Modelling negative symptom domains neurobiology: an observational transdiagnostic, translational study

Technical Summary: Modelling Negative Symptom Domains Neurobiology

Problem Statement
Negative symptoms (NS)—including anhedonia, avolition, asociality, blunted affect, and alogia—are a core dimension of psychopathology across severe mental illnesses (SMI) such as schizophrenia, bipolar disorder, major depressive disorder, and neurocognitive disorders like dementia. Unlike positive symptoms, NS represent a reduction or loss of fundamental psychological functions and are strongly associated with poor functional outcomes, impaired quality of life, and limited response to current therapeutic approaches.

Current psychiatric nosology, largely rooted in a neo-Kraepelinian framework, treats mental disorders as discrete entities with latent pathological processes. However, empirical evidence suggests substantial symptom overlap, high comorbidity, and heterogeneity within diagnoses, challenging the notion that traditional categories map neatly onto distinct biological mechanisms. Furthermore, NS remain a significant unmet treatment need. There is a critical need to characterize these symptoms transdiagnostically, moving beyond diagnostic boundaries to understand shared versus disorder-specific mechanisms, particularly focusing on avolition (the reduction in motivation) as a potential central driver of the negative symptom spectrum.

Methodology
This study is an observational, single-center, transdiagnostic, and translational protocol designed to characterize persistent negative symptoms and their underlying biological correlates.

• Study Design & Setting: Conducted at the Socola Institute of Psychiatry (Iași, Romania), the study recruits approximately 300 participants (targeting ~60 per group) across five diagnostic categories: schizophrenia, schizoaffective disorder, persistent delusional disorder, major depressive disorder, bipolar affective disorder, autism spectrum disorder, and dementia.
• Inclusion/Exclusion: Adults (18–65) with a primary ICD-10 diagnosis in the specified categories and a minimum score of 20 on the negative symptom subscale of the Positive and Negative Syndrome Scale (PANSS) are eligible. Exclusion criteria include inability to speak Romanian, concurrent enrollment in other clinical trials, or somatic comorbidities influencing study outcomes.
• Data Collection Protocol:
  • Psychometric Assessment: Participants undergo a battery of validated scales, including the Brief Negative Symptom Scale (BNSS), PANSS, Hamilton Depression Rating Scale (HAM-D), Barnes Rating Scale (BARS), Personal and Social Performance Scale (PSP), Defeatist Performance Scale (DPAS), Temporal Experience of Pleasure Scale (TEPS), and Brief Assessment of Cognition in Schizophrenia (BACS). BNSS interviews are audio-video recorded for inter-rater reliability.
  • Electrophysiology: An 8-minute resting-state EEG (alternating 2-minute blocks of eyes open/closed) is recorded to analyze event-related potentials and spectral power.
  • Biological Sampling: Venous blood samples (3 mL) are collected for genomic DNA isolation. Polygenic Risk Scores (PRS) for persistent negative symptoms are calculated via genome-wide microarray or whole-genome sequencing.
• Statistical Analysis: The analysis plan employs network theory to model the interrelations of negative symptoms. Key approaches include:
  • Network Analysis: Examining symptom structure, centrality (strength, closeness, betweenness), and connectivity across diagnostic groups to identify central hubs (e.g., avolition) and bridge symptoms.
  • Multimodal Integration: Linking network-derived symptom profiles with electrophysiological measures, biological markers (PRS), and neurocognitive/social cognitive performance.
  • Modeling: Using regression and structural equation models to investigate the role of avolition in shaping other symptoms and to test associations with biological markers, adjusting for covariates and multiple testing.

Key Contributions
The paper outlines a comprehensive protocol that integrates clinical, neurocognitive, electrophysiological, and genetic data to address the characterization of negative symptoms. Its primary contributions include:

1. Transdiagnostic Framework: Shifting focus from specific diagnostic categories to symptom domains (specifically NS) to identify shared mechanisms across SMI and related conditions.
2. Network-Theoretical Approach: Applying network analysis to treat psychopathology as emergent patterns of interacting symptoms rather than passive indicators of latent disease, specifically testing the centrality of avolition.
3. Multimodal Biomarker Integration: Combining polygenic risk scores, EEG data, and detailed phenotypic assessments to identify potential biological and electrophysiological markers associated with negative symptom severity and avolition.
4. Focus on Avolition: Specifically targeting avolition as a potential central node in negative symptom networks, hypothesizing that its improvement may ameliorate the broader spectrum of negative symptoms.

Results
As this document is a study protocol, no empirical results or statistical findings are reported. The text notes that:

• The protocol was developed in February 2024, finalized in August 2024, and updated in January 2025.
• Participant recruitment and data collection commenced on July 1, 2024, and were ongoing at the time of manuscript submission (January 2026).
• Data quality control and preliminary analyses are being performed concurrently with data collection.
• Final statistical analyses and result dissemination are planned for after the completion of the recruitment phase.

Significance and Claims
The authors claim that this study will provide critical insights into the characterization and underlying mechanisms of negative symptoms across psychiatric disorders. By focusing on avolition, reward processing, and their interaction with neurocognitive and social cognitive deficits, the study aims to:

• Identify potential biological and electrophysiological markers of negative symptoms.
• Guide the development of more precise assessment tools.
• Inform novel therapeutic strategies with a broad translational impact for improving outcomes in individuals with serious mental illness.

The study acknowledges limitations, including its single-center nature (potentially limiting generalizability), reliance on clinician-rated and self-report instruments (susceptible to bias), and potential inter-rater variability. However, the authors posit that the comprehensive, multimodal, and transdiagnostic approach provides a strong foundation for future research aimed at refining the biological and clinical understanding of negative symptoms.