Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample

Gur, Ruben C.; Butler, Ellyn R.; Moore, Tyler M.; Rosen, Adon F.G.; Ruparel, Kosha; Satterthwaite, Theodore D.; Roalf, David R.; Gennatas, Efstathios D.; Bilker, Warren B.; Shinohara, Russell T.; Port, Allison M.; Elliott, Mark A.; Verma, Ragini; Davatzikos, Christos; Wolf, Daniel H.; Detre, John A.; Gur, Raquel E.

doi:10.1093/cercor/bhaa282

Cited by 28 publications

(24 citation statements)

References 112 publications

(154 reference statements)

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“…Signature 1 was associated with PS status and with poorer cognition across groups, consistent with prior work linking reduced brain volume, especially in fronto-temporal regions, to worse cognitive performance, and with evidence of impaired cognition in SZ and at-risk states (17)(18)(19). Overall, signature 1 captures a more typical/prevalent SZ-related signature that is also more closely linked to known clinical risk factors in the general population.…”

Section: Differences Between Signaturessupporting

confidence: 79%

“…We hypothesized that SZ neuroanatomical signatures would be replicated in independent schizophrenia samples, and would be more common and prominent in the psychosis-spectrum group from the non-help-seeking PNC youth sample. We expected signature 1 but not signature 2 to be associated with poorer cognition, based on the known relationship between reduced gray matter volume (observed only in signature 1) and worse cognition (17)(18)(19). Given that PRSSZ was defined using a large SZ sample encompassing all potential SZ subtypes (20,21) we hypothesized that both signatures would show elevated PRSSZ.…”

Section: Introductionmentioning

confidence: 99%

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Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

Chand

Singhal

Dwyer

et al. 2022

Preprint

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The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

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Section: Differences Between Signaturessupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

Chand

Singhal

Dwyer

et al. 2022

Preprint

Self Cite

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“…Additional analyses were run using the Philadelphia Neurodevelopmental Cohort (PNC) to illustrate the findings in brain MRI data. Sample details, neuroimaging protocols, and processing can be found in Calkins et al ( 2015 ), Gur et al ( 2021 ), and Satterthwaite et al ( 2014 ). Briefly, participants ages 8–22 were recruited through their primary care providers in the Philadelphia area.…”

Section: Risks Of Using An Modified Brain Age Gapmentioning

confidence: 99%

Pitfalls in brain age analyses

Butler

Chen

Ramadan

et al. 2021

Human Brain Mapping

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Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

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“…Intelligence differences do not occur in isolation, being permeated by other human behaviors and environmental factors. The extended P-FIT (ExtPFIT) was formulated in Gur et al (2021), and its generalizability can be tested in a ML-based framework. Other neuroscientific theories and extensions will probably emerge in the future.…”

Section: Future Workmentioning

confidence: 99%

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Vieira¹,

Pamplona

Fachinello

et al. 2021

Preprint

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Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

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Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample

Cited by 28 publications

References 112 publications

Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

Pitfalls in brain age analyses

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

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