CLASSIFICATION OF PARKINSON DISEASE BASED ON INTER- AND INTRA-REGIONAL BIOMARKERS OF THE BRAIN MOTOR NETWORK USING RESTING STATE FMRI DATA

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GHASEMI MAHDIEH; Mahloojifar ali

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Journal: SIGNAL AND DATA PROCESSING Year:2015 | Volume:- | Issue:2 (SERIAL 22) Page(s): 15-29

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Title

CLASSIFICATION OF PARKINSON DISEASE BASED ON INTER- AND INTRA-REGIONAL BIOMARKERS OF THE BRAIN MOTOR NETWORK USING RESTING STATE FMRI DATA

Author(s)

GHASEMI MAHDIEH | Mahloojifar ali | Issue Writer Certificate

Keywords

FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI)Q2

FUNCTIONAL CONNECTIVITYQ2

RESTING STATEQ2

PARKINSON DISEASEQ2

AMPLITUDE OF LOW FREQUENCY FLUCTUATIONS (ALFF)Q2

REGIONAL HOMOGENEITY (REHO)Q2

CROSS CORRELATION ANALYSIS (CCA)Q2

Abstract

Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movement. Recent studies on the brain function show that there are spontaneous fluctuations between separated regions at rest, known as RESTING STATE network, affected in various disorders. In this paper, we used amplitude of low frequency fluctuation (ALFF) approach for study of intra-regional characteristics and cross-correlation analysis (CCA) for quantifying inter-regional relationship between anatomical regions. We presented FUNCTIONAL CONNECTIVITY networks of the healthy and PD groups based on the results of CCA. By comparing two networks, we conclude some points. Firstly, the activity of cerebellum and basal ganglia areas had a significant negative correlation in PD patients, while this relationship is weak and non-significant in the healthy. We also used mean values of ALFF and ReHo as intra-region biomarkers. These features together with inter-region characteristics are used in discriminative analysis for classification PD and healthy. The result showed 85% accuracy in clustering. In addition, the score index is 89% and Jaccard coefficient of this clustering is 75%. We found that inter-regional features (CCA) were more significant than intra-regional features (ALFF) and FUNCTIONAL CONNECTIVITY between left cerebellum and left putamen was the best discriminator between PD and control.

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APA: Copy

GHASEMI, MAHDIEH, & Mahloojifar, ali. (2015). CLASSIFICATION OF PARKINSON DISEASE BASED ON INTER- AND INTRA-REGIONAL BIOMARKERS OF THE BRAIN MOTOR NETWORK USING RESTING STATE FMRI DATA. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 22)), 15-29. SID. https://sid.ir/paper/160843/en

Vancouver: Copy

GHASEMI MAHDIEH, Mahloojifar ali. CLASSIFICATION OF PARKINSON DISEASE BASED ON INTER- AND INTRA-REGIONAL BIOMARKERS OF THE BRAIN MOTOR NETWORK USING RESTING STATE FMRI DATA. SIGNAL AND DATA PROCESSING[Internet]. 2015;-(2 (SERIAL 22)):15-29. Available from: https://sid.ir/paper/160843/en

IEEE: Copy

MAHDIEH GHASEMI, and ali Mahloojifar, “CLASSIFICATION OF PARKINSON DISEASE BASED ON INTER- AND INTRA-REGIONAL BIOMARKERS OF THE BRAIN MOTOR NETWORK USING RESTING STATE FMRI DATA,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 22), pp. 15–29, 2015, [Online]. Available: https://sid.ir/paper/160843/en

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