Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

After the discovery of rapid eye movement (REM) sleep in 1953, oneiric activity was long thought to be associated uniquely with REM sleep. Subsequent evaluation of sleep in humans combining neurophysiologic, psychophysiologic, and, more recently, functional neuroimaging investigations, has instead shown that dreaming also occurs during non-REM (NREM) sleep. It has been documented that hallucinatory activity during sleep is a normal phenomenon that is not constant throughout the night but increases toward morning when it tends to become present to the same extent in REM and NREM sleep. The role of sleep mechanisms in the generation of visual hallucinations is well-recognized in narcolepsy in the case of hypnagogic hallucinations, which are thought to derive from a REM-dissociation state in which dream imagery intrudes into wakefulness. Similar mechanisms have been hypothesized to play a role in the physiopathogenesis of visual hallucinations in various neuropsychiatric disorders. Furthermore, a growing body of evidence indicates that not only REM but also NREM processes, such as arousal-related processes, may play a role in the physiopathogenesis of hallucinations in the aforementioned disorders. The role of these processes has been most extensively documented in visual hallucinations occurring in the context of delirium tremens and Parkinson's disease.
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PMID:Rapid eye movement sleep, non-rapid eye movement sleep, dreams, and hallucinations. 1593 33

Charles Bonnet syndrome (CBS) is a condition related to patients with visual loss due to age related macular degeneration or glaucoma that are having complex visual hallucinations. The CBS was first described by Swiss physician Charles Bonnet in 1760. Affected patients, who are otherwise mentally healthy people with significant visual loss, have vivid, complex recurrent visual hallucinations (VHs). One characteristic of these hallucinations is that they usually are "Lilliputian hallucinations" as patients experience micropsia (hallucinations in which the characters or objects are distorted and much smaller than normal). The prevalence of Charles Bonnet Syndrome has been reported to be between 10% and 40%; a recent Australian study has found the prevalence to be 17.5%. The high incidence of non-reported CBS is thought to be as a result of patient's fear to report the symptoms as they could be labeled as mentally insane since those type of visual hallucinations could be found in variety of psychiatric and neurological disorders such as drug or alcohol abuse (delirium tremens), Alice in Wonderland syndrome (AIWS), psychosis, schizophrenia, dementia, narcolepsy, epilepsy, Parkinson disease, brain tumors, migraine, as well as, in long term sleep deprivation. VHs can also be presented as the initial sign of the Epstein-Barr virus infection in infectious mononucleosis. Patients who suffer from CBS usually possess insight into the unreality of their visual experiences, which are commonly pleasant but may sometimes cause distress. The hallucinations consist of well-defined, organized, and clear images over which the subject has little control. It is believed that they represent release phenomena due to deafferentiation of the visual association areas of the cerebral cortex, leading to a form of phantom vision. Cognitive defects, social isolation, and sensory deprivation have also been implicated in the etiology of this condition. This study was conducted on 350 patients diagnosed with Age-Related Macular Degeneration (AMD) and shows incidence of CBS in 13% of patients with AMD. Furthermore, we have found higher incidence of CBS in patients with massive loss of vision in peripheral visual field which is not age related.
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PMID:What associates Charles Bonnet syndrome with age-related macular degeneration? 2130 24

Dual-task (DT) paradigms have been used in gait research to assess the automaticity of locomotion, particularly in people with Parkinson's disease (PD). In people with PD, reliance on cortical control during walking leads to greater interference between cognitive and locomotor tasks. Yet, recent studies have suggested that even healthy gait requires cognitive control, and that these cognitive contributions occur at specific phases of the gait cycle. Here, we examined whether changes in gait stability, elicited by simultaneous cognitive DTs, were specific to certain phases of the gait cycle in people with PD. Phase-dependent local dynamic stability (LDS) was calculated for 95 subjects with PD and 50 healthy control subjects during both single task and DT gait at phases corresponding to (1) heel contact-weight transfer, (2) toe-off-early swing, and (3) single-support-mid swing. PD-related DT interference was evident only for the duration of late swing and LDS during the heel contact-weight transfer phase of gait. No PD-related DT costs were found in other traditional spatiotemporal gait parameters. These results suggest that PD-related DT interference occurs only during times where cortical activity is needed for planning and postural adjustments. These results challenge our understanding of DT costs while walking, particularly in people with PD, and encourage researchers to re-evaluate traditional concepts of DT interference.
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PMID:Gait Stability Has Phase-Dependent Dual-Task Costs in Parkinson's Disease. 2989 24

The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson's disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.
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PMID:A comparison of feature selection methods when using motion sensors data: a case study in Parkinson's disease. 3044 64