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Complex networks reveal early MRI markers of Parkinson’s disease

Amoroso, Nicola ; La Rocca, Marianna ; Monaco, Alfonso ; Bellotti, Roberto ; Tangaro, Sabina

Medical image analysis, 2018-08, Vol.48, p.12-24 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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Título:
Complex networks reveal early MRI markers of Parkinson’s disease
Autor: Amoroso, Nicola ; La Rocca, Marianna ; Monaco, Alfonso ; Bellotti, Roberto ; Tangaro, Sabina
Assuntos: Algorithms ; Biomarkers - analysis ; Brain ; Complex networks ; Diagnostic systems ; Disease Progression ; Early Diagnosis ; Female ; Health risks ; Humans ; Image Interpretation, Computer-Assisted - methods ; Machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Markers ; Medical imaging ; Middle Aged ; Movement disorders ; MRI ; Muscular system ; Neural networks ; Neurodegenerative diseases ; Neuroimaging ; NMR ; Nuclear magnetic resonance ; Parkinson Disease - diagnostic imaging ; Parkinson's disease ; Patients ; Risk factors ; Support Vector Machine ; Support vector machines
É parte de: Medical image analysis, 2018-08, Vol.48, p.12-24
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SourceType-Scholarly Journals-1
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Descrição: •Our work demonstrates that MRI data, and in particular complex network measures, provide an efficient and accurate description of PD patterns;•Novel MRI markers combined with clinical scores typical of prodromal PD can be used for an accurate early diagnosis;•The proposed approach compares favorably with state-of-the-art methodologies basing on MRI data;•This work demonstrates that MRI data allows diagnostic accuracy which compares well with methodologies including other imaging modalities such as SPECT. [Display omitted] Parkinson’s disease (PD) is the most common neurological disorder, after Alzheimer’s disease, and is characterized by a long prodromal stage lasting up to 20 years. As age is a prominent factor risk for the disease, next years will see a continuous increment of PD patients, making urgent the development of efficient strategies for early diagnosis and treatments. We propose here a novel approach based on complex networks for accurate early diagnoses using magnetic resonance imaging (MRI) data; our approach also allows us to investigate which are the brain regions mostly affected by the disease. First of all, we define a network model of brain regions and associate to each region proper connectivity measures. Thus, each brain is represented through a feature vector encoding the local relationships brain regions interweave. Then, Random Forests are used for feature selection and learning a compact representation. Finally, we use a Support Vector Machine to combine complex network features with clinical scores typical of PD prodromal phase and provide a diagnostic index. We evaluated the classification performance on the Parkinson’s Progression Markers Initiative (PPMI) database, including a mixed cohort of 169 normal controls (NC) and 374 PD patients. Our model compares favorably with existing state-of-the-art MRI approaches. Besides, as a difference with previous approaches, our methodology ranks the brain regions according to disease effects without any a priori assumption.
Editor: Netherlands: Elsevier B.V
Idioma: Inglês

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Complex networks reveal early MRI markers of Parkinson’s disease

Amoroso, Nicola ; La Rocca, Marianna ; Monaco, Alfonso ; Bellotti, Roberto ; Tangaro, Sabina

Netherlands: Elsevier B.V

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