Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study

Occurrence and characteristics of suicidal ideation in psychiatrically healthy individuals based on ecological momentary assessment

Decedents with no known mental disorder comprise 5-40% of suicides, suggesting that suicide ideation (SI) and behavior may occur in the psychiatrically healthy with important implications for suicide risk screening. Healthy Volunteers (HV) and patients with Major Depressive Disorder (MDD) provided 7 days of Ecological Momentary Assessment (EMA) data about SI and stressors. Longitudinal mixed effects logistic regression models compared HV and patient SI and stressors. Mixed effects linear regression models compared HVs' and patients' SI score change from the previous epoch's SI score when each stressor occurred. HVs (n = 42) reported less frequent (p < 0.001) and less intense SI (p < 0.003) than patients (n = 80), yet did endorse SI and/or SI-related items in 44% of EMA epochs, endorsing SI items in 25% of epochs with non-zero SI scores. For 7 of 8 stressors, patients reported stressors more often than HVs (all p < 0.001) responding to them with increased SI (0.0001 < p < 0.0472). HVs were relatively resilient to stressors, reporting SI increases only in response to neglect (p < 0.0147). Although SI and SAs are documented among psychiatrically healthy individuals, scientific attention to these observations has been scant. Real-time SI measurement showed that HVs' SI was less pronounced than MDD patients', but was endorsed, nonetheless. Patients were more likely to report stressors than HVs, perhaps due to greater sensitivity to the environment, and reported SI in response to stressors, which was less common in HVs. Both MDD patients and HVs most often manifested passive SI (viz, "decreased wish to live"). However, passive SI (viz, "desire for death"), may predict suicide, even absent SI per se (thinking about killing yourself). This study validates the utility of real-time SI assessment, showing that HVs endorse SI items in 11% of epochs, which implies that suicide risk screening focused on those with mental disorders may be too narrow an approach.

Reduced attentional control in individuals with a history of suicide attempts compared to those with suicidal ideation: Results from a systematic review and meta-analysis

BACKGROUND

Neurocognitive profiles may be especially useful to identify factors that facilitate transitioning from contemplating suicide to attempting suicide. Generally, those who attempt suicide show greater disruptions in neurocognitive ability compared to those who think about suicide but do not proceed to attempt. The goal of this systematic review and meta-analysis is to test whether this pattern is observed with attentional control.

METHODS

We systematically searched PubMed, PsychINFO, CINAHL, and Google Scholar to find pertinent studies. All included studies compared attentional functioning using neutral stimuli. Each sample featured adults with a history of suicidal ideation (SI) and no history of suicide attempts (SA) compared to those with a history of SA.

RESULTS

We identified 15 studies with 32 effect sizes (N = 931; n = 506 with SI only; n = 425 with SA). SA groups, compared to SI groups, exhibited worse accuracy yet similar reaction time, suggesting a comparatively blunted speed-accuracy tradeoff. Relative to SI, SA groups performed worse on Stroop-like and Go/NoGo tasks. SA performed better than SI on Trail Making Test B, but not A.

LIMITATIONS

There were few available studies. Most samples were small. We did not differentiate current vs. past SI or high vs. low lethality SA. Only English and Spanish language articles were included.

CONCLUSIONS

Disrupted attentional control may convey risk for transitioning to SA from SI. More work is needed to determine which components of attention are most associated with suicide risk.

Correlation between low frequency fluctuation and cognitive performance in bipolar disorder patients with suicidal ideation

BACKGROUND

Patients with bipolar disorder (BD) are at high risk of suicidal ideation (SI), and BD patients with suicidal ideation (BDSI) have shown marked abnormalities in spontaneous brain function. Cognitive impairment, on the other hand, is considered to be one of the core symptoms of BD. However, few studies have addressed the association between cognitive performance and abnormal spontaneous brain function in BDSI.

METHODS

In the current study, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive performance in BDSI (n = 20), BD subjects without suicidal ideation (BDNSI) (n = 24) and healthy controls (HC) (n = 30). Their cognitive performance was then correlated with amplitude of low frequency fluctuation (ALFF) values obtained by resting-state functional magnetic resonance imaging (rs-fMRI).

RESULTS

We found that ALFF was significantly higher in the left precuneus and right posterior cingulate cortex in the BDSI group and significantly lower in the right precuneus in the BDNSI group than in the HC group. In addition, in the BDSI group, visual learning performance was positively correlated with ALFF values in the left precuneus.

CONCLUSIONS

Our findings support the notion that BD patients present with ALFF abnormalities, which are associated with cognitive performance in BDSI.

Impulsivity in Major Depressive Disorder Patients with Suicidal Ideation: Event-related Potentials in a GoNogo Task

Objective

: Suicidal ideation is one of the strongest predictors of suicide, and its relevance to impulsivity in depressed patients has been accumulated. Furthermore, high impulsivity patients show the attenuation of the Nogo amplitude in the GoNogo event-related potential (ERP). The purpose of the current study is to determine the correlation of Nogo ERP to the suicidal ideation depending on the condition of its presence or absence in major depressive disorder (MDD) patients.

Methods

: A total 162 participants (104 patients with suicidal ideation, 31 patients without suicidal ideation, and 27 healthy controls) were recruited, and performed GoNogo tasks during the electroencephalogram measurement. Depression, anxiety, suicidal ideation and impulsivity were assessed by self-rating scales. The clinical measures, behavioral data and Nogo ERP were compared among groups.

Results

: The MDD with suicidal ideation (SI) group showed significantly decreased Nogo P3 amplitudes compared to MDD without SI (Fz and Cz electrodes) and control group (all electrodes). The MDD with SI group also had significantly low accuracy of both Go and Nogo trails, compared to the MDD without group. The Nogo P3 amplitudes showed the negative relation to the scores of impulsivity, depression, anxiety and SI.

Conclusion

: Our results concluded that the Nogo P3 ERP amplitude was decreased in MDD patients with SI compared to MDD patients without SI and controls. These findings suggest that the decreased Nogo P3 amplitude is the one of the candidate biomarker for impulsivity in MDD patients to evaluating SI.

Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review

BACKGROUND

Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data.

METHODS

We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment.

RESULTS

23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures.

LIMITATIONS

Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development.

CONCLUSIONS

Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

The Experience of Pain is Strongly Associated With Poor Sleep Quality and Increased Risk for Suicide

OBJECTIVE

Effective suicide prevention is hindered by a limited understanding of the neurobiology leading to suicide. We aimed to examine the association between changes in the experience of pain and disturbances in sleep quantity and quality in patients with elevated risk for suicide.

METHODS

Three groups of adult depressed individuals, including patients following a recent suicide attempt (n = 79), patients experiencing current suicidal ideation (n = 131), and patients experiencing depression but no suicidal ideation or behavior in at least 6 months (n = 51), were examined in a case-control study for sleep quantity and quality, physical and psychological pain, pressure pain threshold, suicidal ideation, and recent suicidal behavior.

RESULTS

Sleep quality, physical and psychological pain were positively associated with suicidal ideation severity. In both cases in which sleep quality was added to a model with either physical or psychological pain, physical or psychological pain became more significantly associated with suicidal ideation severity. Pressure pain threshold was elevated in patients suffering from any type of insomnia. There was no significant association between pressure pain threshold and suicidal ideation severity.

CONCLUSIONS

The impact of these findings lies in the identification of both psychological and physical pain, and sleep quality as potential biological mechanisms underlying suicidal risk. HIGHLIGHTSWe assessed the association between pain and sleep quality in suicidal patients.Sleep quality, physical and psychological pain were associated with suicide risk.Pain perception may mediate the progression to suicidal behavior.

Distinct patterns of altered quantitative T1ρ and functional BOLD response associated with history of suicide attempts in bipolar disorder

Despite the high risk for suicide, relatively few studies have explored the relationship between suicide and brain imaging measures in bipolar disorder. In addition, fewer studies have explored the possibility that altered brain metabolism may be associated with suicide attempt. To begin to fill in these gaps, we evaluated functional (task based fMRI) and metabolic (quantitative T1ρ) differences associated with suicide attempt in participants with bipolar disorder. Thirty-nine participants with bipolar disorder underwent fMRI during a flashing checkerboard task and 27 also underwent quantitative T1ρ. The relationship between neuroimaging and history of suicide attempt was tested using multiple regression while adjusting for age, sex, and current mood state. Differences between two measures of suicide attempt (binary: yes/no and continuous: number of attempts) were quantified using the corrected Akaike Information Criterion. Participants who had attempted suicide had greater fMRI task-related activation in visual areas and the cerebellum. The number of suicide attempts was associated with a difference in BOLD response in the amygdala, prefrontal cortex, and cerebellum. Increased quantitative T1ρ was associated with number of suicide attempts in limbic, basal ganglia, and prefrontal cortex regions. This study is a secondary analysis with a modest sample size. Differences between measures of suicide history may be due to differences in statistical power. History of suicide was associated with limbic, prefrontal, and cerebellar alterations. Results comparing those with and without suicide attempts differed from results using number of suicide attempts, suggesting that these variables have different neurobiological underpinnings.

Electrophysiological Changes Between Patients With Suicidal Ideation and Suicide Attempts: An Event-Related Potential Study

INTRODUCTION

Inhibitory control is regarded as an important ability related to the transition from suicidal ideation to suicide attempts. In event-related potential, patients with dysfunction of inhibitory control demonstrate a reduction in the no-go amplitude. This study aimed to determine the association between the no-go event-related potential component and suicidal behaviors among suicide attempters and ideators who never attempted suicide.

METHODS

Overall, 150 patients who visited the emergency room by suicide attempts or patients who visited the psychiatric department with suicidal ideation were recruited and instructed to perform a go/no-go task during electroencephalography recording. The Beck Depression Inventory, Beck Anxiety Inventory, Barratt Impulsivity Scale, Difficulties in Emotional Regulation Scale, and Acquired Capability for Suicide Scale were used. Individuals were divided into two groups: those with suicide attempt group) and with suicidal ideation (SI group) without SA. The psychological characteristics and event-related potentials of the two groups were compared. Correlation analyses were conducted to test the association between the clinical characteristics and event-related potentials.

RESULTS

The SA group had significantly decreased no-go P3 amplitudes at all electrodes compared to the SI group. In the correlation analysis between the clinical measurements and event-related potentials in all the participants, no-go P3 amplitudes in whole electrode sites were negatively correlated with the scores of the acquired capability for the suicide scale.

CONCLUSIONS

This study revealed that suicide attempters have dysfunction in controlling inhibition compared to suicide ideators reflected in the no-go P3. Our findings suggested that no-go P3 can be a biomarker associated suicide attempts in suicide ideators.

Machine learning as the new approach in understanding biomarkers of suicidal behavior

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder

The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.

Functional network alterations differently associated with suicidal ideas and acts in depressed patients: an indirect support to the transition model

The transition from suicidal ideas to a suicide act is an important topic of research for the identification of those patients at risk of acting out. We investigated here whether specific brain activity and connectivity measures at rest may be differently associated with suicidal thoughts and behaviors. A large sample of acutely depressed patients with major depressive disorder was recruited in three different centers (Montreal/Canada, Stanford/USA, and Jena/Germany), covering four different phenotypes: patients with a past history of suicide attempt (n = 53), patients with current suicidal ideas but no past history of suicide attempt (n = 40), patients without current suicidal ideation nor past suicide attempts (n = 42), and healthy comparison subjects (n = 107). 3-T resting-state functional magnetic resonance imaging (fMRI) measures of the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) were obtained and examined in a whole-brain data-driven analysis. Past suicide attempt was associated with a double cortico-subcortical dissociation in ALFF values. Decreased ALFF and DC values mainly in a frontoparietal network and increased ALFF values in some subcortical regions (hippocampus and thalamus) distinguished suicide attempters from suicide ideators, patient controls, and healthy controls. No clear neural differences were identified in relation to suicidal ideas. Suicide attempters appear to be a distinct subgroup of patients with widespread brain alterations in functional activity and connectivity that could represent factors of vulnerability. Our results also indirectly support at the neurobiological level the relevance of the transition model described at the psychological and clinical levels. The brain bases of suicidal ideas occurrence in depressed individuals needs further investigations.

Examination of structural brain changes in recent suicidal behavior

We aimed to identify brain structural changes in cortical and subcortical regions linked to recent suicidal behavior. We performed secondary analyses of structural MRI data of two independent studies, namely the Establishing Moderators/Biosignatures of Antidepressant Response - Clinical Care (EMBARC) study and a Little Rock study on acute suicidal behavior. Study 1 (EMBARC, N = 187), compared individuals with remote suicide attempts (Remote-SA), individuals with lifetime suicide ideation but no attempts (Lifetime-SI only), and depressed individuals without lifetime suicide ideation or attempts (non-suicidal depressed). Study 2 (Little Rock data, N = 34) included patients recently hospitalized for suicide ideation or attempt constituted by: patients who recently attempted suicide (Recent-SA), individuals with remote suicide attempts (Remote-SA), and Lifetime-SI only. Study 3 combined the EMBARC and Little Rock datasets including Recent-SA, Remote-SA, Lifetime-SI only and non-suicidal depressed individuals. In Study 1 and Study 2, no significant differences were observed between groups. In Study 3, significantly lower middle temporal gyrus thickness, insular surface area, and thalamic volume and higher volume in the nucleus accumbens were observed in Recent-SA. This pattern of structural abnormalities may underlie pain and emotion dysregulation, which have been linked to the transition from suicidal thoughts to action.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Slower perception of time in depressed and suicidal patients

Effective suicide prevention is hindered by a limited understanding of the natural progression and neurobiology of the suicidal process. Our objective was to characterize the duration of the suicidal process and its relation to possible determinants: time judgment and cognitive impulsivity. In four groups of adults of both sexes including recent suicide attempters (n = 57), suicidal ideators (n = 131), non-suicidal depressed controls (n = 51) and healthy controls (n = 48) we examined time estimation and production, impulsivity and other cognitive variables. Duration of the suicidal process was recorded in suicide attempters. The suicide process duration, suicide contemplation and action intervals, had a bimodal distribution, ∼40% of attempters took less than 5 min from decision to attempt. Time slowing correlated negatively with the suicidal action interval (time from the decision to kill oneself to suicide attempt) (p = .003). Individuals with suicide contemplation interval shorter than three hours showed increased time slowing, measured as shorter time production at 35 s (p = .011) and 43 s (p = .036). Delay discounting for rewards correlated with time estimation at 25 min (p = .02) and 90 s (p = .01). Time slowing correlated positively with suicidal ideation severity, independently of depression severity (p < .001). Perception of time slowing may influence both the intensity and the duration of the suicidal process. Time slowing may initially be triggered by intense psychological pain, then worsen the perception of inescapability in suicidal patients.

Altered resting-state functional network connectivity is associated with suicide attempt in young depressed patients

The purpose of this study was to investigate the changes in resting-state brain functional network connectivity (FNC) in young depressed patients with and without suicidal behavior, and the relationship between FNC and suicidal attempts in depressed youths using resting-state fMRI (RS-fMRI). We conducted independent component analysis (ICA) to identify intrinsically connected neural networks and analyze the alterations of intra- and inter-network connectivity using FNC analysis in 35 depressed youth with suicidal attempts (SU group), 18 patients without suicidal attempts (NSU group) and 47 healthy controls (HC), and investigate brain-behavior associations between the FNC coefficients and clinical behavior in the SU group. SU group showed significantly decreased internetwork connectivity between anterior default mode network (aDMN) and salience network (SN), as well as the right frontal-parietal network (rFPN). However, the internetwork connectivity between the SN and rFPN in SU group was higher than that in NSU group. Moreover, decreased aDMN-rFPN connectivity was negatively correlated with BHS scores, and the differences in SN-rFPN and aDMN-pDMN connectivity were negatively associated with the HAMD score in the SU group. Our findings may provide new insights into the patterns of functional organization in the brain of suicidal depressed patients.

Promising Neuroimaging Biomarkers in Depression

The neuroimaging has been applied in the study of pathophysiology in major depressive disorder (MDD). In this review article, several kinds of methodologies of neuroimaging would be discussed to summarize the promising biomarkers in MDD. For the magnetic resonance imaging (MRI) and magnetoencephalography field, the literature review showed the potentially promising roles of frontal lobes, such as anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). In addition, the limbic regions, such as hippocampus and amygdala, might be the potentially promising biomarkers for MDD. The structures and functions of ACC, DLPFC, OFC, amygdala and hippocampus might be confirmed as the biomarkers for the prediction of antidepressant treatment responses and for the pathophysiology of MDD. The functions of cognitive control and emotion regulation of these regions might be crucial for the establishment of biomarkers. The near-infrared spectroscopy studies demonstrated that blood flow in the frontal lobe, such as the DLPFC and OFC, might be the biomarkers for the field of near-infrared spectroscopy. The electroencephalography also supported the promising role of frontal regions, such as the ACC, DLPFC and OFC in the biomarker exploration, especially for the sleep electroencephalogram to detect biomarkers in MDD. The positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in MDD demonstrated the promising biomarkers for the frontal and limbic regions, such as ACC, DLPFC and amygdala. However, additional findings in brainstem and midbrain were also found in PET and SPECT. The promising neuroimaging biomarkers of MDD seemed focused in the fronto-limbic regions.

Longitudinal decreases in suicidal ideation are associated with increases in salience network coherence in depressed adolescents

BACKGROUND

Suicidal ideation (SI) is an important predictor of suicide attempt, yet SI is difficult to predict. Given that SI begins in adolescence when brain networks are maturing, it is important to understand associations between network functioning and changes in severity of SI.

METHODS

Thirty-three depressed adolescents were administered the Columbia-Suicide Severity Rating Scale to assess SI and completed resting-state fMRI at baseline (T1) and 6 months later (T2). We computed coherence in the executive control (ECN), default mode (DMN), salience (SN), and non-relevant noise networks and then examined the association between changes in brain network coherence and changes in SI severity from T1 to T2.

RESULTS

A greater reduction in severity of SI was associated with a stronger increase in SN coherence from T1 to T2. There were no associations between the other networks and SI.

LIMITATIONS

We cannot generalize our findings to more psychiatrically diverse samples. More time-points are necessary to understand the trajectory of SI and SN coherence change.

CONCLUSIONS

Our finding that reductions in SI are associated with increases in SN coherence extends previous cross-sectional results documenting a negative association between SI severity and SN coherence. The SN is involved in coordinating activation of ECN and DMN in response to salient information. Given this regulatory role of the SN, the association between SN coherence and SI suggests that adolescents with reduced SN coherence might more easily engage in harmful thoughts. Thus, the SN may be particularly relevant as a target for treatment applications in depressed adolescents.