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Medication management in schizophrenia is a lengthy process, as the lack of clinical response can only be confirmed after at least 4 weeks of antipsychotic treatment at a therapeutic dose. Thus, there is a clear need for the discovery of biomarkers that have the potential to accelerate the management of treatment. Using resting-state functional MRI, we examined the functional connectivity of the ventral tegmental area (VTA), the origin of the mesocorticolimbic dopamine projections, in 21 healthy controls and 21 unmedicated patients with schizophrenia at baseline (pre-treatment) and after 1 week of treatment with the antipsychotic drug risperidone (1-week posttreatment). Group-level functional connectivity maps were obtained and group differences in connectivity were assessed on the groups' participant-level functional connectivity maps. We also examined the relationship between pre-treatment/1-week post-treatment functional connectivity and treatment response. Compared with controls, patients exhibited significantly reduced pre-treatment VTA/midbrain connectivity to multiple cortical and subcortical regions, including the dorsal anterior cingulate cortex (dACC) and thalamus. After 1 week of treatment, VTA/midbrain connectivity to bilateral regions of the thalamus was re-established. Pre-treatment VTA/midbrain connectivity strength to dACC was positively correlated with good response to a 6-week course of risperidone, whereas pre-treatment VTA/midbrain connectivity strength to the default mode network was negatively correlated. Our findings suggest that VTA/midbrain resting-state connectivity may be a useful biomarker for the prediction of treatment response.

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BACKGROUND AND PURPOSE:Depression occurs frequently in PD; however the neural basis of depression in PD remains unclear. The aim of this study was to characterize possible depressionrelated white matter microstructural changes in the thalamus of patients with DPD compared with those with NDPD.

To learn if limb-kinetic apraxia (LKA) is associated with Parkinson disease (PD), participants with PD (on medications) and control subjects performed finger tapping (FT), measuring movement speed, and performed coin rotation (CR), measuring precise coordinated but independent finger movements and speed. There were no group differences in FT, a measure of bradykinesia-rigidity, but CR rotation was impaired in PD. Thus, LKA, not related to bradykinesia-rigidity, is associated with PD.

Spatial covariance mapping can be used to identify and measure the activity of disease-related functional brain networks. While this approach has been widely used in the analysis of cerebral blood flow and metabolic PET scans, it is not clear whether it can be reliably applied to resting state functional MRI (rs-fMRI) data. In this study, we present a novel method based on independent component analysis (ICA) to characterize specific network topographies associated with Parkinson’s disease (PD). Using rs-fMRI data from PD and healthy subjects, we used ICA with bootstrap resampling to identify a PD-related pattern that reliably discriminated the two groups. This topography, termed fPDRP, was similar to previously characterized disease-related patterns identified using metabolic PET imaging. Following pattern identification, we validated the fPDRP by computing its expression in rs-fMRI testing data on a prospective case basis. Indeed, significant increases in fPDRP expression were found in separate sets of PD and control subjects. In addition to providing a similar degree of group separation as PET, fPDRP values correlated with motor disability and declined toward normal with levodopa administration. Finally, we used this approach in conjunction with neuropsychological performance measures to identify a separate PD cognition-related pattern in the patients. This pattern, termed fPDCP, was topographically similar to its PET-derived counterpart. Subject scores for the fPDCP correlated with executive function in both training and testing data. These findings suggest that ICA can be used in conjunction with bootstrap resampling to identify and validate stable disease-related network topographies in rs-fMRI.

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