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Record W2603602965 · doi:10.1093/schbul/sbx022.029

M30. Cortical Thickness Patterns of Cognitive Impairment in Schizophrenia

2017· article· en· W2603602965 on OpenAlexaff
Farena Pinnock, Lindsay C. Hanford, R. Walter Heinrichs

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsYork University
Fundersnot available
KeywordsCognitionSchizoaffective disorderPsychologySchizophrenia (object-oriented programming)Effects of sleep deprivation on cognitive performanceNeuroimagingPsychosisNeuropsychologyPsychopathologyDissociation (chemistry)NeuroscienceAudiologyPsychiatryMedicine

Abstract

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Background: Schizophrenia is characterized by both psychotic illness and cognitive impairment, but it is unclear whether they represent related yet distinct disease processes. There is evidence to suggest dissociation. For example, cognitive impairment occurs in schizophrenia patients during both active psychosis and symptom remission. However, the shared or nonshared neural underpinnings of cognition and psychotic psychopathology are also unclear despite findings of multi-focal cortical thinning in the illness. Accordingly, this study sampled patients and controls with a broad range of cognitive ability to examine relations between cortical thickness and cognitive performance with and without the presence of psychotic illness. Our basic questions were: do regional thickness values primarily index the psychotic disease process or cognitive performance and to what extent do disease and performance interact? Methods: Cognitive functioning of patients diagnosed with schizophrenia or schizoaffective disorder (n = 61) and healthy controls (n = 40) were assessed with the MATRICS Consensus Cognitive Battery (MCCB). Neuroimaging data were obtained with a 3T General Electric System MRI scanner, and cortical thickness was calculated using Freesurfer. General linear models were conducted to examine relations and interactions between cortical thickness, diagnosis, and cognition. Results: Cortical thickness and cognitive performance on MCCB subscales and overall composite score were positively correlated in 34 brain regions, predominantly in the frontal, parietal, and temporal brain areas, irrespective of diagnostic status. Patients showed the same cortical thickness-cognitive performance relationship as controls, but had significantly reduced thickness in 27/34 of these regions despite similar behavioral performance. An interaction of diagnosis, cognition, and cortical thickness was found in the parahippocampal and left caudal middle frontal gyri only. Lastly, there were several regions of reduced cortical thickness among patients with no corresponding relationship to cognitive performance. Conclusion: These findings suggest that despite their high rates of co-occurrence, cognitive impairment and psychosis may be partially independent pathologies of the schizophrenia disease process. Cortical thickness varies with cognition in both schizophrenia patients and healthy controls, but remains significantly reduced in patients. This occurs even when cognitive performance is largely equalized between patients and controls. These findings are consistent with recent neurogenetic research linking liability to schizophrenia with cortical abnormalities including thinning, reduced synaptic structure and excessive pruning. The results point to the importance of studying cognition and psychotic symptoms as potentially separable processes that may also represent independent treatment targets.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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
Published2017
Admission routes1
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