Corticostriatal connectivity fingerprints:Probability maps based on resting-state functional connectivity

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.status: publishe

Temporal characteristics of the influence of punishment on perceptual decision making in the human brain

Perceptual decision making is the process by which information from sensory systems is combined and used to influence our behavior. In addition to the sensory input, this process can be affected by other factors, such as reward and punishment for correct and incorrect responses. To investigate the temporal dynamics of how monetary punishment influences perceptual decision making in humans, we collected electroencephalography (EEG) data during a perceptual categorization task whereby the punishment level for incorrect responses was parametrically manipulated across blocks of trials. Behaviorally, we observed improved accuracy for high relative to low punishment levels. Using multivariate linear discriminant analysis of the EEG, we identified multiple punishment-induced discriminating components with spatially distinct scalp topographies. Compared with components related to sensory evidence, components discriminating punishment levels appeared later in the trial, suggesting that punishment affects primarily late postsensory, decision-related processing. Crucially, the amplitude of these punishment components across participants was predictive of the size of the behavioral improvements induced by punishment. Finally, trial-by-trial changes in prestimulus oscillatory activity in the alpha and gamma bands were good predictors of the amplitude of these components. We discuss these findings in the context of increased motivation/attention, resulting from increases in punishment, which in turn yields improved decision-related processing