20.9.3 Brain, Parkinson's Disease

Chapter Contents (Back)
Brain. Cortex. Parkinson's Disease.

Oh, S.H.[Se-Hong], Jeong, H.J.[Hye-Jin], Kim, J.M.[Jong-Min], Kwon, D.H.[Dae-Hyuk], Park, S.Y.[Sung-Yeon], Park, J.H.[Joshua H.], Kim, Y.B.[Young-Bo], Chi, J.G.[Je-Geun], Park, C.W.[Chan-Woong], Jeon, B.S.[Beom S.], Cho, Z.H.[Zang-Hee],
Quantitative analysis of the SN in Parkinson's disease implementing 3D modeling at 7.0-T MRI,
IJIST(21), No. 3, September 2011, pp. 253-259.
DOI Link 1109

Chauvie, S.[Stephane], Obertino, M.[Margherita], Papaleo, A.[Alberto], Ruspa, M.[Marta], Solano, A.[Ada], Gozzoli, L.[Luigi], Gagliano, A.[Attilio], Biggi, A.[Alberto],
A method for the visual analysis of early-stage Parkinson's disease based on virtual MRI-derived SPECT images,
IJIST(22), No. 3, September 2012, pp. 172-176.
DOI Link 1208

Song, I.U.[In-Uk], Chung, Y.A.[Yong-An], Chung, S.W.[Sung-Woo], Huh, R.[Ryoong],
Clinical value of cardiac I-123 metaiodobenzylguanidine scintigraphy between Parkinson's disease and Parkinson's disease associated dementia,
IJIST(22), No. 4, December 2012, pp. 241-244.
DOI Link 1211

Song, I.U.[In-Uk], Chung, Y.A.[Yong-An], Huh, R.[Ryoong],
Brain perfusion SPECT can differentiate clinical subtypes of Parkinson's diseases,
IJIST(23), No. 3, 2013, pp. 222-226.
DOI Link 1309
Parkinson's disease, tremor, perfusion SPECT BibRef

Song, I.U.[In-Uk], Chung, S.W.[Sung-Woo], Chung, Y.A.[Yong-An],
Efficacy of an NMDA receptor antagonist for Parkinson's disease dementia: A brain perfusion SPECT study,
IJIST(24), No. 4, 2014, pp. 326-331.
DOI Link 1411
Parkinson's disease, dementia, memantine, cerebral blood flow BibRef

Kim, T.W.[Tae-Won], Chung, Y.A.[Yong-An], Song, I.U.[In-Uk], Lee, K.S.[Kwang-Soo],
Analysis of cerebral blood flow in Parkinson's disease with dementia versus subcortical ischemic vascular dementia using single photon emission computed tomography,
IJIST(24), No. 4, 2014, pp. 306-312.
DOI Link 1411
Parkinson's disease with dementia BibRef

Ghayoumi, M.[Mehdi], Zhao, Y.[Ye],
Parkinson Data Analysis and Interpretation with Data Visualization Methods,
ISVC14(II: 884-893).
Springer DOI 1501

Subasi, A.[Abdulhamit],
A decision support system for diagnosis of neuromuscular disorders using DWT and evolutionary support vector machines,
SIViP(9), No. 2, February 2015, pp. 399-408.
WWW Link. 1503

Bailey, C.[Chris], Austin, J.[Jim], Hollier, G.[Garry], Moulds, A.[Anthony], Freeman, M.[Micheal], Fargus, A.[Alex], Lampert, T.[Tom],
Evaluating a Miniature Multisensor Biosignal Recorder for Unsupervised Parkinson's Disease Monitoring,
Sensors(184), No. 1, January 2015, pp. 66-76.
HTML Version. 1504

Rana, B.[Bharti], Juneja, A.[Akanksha], Saxena, M.[Mohit], Gudwani, S.[Sunita], Kumaran, S.S.[S. Senthil], Behari, M.[Madhuri], Agrawal, R.K.,
Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI,
IJIST(25), No. 3, 2015, pp. 245-255.
DOI Link 1509
Parkinson's disease BibRef

Song, I.U.[In-Uk], Kim, T.W.[Tae-Won], Yoo, I.[Ikdong], Chung, Y.A.[Yong-An], Lee, K.S.[Kwan-Sung],
Can COMT-inhibitor delay the clinical progression of Parkinson's disease? 2 years follow up pilot study,
IJIST(26), No. 1, 2016, pp. 38-42.
DOI Link 1604
Parkinson's disease BibRef

Kim, Y.D.[Young-Do], Jeong, H.S.[Hyeonseok S.], Kim, Y.D.[Yong-Duk],
Comparison of regional cerebral blood flow in Parkinson's disease with depression and major depression,
IJIST(27), No. 3, 2017, pp. 209-215.
DOI Link 1708
depression, Parkinson's disease, regional cerebral blood flow, , single, photon, emission, computed, tomography BibRef

Impedovo, D.,
Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting,
SPLetters(26), No. 4, April 2019, pp. 632-636.
Writing, Task analysis, Diseases, Standards, Acceleration, Azimuth, Neuromuscular, Parkinson's disease, computer aided diagnosis, tremor BibRef

Qin, Z., Jiang, Z., Chen, J., Hu, C., Ma, Y.,
sEMG-Based Tremor Severity Evaluation for Parkinson's Disease Using a Light-Weight CNN,
SPLetters(26), No. 4, April 2019, pp. 637-641.
Training, Testing, Parkinson's disease, Task analysis, Feature extraction, Hospitals, Muscles, Parkinson's Disease, similarity learning BibRef

Ariz, M., Abad, R.C., Castellanos, G., Martínez, M., Muńoz-Barrutia, A., Fernández-Seara, M.A., Pastor, P., Pastor, M.A., Ortiz-de-Solórzano, C.,
Dynamic Atlas-Based Segmentation and Quantification of Neuromelanin-Rich Brainstem Structures in Parkinson Disease,
MedImg(38), No. 3, March 2019, pp. 813-823.
Image segmentation, Diseases, Brainstem, Nuclear magnetic resonance, Magnetic resonance imaging, neural network based classifier BibRef

Loconsole, C.[Claudio], Cascarano, G.D.[Giacomo Donato], Brunetti, A.[Antonio], Trotta, G.F.[Gianpaolo Francesco], Losavio, G.[Giacomo], Bevilacqua, V.[Vitoantonio], di Sciascio, E.[Eugenio],
A model-free technique based on computer vision and sEMG for classification in Parkinson's disease by using computer-assisted handwriting analysis,
PRL(121), 2019, pp. 28-36.
Elsevier DOI 1904
Handwriting analysis, Neurodegenerative disease, Parkinson's disease, Neural Network, SVM BibRef

Moetesum, M.[Momina], Siddiqi, I.[Imran], Vincent, N.[Nicole], Cloppet, F.[Florence],
Assessing visual attributes of handwriting for prediction of neurological disorders: A case study on Parkinson's disease,
PRL(121), 2019, pp. 19-27.
Elsevier DOI 1904
Handwriting, Parkinson's disease, Convolutional neural networks, Visual attributes BibRef

Liu, C., Wang, J., Deng, B., Li, H., Fietkiewicz, C., Loparo, K.A.,
Noise-Induced Improvement of the Parkinsonian State: A Computational Study,
Cyber(49), No. 10, October 2019, pp. 3655-3664.
Neurons, Satellite broadcasting, Mathematical model, Computational modeling, Pathology, Biological neural networks, Parkinsonian state BibRef

Almeida, J.S.[Jefferson S.], Filho, P.P.R.[Pedro P. Rebouças], Carneiro, T.[Tiago], Wei, W.[Wei], Damaševicius, R.[Robertas], Maskeliunas, R.[Rytis], de Albuquerque, V.H.C.[Victor Hugo C.],
Detecting Parkinson's disease with sustained phonation and speech signals using machine learning techniques,
PRL(125), 2019, pp. 55-62.
Elsevier DOI 1909
Parkinson's disease, Speech processing, Phonological features, Feature extraction, Machine learning, Diagnosis BibRef

Bernardo, L.S.[Lucas S.], Quezada, A.[Angeles], Munoz, R.[Roberto], Maia, F.M.[Fernanda Martins], Pereira, C.R.[Clayton R.], Wu, W.Q.[Wan-Qing], de Albuquerque, V.H.C.[Victor Hugo C.],
Handwritten pattern recognition for early Parkinson's disease diagnosis,
PRL(125), 2019, pp. 78-84.
Elsevier DOI 1909
Parkinson's disease, machine learning, image processing BibRef

Parziale, A.[Antonio], Cioppa, A.D.[Antonio Della], Senatore, R.[Rosa], Marcelli, A.[Angelo],
A Decision Tree for Automatic Diagnosis of Parkinson's Disease from Offline Drawing Samples: Experiments and Findings,
Springer DOI 1909

Diaz, M.[Moises], Ferrer, M.A.[Miguel Angel], Impedovo, D.[Donato], Pirlo, G.[Giuseppe], Vessio, G.[Gennaro],
Dynamically enhanced static handwriting representation for Parkinson's disease detection,
PRL(128), 2019, pp. 204-210.
Elsevier DOI 1912
Parkinson's disease, e-Health, Computer aided diagnosis, Dynamically enhanced static handwriting, Convolutional neural networks BibRef

Kim, M., Won, J.H., Youn, J., Park, H.,
Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease,
MedImg(39), No. 1, January 2020, pp. 23-34.
Genetics, Neuroimaging, Correlation, Diseases, Magnetic resonance imaging, Sparse matrices, Imaging genetics, single nucleotide polymorphism (SNP) BibRef

Naghsh, E.[Erfan], Sabahi, M.F.[Mohamad Farzan], Beheshti, S.[Soosan],
Spatial analysis of EEG signals for Parkinson's disease stage detection,
SIViP(14), No. 2, March 2020, pp. 397-405.
WWW Link. 2003

Ashour, A.S.[Amira S.], El-Attar, A.[Amira], Dey, N.[Nilanjan], Abd El-Kader, H.[Hatem], Abd El-Naby, M.M.[Mostafa M.],
Long short term memory based patient-dependent model for FOG detection in Parkinson's disease,
PRL(131), 2020, pp. 23-29.
Elsevier DOI 2004
Parkinson's disease, Wearable sensors, Accelerometer sensor, Freezing of gait, Classification, Support vector machine, Long short term memory deep learning model BibRef

Kaur, S.[Sukhpal], Aggarwal, H.[Himanshu], Rani, R.[Rinkle],
Hyper-parameter optimization of deep learning model for prediction of Parkinson's disease,
MVA(31), No. 5, July 2020, pp. Article32.
WWW Link. 2006

Wingate, J.[James], Kollia, I.[Ilianna], Bidaut, L.[Luc], Kollias, S.[Stefanos],
Unified deep learning approach for prediction of Parkinson's disease,
IET-IPR(14), No. 10, August 2020, pp. 1980-1989.
DOI Link 2008

Afonso, L.C.S.[Luis C.S.], Pereira, C.R.[Clayton R.], Weber, S.A.T.[Silke A.T.], Hook, C.[Christian], Falcăo, A.X.[Alexandre X.], Papa, J.P.[Joăo P.],
Hierarchical Learning Using Deep Optimum-Path Forest,
JVCIR(71), 2020, pp. 102823.
Elsevier DOI 2009
Parkinson's disease, Optimum-path forest, Handwriting dynamics, Hierarchical representation
See also Active Learning Paradigms for CBIR Systems Based on Optimum-Path Forest Classification. BibRef

Afonso, L.C.S.[Luis C.S.], Pedronette, D.C.G., de Souza, A.N., Papa, J.P.[Joăo P.],
Improving Optimum-Path Forest Classification Using Unsupervised Manifold Learning,
Measurement, Manifolds, Prototypes, Training, Forestry, Task analysis, Vegetation BibRef

Zhou, Y., Tinaz, S., Tagare, H.D.,
Robust Bayesian Analysis of Early-Stage Parkinson's Disease Progression Using DaTscan Images,
MedImg(40), No. 2, February 2021, pp. 549-561.
Mathematical model, Diseases, Brain modeling, Biological system modeling, Trajectory, Time series analysis, t-distribution BibRef

Khachnaoui, H.[Hajer], Mabrouk, R.[Rostom], Khlifa, N.[Nawres],
Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review,
IET-IPR(14), No. 16, 19 December 2020, pp. 4013-4026.
DOI Link 2103

Jiji, G.W.[G. Wiselin], Rajesh, A., Raj, P.J.D.[P. Johnson Durai],
Diagnosis of Parkinson's Disease Using SVM Classifier,
IJIG(21), No. 2 2021, pp. 2150011.
DOI Link 2105

Jiang, Z.[Zheheng], Zhou, F.[Feixiang], Zhao, A.[Aite], Li, X.[Xin], Li, L.[Ling], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long], Zhou, H.Y.[Hui-Yu],
Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model,
IP(30), 2021, pp. 5490-5504.
Mice, Feature extraction, Hidden Markov models, Computational modeling, Graphical models, Cameras, Video recording, Parkinson's disease (PD) BibRef

Huang, Z.[Zhongwei], Lei, H.[Haijun], Li, S.[Shiqi], Xiao, X.H.[Xiao-Hua], Tan, E.L.[Ee-Leng], Lei, B.[Baiying],
Longitudinal Feature Selection and Feature Learning for Parkinson's Disease Diagnosis and Prediction,
Neuroimaging, Deep learning, Parkinson's disease, Diversity reception, Feature extraction, Data models, Data mining, multiple modalities and relation Classification and Regression BibRef

Sharma, H.[Harsh], Soltaninejad, S.[Sara], Cheng, I.[Irene],
Automated Classification of Parkinson's Disease Using Diffusion Tensor Imaging Data,
Springer DOI 2103

Ali, M.R., Hernandez, J., Dorsey, E.R., Hoque, E., McDuff, D.,
Spatio-Temporal Attention and Magnification for Classification of Parkinson's Disease from Videos Collected via the Internet,
Task analysis, Videos, Motion segmentation, Thumb, Handheld computers, Computer vision, Wearable sensors, Parkinson's, Segmentation BibRef

Guarin, D.L., Dempster, A., Bandini, A., Yunusova, Y., Taati, B.,
Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking,
Feature extraction, Task analysis, Mouth, Cameras, Diseases BibRef

Dias, S.B., Grammatikopoulou, A., Grammalidis, N., Diniz, J.A., Savvidis, T., Konstantinidis, E., Bamidis, P., Stadtschnitzer, M., Trivedi, D., Klingelhoefer, L., Katsarou, Z., Bostantzopoulou, S., Dimitropoulos, K., Hadjileontiadis, L.J.,
Motion Analysis on Depth Camera Data to Quantify Parkinson's Disease Patients' Motor Status Within the Framework of I-Prognosis Personalized Game Suite,
Cameras, Parkinson's disease, Games, Physics, Indexes, Predictive models, i-PROGNOSIS, Deep learning BibRef

Lei, H., Zhao, Y., Huang, Z., Zhou, F., Huang, L., Lei, B.,
Multi-classification of Parkinson's Disease via Sparse Low-Rank Learning,
Sparse matrices, Feature extraction, Diseases, Neuroimaging, Diffusion tensor imaging, Support vector machines, feature selection BibRef

Vlachostergiou, A., Tagaris, A., Stafylopatis, A., Kollias, S.,
Multi-Task Learning for Predicting Parkinson's Disease Based on Medical Imaging Information,
Task analysis, Parkinson's disease, Predictive models, Biomedical imaging, Handheld computers, Deep Neural Networks, Computer-Aided Diagnosis BibRef

Vlachostergiou, A., Tagaris, A., Stafylopatis, A., Kollias, S.,
Investigating the Best Performing Task Conditions of a Multi-Tasking Learning Model in Healthcare Using Convolutional Neural Networks: Evidence from a Parkinson'S Disease Database,
Task analysis, Parkinson's disease, Predictive models, Databases, Computational modeling, Convolutional Neural Networks, context BibRef

Spetsieris, P.G., Dhawan, V., Eidelberg, D.,
Visualizing Network Connectivity in Parkinson'S Disease,
Correlation, Diseases, Principal component analysis, Covariance matrices, Positron emission tomography, FDG PET BibRef

Oikonomou, V.P., Blekas, K., Astrakas, L.,
Functional Connectivity in Parkinson Disease Through Mixture Modelling,
Functional magnetic resonance imaging, Time series analysis, Brain modeling, Task analysis, Analytical models, Mixture models, Data models BibRef

Przybyszewski, A.W.[Andrzej W.], Szlufik, S.[Stanislaw], Habela, P.[Piotr], Koziorowski, D.M.[Dariusz M.],
Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson's Patients,
Springer DOI 1711

Pereira, C.R.[Clayton R.], Passos, L.A.[Leandro A.], Lopes, R.R.[Ricardo R.], Weber, S.A.T.[Silke A. T.], Hook, C.[Christian], Papa, J.P.[Joăo Paulo],
Parkinson's Disease Identification Using Restricted Boltzmann Machines,
CAIP17(II: 70-80).
Springer DOI 1708

Gómez-Orozco, V., Cuellar, J., García, H.F.[Hernán F.], Álvarez, A.M., Álvarez, M.A., Orozco, A.A., Henao, O.A.,
A Kernel-Based Approach for DBS Parameter Estimation,
Springer DOI 1703
deep brain stimulation. VTA: Volume of tissue activated. BibRef

Kao, J.Y.[Jiun-Yu], Nguyen, M.[Minh], Nocera, L.[Luciano], Shahabi, C.[Cyrus], Ortega, A.[Antonio], Winstein, C.[Carolee], Sorkhoh, I.[Ibrahim], Chung, Y.C.[Yu-Chen], Chen, Y.A.[Yi-An], Bacon, H.[Helen],
Validation of Automated Mobility Assessment Using a Single 3D Sensor,
ACVR16(II: 162-177).
Springer DOI 1611
More gait type analysis. BibRef

Adeli-Mosabbeb, E.[Ehsan], Wee, C.Y.[Chong-Yaw], An, L.[Le], Shi, F.[Feng], Shen, D.G.[Ding-Gang],
Joint Feature-Sample Selection and Robust Classification for Parkinson's Disease Diagnosis,
Springer DOI 1608

Padilla, J.B.[José Bestier], Arango, R.[Ramiro], García, H.F.[Hernán F.], Cardona, H.D.V.[Hernán Darío Vargas], Orozco, Á.A.[Álvaro A.], Álvarez, M.A.[Mauricio A.], Guijarro, E.[Enrique],
NEURONAV: A Tool for Image-Guided Surgery - Application to Parkinson's Disease,
ISVC15(I: 349-358).
Springer DOI 1601

Kubis, A.[Anna], Szymanski, A.[Artur], Przybyszewski, A.W.[Andrzej W.],
Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson's Patients,
Springer DOI 1511

Spasojevic, S.[Sofija], Santos-Victor, J.[José], Ilic, T.[Tihomir], Milanovic, S.[Sladan], Potkonjak, V.[Veljko], Rodic, A.[Aleksandar],
A Vision-Based System for Movement Analysis in Medical Applications: The Example of Parkinson Disease,
Springer DOI 1507

Morisi, R.[Rita], Gnecco, G.[Giorgio], Lanconelli, N.[Nico], Zanigni, S.[Stefano], Manners, D.N.[David Neil], Testa, C.[Claudia], Evangelisti, S.[Stefania], Gramegna, L.L.[Laura Ludovica], Bianchini, C.[Claudio], Cortelli, P.[Pietro], Tonon, C.[Caterina], Lodi, R.[Raffaele],
Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines,
Springer DOI 1506

Prashanth, R., Roy, S.D., Mandal, P.K., Ghosh, S.,
Surface fitting in SPECT imaging useful for detecting Parkinson's Disease and Scans Without Evidence of Dopaminergic Deficit,
diseases BibRef

Zhang, Y.[Yuyao], Ogunbona, P.O., Li, W.Q.[Wan-Qing], Munro, B., Wallace, G.G.,
Pathological Gait Detection of Parkinson's Disease Using Sparse Representation,
diseases BibRef

Morales, J.M.[Juan-Miguel], Rodriguez, R.[Rafael], Carballo, M.[Maylen], Batista, K.[Karla],
Accuracy to Differentiate Mild Cognitive Impairment in Parkinson's Disease Using Cortical Features,
Springer DOI 1311

Rodriguez-Rojas, R.[Rafael], Sanabria, G.[Gretel], Melie, L.[Lester], Morales, J.M.[Juan-Miguel],
Using Graph Theory to Identify Aberrant Hierarchical Patterns in Parkinsonian Brain Networks,
Springer DOI 1311

Stawarz, M.[Magdalena], Polanski, A.[Andrzej], Kwiek, S.[Stanislaw], Boczarska-Jedynak, M.[Magdalena], Janik, L.[Lukasz], Przybyszewski, A.[Andrzej], Wojciechowski, K.[Konrad],
A System for Analysis of Tremor in Patients with Parkinson's Disease Based on Motion Capture Technique,
Springer DOI 1210

Chen, L.[Lei], Seidel, G.[Gunter], Mertins, A.[Alfred],
Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image,

Szilágyi, S.M.[Sándor M.], Szilágyi, L.[László], Görög, L.K.[Levente K.], Luca, C.T.[Constantin T.], Cozma, D.[Dragos], Ivanica, G.[Gabriel], Benyó, Z.[Zoltán],
An Enhanced Accessory Pathway Localization Method for Efficient Treatment of Wolff-Parkinson-White Syndrome,
Springer DOI 0809

Lee, J.D.[Jiann-Der], Huang, C.H.[Chung-Hsien], Chen, C.W.[Cheng-Wei], Weng, Y.H.[Yi-Hsin], Lin, K.J.[Kun-Ju], Chen, C.T.[Chin-Tu],
A Brain MRI/SPECT Registration System Using an Adaptive Similarity Metric: Application on the Evaluation of Parkinson's Disease,
Springer DOI 0703

Ericsson, A.[Anders], Lonsdale, M.N.[Markus Nowak], Astrom, K.[Kalle], Edenbrandt, L.[Lars], Friberg, L.[Lars],
Decision Support System for the Diagnosis of Parkinson's Disease,
Springer DOI 0506

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Schizophrenia .

Last update:Aug 2, 2021 at 20:26:03