorder.
      ). The finding discrepancy could arise from the difference in patient age. Aging is particularly associated with reduction in corticostriatal dopaminergic circuitries (
      • Shao R.
      • Lee T.M.C.
      Aging and risk taking: toward an integration of cognitive, emotional, and neurobiological perspectives.
      ), which might render the VLPFC/OFC-caudate connectivity the key determining factor for late-life suicidality. Our LLD findings suggest that old-age suicidality is primarily associated with altered value-based decision making and behavioural control, while negatively-biased emotion processing associated with changes in the ventral PFC-amygdala connectivity may play more important roles in suicidality among younger adults.
      The reversed caudate-to-VLPFC/OFC connectivity in ideators is particularly noteworthy, since it may mean that for ideators, emotional and behavioural regulation operate in largely bottom-up manner, governed by instrumental computations that is under intensive dopaminergic modulations (
      • Kuhnen C.M.
      • Knutson B.
      The neural basis of financial risk taking.
      ). Aging is associated with significant declines in dopaminergic levels in the cortisostriatal circuitries, which may lead to older people’s impaired capacity to learn from positive outcomes but enhanced learning from negative experience (
      • Shao R.
      • Lee T.M.C.
      Aging and risk taking: toward an integration of cognitive, emotional, and neurobiological perspectives.
      ). Therefore, the qualitatively positive caudate-to-VLPFC/OFC connectivity may reflect the increased efforts of the ideators to seek positive life experiences to combat suicide-related thoughts. However, due to deficient cortisostriatal dopaminergic functioning, the caudate output may be progressively declining, hampering this positivity process.
      Our findings have potential clinical implications, particularly in view of the long-remaining difficulties in pinpointing suicidality among LLD patients (
      • Conwell Y.
      • Van Orden K.
      • Caine E.D.
      Suicide in older adults.
      ). LLD individuals tend to be less voicing and attract less medical and social care (
      • Conwell Y.
      • Duberstein P.R.
      • Caine E.D.
      Risk factors for suicide in later life.
      ), meaning it is important to derive objective, unbiased indices that could help identify high-risk elderly for suicide. Building on the current findings, we can progress towards more reliable and earlier identification of old-age suicidality, which in turn informs contingent management and intervention plans. Our finding also has translational implication and offers support for the application of neuromodulation methods targeting on key brain regions, such as the VLPFC/OFC and DMPFC, in treating depression symptoms (
      • Feffer K.
      • Fettes P.
      • Giacobbe P.
      • Daskalakis Z.J.
      • Blumberger D.M.
      • Downar J.
      1 Hz rTMS of the right orbitofrontal cortex for major depression: Safety, tolerability and clinical outcomes.
      ,
      • Rao V.R.
      • Sellers K.K.
      • Wallace D.L.
      • Lee M.B.
      • Bijanzadeh M.
      • Sani O.G.
      • et al.
      Direct electrical stimulation of lateral orbitofrontal cortex acutely improves mood in individuals with symptoms of depression.
      ) and improving suicidal impulse (
      • Feffer K.
      • Peters S.K.
      • Bhui K.
      • Downar J.
      • Giacobbe P.
      Successful dorsomedial prefrontal rTMS for major depression in borderline personality disorder: three cases.
      ).
      This study is inevitably limited by its cross-sectional nature, which demands further validation with longitudinal design. While we were only able to distinguish based on participants’ past suicidal action, the findings should also inform on predicting future attempts given the very close association between past and future suicide attempts. Our sample size was larger than or comparable to that of most imaging studies on suicidality, but was still insufficient to address possible modulatory effect of psychosocial (e.g. loneliness) or personality (e.g. impulsivity) measures. Such investigations could be conducted in future studies with more focused research question. Our LLD patients, while all being on medication, showed varied symptom severity levels. Although we could not account for each patient’s potentially distinct treatment regime or comorbidity in the distant past, we did control for patients’ medication type and load which stayed unchanged for at least 2 months prior to the study, and patients’ comorbidity at the time of study. Also, we had no direct measure of the patients’ historical illness severity, although we controlled for other clinical variables such as cumulative illness duration and number of depression episodes throughout the analyses. Our ROI approach is an empirically informed, powerful method to identify key neural measures using limited patient samples, but it may not guarantee to include all potential suicide-related brain regions. With more substantial patient samples, future studies may extend our findings by adopting more comprehensive whole-brain approach. Last, our sample consisted predominantly of females. While we verified that the main findings were replicated on female-only samples, the findings may not necessarily generalize to male LLD patients, which needs to be tested in future studies.
      In conclusion, among LLD individuals, we identified key lateral prefrontal and DMN regions where volumetric decreases characterised suicide thoughts and action, and the strength and direction of the VLPFC/OFC-caudate circuitry distinguished non-suicidal patients from ideators and attempters. The findings advanced our knowledge on the neurobiological mechanisms of suicidality in LLD, with potential clinical implications in early identification and intervention.

       Data and Code Availability

      All data included in this manuscript are available upon reasonable request from the corresponding author. The Support Vector Machine analysis codes are available upon reasonable request from the corresponding author.

      Disclosure

      The authors report no biomedical financial interests or potential conflicts of interest.

      Author Contributions

      R.S. contributed to data analysis, interpretation and manuscript drafting; M.G. contributed to data analysis and interpretation; C.L. contributed to study conceptualization, design and data collection; C-M.H. contributed to study conceptualization, design and data collection; H-L.L. contributed to study conceptualization and design; C-H.T contributed to study conceptualization and design; C.W. contributed to study conceptualization and design; Y-F.T. contributed to study conceptualization and design; D.Q. contributed to data analysis and interpretation; S-H.L. contributed to study conceptualization, design and data collection; T.M.C.L. contributed to study conceptualization, design and manuscript drafting.

      Acknowledgements

      The project was supported by NMRPG3G6031/32 & NMRPG3J0121 from Ministry of Science and Technology of Taiwan to SH Lee, the medical research grant CRRPG2G2K0021 from Chang Gung Memorial Hospital and NRRPG2K6011 from Ministry of Science and Technology of Taiwan to C Lin, and the Key-Area Research and Development Program of Guangdong Province (2018B30334001) and The University of Hong Kong May Endowed Professorship in Neuropsychology to Tatia Lee. The funding bodies played no role in the original study conductance or the preparation of the present manuscript.
      We thank Dr. Clive Wong for his kind advice on data analysis.

      Supplementary Material

      References

        • Conwell Y.
        • Van Orden K.
        • Caine E.D.
        Suicide in older adults.
        Psychiatr Clin North Am. 2011; 34: 451-468
        • Nock M.K.
        • Borges G.
        • Bromet E.J.
        • Alonso J.
        • Angermeyer M.
        • Beautrais A.
        • et al.
        Cross-national prevalence and risk factors for suicidal ideation, plans and attempts.
        Br J Psychiatry. 2008; 192: 98-105
        • Conwell Y.
        • Duberstein P.R.
        • Caine E.D.
        Risk factors for suicide in later life.
        Biol Psychiatry. 2002; 52: 193-204
        • Hawton K.
        • i Comabella C.C.
        • Haw C.
        • Saunders K.
        Risk factors for suicide in individuals with depression: a systematic review.
        J Affect Disord. 2013; 147: 17-28
        • Conwell Y.
        Suicide in later life: a review and recommendations for prevention.
        Suicide Life Threat Behav. 2001; 31: 32-47
        • Phillips M.R.
        • Li X.
        • Zhang Y.
        Suicide rates in China, 1995–99.
        Lancet. 2002; 359: 835-840
        • Richard-Devantoy S.
        • Turecki G.
        • Jollant F.
        Neurobiology of elderly suicide.
        Arch Suicide Res. 2016; 20: 291-313
        • May A.M.
        • Klonsky E.D.
        • Klein D.N.
        Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study.
        J Psychiatr Res. 2012; 46: 946-952
        • Cochrane-Brink K.A.
        • Lofchy J.S.
        • Sakinofsky I.
        Clinical rating scales in suicide risk assessment.
        Gen Hosp Psychiatry. 2000; 22: 445-451
        • Mackin R.S.
        • Tosun D.
        • Mueller S.G.
        • Lee J.Y.
        • Insel P.
        • Schuff N.
        • et al.
        Patterns of reduced cortical thickness in late-life depression and relationship to psychotherapeutic response.
        A J Geriatr Psychiatry. 2013; 21: 794-802
        • Nock M.K.
        • Park J.M.
        • Finn C.T.
        • Deliberto T.L.
        • Dour H.J.
        • Banaji M.R.
        Measuring the suicidal mind: Implicit cognition predicts suicidal behavior.
        Psychol Sci. 2010; 21: 511-517
        • Kim J.U.
        • Weisenbach S.L.
        • Zald D.H.
        Ventral prefrontal cortex and emotion regulation in aging: A case for utilizing transcranial magnetic stimulation.
        Int J Geriatr Psychiatry. 2019; 34: 215-222
        • Ochsner K.N.
        • Silvers J.A.
        • Buhle J.T.
        Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion.
        Ann N Y Acad Sci. 2012; 1251: E1
        • McTeague L.M.
        • Rosenberg B.M.
        • Lopez J.W.
        • Carreon D.M.
        • Huemer J.
        • Jiang Y.
        • et al.
        Identification of common neural circuit disruptions in emotional processing across psychiatric disorders.
        Am J Psychiatry. 2020; 177: 411-421
        • Rolls E.T.
        A non-reward attractor theory of depression.
        Neurosci Biobehav Rev. 2016; 68: 47-58
        • Jarbo K.
        • Verstynen T.D.
        Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum.
        J Neurosci. 2015; 35: 3865-3878
        • Jaspers E.
        • Balsters J.H.
        • Kassraian Fard P.
        • Mantini D.
        • Wenderoth N.
        Corticostriatal connectivity fingerprints: Probability maps based on resting‐state functional connectivity.
        Hum Brain Mapp. 2017; 38: 1478-1491
        • Rudebeck P.H.
        • Murray E.A.
        The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.
        Neuron. 2014; 84: 1143-1156
        • Shao R.
        • Lee T.M.C.
        Aging and risk taking: toward an integration of cognitive, emotional, and neurobiological perspectives.
        Neurosci Neuroecon. 2014; 3: 47-62
        • Clark L.
        • Dombrovski A.Y.
        • Siegle G.J.
        • Butters M.A.
        • Shollenberger C.L.
        • Sahakian B.J.
        • Szanto K.
        Impairment in risk-sensitive decision-making in older suicide attempters with depression.
        Psychol Aging. 2011; 26: 321-330
        • Sridharan D.
        • Levitin D.J.
        • Menon V.
        A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.
        P Natl Acad Sci U.S.A. 2008; 105: 12569-12574
        • Benedetti F.
        • Radaelli D.
        • Poletti S.
        • Locatelli C.
        • Falini A.
        • Colombo C.
        • Smeraldi E.
        Opposite effects of suicidality and lithium on gray matter volumes in bipolar depression.
        J Affect Disord. 2011; 135: 139-147
        • Hwang J.P.
        • Lee T.W.
        • Tsai S.J.
        • Chen T.J.
        • Yang C.H.
        • Lirng J.F.
        • Tsai C.F.
        Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry.
        J Geriatr Psychiatry Neurol. 2010; 23: 171-184
        • Monkul E.S.
        • Hatch J.P.
        • Nicoletti M.A.
        • Spence S.
        • Brambilla P.
        • Lacerda A.D.
        • et al.
        Fronto-limbic brain structures in suicidal and non-suicidal female patients with major depressive disorder.
        Mol Psychiatry. 2007; 12: 360-366
        • Schmaal L.
        • van Harmelen A.L.
        • Chatzi V.
        • Lippard E.T.
        • Toenders Y.J.
        • Averill L.A.
        • et al.
        Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.
        Mol Psychiatry. 2020; 25: 408-427
        • Cao J.
        • Chen X.
        • Chen J.
        • Ai M.
        • Gan Y.
        • Wang W.
        • et al.
        Resting-state functional MRI of abnormal baseline brain activity in young depressed patients with and without suicidal behavior.
        J Affect Disord. 2016; 205: 252-263
        • Kang S.G.
        • Na K.S.
        • Choi J.W.
        • Kim J.H.
        • Son Y.D.
        • Lee Y.J.
        Resting-state functional connectivity of the amygdala in suicide attempters with major depressive disorder.
        Prog Neuropsychopharmacol Biol Psychiatry. 2017; 77: 222-227
        • Qiu H.
        • Cao B.
        • Cao J.
        • Li X.
        • Chen J.
        • Wang W.
        • et al.
        Resting-state functional connectivity of the anterior cingulate cortex in young adults depressed patients with and without suicidal behavior.
        Behav Brain Res. 2020; 384: 112544
        • Wang H.
        • Zhu R.
        • Dai Z.
        • Tian S.
        • Shao J.
        • Wang X.
        • et al.
        Aberrant functional connectivity and graph properties in bipolar II disorder with suicide attempts.
        J Affect Disord. 2020; 275: 202-209
        • Marchand W.R.
        • Lee J.N.
        • Johnson S.
        • Thatcher J.
        • Gale P.
        • Wood N.
        • Jeong E.K.
        Striatal and cortical midline circuits in major depression: implications for suicide and symptom expression.
        Prog Neuropsychopharmacol Biol Psychiatry. 2012; 36 (2012): 290-299
        • Minzenberg M.J.
        • Lesh T.A.
        • Niendam T.A.
        • Yoon J.H.
        • Rhoades R.N.
        • Carter C.S.
        Frontal cortex control dysfunction related to long-term suicide risk in recent-onset schizophrenia.
        Schizophr Res. 2014; 157: 19-25
        • Matthews S.C.
        • Spadoni A.D.
        • Lohr J.B.
        • Strigo I.A.
        • Simmons A.N.
        Combat-exposed war veterans at risk for suicide show hyperactivation of prefrontal cortex and anterior cingulate during error processing.
        Psychosom Med. 2012; 74: 471-475
        • van Heeringen K.
        • Bijttebier S.
        • Desmyter S.
        • Vervaet M.
        • Baeken C.
        Is there a neuroanatomical basis of the vulnerability to suicidal behavior? A coordinate-based meta-analysis of structural and functional MRI studies.
        Front Hum Neurosci. 2014; 8: 824
        • Reisch T.
        • Seifritz E.
        • Esposito F.
        • Wiest R.
        • Valach L.
        • Michel K.
        An fMRI study on mental pain and suicidal behavior.
        J Affect Disord. 2010; 126: 321-325
        • Just M.A.
        • Pan L.
        • Cherkassky V.L.
        • McMakin D.L.
        • Cha C.
        • Nock M.K.
        • Brent D.
        Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.
        Nat Hum Behav. 2017; 1: 911-919
        • Feffer K.
        • Peters S.K.
        • Bhui K.
        • Downar J.
        • Giacobbe P.
        Successful dorsomedial prefrontal rTMS for major depression in borderline personality disorder: three cases.
        Brain Stimul. 2017; 10: 716-717
        • Sackeim H.A.
        • Aaronson S.T.
        • Bunker M.T.
        • Conway C.R.
        • Demitrack M.A.
        • George M.S.
        • et al.
        The assessment of resistance to antidepressant treatment: rationale for the antidepressant treatment history form: short form (ATHF-SF).
        J Psychiatr Res. 2019; 113: 125-136
      1. Beck AT, Steer RA. (1991): Manual for the Beck Scale for Suicide Ideation. (TX: Psychological Corporation, San Antonio).

        • Hockberger R.S.
        • Rothstein R.J.
        Assessment of suicide potential by nonpsychiatrists using the SAD PERSONS score.
        J Emerg Med. 1988; 6: 99-107
        • Lee S.H.
        • Tsai Y.F.
        • Wang Y.W.
        • Chen Y.J.
        • Tsai H.H.
        Development and psychometric testing of the triggers of Suicidal Ideation Inventory for assessing older outpatients in primary care settings.
        Int J Geriatr Psychiatry. 2017; 32: 1114-1121
        • Ashburner J.
        A fast diffeomorphic image registration algorithm.
        Neuroimage. 2007; 38: 95-113
        • Yan C.G.
        • Wang X.D.
        • Zuo X.N.
        • Zang Y.F.
        DPABI: data processing & analysis for (resting-state) brain imaging.
        Neuroinformatics. 2016; 14: 339-351
        • Smith S.M.
        • Nichols T.E.
        Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.
        Neuroimage. 2009; 44: 83-98
        • Razi A.
        • Kahan J.
        • Rees G.
        • Friston K.J.
        Construct validation of a DCM for resting state fMRI.
        Neuroimage. 2015; 106: 1-14
        • Penny W.D.
        • Stephan K.E.
        • Daunizeau J.
        • Rosa M.J.
        • Friston K.J.
        • Schofield T.M.
        • Leff A.P.
        Comparing families of dynamic causal models.
        PLoS Comput Biol. 2010; 6e1000709
        • Lin K.
        • Shao R.
        • Geng X.
        • Chen K.
        • Lu R.
        • Gao Y.
        • et al.
        Illness, at-risk and resilience neural markers of early-stage bipolar disorder.
        J Affect Disord. 2018; 238: 16-23
        • Ridderinkhof K.R.
        • Van Den Wildenberg W.P.
        • Segalowitz S.J.
        • Carter C.S.
        Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning.
        Brain Cogn. 2004; 56: 129-140
        • Eshel N.
        • Nelson E.E.
        • Blair R.J.
        • Pine D.S.
        • Ernst M.
        Neural substrates of choice selection in adults and adolescents: development of the ventrolateral prefrontal and anterior cingulate cortices.
        Neuropsychologia. 2007; 45: 1270-1279
        • Ostlund S.B.
        • Balleine B.W.
        The contribution of orbitofrontal cortex to action selection.
        Ann N Y Acad Sci. 2007; 1121: 174-192
        • Young J.J.
        • Shapiro M.L.
        The orbitofrontal cortex and response selection.
        Ann N Y Acad Sci. 2011; 1239: 25-32
        • Yang L.
        • Masmanidis S.C.
        Differential encoding of action selection by orbitofrontal and striatal population dynamics.
        J Neurophysiol. 2020; 124: 634-644
        • Tricomi E.M.
        • Delgado M.R.
        • Fiez J.A.
        Modulation of caudate activity by action contingency.
        Neuron. 2004; 41: 281-292
        • Cooper J.C.
        • Dunne S.
        • Furey T.
        • O'Doherty J.P.
        Human dorsal striatum encodes prediction errors during observational learning of instrumental actions.
        J Cogn Neurosci. 2012; 24: 106-118
        • Peak J.
        • Hart G.
        • Balleine B.W.
        From learning to action: the integration of dorsal striatal input and output pathways in instrumental conditioning.
        Eur J Neurosci. 2019; 49: 658-671
        • Taylor S.F.
        • Liberzon I.
        Neural correlates of emotion regulation in psychopathology.
        Trends Cogn Sci. 2007; 11: 413-418
        • Ochsner K.N.
        • Gross J.J.
        The cognitive control of emotion.
        Trends Cogn Sci. 2005; 9: 242-249
        • Eickhoff S.B.
        • Laird A.R.
        • Fox P.T.
        • Bzdok D.
        • Hensel L.
        Functional segregation of the human dorsomedial prefrontal cortex.
        Cereb Cortex. 2016; 26: 304-321
        • Humphreys G.F.
        • Lambon Ralph M.A.
        Fusion and fission of cognitive functions in the human parietal cortex.
        Cereb Cortex. 2015; 25: 3547-3560
        • Richard-Devantoy S.
        • Berlim M.T.
        • Jollant F.
        Suicidal behaviour and memory: A systematic review and meta-analysis.
        World J Biol Psychiatry. 2015; 16: 544-566
        • Cavanna A.E.
        • Trimble M.R.
        The precuneus: a review of its functional anatomy and behavioural correlates.
        Brain. 2006; 129: 564-583
        • Shao R.
        • Keuper K.
        • Geng X.
        • Lee T.M.
        Pons to posterior cingulate functional projections predict affective processing changes in the elderly following eight weeks of meditation training.
        EBioMedicine. 2016; 10: 236-248
        • Robinson J.L.
        • Laird A.R.
        • Glahn D.C.
        • Blangero J.
        • Sanghera M.K.
        • Pessoa L.
        • et al.
        The functional connectivity of the human caudate: an application of meta-analytic connectivity modeling with behavioral filtering.
        Neuroimage. 2012; 60: 117-129
        • Graff-Radford J.
        • Williams L.
        • Jones D.T.
        • Benarroch E.E.
        Caudate nucleus as a component of networks controlling behavior.
        Neurology. 2017; 89: 2192-2197
        • Fineberg N.A.
        • Potenza M.N.
        • Chamberlain S.R.
        • Berlin H.A.
        • Menzies L.
        • Bechara A.
        • et al.
        Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review.
        Neuropsychopharmacology. 2010; 35: 591-604
        • Kuhnen C.M.
        • Knutson B.
        The neural basis of financial risk taking.
        Neuron. 2005; 47: 763-770
        • Feffer K.
        • Fettes P.
        • Giacobbe P.
        • Daskalakis Z.J.
        • Blumberger D.M.
        • Downar J.
        1 Hz rTMS of the right orbitofrontal cortex for major depression: Safety, tolerability and clinical outcomes.
        Eur Neuropsychopharmacol. 2018; 28: 109-117
        • Rao V.R.
        • Sellers K.K.
        • Wallace D.L.
        • Lee M.B.
        • Bijanzadeh M.
        • Sani O.G.
        • et al.
        Direct electrical stimulation of lateral orbitofrontal cortex acutely improves mood in individuals with symptoms of depression.
        Curr Biol. 2018; 28: 3893-3902