You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Identifying the presence of Parkinson's disease using low-frequency fluctuations in BOLD signals Parkinson's disease (PD) is a chronic, progressive, and degenerative neurological disorder that is characterized by the degeneration of dopamine neurons in the substantia nigra and the formation of intracellular Lewy inclusion bodies. Resting-state functional magnetic resonance imaging (RS-fMRI) has demonstrated evidence of changes in metabolic patterns in individuals with PD. The purpose of this study was to determine whether the presence of PD could be " predicted " based on resting fluctuations in the blood oxygenation level dependent signal. We utilized RS-fMRI to measure the amplitude of low-frequency fluctuation (ALFF) and the fractional ALFF (fALFF) in 51 patients with PD and 50 age - and sex -matched healthy controls. Compared with the healthy controls, the individuals with PD exhibited altered ALFFs in the bilateral lingual gyrus and left putamen and an altered fALFF in the right cerebellum posterior lobe. Support vector machines (SVMs), which comprise a supervised pattern recognition method that enables predictions at the individual level, were trained to separate individuals with PD from healthy controls based on the ALFF and fALFF. Using the leave-one-out cross-validation method to analyze our sample, we reliably distinguished the participants with PD from the controls with 92% sensitivity and 87% specificity. Overall, these findings suggest that the SVM - neuroimaging approach may be of particular clinical value because it enables the accurate identification of PD at the individual level. RS-fMRI should be considered for development as a biomarker and an analytical tool for the evaluation of PD.
{"AnatomicalStructure": ["lingual gyrus", "left putamen", "Lewy inclusion bodies", "dopamine neurons", "substantia nigra"], "Bacterium": [], "BiologicFunction": ["pattern recognition", "blood oxygenation level dependent signal", "Parkinson\u0027s disease", "PD", "neurological disorder"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": ["biomarker"], "Eukaryote": [], "Finding": ["fluctuation", "fluctuations", "presence", "ALFF", "ALFFs", "fALFF"], "Food": [], "HealthCareActivity": ["RS-fMRI", "evaluation", "neuroimaging", "BOLD signals", "Resting-state functional magnetic resonance imaging"], "InjuryOrPoisoning": [], "IntellectualProduct": ["method"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["participants", "individuals", "individual"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["study", "analyze", "cross-validation method"], "SpatialConcept": ["intracellular", "patterns", "bilateral", "amplitude", "right cerebellum posterior lobe"], "Virus": []}