Psychotic depression (PD) is increasingly recognized as a distinct psychiatric condition with unique neurobiological features. To identify neuroimaging biomarkers of PD, we systematically reviewed evidence across structural magnetic resonance imaging (MRI), functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopy, following PRISMA guidelines. PubMed, MEDLINE, and Embase were searched through December 2025; 28 of 1195 records met PICOS-based inclusion criteria. PD showed distinct structural and functional abnormalities versus healthy controls and non-psychotic depression, particularly involving lower gray matter volume in prefrontal, insular, anterior cingulate, and temporal regions; aberrant temporoparietal and frontoparietal function; altered dorsal anterior cingulate metabolism; and default mode network-cortical hypoconnectivity. Pharmacological and electroconvulsive therapies may partially reverse frontotemporal and visual network abnormalities. Evidence for subcortical volume changes was inconsistent. Despite small, heterogeneous studies precluding meta-analysis, symptom-correlated fronto-insular-limbic, and precuneus alterations may represent potential biomarkers warranting further investigation.

Bio-psycho-social models of psychopathology have been long proposed. Despite this, few details have been identified in part due to the poor match between clinical phenomenology and diagnostic categories (e.g., overlapping symptoms and comorbidity). In the present paper, the author reviews the evidence for three factors thought to affect vulnerability across diagnoses: mesocorticolimbic function, early life adversity, and early propensities to high emotional and behavioral reactivity. All three factors now have compelling support but their effects on clinical outcomes are small to moderate in isolation. In comparison, our preliminary studies suggest that combining these factors yields a powerful model that predicts liability to all common psychiatric disorders. Narrower mental health problems are identified when the model incorporates high brain reactivity to disorder-specific stimuli. Since the combination of these factors accounts for more variance than the sum of their parts, this novel, hierarchical model might explain how the structure of psychopathology arises.

Background: Depression is a common psychological and behavioural symptom experienced by people with migraines. However, the underlying neural mechanisms of depressive symptoms in this population are unclear. Methods: In this study, we investigated the neural mechanisms underlying excitation-inhibition imbalance in patients with migraine accompanied by depressive symptoms (MD) using the Hurst exponent. We used Analysis of covariance to compare differences in the Hurst exponent between patients with MD, patients with migraine accompanied by non-depressive symptoms (MnD), and healthy controls (HC). Spearman's correlation analysis was used to calculate the correlation between the Hurst exponent of different brain regions and clinical variables. Results: Compared with patients with migraine accompanied by MnD, patients with MD showed an increased Hurst exponent in the left lingual gyrus and medial superior frontal cortex. Compared with HC, MD showed an increased Hurst exponent in the right cerebellum crus 1 and right lingual gyrus. Compared with HC, MnD showed an increased Hurst exponent in the left cerebellum crus 2 and medial superior frontal cortex. The Hurst exponent of the medial superior frontal cortex in patients with migraine was significantly positively correlated with depression scores (r = 0.3501, p = 0.0001). Conclusions: Our study has identified imaging features of excitatory-inhibitory imbalance in MD. Imbalances in excitation and inhibition in the medial superior frontal cortex may serve as a promising candidate imaging marker for diagnosing and intervening in depressive symptoms in migraine.

Background: Attention-deficit/hyperactivity disorder (ADHD) is characterized by emotional dysregulation, such as emotional instability, and by non-emotional symptoms, including inattention. These symptom domains are dimensionally distributed in the general population and are hypothesized to reflect both overlapping and distinct structural and functional features of cortical and subcortical brain networks. Methods: We applied a dimensional symptom approach in a sample of adults with ADHD (n = 71) and healthy controls (n = 59) to investigate structural brain correlates of inattention and emotional instability, as measured by the Brown Attention-Deficit Disorder Scales. Structural magnetic resonance imaging (MRI) data were analysed to examine cortical and subcortical associations with these symptom dimensions. Performance in an emotional Stroop task was assessed in conjunction with MRI acquisition in a subsample to explore neural correlates of emotion-related cognitive control. Data were collected between 2012 and 2017. Results: Emotional instability was negatively associated with nucleus accumbens volume, independent of inattention symptoms and ADHD diagnosis (β = -0.29, p = 0.016). Additionally, while controlling for inattention symptoms, a negative correlation between emotional instability and cortical surface area was found in a cluster comprising the left dorsolateral prefrontal cortex and right medial/superior frontal cortex. Moreover, cortical thickness in the lateral orbitofrontal cortex was positively associated with performance in the Stroop task in ADHD (r = 0.44, p = 0.027) while being negatively associated in healthy controls (r = -0.40 p = 0.028). Limitations: Medication use within the ADHD group might influence brain-symptom-associations. The cross-sectional design of the study precludes conclusions about causality. Conclusions: These findings suggest that structural alterations in the nucleus accumbens are more strongly related to emotional, rather than attentional symptoms after accounting for their shared variance. Furthermore, altered prefrontal and orbitofrontal morphology may underlie impairments in emotional regulation, supporting the role of frontostriatal circuits in the dimensional expression of emotional instability across clinical and non-clinical populations.

Thoughts are traditionally classified by their cognitive contents, such as task-related versus task-unrelated or internally versus externally oriented. However, content-based distinctions often lack diagnostic specificity in psychiatric disorders, where similar thought contents may manifest across conditions such as schizophrenia and major depressive disorder. Here, we propose a complementary framework that characterizes thoughts by their dynamics, the pattern of changes in the thought contents over time-that is, their duration, speed, and temporal precision. Converging evidence in healthy individuals shows that distinct thought dimensions map onto specific neural timescales, ranging from fine-grained oscillatory phase dynamics to coarse-grained temporal windows. Extending this framework to psychopathology, schizophrenia is characterized by temporal imprecision of thought contents across shorter and longer timescales, yielding fragmented and poorly differentiated thoughts. In contrast, major depressive disorder is marked by abnormally prolonged timescales with abnormal slowness leading to rarely changing thought contents, e.g., ruminations. We propose that the timescales of thought contents and, more generally, thought dynamics may serve as a promising marker for the differential diagnosis of thought disturbances in mental disorders.