Relevant bibliographies by topics / Functional data analysis

Academic literature on the topic 'Functional data analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Functional data analysis"

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MIZUTA, Masahiro. "Functional Data and Functional Data Analysis." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 17, no. 4 (2005): 413–17. http://dx.doi.org/10.3156/jsoft.17.413.

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Wang, Jane-Ling, Jeng-Min Chiou, and Hans-Georg Müller. "Functional Data Analysis." Annual Review of Statistics and Its Application 3, no. 1 (June 2016): 257–95. http://dx.doi.org/10.1146/annurev-statistics-041715-033624.

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Marron, J. S., J. O. Ramsey, and B. W. Silverman. "Functional Data Analysis." Journal of the American Statistical Association 93, no. 443 (September 1998): 1232. http://dx.doi.org/10.2307/2669864.

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Georgiev, Alexander A. "Functional Data Analysis." Technometrics 40, no. 3 (August 1998): 260–61. http://dx.doi.org/10.1080/00401706.1998.10485535.

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Muraki, Eiji, J. O. Ramsay, and B. W. Silverman. "Functional Data Analysis." Journal of Educational and Behavioral Statistics 24, no. 1 (1999): 101. http://dx.doi.org/10.2307/1165264.

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Ahn, Kyungmin. "Comparative study between functional data analysis and multivariate data analysis for functional data." Journal of the Korean Data And Information Science Society 33, no. 5 (September 30, 2022): 817–27. http://dx.doi.org/10.7465/jkdi.2022.33.5.817.

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Yamanishi, Yoshihiro, and Yutaka Tanaka. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 307–17. http://dx.doi.org/10.5183/jjscs1988.15.2_307.

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Tokushige, Shuichi, Koichi Inada, and Hiroshi Yadohisa. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 319–26. http://dx.doi.org/10.5183/jjscs1988.15.2_319.

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Mizuta, Masahiro. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 327–33. http://dx.doi.org/10.5183/jjscs1988.15.2_327.

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Altland, Henry W. "Applied Functional Data Analysis." Technometrics 45, no. 1 (February 2003): 101–2. http://dx.doi.org/10.1198/tech.2003.s16.

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Dissertations / Theses on the topic "Functional data analysis"

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Yao, Fang. "Functional data analysis for longitudinal data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Hadjipantelis, Pantelis-Zenon. "Functional data analysis in phonetics." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/62527/.

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The study of speech sounds has established itself as a distinct area of research, namely Phonetics. This is because speech production is a complex phenomenon mediated by the interaction of multiple components of a linguistic and non-linguistic nature. To investigate such phenomena, this thesis employs a Functional Data Analysis framework where speech segments are viewed as functions. FDA treats functions as its fundamental unit of analysis; the thesis takes advantage of this, both in conceptual as well as practical terms, achieving theoretical coherence as well as statistical robustness in its insights. The main techniques employed in this work are: Functional principal components analysis, Functional mixed-effects regression models and phylogenetic Gaussian process regression for functional data. As it will be shown, these techniques allow for complementary analyses of linguistic data. The thesis presents a series of novel applications of functional data analysis in Phonetics. Firstly, it investigates the influence linguistic information carries on the speech intonation patterns. It provides these insights through an analysis combining FPCA with a series of mixed effect models, through which meaningful categorical prototypes are built. Secondly, the interplay of phase and amplitude variation in functional phonetic data is investigated. A multivariate mixed effects framework is developed for jointly analysing phase and amplitude information contained in phonetic data. Lastly, the phylogenetic associations between languages within a multi-language phonetic corpus are analysed. Utilizing a small subset of related Romance languages, a phylogenetic investigation of the words' spectrograms (functional objects defined over two continua simultaneously) is conducted to showcase a proof-of-concept experiment allowing the interconnection between FDA and Evolutionary Linguistics.
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Bacchetti, Enrico <1997&gt. "Functional Data Analysis - An application to weather data." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19503.

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The goal of this project is to introduce the topic of Functional Data Analysis (FDA) a relative new research area that resembles and puts together different fields and sectors, such as Statistics, Econometrics, Machine Learning, ... Actually, it can be considered a branch of Statistics. Functional Data Analysis was developed in order to address some data analysis problem, especially for what concerns phenomena that show, by nature, a relative ”smooth” behavior, i.e. that can be represented by curves that present some sort of regularity, and that varies over a continuum. My work has been articulated in the following manner: the first Chapter (1) is dedicated to the main and must-known theory necessary for understanding what FDA is and what kind of works can be devised using its particular techniques. Chapter 2 presents the data that are deployed in carrying out my empirical analysis and in particular: the type of data with all their characteristics, the preliminary analysis through which data has been processed in order to clean and finalize them. The third Chapter (3) contains the empirical analysis and includes all the graphs and plots that are helpful to understand the results achieved. Finally, Chapter 4 is devoted to conclusion and remarks and provide hints for developing further the work. As for the empirical part, I have focused my attention on weather and climate and in particular on daily temperature and precipitation amounts related to 30 different weather stations in Europe. As for the code part, nowadays many packages or toolboxes for different languages (R, MATLAB, python) have been devised by practitioners aiming to address all the complexity of functional data analysis. In my application, I have opted for using MATLAB and in particular the FDA Toolbox.
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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.
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Zoglat, Abdelhak. "Analysis of variance for functional data." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10136.

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In this dissertation we present an extension to the well known theory of multivariate analysis of variance. In various situations data are continuous stochastic functions of time or space. The speed of pollutants diffusing through a river, the real amplitude of a signal received from a broadcasting satellite, or the hydraulic conductivity rates at a given region are examples of such processes. After the mathematical background we develop tools for analyzing such data. Namely, we develop estimators, tests, and confidence sets for the parameters of interest. We extend these results, obtained under the normality assumption, and show that they are still valid if this assumption is relaxed. Some examples of applications of our techniques are given. We also outline how the latter can apply to random and mixed models for continuous data. In the appendix, we give some programs which we use to compute the distributions of some of our tests statistics.
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Friman, Ola. "Adaptive analysis of functional MRI data /." Linköping : Univ, 2003. http://www.bibl.liu.se/liupubl/disp/disp2003/tek836s.pdf.

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Martinenko, Evgeny. "Functional Data Analysis and its application to cancer data." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6323.

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The objective of the current work is to develop novel procedures for the analysis of functional data and apply them for investigation of gender disparity in survival of lung cancer patients. In particular, we use the time-dependent Cox proportional hazards model where the clinical information is incorporated via time-independent covariates, and the current age is modeled using its expansion over wavelet basis functions. We developed computer algorithms and applied them to the data set which is derived from Florida Cancer Data depository data set (all personal information which allows to identify patients was eliminated). We also studied the problem of estimation of a continuous matrix-variate function of low rank. We have constructed an estimator of such function using its basis expansion and subsequent solution of an optimization problem with the Schattennorm penalty. We derive an oracle inequality for the constructed estimator, study its properties via simulations and apply the procedure to analysis of Dynamic Contrast medical imaging data.
Ph.D.
Doctorate
Mathematics
Sciences
Mathematics
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Kröger, Viktor. "Classification in Functional Data Analysis : Applications on Motion Data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184963.

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Anterior cruciate knee ligament injuries are common and well known, especially amongst athletes.These injuries often require surgeries and long rehabilitation programs, and can lead to functionloss and re-injuries (Marshall et al., 1977). This work aims to explore the possibility of applyingsupervised classification on knee functionality, using different types of models, and testing differentdivisions of classes. The data used is gathered through a performance test, where individualsperform one-leg hops with motion sensors attached to their bodies. The obtained data representsthe position over time, and is considered functional data.With functional data analysis (FDA), a process can be analysed as a continuous function of time,instead of being reduced to finite data points. FDA includes many useful tools, but also somechallenges. A functional observation can for example be differentiated, a handy tool not found inthe multivariate tool-box. The speed, and acceleration, can then be calculated from the obtaineddata. How to define "similarity" is, on the other hand, not as obvious as with points. In this work,an FDA-approach is taken on classifying knee kinematic data, from a long-term follow-up studyon knee ligament injuries.This work studies kernel functional classifiers, and k-nearest neighbours models, and performssignificance tests on the model accuracy, using re-sampling methods. Additionally, depending onhow similarity is defined, the models can distinguish different features of the data. Attempts atutilising more information through incorporation of ensemble-methods, does not exceed the singlemodels it is created from. Further, it is shown that classification on optimised sub-domains, canbe superior to classifiers using the full domain, in terms of predictive power.
Främre korsbandsskador är vanliga och välkända skador, speciellt bland idrottsutövare. Skadornakräver ofta operationer och långa rehabiliteringsprogram, och kan leda till funktionell nedsättningoch återskador (Marshall et al., 1977). Målet med det här arbetet är att utforska möjligheten attklassificera knän utifrån funktionalitet, där utfallet är känt. Detta genom att använda olika typerav modeller, och genom att testa olika indelningar av grupper. Datat som används är insamlatunder ett prestandatest, där personer hoppat på ett ben med rörelsesensorer på kroppen. Deninsamlade datan representerar position över tid, och betraktas som funktionell data.Med funktionell dataanalys (FDA) kan en process analyseras som en kontinuerlig funktion av tid,istället för att reduceras till ett ändligt antal datapunkter. FDA innehåller många användbaraverktyg, men även utmaningar. En funktionell observation kan till exempel deriveras, ett händigtverktyg som inte återfinns i den multivariata verktygslådan. Hastigheten och accelerationen kandå beräknas utifrån den insamlade datan. Hur "likhet" är definierat, å andra sidan, är inte likauppenbart som med punkt-data. I det här arbetet används FDA för att klassificera knärörelsedatafrån en långtidsuppföljningsstudie av främre korsbandsskador.I detta arbete studeras både funktionella kärnklassificerare och k-närmsta grannar-metoder, och ut-för signifikanstest av modellträffsäkerheten genom omprovtagning. Vidare kan modellerna urskiljaolika egenskaper i datat, beroende på hur närhet definieras. Ensemblemetoder används i ett försökatt nyttja mer av informationen, men lyckas inte överträffa någon av de enskilda modellerna somutgör ensemblen. Vidare så visas också att klassificering på optimerade deldefinitionsmängder kange en högre förklaringskraft än klassificerare som använder hela definitionsmängden.
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Alshabani, Ali Khair Saber. "Statistical analysis of human movement functional data." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421478.

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Prentius, Wilmer. "Exploring Cumulative Incomefunctions by Functional Data Analysis." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122685.

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Cumulative incomes can be seen as the added yearly incomes for some distinct amount of years. It can also be thought of as a continuous curve, where income continuously flows into ones account. The analyzing of curves, or functions, instead of uni- or multivariate data, needs and enables different approaches. In this thesis, methods called Functional Data Analysis are used to show how analyzes of such cumulative income curves can be done, mainly through functional adaptions of principal component analysis and linear regression. Results shows how the smoothing of curves helps to decrease variances in a bias-variance trade-off, while having problems accounting for data containing many low valued observations. Furthermore, results indicates that education might have an effect, when controlling for employment rate, in the sample.
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Books on the topic "Functional data analysis"

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1952-, Silverman B. W., ed. Functional data analysis. New York: Springer, 1997.

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Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-7107-7.

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Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/b98888.

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1952-, Silverman B. W., and Knovel (Firm), eds. Functional data analysis. 2nd ed. New York: Springer, 2006.

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author, Silverman B. W., ed. Functional Data Analysis. New York, NY: Springer New York, 1997.

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The Oxford handbook of functional data analysis. Oxford: Oxford University Press, USA, 2011.

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Srivastava, Anuj, and Eric P. Klassen. Functional and Shape Data Analysis. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4020-2.

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Morettin, Pedro A., Aluísio Pinheiro, and Brani Vidakovic. Wavelets in Functional Data Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59623-5.

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Analysis of variance for functional data. Boca Raon: CRC Press, Taylor & Francis Group, 2014.

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Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge: Cambridge University Press, 2011.

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Book chapters on the topic "Functional data analysis"

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Müller, Hans-Georg. "Functional Data Analysis." In International Encyclopedia of Statistical Science, 554–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_263.

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Xu, Yuhang. "Functional Data Analysis." In Springer Handbook of Engineering Statistics, 67–85. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-7503-2_4.

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García de Linares, A. J., and L. de la Peña Fernández. "An Anatomical and Functional Model for the Study of Cortical Functions." In Medical Data Analysis, 101–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45497-7_15.

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Eidhammer, Ingvar, Harald Barsnes, and Svein-Ole Mikalsen. "MassSorter: Peptide Mass Fingerprinting Data Analysis." In Functional Proteomics, 345–59. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-398-1_23.

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Ramsay, James O., Tim Ramsay, and Laura M. Sangalli. "Spatial Functional Data Analysis." In Contributions to Statistics, 269–75. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_42.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Functional Data Analysis I." In Regression Analysis in Medical Research, 393–406. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71937-5_25.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Functional Data Analysis II." In Regression Analysis in Medical Research, 407–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71937-5_26.

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Crainiceanu, Ciprian M., Jeff Goldsmith, Andrew Leroux, and Erjia Cui. "Multilevel Functional Data Analysis." In Functional Data Analysis with R, 243–64. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003278726-8.

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Silverman, Bernard W. "Function Estimation and Functional Data Analysis." In Progress in Mathematics, 407–27. Basel: Birkhäuser Basel, 1994. http://dx.doi.org/10.1007/978-3-0348-9112-7_17.

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Xu, Ying. "Microarray Gene Expression Data Analysis." In Microbial Functional Genomics, 177–206. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471647527.ch7.

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Conference papers on the topic "Functional data analysis"

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Jou, Yow-Jen, Chien-Chia Huang, Jennifer Yuh-Jen Wu, George Maroulis, and Theodore E. Simos. "Functional Canonical Analysis Between Functional and Interval Data." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225345.

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Frončková, Kateřina, and Pavel Pražák. "Functional Data Analysis in Econometrics." In Hradec Economic Days 2021, edited by Jan Maci, Petra Maresova, Krzysztof Firlej, and Ivan Soukal. University of Hradec Kralove, 2021. http://dx.doi.org/10.36689/uhk/hed/2021-01-017.

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Hamdi, H., E. Zirbes, and M. Costa Sousa. "Analysis of Well Production Data Using Functional Data Analysis." In 84th EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.2023101054.

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Gattone, Stefano A., and Tonio Di Battista. "Density modelling with functional data analysis." In CARMA 2023 - 5th International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2023. http://dx.doi.org/10.4995/carma2023.2023.16467.

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Recent technological advances have eased the collection of big amounts of data in many research fields. In this scenario density estimation may represent an important source of information. One dimensional density functions represent a special case of functional data subject to the constraints to be non-negative and with a constant integral equal to one. Because of these constraints, a naive application of functional data analysis (FDA) methods may lead to non-valid results. To solve this problem, by means of an appropriate transformation, densities are embedded in the Hilbert space of square integrable functions where standard FDA methodologies can be applied.
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Allen, Genevera I., and Michael Weylandt. "Sparse and Functional Principal Components Analysis." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755778.

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Gurram, Mohana M., and Christopher D. Knight. "Functional Hierarchical Search Results Data Analysis." In 2008 IEEE Aerospace Conference. IEEE, 2008. http://dx.doi.org/10.1109/aero.2008.4526583.

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Lachish, O., E. Marcus, S. Ur, and A. Ziv. "Hole analysis for functional coverage data." In Proceedings of 39th Design Automation Conference. IEEE, 2002. http://dx.doi.org/10.1109/dac.2002.1012733.

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Lachish, Oded, Eitan Marcus, Shmuel Ur, and Avi Ziv. "Hole analysis for functional coverage data." In the 39th conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/513918.514119.

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Chawla, Manisha, and Krishna P. Miyapuram. "Meta-analysis of functional neuroimaging data." In 2013 IEEE Second International Conference on Image Information Processing (ICIIP). IEEE, 2013. http://dx.doi.org/10.1109/iciip.2013.6707594.

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Gritsenko, Pavel S., Igor S. Gritsenko, Askar Zh Seidakhmet, and Azizbek E. Abduraimov. "Generation of RGB-D data for SLAM using robotic framework V-REP." In INTERNATIONAL CONFERENCE “FUNCTIONAL ANALYSIS IN INTERDISCIPLINARY APPLICATIONS” (FAIA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5000659.

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Reports on the topic "Functional data analysis"

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Parzen, Emanuel. Multi-Sample Functional Statistical Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada210992.

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Parzen, Emanuel, Scott D. Grimshaw, and William P. Alexander. Functional Statistical Data Analysis and Modeling. Fort Belvoir, VA: Defense Technical Information Center, February 1990. http://dx.doi.org/10.21236/ada219387.

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Watts, Adam. Analysis of Variance of Functional Data (F-ANOVA). Office of Scientific and Technical Information (OSTI), January 2024. http://dx.doi.org/10.2172/2282510.

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Gao, Wenjie, Luisa Gianuca, (Kevin) Kong, Justin Lee, Kimberly Sheldon, Allon Percus, Jeffrey Hyman, et al. Harnessing Uncertainty through Functional Data Analysis in Gas Breakthrough. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1996134.

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Moonwichit, Nitipon, Catherine Ma, Theeraphat Pothisawang, Joy Shin, Allon Percus, Jeffrey Hyman, Philip Stauffer, and Justin Strait. Improving Density Curve Predictions and Uncertainty Quantification with Functional Data Analysis. Office of Scientific and Technical Information (OSTI), May 2024. http://dx.doi.org/10.2172/2346044.

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Liming, Kieran. Tests of Equality Between Groups of Spatially Correlated Temporal Data using a Functional Data Analysis Approach. Ames (Iowa): Iowa State University, December 2020. http://dx.doi.org/10.31274/cc-20240624-1391.

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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 18. Hilbert Press, 2018. http://dx.doi.org/10.56441/hilbertpress.1818.9585.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 20. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2048.3738.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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9

Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 19. Hilbert Press, 2019. http://dx.doi.org/10.56441/hilbertpress.1927.9364.

Full text
Abstract:
CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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10

Liu, Baiyan, Bing Yan, Hailin Jiang, Xuewei Zhao, Luyao Wang, Tie Li, and Fuchun Wang. The effectiveness of herbal acupoint application for functional diarrhea Protocol for a meta-analysis and data mining. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2021. http://dx.doi.org/10.37766/inplasy2021.7.0094.

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