The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

Cognitive deficits are recognized in Parkinson's disease. Understanding cognitive functions mediated by the striatum can clarify some of these impairments and inform treatment strategies. The dorsal striatum, a region impaired in Parkinson's disease, has been implicated in stimulus-response learning. However, most investigations combine acquisition of associations between stimuli, responses, or outcomes (i.e., learning) and expression of learning through response selection and decision enactment, confounding these separate processes. Using neuroimaging, we provide evidence that dorsal striatum does not mediate stimulus-response learning from feedback but rather underlies decision making once associations between stimuli and responses are learned. In the experiment, 11 males and 5 females (mean age 22) learned to associate abstract images to specific button-press responses through feedback in Session 1. In Session 2, they were asked to provide responses learned in Session 1. Feedback was omitted, precluding further feedback-based learning in this session. Using functional magnetic resonance imaging, dorsal striatum activation in healthy young participants was observed at the time of response selection and not during feedback, when greatest learning presumably occurs. Moreover, dorsal striatum activity increased across the duration of Session 1, peaking after most associations were well learned, and was significant during Session 2 where no feedback was provided, and therefore no feedback-based learning occurred. Preferential ventral striatum activity occurred during feedback and was maximal early in Session 1. Taken together, the results suggest that the ventral striatum underlies learning associations between stimuli and responses via feedback whereas the dorsal striatum mediates enacting decisions.

Parkinson's disease (PD) is a progressive neurological disorder that has no reliable biomarkers. The aim of this study was to explore the potential of semi-automated sub-regional analysis of the striatum with magnetic resonance imaging (MRI) to distinguish PD patients from controls (i.e., as a diagnostic biomarker) and to compare PD patients at different stages of disease.With 3 Tesla MRI, diffusion- and T1-weighted scans were obtained on two occasions in 24 PD patients and 18 age-matched, healthy controls. PD patients completed one session on and the other session off dopaminergic medication. The striatum was parcellated into seven functionally disparate sub-regions. The segmentation was guided by reciprocal connections to distinct cortical regions. Volume, surface-based morphometry, and integrity of white matter connections were calculated for each striatal sub-region.Test-retest reliability of our volume, morphometry, and white matter integrity measures across scans was high, with correlations ranging from r = 0.452, p < 0.05 and r = 0.985, p < 0.001. Global measures of striatum such as total striatum, nucleus accumbens, caudate nuclei, and putamen were not significantly different between PD patients and controls, indicating poor sensitivity of these measures, which average across sub-regions that are functionally heterogeneous and differentially affected by PD, to act as diagnostic biomarkers. Further, these measures did not correlate significantly with disease severity, challenging their potential to serve as progression biomarkers. In contrast, a) decreased volume and b) inward surface displacement of caudal-motor striatum—the region first and most dopamine depleted in PD—distinguished PD patients from controls. Integrity of white matter cortico-striatal connections in caudal-motor and adjacent striatal sub-regions (i.e., executive and temporal striatum) was reduced for PD patients relative to controls. Finally, volume of limbic striatum, the only striatal sub-region innervated by the later-degenerating ventral tegmental area in PD, was reduced in later-stage compared to early stage PD patients a potential progression biomarker.Segmenting striatum based on distinct cortical connectivity provided highly sensitive MRI measures for diagnosing and staging PD.

We interpret these findings as support for the increasingly accepted notion that DS mediates cognitive control specifically and does not simply index cognitive effort per se.

Cognitive dysfunction is a feature of Parkinson's Disease (PD). Some cognitive functions are impaired by dopaminergic medications prescribed to address the movement symptoms that typify PD. Learning appears to be the cognitive function most frequently worsened by dopaminergic therapy. However, this result could reflect either impairments in learning (i.e., acquisition of associations among stimuli, responses, and outcomes) or deficits in performance based on learning (e.g., selecting responses). We sought to clarify the specific effects of dopaminergic medication on (a) stimulus-response association learning from outcome feedback and (b) response selection based on learning, in PD. We tested 28 PD patients on and/or off dopaminergic medication along with 32 healthy, age- and education-matched controls. In Session 1, participants learned to associate abstract images with specific key-press responses through trial and error via outcome feedback. In Session 2, participants provided specific responses to abstract images learned in Session 1, without feedback, precluding new feedback-based learning. By separating Sessions 1 and 2 by 24 h, we could distinguish the effect of dopaminergic medication on (a) feedback-based learning and response selection processes in Session 1 as well as on (b) response selection processes when feedback-based learning could not occur in Session 2. Accuracy achieved at the end of Session 1 were comparable across groups. PD patients on medication learned stimulus-response associations more poorly than PD patients off medication and controls. Medication did not influence decision performance in Session 2. We confirm that dopaminergic therapy impairs feedback-based learning in PD, discounting an alternative explanation that warranted consideration.