GABA and Negative Affect—Catatonia as Model of RDoC-Based Investigation in Psychiatry
Bibliographic record
Abstract
We were very pleased to read the excellent article by Taylor and colleagues,1 which has highlighted the central role of the GABAergic system in determining stress vulnerability and modulation of negative affect (NA) in schizophrenia. We fully agree that the relevance of GABAergic system goes far beyond schizophrenia, because GABAergic dysfunction might be responsible for a number of affective, behavioral, and cognitive symptoms through neurodevelopmental disturbance of emotional regulation across several mental disorders.1 Here, we propose that a first step toward better understanding of the transdiagnostic contributions of aberrant GABAergic system would be through investigating catatonia in the literal sense of the terms “psycho” and “motor.” Catatonia is one of the oldest syndromes described in clinical psychiatry which occurs in 9%–17% of acute mental disorders and is characterized by a triad of affective, motor, and behavioral symptoms.2 Catatonia can be classified as “catatonic schizophrenia” in ICD-10 and as “catatonia not otherwise specified,” that is, as residual category in DSM-5.3 In DSM-5, unlike in ICD-10, catatonia is also linked to other mental disorders or specific medical conditions. Still, there is intensive effort to recognize catatonia as an independent diagnostic entity in ICD-11.4 For about a decade, the RDoC initiative provides a platform for neuroscientific research of mental disorders based on dimensions of observable behavior and neurobiological measures.5–7 In line with this framework, there are two main reasons to support catatonia as a paradigmatic model for RDoC-based investigation of GABAergic system: (1) Clinical syndrome and its pathophysiological basis: The precise clinical description of former psychiatrists could achieve a good differentiation of catatonia as a psychomotor syndrome from other psychiatric disorders including both affective and schizophrenic psychoses. More recent researchers showed that catatonia characterized by its three symptom dimensions (motor, affective, and behavioral) is based on dysfunction of GABAergic cortical circuits.8–12 Targeting the GABAergic system in frontoparietal regions with lorazepam (positive allosteric modulation at the GABAA receptor)13,14 or GABAergic mediated electroconvulsive therapy (ECT)15 leads to an improvement of motor, affective and behavioral symptoms not only in schizophrenia, but also in autism and affective disorders.16 This is in line with Taylor and colleagues1 and their emphasis on the relation of GABA and NA as catatonic patients often experience/show extreme uncontrollable fear and anxiety from which they can be relieved by GABAergic drugs. Hence, the case of catatonia strongly extend the dimensional as well as the syndromal nature of GABA and NA beyond schizophrenia as emphasized by Taylor and colleagues.1 (2) Future directions: We expect that the different levels of the relation of GABA and NA can be extended even more in the future in the case of catatonia. There is a mechanistic animal model of catatonia, which will help us to understand genes, molecules, and cells of the GABAergic system in mice and men.17 Catatonic symptoms can be easily measured using instrumental assessments for detecting sensorimotor dysfunction and multimodal MRI.18 That’s what makes the investigation of aberrant circuits, physiology, and behavior associated with GABAergic dysfunction so convenient. Studying the dysfunction of the GABAergic system (dysbalance between GABAA and GABAB) in animal models and human beings will help to reduce the risk of failure in clinical trials (which we are still lacking).19 Multimodal MRI research on catatonia will provide important clues to the complex interplay between dysfunctions and dynamics of neural circuitry20 underlying sensorimotor function, behavior, affective processing, and cognition.7 In particular, we will better delineate the interaction between basal ganglia, cerebellar, and cortico-motor circuits, which are not solely responsible for sensorimotor function/dysfunction.7 Not to be forgotten are also first-person reports or citations of patients’ statements to investigate the structure of patients’ subjective (a priori) experience (eg, following a phenomenological approach)21 before and after development of catatonic symptoms.22 Therefore, we strongly endorse the notion that mental disorders must be understood as a dysfunction of individual neurotransmitter systems with a focus on specifically GABA and associated brain circuits (and not as rigid categories) to develop neurobiologically plausible therapies. Finally, catatonia can be defined as primarily “psycho” and “motor” disorder that is based on dysbalance between GABAergic and serotonergic as well as dopaminergic neurotransmission that essentially modulates both affective and motor systems, as well as their cortico-subcortical functional interplay.23,24 Pathophysiology- and dimension-based research framework on catatonia as proposed by RDoC initiative does not only open the door for developing more proper treatment of this devastating condition but also into the psycho-motor, for example, affective-motor and cognitive-motor mechanisms and functions of the healthy brain.25 The authors have declared that there are no conflicts of interest in relation to the subject of this commentary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".