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Shen T, Pu JL, Jiang YS, Yue YM, He TT, Qu BY, Zhao S, Yan YP, Lai HY, Zhang BR. Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease. Neural Regen Res 2022; 18:1154-1160. [PMID: 36255006 PMCID: PMC9827791 DOI: 10.4103/1673-5374.355764] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson's disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease. Forty-eight Parkinson's disease patients and 39 matched healthy controls underwent genotyping and 7T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson's disease diagnosis. We found that, in Parkinson's disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein (SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson's disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson's disease.
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Affiliation(s)
- Ting Shen
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China,Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jia-Li Pu
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ya-Si Jiang
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China,Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yu-Mei Yue
- Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ting-Ting He
- Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China,College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Bo-Yi Qu
- Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China,College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Shuai Zhao
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ya-Ping Yan
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hsin-Yi Lai
- Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China,Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China,College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang Province, China,Correspondence to: Bao-Rong Zhang, ; Hsin-Yi Lai, .
| | - Bao-Rong Zhang
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China,Correspondence to: Bao-Rong Zhang, ; Hsin-Yi Lai, .
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The role of the deep convolutional neural network as an aid to interpreting brain [ 18F]DOPA PET/CT in the diagnosis of Parkinson's disease. Eur Radiol 2021; 31:7003-7011. [PMID: 33686474 DOI: 10.1007/s00330-021-07779-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/12/2020] [Accepted: 02/12/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To test the performance of a 3D convolutional neural network (CNN) in analysing brain [18F]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR). METHODS We analyzed patients who had undergone [18F]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [18F]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [18F]DOPA PET images were first co-registered. RESULTS Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5th percentile of the negative cases' distribution. CONCLUSIONS 3D CNNs are able to interpret [18F]DOPA PET/CT properly, revealing patients affected by Parkinson's disease. KEY POINTS • [18F]DOPA PET/CT is a sensitive diagnostic tool to identify patients with nigro-striatal neurodegeneration. • A semiquantitative evaluation of the images allows a more confident interpretation of the PET findings. • 3D convolutional neural network allows an accurate interpretation of 18F-DOPA PET/CT images, revealing patients affected by Parkinson's disease.
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Benito-León J, Serrano JI, Louis ED, Holobar A, Romero JP, Povalej-Bržan P, Bermejo-Pareja F, Del Castillo MD, Posada IJ, Rocon E. Tremor severity in Parkinson's disease and cortical changes of areas controlling movement sequencing: A preliminary study. J Neurosci Res 2018; 96:1341-1352. [PMID: 29660812 DOI: 10.1002/jnr.24248] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 03/24/2018] [Accepted: 03/26/2018] [Indexed: 11/07/2022]
Abstract
There remains much to learn about the changes in cortical anatomy that are associated with tremor severity in Parkinson's disease (PD). For this reason, we used a combination of structural neuroimaging to measure cortical thickness and neurophysiological studies to analyze whether PD tremor was associated with cortex integrity. Magnetic resonance imaging and neurophysiological assessment were performed in 13 nondemented PD patients (9 women, 69.2%) with a clearly tremor-dominant phenotype. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis software. Assessment of tremor was performed by means of high-density surface electromyography (hdEMG) and inertial measurement units (IMUs). Individual motor unit discharge patterns were identified from surface hdEMG and tremor metrics quantifying motor unit synchronization from IMUs. Increased motor unit synchronization (i.e., more severe tremor) was associated with cortical changes (i.e., atrophy) in wide-spread cortical areas, including caudal middle frontal regions bilaterally (dorsal premotor cortices), left inferior parietal lobe (posterior parietal cortex), left lateral orbitofrontal cortex, cingulate cortex bilaterally, left posterior and transverse temporal cortex, and left occipital lobe, as well as reduced left middle temporal volume. Given that the majority of these areas are involved in controlling movement sequencing, our results support Albert's classic hypothesis that PD tremor may be the result of an involuntary activation of a program of motor behavior used in the genesis of rapid voluntary alternating movements.
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Affiliation(s)
- Julián Benito-León
- Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain
- Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Spain
- Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
| | - J Ignacio Serrano
- Neural and Cognitive Engineering group, Centre for Automation and Robotics (CAR) CSIC-UPM, Arganda del Rey, Spain
| | - Elan D Louis
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA
- Yale School of Medicine and Yale School of Public Health, Center for Neuroepidemiology and Clinical Neurological Research, New Haven, Connecticut, USA
| | - Ales Holobar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Juan P Romero
- Faculty of Biosanitary Sciences, Francisco de Vitoria University, Pozuelo de Alarcón, Madrid, Spain
- Brain Damage Service, Hospital Beata Maria Ana, Madrid, Spain
| | - P Povalej-Bržan
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
- Faculty of Health Sciences, Faculty of Health Sciences, University of Maribor, Maribor, Slovenia
| | - Félix Bermejo-Pareja
- Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Spain
- Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
- Clinical Research Unit, University Hospital 12 de Octubre, Madrid, Spain
| | - M Dolores Del Castillo
- Neural and Cognitive Engineering group, Centre for Automation and Robotics (CAR) CSIC-UPM, Arganda del Rey, Spain
| | - Ignacio J Posada
- Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain
- Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
| | - Eduardo Rocon
- Neural and Cognitive Engineering group, Centre for Automation and Robotics (CAR) CSIC-UPM, Arganda del Rey, Spain
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Learning "How to Learn": Super Declarative Motor Learning Is Impaired in Parkinson's Disease. Neural Plast 2017; 2017:3162087. [PMID: 28828186 PMCID: PMC5554559 DOI: 10.1155/2017/3162087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 07/09/2017] [Indexed: 11/18/2022] Open
Abstract
Learning new information is crucial in daily activities and occurs continuously during a subject's lifetime. Retention of learned material is required for later recall and reuse, although learning capacity is limited and interference between consecutively learned information may occur. Learning processes are impaired in Parkinson's disease (PD); however, little is known about the processes related to retention and interference. The aim of this study is to investigate the retention and anterograde interference using a declarative sequence learning task in drug-naive patients in the disease's early stages. Eleven patients with PD and eleven age-matched controls learned a visuomotor sequence, SEQ1, during Day1; the following day, retention of SEQ1 was assessed and, immediately after, a new sequence of comparable complexity, SEQ2, was learned. The comparison of the learning rates of SEQ1 on Day1 and SEQ2 on Day2 assessed the anterograde interference of SEQ1 on SEQ2. We found that SEQ1 performance improved in both patients and controls on Day2. Surprisingly, controls learned SEQ2 better than SEQ1, suggesting the absence of anterograde interference and the occurrence of learning optimization, a process that we defined as “learning how to learn.” Patients with PD lacked such improvement, suggesting defective performance optimization processes.
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Marinelli L, Quartarone A, Hallett M, Frazzitta G, Ghilardi MF. The many facets of motor learning and their relevance for Parkinson's disease. Clin Neurophysiol 2017; 128:1127-1141. [PMID: 28511125 DOI: 10.1016/j.clinph.2017.03.042] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 02/14/2017] [Accepted: 03/19/2017] [Indexed: 12/16/2022]
Abstract
The final goal of motor learning, a complex process that includes both implicit and explicit (or declarative) components, is the optimization and automatization of motor skills. Motor learning involves different neural networks and neurotransmitters systems depending on the type of task and on the stage of learning. After the first phase of acquisition, a motor skill goes through consolidation (i.e., becoming resistant to interference) and retention, processes in which sleep and long-term potentiation seem to play important roles. The studies of motor learning in Parkinson's disease have yielded controversial results that likely stem from the use of different experimental paradigms. When a task's characteristics, instructions, context, learning phase and type of measures are taken into consideration, it is apparent that, in general, only learning that relies on attentional resources and cognitive strategies is affected by PD, in agreement with the finding of a fronto-striatal deficit in this disease. Levodopa administration does not seem to reverse the learning deficits in PD, while deep brain stimulation of either globus pallidus or subthalamic nucleus appears to be beneficial. Finally and most importantly, patients with PD often show a decrease in retention of newly learned skill, a problem that is present even in the early stages of the disease. A thorough dissection and understanding of the processes involved in motor learning is warranted to provide solid bases for effective medical, surgical and rehabilitative approaches in PD.
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Affiliation(s)
- Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Italy
| | - Angelo Quartarone
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Department of Neuroscience, University of Messina, Italy; The Fresco Institute for Parkinson's & Movement Disorders, NYU-Langone School of Medicine, New York, NY, USA
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Giuseppe Frazzitta
- Department of Parkinson's Disease and Brain Injury Rehabilitation, "Moriggia-Pelascini" Hospital, Gravedona ed Uniti, Como, Italy
| | - Maria Felice Ghilardi
- Department of Physiology, Pharmacology & Neuroscience, CUNY School of Medicine, New York, NY, USA; The Fresco Institute for Parkinson's & Movement Disorders, NYU-Langone School of Medicine, New York, NY, USA.
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