Yashar Zeighami

Impulse Control Disorders in Parkinson's Disease Are Associated with Dysfunction in Stimulus Valuation But Not Action Valuation

A substantial subset of Parkinsons disease (PD) patients suffers from impulse control disorders (ICDs), which are side effects of dopaminergic medication. Dopamine plays a key role in reinforcement learning processes. One class of reinforcement learning models, known as the actor-critic model, suggests that two components are involved in these reinforcement learning processes: a critic, which estimates values of stimuli and calculates prediction errors, and an actor, which estimates values of potential actions. To understand the information processing mechanism underlying impulsive behavior, we investigated stimulus and action value learning from reward and punishment in four groups of participants: on-medication PD patients with ICD, on-medication PD patients without ICD, off-medication PD patients without ICD, and healthy controls. Analysis of responses suggested that participants used an actor-critic learning strategy and computed prediction errors based on stimulus values rather than action values. Quantitative model fits also revealed that an actor-critic model of the basal ganglia with different learning rates for positive and negative prediction errors best matched the choice data. Moreover, whereas ICDs were associated with model parameters related to stimulus valuation (critic), PD was associated with parameters related to action valuation (actor). Specifically, PD patients with ICD exhibited lower learning from negative prediction errors in the critic, resulting in an underestimation of adverse consequences associated with stimuli. These findings offer a specific neurocomputational account of the nature of compulsive behaviors induced by dopaminergic drugs.

Clinical criteria for subtyping Parkinson’s disease: biomarkers and longitudinal progression

Parkinsons disease varies widely in clinical manifestations, course of progression and biomarker profiles from person to person. Identification of distinct Parkinsons disease subtypes is of great priority to illuminate underlying pathophysiology, predict progression and develop more efficient personalized care approaches. There is currently no clear way to define and divide subtypes in Parkinsons disease. Using data from the Parkinsons Progression Markers Initiative, we aimed to identify distinct subgroups via cluster analysis of a comprehensive dataset at baseline (i.e. cross-sectionally) consisting of clinical characteristics, neuroimaging, biospecimen and genetic information, then to develop criteria to assign patients to a Parkinsons disease subtype. Four hundred and twenty-one individuals with de novo early Parkinsons disease were included from this prospective longitudinal multicentre cohort. Hierarchical cluster analysis was performed using data on demographic and genetic information, motor symptoms and signs, neuropsychological testing and other non-motor manifestations. The key classifiers in cluster analysis were a motor summary score and three non-motor features (cognitive impairment, rapid eye movement sleep behaviour disorder and dysautonomia). We then defined three distinct subtypes of Parkinsons disease patients: 223 patients were classified as mild motor-predominant (defined as composite motor and all three non-motor scores below the 75th percentile), 52 as diffuse malignant (composite motor score plus either ≥1/3 non-motor score >75th percentile, or all three non-motor scores >75th percentile) and 146 as intermediate. On biomarkers, people with diffuse malignant Parkinsons disease had the lowest level of cerebrospinal fluid amyloid-β (329.0 ± 96.7 pg/ml, P = 0.006) and amyloid-β/total-tau ratio (8.2 ± 3.0, P = 0.032). Data from deformation-based magnetic resonance imaging morphometry demonstrated a Parkinsons disease-specific brain network had more atrophy in the diffuse malignant subtype, with the mild motor-predominant subtype having the least atrophy. Although disease duration at initial visit and follow-up time were similar between subtypes, patients with diffuse malignant Parkinsons disease progressed faster in overall prognosis (global composite outcome), with greater decline in cognition and in dopamine functional neuroimaging after an average of 2.7 years. In conclusion, we introduce new clinical criteria for subtyping Parkinsons disease based on a comprehensive list of clinical manifestations and biomarkers. This clinical subtyping can now be applied to individual patients for use in clinical practice using baseline clinical information. Even though all participants had a recent diagnosis of Parkinsons disease, patients with the diffuse malignant subtype already demonstrated a more profound dopaminergic deficit, increased atrophy in Parkinsons disease brain networks, a more Alzheimers disease-like cerebrospinal fluid profile and faster progression of motor and cognitive deficits.

Network structure of brain atrophy in de novo Parkinson's disease

We mapped the distribution of atrophy in Parkinsons disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinsons Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation. DOI: http://dx.doi.org/10.7554/eLife.08440.001

Network connectivity determines cortical thinning in early Parkinson’s disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (nu2009=u2009105) and matched controls (nu2009=u200957) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.

Pattern of Reduced Functional Connectivity and Structural Abnormalities in Parkinson’s Disease: An Exploratory Study

Background MRI brain changes in Parkinson’s disease (PD) are controversial. Objectives We aimed to describe structural and functional changes in PD. Methods Sixty-six patients with PD (57.94u2009±u200910.25u2009years) diagnosed according to the UK Brain Bank criteria were included. We performed a whole brain analysis using voxel-based morphometry (VBM–SPM 8 software), cortical thickness (CT) using CIVET, and resting-state fMRI using the Neuroimaging Analysis Kit software to compare patients and controls. For VBM and CT we classified subjects into three groups according to disease severity: mild PD [Hoehn and Yahr scale (HY) 1–1.5], moderate PD (HY 2–2.5), and severe PD (HY 3–5). Results We observed gray matter atrophy in the insula and inferior frontal gyrus in the moderate PD and in the insula, frontal gyrus, putamen, cingulated, and paracingulate gyri in the severe groups. In the CT analysis, in mild PD, cortical thinning was restricted to the superior temporal gyrus, gyrus rectus, and olfactory cortex; in the moderate group, the postcentral gyrus, supplementary motor area, and inferior frontal gyrus were also affected; in the severe PD, areas such as the precentral and postentral gyrus, temporal pole, fusiform, and occipital gyrus had reduced cortical thinning. We observed altered connectivity at the default mode, visual, sensorimotor, and cerebellar networks. Conclusion Subjects with mild symptoms already have cortical involvement; however, further cerebral involvement seems to follow Braak’s proposed mechanism. Similar regions are affected both structurally and functionally. We believe the combination of different MRI techniques may be useful in evaluating progressive brain involvement and they may eventually be used as surrogate markers of disease progression.

Reward Prediction Errors in Drug Addiction and Parkinson’s Disease: from Neurophysiology to Neuroimaging

Purpose of ReviewSurprises are important sources of learning. Cognitive scientists often refer to surprises as “reward prediction errors,” a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson’s disease.Recent FindingsBy increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson’s disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes.SummaryThe present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Differential functions of ventral and dorsal striatum

Sir,nnThe case study by Vo and colleagues (2014) aims to address the differential roles of ventral versus dorsal striatum in learning, specifically, whether they are essential for learning or simply involved in it. The authors reported a dissociation between action-value (based on the outcomes, values will be assigned to actions) and stimulus-value learning (values will be associated with the stimuli), and how impairment of the dorsal striatum will affect each of these processes.nnTo achieve this, Vo and colleagues tested a patient (known as XG) who has bilateral damage to the dorsal striatum, while the ventral striatum including the nucleus accumbens is spared. To compare XG’s performance in different tasks with a healthy population statistically, the researchers tested 11 matched control subjects. Among the seven reinforcement learning tasks employed, three could be solved only by learning stimulus-values, one only by learning action-values, and the remaining tasks, with either strategy. Surprisingly, they found that Patient XG was able to learn all the tasks involving action-value learning and his performance resembled those of healthy controls. However, he was impaired at learning the tasks which could only be learned using stimulus values; his performance in those tasks was significantly poorer than controls and was no different from random.nnVo et al. also analysed …

Neurobehavioral correlates of obesity are largely heritable

Significance Obesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in body mass index (BMI) is explained by brain and behavioral factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in BMI had genetic correlations 0.25–0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, BMI was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioral factors. In summary, the genetic vulnerability to BMI is expressed in the brain. This may inform intervention strategies. Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.

White matter hyperintensities are linked to future cognitive decline in de novo Parkinson's disease patients

White Matter Hyperintensities (WMHs) are associated with cognitive decline in aging and Alzheimers disease. However, the pathogenesis of cognitive decline in Parkinsons disease (PD) is not as clearly related to vascular causes, and therefore the role of WMHs as a marker of small-vessel disease (SVD) in PD is less clear. Currently, SVD in PD is assessed and treated independently of the disease. However, if WMH as the major MRI sign of SVD has a higher impact on cognitive decline in PD patients than in healthy controls, vascular pathology needs to be assessed and treated with a higher priority in this population. Here we investigate whether the presence of WMHs leads to increased cognitive decline in de novo PD, and if these effects relate to cortical atrophy. WMHs and cortical thickness were measured in de novo PD patients and age-matched controls (NPDu202f=u202f365, NControlu202f=u202f174) from Parkinsons Progression Markers Initiative (PPMI) to study the relationship between baseline WMHs, future cognitive decline (follow-up: 4.09u202f±u202f1.14u202fyears) and cortical atrophy (follow-up: 1.05u202f±u202f0.10u202fyears). PD subjects with high baseline WMH loads had significantly greater cognitive decline than i) PD subjects with low WMH load, and ii) control subjects with high WMH load. Furthermore, in PD subjects, high WMH load resulted in more cortical thinning in the right frontal lobe. Theses results show that the presence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical atrophy than in normal aging.

Ghrelin enhances food odor conditioning in healthy humans: an fMRI study

Vulnerability to obesity includes eating in response to food cues, which acquire incentive value through conditioning. The conditioning process is largely subserved by dopamine, theorized to encode the discrepancy between expected and actual rewards, known as the reward prediction error (RPE). Ghrelin is a gut-derived homeostatic hormone that triggers hunger and eating. Despite extensive evidence that ghrelin stimulates dopamine, it remains unknown in humans if ghrelin modulates food cue learning. Here we show using functional magnetic resonance imaging that intravenously administered ghrelin increased RPE-related activity in dopamine-responsive areas during food odor conditioning in healthy volunteers. Participants responded faster to food odor-associated cues and perceived them to be more pleasant following ghrelin injection. Ghrelin also increased functional connectivity between hippocampus and ventral striatum. Our work demonstrates that ghrelin promotes the ability of cues to acquire incentive salience, and has implications for the development of vulnerability to obesity.