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Record W3028177859 · doi:10.1093/schbul/sbaa031.224

S158. URBANICITY INDEX AND CORTICAL GYRIFICATION IN SCHIZOPHRENIA

2020· article· en· W3028177859 on OpenAlexaboutno aff
Vittal Korann, Umesh Thonse, Arpitha Jacob, Vaishnavi A. Patil, Sahana Shiri, Priyanka Devi, Bhargavi Nagendra, Mugdha Kunte, Ayushi Shukla, Anantha Padmanabha, Keshav Kumar, Shivaram Varambally, Ganesan Venkatasubramanian, Naren P. Rao

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGyrificationSchizophrenia (object-oriented programming)PsychologyClinical psychologyCensusPositive and Negative Syndrome ScaleGerontologyPsychiatryMedicinePsychosisEnvironmental healthPopulationCerebral cortex

Abstract

fetched live from OpenAlex

Abstract Background Urban birth and upbringing are considered to be risk factors for schizophrenia, but recent studies do not support the same. While several hypotheses are suggested, the pathogenic mechanisms are not known. Notably, no study has examined brain changes, if any, associated with an urban upbringing in schizophrenia. Hence, in this study, we examined the effect of urban upbringing on the cortical gyrification in schizophrenic patients. Methods We recruited 108 persons with DSM-IV schizophrenia and 74 healthy volunteers. Study participants underwent clinical assessments using Positive and negative syndrome scale, Calgary depression scale to measure severity of clinical symptoms. All participants were scanned using 3T MRI scanner and a high resolution T1 structural scan was obtained. Cortical gyrification measurements were conducted on these images using Freesurfer software. Statistical maps were generated in Query, design, Estimate, Contrast (QDEC) interface. A Monte Carlo Simulation (MCS) was run for FWE correction with the threshold 1.3 (p<0.05) in QDEC. Participants upbringing place was noted with respect to the place where they lived for the first 15 years of life. Based on Census India of 2011, the places were categorized into 1) rural 2) statutory town and 3) census town. These 3 groups and assigned values 1,2 and 3, respectively, which were then rated for each year of life (1–15) and urbanicity index was calculated using a previously used method (range from 15 to 45). A regression analysis was implemented in QDEC with age, sex, education and demean ICV as covariates to examine the relation between Urban upbringing and cortical gyrification index. Results In the overall population, HV had higher gyrification index in the left superior parietal cortex (p<0.008) than SCZ. There was a significant negative correlations between gyrification index and urbanicity index in left postcentral (p<0.0001), left insula (p<0.0001), left fusiform (p<0.0005), left rostral middle frontal (p<0.01), right supramarginal (p<0.0001), right fusiform (p<0.0001), and right superior temporal (p<0.005) cortices. Discussion The results indicate a significant effect of urbanicity on cortical gyrification in patients with SCZ as well as HV. The presence of deficits in frontal areas indicate the likely effect of urban upbringing on growth and maturation of the frontal cortex. Interestingly, the presence of difference in areas implicated in schizophrenia provides support to the possible increased risk of urban upbringing on schizophrenia. The preliminary evidence from this analysis provides the necessary rationale to further examine the impact of urban upbringing on brain structure/ function and the risk of developing schizophrenia.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.031
GPT teacher head0.240
Teacher spread0.209 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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