Using principal component analysis to visualize the spatial distribution of functional areas of the brain as studied with MRI during motor and sensory activation

1994 ◽  
Author(s):  
Finn Pedersen ◽  
Ewert W. Bengtsson ◽  
Tomas Hindmarsh ◽  
Bo Nordell ◽  
Hans Forssberg
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Activation ◽  
Functional Areas ◽  
The Brain

scholarly journals On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

2016 ◽  
Vol 38 (3) ◽  
pp. 1208-1223 ◽  
Author(s):  
Francisco Jesús Martinez-Murcia ◽  
Meng-Chuan Lai ◽  
Juan Manuel Górriz ◽  
Javier Ramírez ◽  
Adam M. H. Young ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Brain Structure ◽  
Site Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Weighted Principal Component ◽  
Structure Heterogeneity ◽  
The Brain

scholarly journals The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021 ◽  
Vol 13 (24) ◽  
pp. 13859
Author(s):  
Shu Wu
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Forest Fire ◽  
Forest Fires ◽  
Inner Mongolia ◽  
Information Diffusion ◽  
Principal Component ◽  
Component Analysis ◽  
Expected Number

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

scholarly journals A Novel Method of Early Diagnosis of Alzheimer’s Disease Based on EEG Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dhiya Al-Jumeily ◽  
Shamaila Iram ◽  
Francois-Benois Vialatte ◽  
Paul Fergus ◽  
Abir Hussain
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component Analysis ◽  
Healthy Subjects ◽  
Early Stage ◽  
Measurement Techniques ◽  
Principal Component ◽  
Component Analysis ◽  
Neural Synchrony ◽  
The Brain

Studies have reported that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer’s disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-WhitneyUtest) to compare the results.

scholarly journals HUBUNGAN ANTARA KONDISI OSEANOGRAFI DAN DISTRIBUSI SPASIAL IKAN PELAGIS DI WILAYAH PENGELOLAAN PERIKANAN NEGARA REPUBLIK INDONESIA (WPP NRI) 712 LAUT JAWA

2019 ◽  
Vol 25 (1) ◽  
pp. 1
Author(s):  
Asep Ma'mun ◽  
Asep Priatna ◽  
Khairul Amri ◽  
Erfind Nurdin
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Environmental Factors ◽  
Direct Interaction ◽  
Principal Component ◽  
Environmental Parameters ◽  
Component Analysis ◽  
Pelagic Fish ◽  
Oceanographic Parameter ◽  
October 17

Kepadatan dan penyebaran sumber daya ikan di perairan banyak dipengaruhi oleh variasi kondisi oseanografinya. Untuk mengkaji interaksi antara kondisi oseanografi dengan sebaran spasial ikan pelagis di Laut Jawa, telah dilakukan penelitian hydro acoustic dengan menggunakan KR. Bawal Putih III pada 17 Oktober-11 November 2017. Akuisisi data akustik menggunakan multi beam Simrad ME (70-120 kHz) dengan posisi transduser dipasang pada lunas kapal. Parameter lingkungan (oksigen, pH, salinitas, klorofil, suhu) diukur menggunakan CTD SBE 19 plus V2 dan parameter oseanogafi fisik (arah dan kecepatan arus) menggunakan ARM current meter, keduanya diturunkan secara vertikal sesuai kedalaman pada 48 stasiun. Analisa korelasi antara parameter oseanografi dengan kelimpahan ikan dan distribusi spasial menggunakan analisis statistik PCA (Principal Component Analysis). Hasil penelitian menunjukkan densitas ikan pelagis dipengaruhi secara berturut-turut oleh salinitas, oksigen, klorofil, pH dan suhu. Urutan ini didasarkan pada jarak dan kedekatan terhadap garis yang dibentuk faktor lingkungan terhadap titik pusat korelasi. Komponen lingkungan yang memiliki interaksi langsung dengan kelimpahan ikan pelagis adalah salinitas dan oksigen. Kedua faktor ini merupakan faktor utama dalam kegiatan osmoregulasi dan pembentukan energi untuk tubuh ikan, sementara keempat faktor lingkungan lainnya (klorofil pH, suhu dan kecepatan arus) berkorelasi secara parsial terhadap keberadaan ikan pelagis.The density and distribution of fish resources in the waters are much influenced by variations in oceanographic conditions. To examine interaction between oceanographic condition with spatial distribution of pelagic fish in Java Sea, hydroacoustic research was done using KR. Bawal Putih III on October 17 to November 11, 2017. Acoustic data acquisition used Simrad ME multi beam (70-120 kHz) with the position of the transducer installed on the keel. Environmental parameters (oxygen, pH, salinity, chlorophyll, temperature) were measured using the SBE 19 plus V2 CTD and physical oceanographic parameter (current direction and speed) using the ARM current meter, both are lowered vertically according to depth at 48 station. Correlation analysis between oceanographic parameter with fish abundance and spatial distribution using PCA (Principal Component Analysis) statistical analysis. Results show that density of pelagic fish was influenced respectively by salinity, oxygen, chlorophyll, pH and temperature. This sequence based on distance and proximity to the line formed by environmental factors towards the center of correlation. The environmental components that have a direct interaction with the abundance of pelagic fish are salinity and oxygen. These two factors are the main factors in osmoregulation and energy formation for fish bodies, while the other four environmental factors (chlorophyll pH, temperature and current velocity) correlate partially to the presence of pelagic fish. 

scholarly journals Principal component analysis of rainwater composition at BAPMoN stations in India

MAUSAM ◽  
2022 ◽  
Vol 44 (2) ◽  
pp. 179-184
Author(s):  
B. MUKHOPADHYAY ◽  
S.S. SINGH ◽  
S. V. DATAR
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Distribution Characteristics ◽  
Temporal And Spatial Distribution ◽  
Rainwater Composition ◽  
Spatial Distribution Characteristics ◽  
Temporal And Spatial

Data from Indian BAPMoN stations were analyzed using the Principal Component Analysis (PCA) by examining broadly the temporal and spatial distribution characteristics of the ions, from mineral and gaseous sources, observed in rainwater samples collected over the Indian BAPMoN stations over along period (1976-87), The results show that the pH of rainwater can be generally explained In terms of the concentration of SO.-2 , NO3 -1, CI-l, Ca+2 and Na+1 ion~, However, other mechanisms could determine the overall nature of the Interactions, These mechanisms have become more clear by performing principal component analysis.

scholarly journals Applicability of hyperspectral technology for in situ phytoremediation

2008 ◽  
pp. 71-78
Author(s):  
Attila Nagy ◽  
János Tamás
Keyword(s):  
Heavy Metal ◽  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Metal Content ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Vegetation Types ◽  
Barren Area ◽  
Spectral Profiles

The characterization of heavy metal polluted abandoned mining sites is a complicated assignment due to the variable spatial distribution of the pollutants, therefore complex integrated method is required in order to assess precisely the amount and the distribution of the contaminants. The examined area is flotation sludge reservoir of abandoned Pb-Zn mining site with serious heavy metal contamination. located in Gyöngyösoroszi, Northern Hungary.The hyperspectral image of the flotation sludge is obtained by using a Digital Airborne Imaging Spectrometer DAIS 7915, in the frame of DLR HySens first Hungarian hyperspectral flight campaign (21/08/2002). Parallel to the flight campaign heavy metal content of soil samples were examined from the area of the flotation sludge. The analysis of hyperspectral data was verified by the examination of mine tailing samples by FPXRF (Field Portable X-ray Fluorescence spectrometry) (NITON XL-703).Determinations of heavy metal containing minerals are based on the spectral profiles of the pixels of the area with using USGIS standard spectral profiles of the examined materials (galena, pyrite, sphalerite, goethite and jarosit). Applying the Spectral Angle Mapper with BandMax classification the distribution of minerals (galena, pyrite, sphalerite, goethite, jarosit) in the area was defined. The mineral formation occurs especially at the levees and the barren places of the Szárazvölgyi flotation sludge reservoir. Based on the statistic results of the samples, principal component analysis and correlation coefficient between the different metal content of the samples were calculated. The highest correlations were found between Pb-Zn, Fe-Zn and between Fe-Pb. This prove the results of the principal component analysis, where usually Pb, Zn, Fe introduce the main component. Canopy analysis was also carried out with the hyperspectal image in order to classify the differences between vegetation types at the Szárazvölgy flotation sludge reservoir and analyse the applicability of it. Supervised classification methods were used to distinguish 8 vegetation types based on the spectral properties of the area. The results of the classifications were compared to a ground truth image, based on ortophoto, topographic map, and GPS based field data collection. According to results of the comparison, the paralellpiped classification method is proved to be appropriate method based on the overall accuracy of canopy classification, which was 54% due to heterogeneity of the vegetation.  The results of hyperspectral data and FPXRF analysis suggest that Pb, Zn and Fe containing minerals have similar spatial distribution in the examined and barren area. Based on this study hyperspectral remote sensing is likely to be an effective tool for the characterization and modeling the distribution of Pb, Zn and Fe containing minerals at the examined heavy metal polluted sites. Further more, based on the vegetation analysis plant species for phytoremediation can be defined.

Topographic Structure of Alpha Wave Activity by Means of Principal Component Analysis

1993 ◽  
Vol 5 (3) ◽  
pp. 209-213
Author(s):  
Yoichi Tsuji ◽  
◽  
Hidekazu Takase ◽  
Kazuyuki Nagasawa ◽  
Misao Itoi
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Wave Activity ◽  
Alpha Activity ◽  
Analysis Method ◽  
Alpha Wave ◽  
The Brain ◽  
Topographic Structure

In order to find out the topographic structure in alpha wave activity, the principal component analysis method was applied to 17 channel scalp EEGs. Three kind of topographic structures were obtained from the factor loadings of principal components as follows: frontal-occipital, centoral or lateral activity. These structures may be related to the mechanism of alpha activity on the brain.

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