Aug 2026· Biological Psychiatry· 0 citations· 98 references
Medicine
TL;DR
The latest developments in connectome-based modeling of the suicidal brain can not only inform neurobiological mechanisms but also help to advance clinical translation, and a novel paradigm using normative models to develop a connectome-based suicide risk calculator is proposed.
Abstract
Suicide represents a global public health crisis, claiming over 700,000 lives worldwide every year. Deep understanding of the neurobiological mechanisms will enable objective risk assessment and more effective prevention of suicide. Recent advances in neuroimaging have revealed that suicidal thoughts and behaviors are associated with disruptions in both structural and functional brain connectome organization. In addition, brain connectome profiles may represent "fingerprints" that capture individual variability in suicidality, offering a transformative framework for personalized assessment. In this review, we discuss the latest developments in connectome-based modeling of the suicidal brain, which can not only inform neurobiological mechanisms but also help to advance clinical translation. We first summarize previous MRI-based connectomic findings, emphasizing convergent evidence for disrupted prefrontal-limbic-subcortical circuitry and reduced global network integration as core features of suicidality. Next, we review PET and EEG/MEG studies to highlight future directions for multimodal and integrative connectomic research. With a specific focus on individualized application, we also highlight connectome-based machine learning findings and propose a novel paradigm using normative models to develop a connectome-based suicide risk calculator. Finally, we present current challenges and future directions to improve brain connectome research in suicidality, emphasizing the imperative of using high-quality longitudinal cohorts for validation.
This Perspective discusses mechanistic approaches that focus on excitation-inhibition balance, reward and aversion circuits, hippocampal-prefrontal neuroplasticity, and processing of social cues, highlighting how these frameworks can elucidate drug mechanisms and predict treatment responses.
Amit Etkin, P. O’Donnell, K. Ressler et al.· Nature reviews. Drug discove...· 0 citations
With growing recognition that somatic symptoms constitute a clinically significant yet underexplored dimension of major depressive disorder (MDD), symptoms including sleep disturbances, gastrointestinal discomfort, pain, appetite changes, and fatigue have attracted increasing research attention. These symptoms are not only highly prevalent but are also closely associated with poor prognosis, treatment resistance, and elevated suicide risk. In this context, neuroimaging studies of the somatic manifestations of MDD have provided critical insights to advance precision diagnosis and individualized intervention. This review comprehensively integrates functional and structural neuroimaging findings on discrete somatic symptoms and somatic symptom clusters in MDD. It identifies the specific neural circuit abnormalities associated with individual symptom domains while elucidating shared pathophysiological mechanisms across symptom types, including disrupted interoceptive processing, pathological default mode network activity, and impaired sensory gating. On this basis, the review discusses the therapeutic and predictive implications of these findings and proposes future research directions oriented toward network-based brain-symptom mapping, transdiagnostic and longitudinal designs, and multimodal multilevel integration. By synthesizing existing evidence, this review provides a framework for understanding the neural substrates of somatic symptoms in MDD, developing somatic phenotype-based biomarkers and targeted neuromodulation therapies, and integrating systems-level neuroimaging into precision psychiatry to advance biologically informed diagnosis/treatment.
Results indicate that multi-omics integration, in addition to explaining the molecular architecture of MDD, also characterizes patient subgroups with pathophysiological mechanisms, dimensions of symptoms, and disease treatment, which demonstrates that there is a shift in psychiatry toward a more mechanistic approach.
E. Amjad, B. Sokouti· OBM Neurobiology· 0 citations
Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for identifying individuals before symptom onset are lacking. Here, we combine functional neuroimaging with computational modeling to identify a mechanistic biomarker of depression risk. In a population-based adolescent cohort (IMAGEN, N = 1332), we found that weakened neural representations of emotional signals were linked to depressive symptoms. Perturbation experiments in a brain-aligned deep learning model showed that this deficit reflects overregularized emotion perception, producing a negative perceptual bias. A neurocomputational signature of this mechanism predicted depression symptom onset up to 4 years later at the IMAGEN follow-up (N = 725), was associated with both a genetic-risk variant and polygenic risk for depression, and improved depression classification in a patient cohort (STRATIFY, N = 411). These findings suggest a possible mechanism linking genetic vulnerability to altered emotion perception and future depression, and propose a predictive computational marker with potential for early detection and prevention.
Han Lu, Xiaoqian Yan, Benjamin Becker et al.· Science Advances· 0 citations
Major depressive disorder (MDD) is characterized by dysfunction in higher-order cortical regions involved in emotional and cognitive processes; however, its neurobiological basis remains unclear. Pharmacological treatments, including esketamine and sertraline, produce rapid antidepressant effects. We investigated the local intrinsic neural dynamics underlying these antidepressant effects using intrinsic neural timescales (INT), which measure the duration and capacity of information integration in localized brain regions.
A total of 57 healthy controls and 41 patients with MDD were included. All patients with MDD received six intravenous infusions of esketamine (0.25 mg/kg) and two weeks of sertraline treatment. All participants underwent resting-state fMRI to quantify INT alterations at the voxel and brain network levels, followed by spatial correlation analyses linking autocorrelation metrics of INT signals to transcriptomic data from the Allen Human Brain Atlas and PET-derived neurotransmitter receptor maps. The spatial correlation between PLS2 scores and case–control t-statistic maps did not pass rigorous spin permutation testing (
r
= 0.484,
p
= 0.068) and did not meet the conventional significance threshold of 0.05. Accordingly, all subsequent enrichment analyses based on PLS2 results are presented as exploratory preliminary observations for hypothesis generation.
Compared with healthy controls, patients with MDD exhibited significantly elevated INT levels in the left and right precuneus. Following treatment, patients with MDD showed significantly increased INT in the left occipital midline region and left calcarine cortex. At the brain network level, INT levels were significantly reduced in the default mode network (DMN) in patients with MDD compared with healthy controls. Compared with the pre-treatment state, the cerebellar network (CN) showed significantly elevated INT levels after treatment. Partial least squares regression analysis suggested potential associations between INT alterations and spatial gene expression gradients particularly those associated with immune responses, hormonal regulation, neutrophils, regulatory T cells, and glutamatergic synapses. Cell-type enrichment analysis identified excitatory and inhibitory neurons as key cellular contributors. Alterations in INT also correlated with cortical 5-HT1b and NAT receptor density, suggesting a role for inhibitory neurotransmission in temporal integration deficits.
This study advances understanding of treatment-related brain abnormalities in patients with MDD from the perspective of local neural dynamics. These findings support a multiscale pathophysiological framework involving brain connectivity dynamics, molecular architecture, and neurochemical regulation in MDD.
Xiang Liu, Yuanzhi He, Lifeng Li et al.· BMC Psychiatry· 0 citations
BACKGROUND
Subcortical regions are widely implicated in the pathological mechanisms and treatment of schizophrenia, and accumulating evidence, including our prior findings, suggests that subcortical functional dysconnectivity is closely associated with treatment response. Accordingly, the present study aimed to examine the relationship between the subcortical functional connectivity (FC) and treatment outcomes in schizophrenia using multivariate analytical approaches and machine learning algorithms.
METHODS
One hundred and nineteen individuals with first-episode schizophrenia were recruited for this study. All patients underwent MRI scanning and completed assessments with the Positive and Negative Syndrome Scale (PANSS) at baseline and at follow-up after 12 weeks of antipsychotic medication. We employed partial least squares analysis to explore the multivariate associations between changes in subcortical FC (∆FC) and changes in symptom severity (∆PANSS). In addition, a machine learning algorithm was used to predict the antipsychotic treatment outcome based on the distinctive subcortical FC pattern at baseline.
RESULTS
We identified a distinctive subcortical FC pattern dominated by the striatum that was associated with overall treatment outcomes in first-episode schizophrenia. Furthermore, the reduction in PANSS total scores predicted using baseline subcortical FC patterns was positively correlated with the actual reduction in PANSS total scores following antipsychotic treatment.
CONCLUSION
These results indicate that the distinctive subcortical FC pattern holds promise as a biomarker for schizophrenia, supporting individualized treatment approaches and facilitating early intervention to improve clinical outcomes.
C. Hou, Huan Huang, Sisi Jiang et al.· Schizophrenia Research· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
