2020
DOI: 10.1017/s0033291720003463
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Developmental influences on symptom expression in antipsychotic-naïve first-episode psychosis

Abstract: Background The neurodevelopmental model of psychosis was established over 30 years ago; however, the developmental influence on psychotic symptom expression – how age affects clinical presentation in first-episode psychosis – has not been thoroughly investigated. Methods Using generalized additive modeling, which allows for linear and non-linear functional forms of age-related change, we leveraged symptom data from a large sample of antipsychotic-naïve individuals with first-episode psyc… Show more

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Cited by 8 publications
(5 citation statements)
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“…Several brain regions implicated in psychosis show protracted developmental courses continuing through adolescence [53][54][55][56][57][58][59][60][61][62][63] , suggesting that morphometric alterations associated with psychosis risk vary with age. Indeed, there are developmental influences on psychotic symptom presentation [64][65][66][67][68] , perhaps driven by differences in regional brain changes. It is not fully understood how age-related patterns in brain morphometry in CHR differ from normal development.…”
Section: Introductionmentioning
confidence: 99%
“…Several brain regions implicated in psychosis show protracted developmental courses continuing through adolescence [53][54][55][56][57][58][59][60][61][62][63] , suggesting that morphometric alterations associated with psychosis risk vary with age. Indeed, there are developmental influences on psychotic symptom presentation [64][65][66][67][68] , perhaps driven by differences in regional brain changes. It is not fully understood how age-related patterns in brain morphometry in CHR differ from normal development.…”
Section: Introductionmentioning
confidence: 99%
“…To determine time periods in which significant change was occurring in each group, we used the R package gratia 130 to estimate a multivariate normal distribution whose vector of means and covariance were defined by the fitted GAM parameters to simulate 10,000 GAM fits and their first derivatives, generated at 0.1-year age intervals. Similar to previous publications 57 , 61 , 131 and in line with recent guidelines 132 , we defined significant intervals of age-related change in MRI measures as ages when the 95% confidence intervals of simulated GAM fits did not include zero.…”
Section: Methodsmentioning
confidence: 91%
“…To determine time periods in which significant change was occurring in each group, we used a multivariate normal distribution whose vector of means and covariance were defined by the fitted GAM parameters to simulate 10,000 GAM fits and their first derivatives, generated at 0.1-year age intervals. Similar to previous publications [85][86][87] and in line with recent guidelines, 88 we defined significant intervals of age-related change in MRI measures as ages when the 95% confidence intervals of simulated GAM fits did not include zero.…”
Section: Statisticsmentioning
confidence: 94%