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Parkinson's disease (PD) patients receiving dopaminergic treatment may experience bursts of creativity. Although this phenomenon is sometimes recognized among patients and their clinicians, the association between dopamine replacement therapy (DRT) in PD patients and creativity remains underexplored. It is unclear, for instance, whether DRT affects creativity through convergent or divergent thinking, idea generation, or a general lack of inhibition. It is also unclear whether DRT only augments pre-existing creative attributes or generates creativity de novo . Here, we tested a group of PD patients when “on” and “off” dopaminergic treatment on a series of tests of creative problem-solving (Alternative Uses Task, Compound Remote Associates, Rebus Puzzles), and related their performance to a group of matched healthy controls as well as to their pre-PD creative skills and measures of inhibition/impulsivity. Results did not provide strong evidence that DRT improved creative thinking in PD patients. Rather, PD patients “on” medication showed less flexibility in divergent thinking, generated fewer ideas via insight, and showed worse performance in convergent thinking overall (by making more errors) than healthy controls. Pre-PD creative skills predicted enhanced flexibility and fluency in divergent thinking when PD patients were “on” medication. However, results on convergent thinking were mixed. Finally, PD patients who exhibited deficits in a measure of inhibitory control showed weaker convergent thinking while “on” medication, supporting previous evidence on the importance of inhibitory control in creative problem-solving. Altogether, results do not support the hypothesis that DRT promotes creative thinking in PD. We speculate that bursts of artistic production in PD are perhaps conflated with creativity due to lay conceptions of creativity (i.e., an art-bias). 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.