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Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of ‘risk calculators’ for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples.
We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting ‘psychosis risk’ enriched community samples. We assessed 632 participants age 8–21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines.
Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70.
Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.
Structural brain abnormalities have been described in individuals with an
at-risk mental state for psychosis. However, the neuroanatomical
underpinnings of the early and late at-risk mental state relative to
clinical outcome remain unclear.
To investigate grey matter volume abnormalities in participants in a
putatively early or late at-risk mental state relative to their
prospective clinical outcome.
Voxel-based morphometry of magnetic resonance imaging data from 20 people
with a putatively early at-risk mental state (ARMS–E group) and 26 people
with a late at-risk mental state (ARMS–L group) as well as from 15
participants with at-risk mental states with subsequent disease
transition (ARMS–T group) and 18 participants without subsequent disease
transition (ARMS–NT group) were compared with 75 healthy volunteers.
Compared with healthy controls, ARMS–L participants had grey matter
volume losses in frontotemporolimbic structures. Participants in the
ARMS–E group showed bilateral temporolimbic alterations and subtle
prefrontal abnormalities. Participants in the ARMS–T group had prefrontal
alterations relative to those in the ARMS–NT group and in the healthy
controls that overlapped with the findings in the ARMS–L group.
Brain alterations associated with the early at-risk mental state may
relate to an elevated susceptibility to psychosis, whereas alterations
underlying the late at-risk mental state may indicate a subsequent
transition to psychosis.
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