IntroductionSchizophrenia is a neurodevelopmental disorder with systemic inflammation that is thought to play a role in its pathophysiology. The pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α) has been reported to be dysregulated in individuals with schizophrenia, but its relationship with brain volume changes remains inconsistent. This study aimed to analyze differences in TNF-α levels and brain volume (hippocampus, prefrontal cortex, and lateral ventricles) and their correlations in schizophrenia patients compared to healthy controls.MethodsThis analytical cross-sectional investigation was conducted on 50 schizophrenia patients and 50 healthy Batak controls at the Prof. M. Ildrem Provincial Mental Hospital, Medan. Serum TNF-α levels were measured by ELISA, while brain structure volume was measured by 1.5 T MRI and analyzed using AnalyzePro software.ResultsTNF-α levels in the schizophrenia group were significantly lower (3.35 pg/dl) than in the control group (16.90 pg/dl). No significant differences in hippocampal and prefrontal cortex volume were found between the groups. However, lateral ventricle volume was significantly larger in schizophrenia. Correlation analysis showed a weak negative relationship between TNF-α and prefrontal cortex volume only in the schizophrenia group.DiscussionThe finding of low TNF-α in schizophrenia supports the complexity of immune dysfunction in schizophrenia, which may be influenced by medication or disease phase. Consistent ventricular enlargement reinforces previous neuroanatomical findings. The association of TNF-α with the prefrontal cortex only within the schizophrenia cohort indicates a specific interaction between inflammation and brain morphology in this patient population.ConclusionsThe findings support TNF-α dysregulation in schizophrenia, albeit at lower levels. Lateral ventricular enlargement was consistently found, while changes in hippocampal and prefrontal cortex volume may be more subtle in this population. The negative correlation between TNF-α and the prefrontal cortex in schizophrenia suggests a potential role for inflammation in the pathology of this brain region.
ESMR: a process framework for calibration dynamics across psychosomatic and psychiatric phenomena
The brain can be modeled as a generative system that predicts bodily and environmental input and reduces mismatch through updating and action. From this perspective, stress is treated not as prediction error per se, but as a condition in which mismatch persists without sufficient recalibration. Health is therefore defined not as the absence of symptoms, but as the preservation of recalibrability, whereas psychopathology is conceptualized as the progressive fixation of recalibration failure. This paper proposes ESMR as a four-layer, two-timescale process framework for comparing psychosomatic conditions, psychiatric disorders, and psychosis-level phenomena in terms of partially shared failures of recalibration rather than as a single disease continuum. ESMR distinguishes E (Embodied Constraints), S (Salience Calibration), M (Metacognitive Model Revision), and R (Reality-Grounding Interface) across learning (L) and development (D). Development is treated not merely as slower accumulation of prior learning, but as change in the biological, representational, and self-regulatory architecture that alters both what forms of recalibration are possible and the background conditions under which learning can be taken up. A central distinction is that M concerns whether corrective information can be converted into updating, whereas R concerns whether already understood correction can be adopted as self-relevant reality under embodied, practical, and interpersonal constraints, with downstream transfer treated as a consequence rather than part of the R core. The framework predicts layer-specific cross-signatures rather than a single undifferentiated burden effect and outlines a staged empirical program using framing, disconfirmation, reality-grounding, and embodied/interoceptive tasks. ESMR is offered as a revisable, clinically oriented intermediate framework rather than a completed computational model.
Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers
Background and hypothesisAssessing schizophrenia risk factors is crucial for developing early preventive interventions. We hypothesized that unaffected siblings, who share high genetic risk, exhibit neuroanatomical signatures similar to affected patients, potentially reflecting early pathogenic processes.Study designTo overcome single-center limitations, we analyzed 1,018 participants from five independent, public databases. Brain MRIs were standardized via voxel-based morphometry, and covariate-adjusted z-scores were calculated for regional volumes. An ensemble support vector machine (SVM) approach, incorporating multiple models to ensure robustness, was employed to extract a multidimensional brain signature, from which a schizophrenia-like score (SPS) was derived.ResultsThe ensemble SVM achieved high classification performance (AUC = 0.99861). Across all databases, patients exhibited consistent volume reductions in frontal, temporal, insular, and thalamic regions, alongside globus pallidus enlargement. Notably, unaffected siblings were 3.8 times more likely to show brain morphological similarities to patients than were healthy controls. Furthermore, we identified a novel imaging phenotype in siblings: increased ventral striatal volume, which positively correlated with the SPS. This feature, absent in established schizophrenia, suggests a potential compensatory mechanism or a transient developmental marker of risk.ConclusionApplying machine learning to large-scale, multi-site neuroimaging data effectively identifies structural endophenotypes. Our findings highlight unique structural characteristics, specifically the enlarged ventral striatum, as a critical biological metric for identifying high-risk individuals before clinical onset.
The many manifestations of magical thinking: a systematic review
Magical thinking (MT) involves beliefs that thoughts or actions can influence events in unrealistic ways. While MT is integral to obsessive compulsive disorder, and reflected in the cognitive features of schizophrenia, it is observable across the general population in various forms. Given its prevalence and potential relevance to a range of psychiatric conditions, understanding more about what may predispose an individual to MT, and how it may in some cases culminate in psychological distress or dysfunction would be helpful. This paper reports a systematic review of studies investigating MT, encompassing both magical ideation and thought-action fusion specifically, across the disciplines of psychiatry and psychology, to shed further light on the likely predisposing factors and behavioural consequences of MT, its potential neurobiological underpinnings, and role in psychiatric symptomatology. After exclusions, 191 studies were identified that explored MT in association with a diverse array of secondary topics, from gambling compulsions to childhood trauma, within both clinical and non-clinical samples, across a range of cultural contexts. On an intra-individual level, MT demonstrates numerous cognitive and emotional correlates, and on a societal level it may influence both social custom and religious tradition. A synthesis of the available evidence uncovers unexplored relationships with social cognition and mental health, and future research investigating its emerging relationships with stress, mood and social connection, may uncover functions beyond those exhibited by a simple marker of psychopathology.
<![CDATA[Why schizophrenia shortens life: smoking, metabolic risks, and care gaps drive a 15-year mortality loss—and clinicians can help close it.]]>
Deep Brain Stimulation for Treatment-Resistant Schizophrenia
Conditions: Treatment Resistant Disorders
Interventions: Device: Medtronic Percept with SensSight Deep Brain Stimulation System
Sponsors: Johns Hopkins University
Not yet recruiting
Interventions: Device: Medtronic Percept with SensSight Deep Brain Stimulation System
Sponsors: Johns Hopkins University
Not yet recruiting
NST-SPARK Open-Label Phase 1 STTR (IGNITE)
Conditions: Schizophrenia / Schizoaffective Disorder
Interventions: Device: NST-SPARK
Sponsors: North Shore Therapeutics, Inc.; Northwell Health; University of Pennsylvania
Not yet recruiting
Interventions: Device: NST-SPARK
Sponsors: North Shore Therapeutics, Inc.; Northwell Health; University of Pennsylvania
Not yet recruiting
Confirmatory Trial of Gamma Neurofeedback to Improve Working Memory in Schizophrenia
Conditions: SCHIZOPHRENIA 1 (Disorder); Schizoaffecitve Disorder
Interventions: Behavioral: EEG Neurofeedback
Sponsors: University of California, San Diego
Not yet recruiting
Interventions: Behavioral: EEG Neurofeedback
Sponsors: University of California, San Diego
Not yet recruiting
<![CDATA[Analysis of speech, acoustics, and facial cues reveals early psychosis and suicide risk.]]>
<![CDATA[Computational phenotyping reveals prodromal psychosis clues, connects biomarkers to symptoms, and shows why stress and cannabis reduction matters.]]>