scholarly journals Human moral decision-making through the lens of Parkinson’s disease

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Giorgia Ponsi ◽  
Marina Scattolin ◽  
Riccardo Villa ◽  
Salvatore Maria Aglioti

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the basal ganglia (BG) and thalamocortical circuitry. While defective motor control has long been considered the defining symptom of PD, mounting evidence indicates that the BG are fundamentally important for a multitude of cognitive, emotional, and motivational processes in addition to motor function. Here, we review alterations in moral decision-making in people with PD, specifically in the context of deceptive behavior. We report that PD patients exhibit two opposite behavioral patterns: hyper- and hypo-honesty. The hyper-honest subgroup engages in deception less often than matched controls, even when lying is associated with a monetary payoff. This behavioral pattern seems to be linked to dopaminergic hypo-activity, implying enhanced harm avoidance, risk aversion, non-impulsivity, and reduced reward sensitivity. On the contrary, the hypo-honest subgroup—often characterized by the additional diagnosis of impulse control disorders (ICDs) and dopamine dysregulation syndrome (DDS)—deceives more often than both PD patients without ICDs/DDS and controls. This behavioral pattern appears to be associated with dopaminergic hyperactivity, which underpins enhanced novelty-seeking, risk-proneness, impulsivity, and reward sensitivity. We posit that these two complementary behavioral patterns might be related to dysfunction of the dopaminergic reward system, leading to reduced or enhanced motivation to deceive. Only a few studies have directly investigated moral decision-making in PD and other neurodegenerative disorders affecting the BG, and further research on the causal role of subcortical structures in shaping moral behavior is needed.

2013 ◽  
Vol 27 (5) ◽  
pp. 562-572 ◽  
Author(s):  
Jan B. Rosen ◽  
Matthias Brand ◽  
Christin Polzer ◽  
Georg Ebersbach ◽  
Elke Kalbe

2015 ◽  
Vol 21 (7) ◽  
pp. 709-716 ◽  
Author(s):  
Manuela Fumagalli ◽  
Sara Marceglia ◽  
Filippo Cogiamanian ◽  
Gianluca Ardolino ◽  
Marta Picascia ◽  
...  

2021 ◽  
Vol 11 (6) ◽  
pp. 769
Author(s):  
Paola Feraco ◽  
Cesare Gagliardo ◽  
Giuseppe La Tona ◽  
Eleonora Bruno ◽  
Costanza D’angelo ◽  
...  

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.


Parkinson's disease is a neurodegenerative disorder that affects millions of people around the globe. Detecting Parkinson's disease at an earlier stage could help to better diagnose the disease. Machine learning provides potentially large opportunities for computer-aided identification and diagnosis that could minimize unavoidable health care errors and inherent clinical uncertainty, provide guidance, and improve decision-making. In this paper, we explore the feature extraction and prediction algorithms used to predict Parkinson's disease and provide a comprehensive comparison of these algorithms


Author(s):  
P.V.Rama Raju ◽  
P.N.T.L. Durga ◽  
B.G.S. Anusha ◽  
A. Bhogeswararao ◽  
M.BalaSai Krishna ◽  
...  

Parkinson's disease (PD) is a gradual progressive central neurodegenerative disorder that affects body movement and is characterized by symptoms such as muscle rigidity, resting tremors, loss of facial expression, hypophonia, diminished blinking, and akinesia [4]. This work aims at providing new insights on the Parkinson's disease fragmentation problem using wavelets [1, 2, 3]. The present work describes a computer model to provide a more accurate picture of the Parkinson's disease (PD) signal processing via Wavelet Transform [7, 8, 9, 10]. The Matlab techniques have been uses which provide a system oriented scientific decision making modal [7, 8]. Within this practice the applied signal has been compared in a sequential order with dissimilar cases in attendance in the database. Special biomedical signals have been considered from Gait in Aging and Disease Database [6] and Physio bank [5]. Analyze the signal under consideration and renowned the holder 100% truthfully.


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