A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study.

BACKGROUND: MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD). METHODS: 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. RESULTS: The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. CONCLUSIONS: MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T.

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability.

Human behavior is surprisingly variable, even when facing the same problem under identical circumstances. A prominent example is risky decision making. Economic theories struggle to explain why humans are so inconsistent. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes, but it remains unknown whether endogenous fluctuations also influence high-level cognitive processes including decision making. Here, using real-time functional magnetic resonance imaging, we tested whether risky decision making is influenced by endogenous fluctuations in blood oxygenation level-dependent (BOLD) activity in the dopaminergic midbrain, encompassing ventral tegmental area and substantia nigra. We show that low prestimulus brain activity leads to increased risky choice in humans. Using computational modeling, we show that increased risk taking is explained by enhanced phasic responses to offers in a decision network. Our findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex human behavior.

Distinct Contributions of Ventromedial and Dorsolateral Subregions of the Human Substantia Nigra to Appetitive and Aversive Learning.

The role of neurons in the substantia nigra (SN) and ventral tegmental area (VTA) of the midbrain in contributing to the elicitation of reward prediction errors during appetitive learning has been well established. Less is known about the differential contribution of these midbrain regions to appetitive versus aversive learning, especially in humans. Here we scanned human participants with high-resolution fMRI focused on the SN and VTA while they participated in a sequential Pavlovian conditioning paradigm involving an appetitive outcome (a pleasant juice), as well as an aversive outcome (an unpleasant bitter and salty flavor). We found a degree of regional specialization within the SN: Whereas a region of ventromedial SN correlated with a temporal difference reward prediction error during appetitive Pavlovian learning, a dorsolateral area correlated instead with an aversive expected value signal in response to the most distal cue, and to a reward prediction error in response to the most proximal cue to the aversive outcome. Furthermore, participants' affective reactions to both the appetitive and aversive conditioned stimuli more than 1 year after the fMRI experiment was conducted correlated with activation in the ventromedial and dorsolateral SN obtained during the experiment, respectively. These findings suggest that, whereas the human ventromedial SN contributes to long-term learning about rewards, the dorsolateral SN may be particularly important for long-term learning in aversive contexts. SIGNIFICANCE STATEMENT: The role of the substantia nigra (SN) and ventral tegmental area (VTA) in appetitive learning is well established, but less is known about their contribution to aversive compared with appetitive learning, especially in humans. We used high-resolution fMRI to measure activity in the SN and VTA while participants underwent higher-order Pavlovian learning. We found a regional specialization within the SN: a ventromedial area was selectively engaged during appetitive learning, and a dorsolateral area during aversive learning. Activity in these areas predicted affective reactions to appetitive and aversive conditioned stimuli over 1 year later. These findings suggest that, whereas the human ventromedial SN contributes to long-term learning about rewards, the dorsolateral SN may be particularly important for long-term learning in aversive contexts.

AAV.shRNA-mediated downregulation of ROCK2 attenuates degeneration of dopaminergic neurons in toxin-induced models of Parkinson's disease in vitro and in vivo.

Parkinson's disease (PD) is a neurodegenerative disorder with prominent neuronal cell death in the substantia nigra (SN) and other parts of the brain. Previous studies in models of traumatic and neurodegenerative CNS disease showed that pharmacological inhibition of Rho-associated kinase (ROCK), a molecule involved in inhibitory signaling in the CNS, by small-molecule inhibitors improves neuronal survival and increases regeneration. Most small-molecule inhibitors, however, offer only limited target specificity and also inhibit other kinases, including both ROCK isoforms. To establish the role of the predominantly brain-expressed ROCK2 isoform in models of regeneration and PD, we used adeno-associated viral vectors (AAV) to specifically knockdown ROCK2 in neurons. Rat primary midbrain neurons (PMN) were transduced with AAV expressing short-hairpin-RNA (shRNA) against ROCK2 and LIM-domain kinase 1 (LIMK1), one of the downstream targets of ROCK2. While knock-down of ROCK2 and LIMK1 both enhanced neurite regeneration in a traumatic scratch lesion model, only ROCK2-shRNA protected PMN against 1-methyl-4-phenylpyridinium (MPP+) toxicity. Moreover, AAV.ROCK2-shRNA increased levels of the pro-survival markers Bcl-2 and phospho-Erk1. In vivo, AAV.ROCK2-shRNA vectors were injected into the ipsilateral SN and a unilateral 6-OHDA striatal lesion was performed. After four weeks, behavioral, immunohistochemical and biochemical alterations were investigated. Downregulation of ROCK2 protected dopaminergic neurons in the SN from 6-OHDA-induced degeneration and resulted in significantly increased TH-positive neuron numbers. This effect, however, was confined to nigral neuronal somata as striatal terminal density, dopamine and metabolite levels were not significantly preserved. Interestingly, motor behavior was improved in the ROCK2-shRNA treated animals compared to control after four weeks. Our studies thus confirm ROCK2 as a promising therapeutic target in models of PD and demonstrate that neuron-specific inhibition of ROCK2 promotes survival of lesioned dopaminergic neurons.

Protection of Primary Dopaminergic Midbrain Neurons Through Impact of Small Molecules Using Virtual Screening of GPR139 Supported by Molecular Dynamic Simulation and Systems Biology.

INTRODUCTION: GPCR share a common structural feature, i.e., the presence of seven trans-membrane helices having three intracellular and three extracellular loops. The carboxyl terminal is intracellular whereas amino terminal is extracellular. Various conformational changes are observed in structure of GPCR during the binding with ligand, coupling with G protein and interaction with other proteins. In Rhodopsin class of GPCR the basic structure of GPCR is resolved by X-ray crystallography. Ligand acts as an extracellular stimulus for GPCRs to bring physiological changes in organisms. GPR139 has been found to have effective physiological role in primary dopaminergic midbrain neurons and in central nervous system. Recent reports suggested that the ligand of GPR139 protein inhibits the growth of primary dopaminergic midbrain neurons in central nervous system. These discoveries indicated the potential involvement and influence of GPR139 protein in central nervous system METHOD: Therefore, we used multi-approach analysis to investigate the role of GPR139 in the molecular mechanisms of central nervous system. In silico screening was performed to study compound 1 binding with GPR139 protein in their predicted three-dimensional structures. Compound 1 was subjected to molecular dynamics (MD) simulation and stability analysis. RESULTS: The results of MD analysis suggested that the loop region in GPR139 protein structure could affect its binding with drugs. Finally, we cross-validated the predicted compound 1 through systems biology approach. Our results suggested that GPR139 might play an important role in primary dopaminergic midbrain neurons therapy.