DOCSLIB.ORG

Image Analysis and Statistical Inference in Neuroimaging with R

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Image Analysis and Statistical Inference in Neuroimaging with R Image Analysis and Statistical Inference in Neuroimaging with R K. Tabelowa,∗, J.D. Claydenb, P. Lafaye de Micheauxc, J. Polzehla, V.J. Schmidd, B. Whitchere aWeierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany bInstitute of Child Health, University College London, London, United Kingdom cMathematics and Statistics Department, Universit´ede Montr´eal,Canada dDepartment of Statistics, Ludwig-Maximilians-Universit¨at,Munich, Germany eGlaxoSmithKline, Clinical Imaging Centre, London, United Kingdom Abstract R is a free software environment for statistical computing and graphics. It compiles and runs on almost every UNIX platform, Windows, and Mac OS. In this paper, we report on several R packages built for the analysis of neuroimaging data in the context of functional Magnetic Resonance Imaging, Diffusion Tensor Imaging, and Dynamic Contrast-Enhanced Magnetic Resonance Imaging. We will review their methodology and give an overview over their capabilities for neuroimaging. Finally, we summarize some of the current activities in the area of neuroimaging software in R in general. Introduction is in addition to a complete set of open-access manuals about the R language. The rapid progress of research in the neuroscience and neu- Why is it worthwhile to consider just another programming roimaging fields has been accompanied by the development of environment for neuroimaging? R is the free and platform- many excellent analysis software tools. These are implemented independent quasi-standard computational environment within in a variety of computer languages and programming environ- the statistics community. R grants access to many well- ments, such as Matlab, IDL, Python, C/C++ and others. This developed statistical tools needed for the analysis of neuroimag- diversity has developed over time through a combination of ing data. R easily integrates, other software can be used from user preferences and the strengths/weaknesses of the computing within R and R can be used within a more general workflow. environments. Many of these software tools are freely avail- Finally, special R packages for neuroimaging provide enhanced able, like SPM (Ashburner et al., 2008), AFNI (Cox, 1996), functionality which is not available elsewhere. FSL (Smith et al., 2004), Freesurfer, or BrainVISA, some are In this paper, we report on several R packages built for the commercial like BrainVoyager, and Analyze. Typically, the analysis of neuroimaging data in various context. We will software tools can be extended by the user to fit their needs in shortly review the methodology of each package to give an im- the data analysis. The NIPY (Millman and Brett, 2007) project pression of their capabilities for neuroimaging. The paper is e.g. is written in Python and a explicitely allows for the inte- organized by the type of data which is to be analyzed and cov- gration, modification and extension of code. ers functional Magnetic Resonance Imaging (fMRI), Diffusion R is a free software environment for statistical computing Tensor Imaging (DTI), and Dynamic Contrast-Enhanced MRI and graphics (R Development Core Team, 2010). It com- (DCE-MRI). In the discussion we want to summarize some of piles and runs on almost every UNIX platform, Windows, the activities in the area of neuroimaging software in R in gen- R and Mac OS. Access to is provided via the Comprehensive eral. R Archive Network (CRAN, http://cran.r-project.org) and R-Forge (http://r-forge.r-project.org). R pro- vides a wide range of statistical (linear and nonlinear regres- On features of R sion modelling, classical statistical tests, time-series analysis, classification, clustering, etc...) and graphical techniques, and R is a high-level programming environment which is typ- is highly extensible. As of November 2010, the CRAN package ically used by its command-line interface (though Graphical repository features over 2600 separate packages contributed by User Interfaces (GUI) do also exist). It allows for interactive R users. A recent community website (http://crantastic. data analysis as well as easy creation of scripts for bulk data org) provides the facilities to search for, review and tag CRAN processing. R is the quasi-standard environment for the devel- packages. Several mailing lists are maintained in order to pro- opment of new statistical methods, often overtaking commer- vide updates and access to literally thousands of R users. This cial statistical packages. Yet its matrix computational capabili- ties also compare well with those e.g. in Matlab. ∗Corresponding author The concept of packages as extensions to the R base system is Email address: [email protected] (K. Tabelow) one of its greatest strengths. R easily integrates compiled code Preprint submitted to Elsevier December 2, 2010 written in low-level languages like C/C++, or FORTRAN pro- the same name) at the Mayo Foundation (Biomedical Imaging viding the ground for efficient programming of computationally Resource, 2001). An Analyze (7.5) format image is comprised expensive algorithms combined with an easy-to-use interface at of two files, the “img” and “hdr” files, that contain the data and the scripting level. information about the acquisition itself. A more recent adaption One of the major difficulties when using R with neuroimag- of this format is known as NIfTI-1 and is a product of the Data ing datasets is R’s memory usage. By default R stores data in Format Working Group (DFWG) from the Neuroimaging Infor- computer main memory. Data are usually retained in double matics Technology Initiative (NIfTI). The NIfTI-1 data format precision. R has no reference type, and therefore usually gen- is almost identical to the ANALYZE format, but offers a few erates a copy of the data every time a function is called. This improvements: merging of the header and image information can, in case of inefficient programming, lead to several copies into one file (.nii), re-organization of the 348-byte fixed header of the data in memory and a situation where demand exceeds into more relevant categories and the possibility of extending the available memory. This can be avoided by calls to low-level the header information. languages. The packages AnalyzeFMRI (Marchini and Lafaye de Regardless of largely increased memory in recent years, there Micheaux, 2010), fmri, tractor.base, and dcemriS4 (Whitcher are also several projects and packages for R to overcome the et al., 2010a) all provide functions that read/write ANALYZE limitations for huge datasets and grant access to it as fast and and NIfTI files. easy as in memory. One of them is the ff (Adler et al., 2010) Additionally fmri provides capabilities to read and write AF- which provides flexible data structures that are stored on disk NIs HEAD/BRIK files. but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory. Another Functional MRI project is the BigMemory project (http://www.bigmemory. org) which supports the creation, storage, access, and manip- Functional Magnetic Resonance Imaging (fMRI) has become ulation of massive matrices. On mid-term these technologies the most informative tool for in-vivo examination of human will be included in the packages described in this paper. brain function on small spatial scales. It is nowadays utilized R supports modern concepts like object-oriented program- both in research as well as in clinical applications such as diag- ming, which is provided as implementation of so-called S4- nosis and treatment of brain lesions. classes and methods (Chambers, 2008). While not as strict as in low-level languages like C/C++ it allows for class/method Package AnalyzeFMRI definition, data encapsulation, polymorphism, etc. The analysis of huge datasets with sophisticated methodol- AnalyzeFMRI is a package originally written by J. Mar- ogy comes at the cost of large computational expenses. R pro- chini (Marchini, 2002) for the processing and analysis of vides the possibility to parallelize code natively in R, or within large structural and functional MRI data sets under the ANA- the C/C++ code. It is also possible to include computation on a LYZE format. It has been updated to include new functional- graphic card (GPU) instead of CPU, which in appropriate cases ity: conversion from ANALYZE to NIfTI, complete NIfTI in- may benefit by orders of magnitude from the speed of modern put/output, functions to obtain spatial coordinates from voxel GPU’s. indices and vice-versa, various geometrical utilities, cross- platform visualization based on Tcl/Tk components, and spa- tial/temporal ICA (Independent Component Analysis) via a Data Input/Output graphical user interface (GUI), see figure 1. Independent component analysis is a statistical technique The industry standard format, for data coming off a clinical that can recover hidden underlying source signals from an ob- imaging device, is DICOM (Digital Imaging and Communica- served mixture of these sources. The only hypothesis made to tions in Medicine). The DICOM “standard” is very broad and solve this problem (known as the blind source separation prob- very complicated. Roughly speaking each DICOM-compliant lem) is that the sources are statistically mutually independent file is a collection of fields organized into two four-byte se- and not Gaussian. ICA is now used to analyze fMRI data since quences (group,element) that are represented as hexadecimal the late 1990’s (McKeown et al., 1998) and has been detailed numbers and form a tag. The (group,element) combination an- in many papers (see e.g. Stone, 2002; Thomas et al., 2002). nounces what type of information is coming next. There is no Note that ICA can be defined in two dual approaches: spatial fixed number of bytes for a DICOM header. ICA (sICA) and temporal ICA (tICA), the latter being not really The packages oro.dicom (Whitcher, 2010), fmri (Tabelow used in fMRI studies due to computational difficulties in diag- and Polzehl, 2010c) and tractor.base (Clayden, 2010b) provide onalizing the huge data correlation matrix in this case.
Load more

Recommended publications
  • Accurate Segmentation of Brain MR Images
    Accurate segmentation of brain MR images Master of Science Thesis in Biomedical Engineering ANTONIO REYES PORRAS PÉREZ Department of Signals and Systems Division of Biomedical Engineering CHALMERS UNIVERSITY OF TECHNOLOGY Göteborg, Sweden, 2010 Report No. EX028/2010 Abstract Full brain segmentation has been of significant interest throughout the years. Recently, many research groups worldwide have been looking into development of patient-specific electromagnetic models for dipole source location in EEG. To obtain this model, accurate segmentation of various tissues and sub-cortical structures is thus required. In this project, the performance of three of the most widely used software packages for brain segmentation has been analyzed: FSL, SPM and FreeSurfer. For the analysis, real images from a patient and a set of phantom images have been used in order to evaluate the performance r of each one of these tools. Keywords: dipole source location, brain, patient-specific model, image segmentation, FSL, SPM, FreeSurfer. Acknowledgements To my advisor, Antony, for his guidance through the project. To my partner, Koushyar, for all the days we have spent in the hospital helping each other. To the staff in Sahlgrenska hospital for their collaboration. To MedTech West for this opportunity to learn. Table of contents 1. Introduction ......................................................................................................................................... 1 2. Magnetic resonance imaging ..............................................................................................................
    [Show full text]
  • 1 Structural and Functional Alterations in the Brain Gray Matter
    Structural and functional alterations in the brain gray matter among first-degree relatives of schizophrenia patients: a multimodal meta-analysis of fMRI and VBM studies Running title: Familial risk for schizophrenia and alterations in the brain Aino I. L. Saarinen, PhD1,2,3,*, Sanna Huhtaniska, MD, PhD4, Juho Pudas, MB3, Lassi Björnholm, MD3, Tuomas Jukuri, MD, PhD3, Jussi Tohka, MD, PhD5, Niklas Granö, PhD6, Jennifer H. Barnett, PhD7,8, Vesa Kiviniemi, MD, PhD9,10, Juha Veijola, MD, PhD3,10,11, Mirka Hintsanen, PhD1, Johannes Lieslehto, MD, PhD 12 1 Research Unit of Psychology, University of Oulu, Finland 2 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland 3 Research Unit of Clinical Neuroscience, Department of Psychiatry, University of Oulu, Finland 4 Center for Life Course Health Research, University of Oulu, Finland 5 A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland 6 Helsinki University Hospital, Department of Adolescent Psychiatry, Finland 7 Cambridge Cognition, Cambridge, UK 8 Department of Psychiatry, University of Cambridge, Cambridge, UK 9 Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland 10 Department of Psychiatry, Oulu University Hospital, Oulu, Finland 11 Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland 12 Section for Neurodiagnostic Applications, Department of Psychiatry, Ludwig Maximilian University, Nussbaumstrasse 7, 80336 Munich, Bavaria, * Corresponding author: Aino Saarinen. Department of Psychology and Logopedics, Faculty of Medicine, Haartmaninkatu 3, P.O. Box 21, 00014 University of Helsinki, Finland. E-mail: [email protected], Tel.: +35844 307 1204. 1 Abstract Objective: Schizophrenia has one of the highest heritability estimates in psychiatry, but the genetically- based underlying neuropathology has mainly remained unclear.
    [Show full text]
  • Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting
    UNIVERSITY OF PADOVA Department of Information Engineering Ph.D. School on Information Engineering Curriculum: Bioengineering - Cycle: XXX Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting Headmaster of the School: Prof. Andrea NEVIANI Supervisor: Prof. Alessandra BERTOLDO PhD. Candidate: Eng. Erica SILVESTRI 2018 iii University of Padova Abstract Ph.D. School on Information Engineering Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting by Eng. Erica SILVESTRI In recent years, the study of brain connectivity has received growing inter- est from neuroscience field, from a point of view both of analysis of patho- logical condition and of a healthy brain. Hybrid PET/MRI scanners are promising tools to study this complex phenomenon. This thesis presents a general framework for the acquisition and analysis of simultaneous multi- modal PET/MRI imaging data to study brain connectivity in a clinical set- ting. Several aspects are faced ranging from the planning of an acquisition protocol consistent with clinical constraint to the off-line PET image recon- struction, from the selection and implementation of methods for quantifying the acquired data to the development of methodologies to combine the com- plementary informations obtained with the two modalities. The developed analysis framework was applied to two different studies, a first conducted on patients affected by Parkinson’s Disease and dementia, and a second one on high grade gliomas, as proof of concept evaluation that the pipeline can be extended in clinical settings. v Università degli Studi di Padova Sommario Scuola di Dottorato in Ingegneria dell’Informazione Acquisizioni simultanee PET/MR per lo studio della connettività: metodi quantitativi in ambito clinico di Ing.
    [Show full text]
  • Official Program
    OFFICIAL PROGRAM ___________________________________________________________________ Generously Sponsored by: TABLE OF CONTENTS SCHEDULE 3 KEYNOTE SPEAKERS 5 BREAKOUT SESSIONS 7 ORAL PRESENTATIONS 11 POSTER SESSION 1 (ORGANIZED BY POSTER NUMBER) 13 POSTER SESSION 2 (ORGANIZED BY POSTER NUMBER) 16 ABSTRACTS – ORAL PRESENTATIONS 19 ABSTRACTS – POSTER SESSION 1 44 ABSTRACTS – POSTER SESSION 2 91 SEMSS ORGANIZING COMMITTEE 135 LIGHT HALL & CAMPUS MAPS 136 SEMSS encourages open and honest intellectual debate as part of a welcoming and inclusive atmosphere at every conference. SEMSS asks each participant to foster rigorous analysis of all science presented or discussed in a manner respectful to all conferees. To help maintain an open and respectful community of scientists, SEMSS does not tolerate illegal or inappropriate behavior at any conference site, including violations of applicable laws pertaining to sale or consumption of alcohol, destruction of property, or harassment of any kind, including sexual harassment. SEMSS condemns inappropriate or suggestive acts or comments that demean another person by reason of his or her gender, gender identity or expression, race, religion, ethnicity, age or disability or that are unwelcome or offensive to other members of the community or their guests. * *Adapted from the language of Gordon Research Conferences 2 SEMSS 2018 SCHEDULE SATURDAY, NOVEMEBER 10, 2018 _____________________________________________________________________________________ REGISTRATION 12:30 – 1:00 PM Location: Light Hall, North
    [Show full text]
  • A Simple Rapid Process for Semi-Automated Brain Extraction from Magnetic Resonance Images of the Whole Mouse Head
    Accepted Manuscript Title: A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head Author: Adam Delora Aaron Gonzales Christopher S. Medina Adam Mitchell Abdul Faheem Mohed Russell E. Jacobs Elaine L. Bearer PII: S0165-0270(15)00370-2 DOI: http://dx.doi.org/doi:10.1016/j.jneumeth.2015.09.031 Reference: NSM 7360 To appear in: Journal of Neuroscience Methods Received date: 10-8-2015 Revised date: 28-9-2015 Accepted date: 30-9-2015 Please cite this article as: Delora A, Gonzales A, Medina CS, Mitchell A, Mohed AF, Jacobs RE, Bearer EL, A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head, Journal of Neuroscience Methods (2015), http://dx.doi.org/10.1016/j.jneumeth.2015.09.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Graphical Abstract (for review) Accepted Manuscript Page 1 of 27 A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head Delora, Gonzales, Medina, Mitchell, Mohed, Jacobs and Bearer Highlights • We present a new software tool for automated mouse brain extraction • The new software tool is rapid and applies to any MR dataset • We validate the output by comparing with manual extraction (the gold standard) • Brain extraction with this tool preserves individual volume, and improves alignments Accepted Manuscript Page 2 of 27 Fast automated skull-stripping Delora et al.
    [Show full text]
  • Software Tools for the Analysis of Functional Magnetic Resonance Imaging
    Basic and Clinical Autumn 2012, Volume 3, Number 5 Software Tools for the Analysis of Functional Magnetic Resonance Imaging Mehdi Behroozi 1,2, Mohammad Reza Daliri 1* 1. Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. 2. School of Cognitive Sciences (SCS), Institute for Research in Fundamental Science (IPM), Niavaran, Tehran, Iran. Article info: A B S T R A C T Received: 12 July 2012 First Revision: 7 August 2012 Functional magnetic resonance imaging (fMRI) has become the most popular method for Accepted: 25 August 2012 imaging of brain functions. Currently, there is a large variety of software packages for the analysis of fMRI data, each providing many features for users. Since there is no single package that can provide all the necessary analyses for the fMRI data, it is helpful to know the features of each software package. In this paper, several software tools have been introduced and they Key Words: have been evaluated for comparison of their functionality and their features. The description of fMRI Software Packages, each program has been discussed and summarized. Preprocessing, Segmentation, Visualization, Registration. 1. Introduction analysis the fMRI data to extract information about the different stimuli (Matthews, Shehzad, & Kelly, 2006). unctional Magnetic resonance imaging (fMRI), a modern technique of imaging, is a Generated data from fMRI have very large amount. powerful non-invasive and safe tool which The handling, processing, analysis and visualization Downloaded from bcn.iums.ac.ir at 18:01 CET on Friday February 20th 2015 F is used for the study of the function of the of fMRI data are not feasible without computer-based brain based on measure of the brain neural methods.
    [Show full text]
  • A Study on the Core Area Image Processing: Current Research
    The International journal of analytical and experimental modal analysis ISSN NO: 0886-9367 A Study on the Core Area Image Processing: Current Research, Softwares and Applications K.Kanimozhi1, Dr.K.Thangadurai2 1Research Scholar, PG and Research Department of Computer Science, Government Arts College, Karur-639005 2Assistant professor and Head, PG and Research Department of Computer Science, Government Arts College, Karur-639005 [email protected] [email protected] Abstract —Among various core subjects such as, networking, data mining, data warehousing, cloud computing, artificial intelligence, image processing, place a vital role on new emerging ideas. Image processing is a technique to perform some operations with an image to get its in enhanced version or to retrieve some information from it. The image can be of signal dispension, video for frame or a photograph while the system treats that as a 2D- signal. This image processing area has its applications in various wide range such as business, engineering, transport systems, remote sensing, tracking applications, surveillance systems, bio medical fields, visual inspection systems etc.., Its purpose can be of: to create best version of images(image sharpening and restoring), retrieving of images, pattern measuring and recognizing images. Keywords — Types, Flow, Projects, MATLAB, Python. I. INTRODUCTION In computer concepts, digital image processing technically means that of using computer algorithms to process digital images. Initially in1960’s the image processing had been applied for those highly technical applications such as satellite imagery, medical imaging, character recognition, wire-photo conversion standards, video phone and photograph enhancement. Since the cost of processing those images was very high however with the equipment used too.
    [Show full text]
  • LONI Pipeline Handbook
    LONI Pipeline Neuroimaging Solutions A practical, hands-on guide to using the LONI Pipeline for neuroimaging analysis LONI Pipeline version 7.0.1 Handbook version 1.6 http://pipeline.loni.usc.edu LONI Pipeline training is funded by the National Institutes of Health through NIBIB grant P41EB015922 Contents Getting Started 1 Overview: About LONI Pipeline 1 Benefits 1 Requirements 1 Installation 2 Software Download 2 LONI Cluster Usage Account 2 Platform-Specific Installation 2 OS X 2 Windows 3 Linux/Unix 3 Interface 4 Overview 4 Data Sources & Sinks 6 Overview 6 How-to 6 Find & Replace 7 LONI Pipeline Viewer 8 Overview 8 Beginning Example Workflows 9 Registration Using AIR 10 Image Processing with the Quantitative Imaging Toolkit - Denoising 29 Intermediate Topics 37 LONI Image Data Archive 38 Overview 38 Change Server for Entire Workflow 40 Overview 40 How-to 40 Intermediate Example Workflows 41 fMRI First Level Analyses 42 DTI Analyses 59 DSI Analyses 65 Image Analysis with the Quantitative Imaging Toolkit - Tractography Metrics 73 Advanced Topics 85 Building a Module 86 Overview 86 How-to 86 Optional Actions 87 Required Options 88 Adding additional parameters 90 Specify dependencies and manipulate file names 90 LONI Pipeline Provenance 92 Overview 92 How-to 92 Adding Metadata 95 Overview 95 How-to 96 Conditional Module 98 Overview 98 How-to 98 Distributed Pipeline Server Installation 102 Introduction 102 GUI Installation 102 Command Line Installation 110 Glossary 113 Bibliography 116 Getting Started Overview: About LONI Pipeline The LONI Pipeline is a free workflow application primarily aimed at neuroimaging researchers. With the LONI Pipeline, users can quickly create workflows that take advantage of any and all neuroimaging tools available.
    [Show full text]
  • In Vivo Assessment of the Neural Substrate Linked with Vocal Imitation
    RESEARCH ARTICLE In vivo assessment of the neural substrate linked with vocal imitation accuracy Julie Hamaide1, Kristina Lukacova2, Jasmien Orije1, Georgios A Keliris1, Marleen Verhoye1, Annemie Van der Linden1* 1Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium; 2Centre of Biosciences, Institute of Animal Biochemistry and Genetics, Slovak Academy of Sciences, Bratislava, Slovakia Abstract Human speech and bird song are acoustically complex communication signals that are learned by imitation during a sensitive period early in life. Although the brain areas indispensable for speech and song learning are known, the neural circuits important for enhanced or reduced vocal performance remain unclear. By combining in vivo structural Magnetic Resonance Imaging with song analyses in juvenile male zebra finches during song learning and beyond, we reveal that song imitation accuracy correlates with the structural architecture of four distinct brain areas, none of which pertain to the song control system. Furthermore, the structural properties of a secondary auditory area in the left hemisphere, are capable to predict future song copying accuracy, already at the earliest stages of learning, before initiating vocal practicing. These findings appoint novel brain regions important for song learning outcome and inform that ultimate performance in part depends on factors experienced before vocal practicing. Introduction Human speech and bird song are highly complex and rapid motor behaviours that are learned
    [Show full text]
  • Towards a Brain Imaging Biomarker for Parkinson’S Disease
    Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson’s Disease Amir Dehsarvi Doctor of Philosophy University of York Electronic Engineering January 2018 To my Jennifer & our soon to be born baby و به پدر و مادر نازنینم با عشق... Abstract It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines.
    [Show full text]
  • Assessment of Functional Connectivity Impairment in Rat Brains
    DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2019 Assessment of functional connectivity impairment in rat brains ANDROULA SAVVA KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH Acknowledgements I would like to express my deep gratitude to my supervisor Rodrigo Moreno for trusting me with this project, for providing me with his valuable guidance and for allowing me to take initiatives through the progress of this project. Special thanks are due to my examiner, Professor Örjan Smedby for the insightful comments and suggestions during the development of this thesis. I would also like to thank Daniel Jörgens, PhD student, for his valuable help on the software development of this pipeline and Malin Siegbahn, MD, PhD student, for providing me with her medical expertise and her time contribution. I wish to thank my family and close friends for their support and encouragement through- out this challenging period. Lastly, to Aria, for her patience and support during my studies. Abstract While the rodent model has long been used in brain research, there exists no standardised processing routine that can be employed for analysis and investigation of disease models. The present thesis attempts to investigate a diseased brain model by implementing a collection of scripts, combined with algorithms from existing neuroimaging software, and adapting them to the rodent brain, in an attempt to examine when and how monaural canal atresia affects the functional connectivity of the brain. We show that it is possible to use software tailored to the human brain to pre-process the rodent model.
    [Show full text]
  • ACNP 56Th Annual Meeting: Poster Session II, December 5, 2017
    Neuropsychopharmacology (2017) 42, S294–S475 © 2017 American College of Neuropsychopharmacology. All rights reserved 0893-133X/17 www.neuropsychopharmacology.org Poster Session II analyses revealed that the SASP index was positively correlated Palm Springs, California, December 3-7, 2017 with age (r = 0.2, p = 0.03) and CIRS score (r = 0.27, p = 0.005), and negatively correlated with information processing speed Sponsorship Statement: Publication of this supplement is (r = − 0.34, p = 0.001), executive function (r = − 0.27, p = 0.004) sponsored by the ACNP. and global cognitive performance (r = − 0.28, p = 0.007). Individual contributor disclosures may be found within the Conclusions: this is the first study to show that a set of abstracts. Part 1: All Financial Involvement with a pharma- proteins (i.e., SASP index) primarily associated with cellular ceutical or biotechnology company, a company providing aging is abnormally regulated and elevated in LLD. These clinical assessment, scientific, or medical products or compa- results suggest that individuals with LLD display enhanced nies doing business with or proposing to do business with aging related molecular patterns that are associated with ACNP over past 2 years (Calendar Years 2014–Present); Part higher medical comorbidity and worse cognitive function. 2: Income Sources & Equity of $10,000 per year or greater Finally, we provide a set of proteins that can serve as (Calendar Years 2014 - Present): List those financial relation- potential therapeutic targets and biomarkers to monitor the ships which are listed in part one and have a value greater than effects of therapeutic or preventative interventions in LLD.
    [Show full text]