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Record W4410005121 · doi:10.3389/fpsyg.2025.1606517

Editorial: Standardizing cognitive endophenotype profiling in bipolar disorder and schizophrenia

2025· editorial· en· W4410005121 on OpenAlexaboutno aff
Pau Soldevila-Matías, Renato de Filippis, Joan Vicent Sánchez‐Ortí, Inmaculada Fuentes-Durá, Rosa Ayesa‐Arriola, Patricia Correa‐Ghisays

Bibliographic record

VenueFrontiers in Psychology · 2025
Typeeditorial
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEndophenotypeBipolar disorderPsychologySchizophrenia (object-oriented programming)CognitionProfiling (computer programming)Schizoaffective disorderPsychiatryClinical psychologyCognitive psychologyPsychosis

Abstract

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The study of cognitive endophenotypes has gained prominence in psychiatric research as a means to elucidate the underlying mechanisms of severe mental illnesses such as bipolar disorder (BD) and schizophrenia (SCZ) (1,2). These endophenotypes, encompassing deficits in executive function, attention, and memory, have been consistently observed in both affected individuals and their firstdegree relatives, supporting their heritability and potential role in early risk detection (3,4).However, a significant challenge in the field remains the lack of consensus on standardized criteria for defining these cognitive markers, leading to discrepancies across studies (5). Recent advancements, such as the MICEmi framework (6), offer structured methodologies to enhance consistency and reliability in cognitive endophenotype profiling. Standardization in this domain has the potential to refine psychiatric nosology, improve early identification of at-risk individuals, and inform targeted interventions (7). Further research is necessary to validate these approaches across diverse populations and determine their predictive value for disease onset and progression. This Research Topic was developed to address the pressing need for standardization in cognitive endophenotype research for mental illnesses such as BD and SCZ. By promoting the adoption of structured methodologies like MICEmi, this collection seeks to consolidate previous findings, reduce methodological variability, and enhance the reproducibility of results. Through this initiative, we aim to advance the integration of cognitive endophenotypes into both clinical practice and research frameworks, ultimately improving diagnostic accuracy, refining classification systems, and informing personalized treatment strategies.The study conducted by Oscoz-Irurozqui and colleagues explored the link between cannabis use and genetic variability in endocannabinoid receptors in patients suffering from first episode psychosis (FEP). Authors examined 50 FEP patients of European ancestry (mean age 26.14 years, 76% male), classified as cannabis users (58%) or non-users. Two Single Nucleotide Polymorphisms (SNPs) were analyzed: CNR1 rs1049353 and CNR2 rs2501431.According to their results, cannabis users showed a trend toward more severe positive psychotic symptoms and better manipulative abilities. Moreover, carriers of the T allele in CNR1 rs1049353 had higher disorganization scores. The cognitive benefits of cannabis use on manipulative abilities were modified by CNR2 rs2501431, with G allele carriers performing better than AA carriers, while the opposite was seen in non-users. Although preliminary, these results suggest that CNR1 and CNR2 genetic variants influence symptom severity and cognitive performance in FEP, interacting with cannabis use (8).The paper published by Liao et al. investigated the effects of comorbid alexithymia on cognitive impairment in chronic schizophrenia, through a cross-sectional analysis of 695 patients with schizophrenia (464 males, 231 females), and assessing alexithymia using the Toronto Alexithymia Scale (TAS-20), cognitive function with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and psychiatric symptoms via the Positive and Negative Syndrome Scale (PANSS). Authors found a prevalence of 31.4% of patients having comorbid alexithymia, with a higher occurrence in males. Still, patients with alexithymia had significantly higher PANSS negative symptom and total scores. Moreover, patients suffering from alexithymia showed greater deficits in immediate memory, delayed memory, and language, reflected in lower RBANS scores. Therefore, regression analysis identified alexithymia as a predictor of language deficits and overall lower cognitive performance in schizophrenia.Authors concluded that patients with schizophrenia and comorbid alexithymia experience worse cognitive function than those without. Alexithymia, along with certain demographic factors, may contribute to cognitive impairment in chronic schizophrenia (9).In the third article included in our Research Topic, the group lead by Wang analyzed the role of negative cognitive biases in depression and their influencing factors, including subtype, age, gender, age of onset, family history, and education level. According to the reported results, depression was linked to increased negative attention, memory, interpretation, and rumination biases. Negative rumination bias was more pronounced in the melancholic subgroup than in the anxious subgroup. In addition, correlation analysis showed that negative rumination bias was associated with family history and age of onset. These findings enhance understanding of cognitive biases in depression and suggest that rumination-focused therapies should be tailored differently for melancholic and anxious depression. This could aid in developing personalized treatment approaches for major depressive disorder (10).Finally, Giralt-López and colleagues examined whether Theory of Mind (ToM) deficits are trait-or state-dependent by analyzing the influence of clinical vulnerability markers (basic symptoms and psychotic-like experiences) in unaffected siblings of individuals with schizophrenia. They enrolled 65 individuals (38 schizophrenia patients, 27 healthy siblings), and administrated the Hinting Task (HT) to explore the ToM impairment, the Frankfurt Complaint Questionnaire (FCQ) for basic symptoms, the Community Assessment of Psychic Experiences (CAPE) for psychotic-like experiences, and the Family Interview for Genetic Studies for family history. They found that patients had significantly lower ToM scores than their siblings, but this difference became non-significant after adjusting for clinical vulnerability markers. Among healthy siblings, higher depressive symptoms (FCQ) and negative psychotic-like experiences (CAPE) were associated with poorer ToM performance. These results suggest that ToM deficits are not exclusive to SZ but also linked to clinical vulnerability factors. ToM could serve as an endophenotypic marker, helping identify individuals at higher risk due to genetic predisposition (11).The standardization of cognitive endophenotype profiling represents a critical step forward in psychiatric research, particularly for BD and SCZ. By addressing the methodological inconsistencies that have hindered progress in the field, structured approaches like the ones described in this collection offer a promising avenue for enhancing the reliability and reproducibility of findings. This Research Topic highlights the importance of establishing consensus-driven criteria to improve early risk detection, refine psychiatric classification, and facilitate the development of personalized treatment strategies. Future studies should focus on validating these standardized methodologies across diverse populations and evaluating their predictive utility for disease onset and progression.Ultimately, the integration of standardized cognitive endophenotype profiling into clinical and research settings has the potential to transform our understanding of severe mental illnesses and improve patient outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.017
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.037
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0030.001
Science and technology studies0.0030.004
Scholarly communication0.0060.005
Open science0.0050.001
Research integrity0.0170.019
Insufficient payload (model declined to judge)0.0150.009

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.

Opus teacher head0.007
GPT teacher head0.306
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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