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By members & collaborators of the Wellcome Trust Centre for Neuroimaging Introduction| Software | Documentation | Courses | Email list | Data |ExtensionsSPM Menu:Introduction Software Documentation Courses Email list Data setsExtensionsThis page:Introduction Getting started Latest news SPM in briefStatistical Parametric Mapping IntroductionStatistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM.The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI,PET, SPECT, EEG and MEG.Getting StartedThe best starting point is to read the introductory article on SPM available here. You could then download the latest version of the software and a data set to analyse. Step-by-step instructions for this analysis are available in the SPM manual.If you're new to imaging, perhaps an epoch fMRI data set would be appropriate. The data sets are provided with instructions on how to use SPM to analyse them. These tutorials therefore give practical instructions on how to implement the various methodologies. Our methods have been written up in books, technical reports and journal papers which are available from our Online Bibliography. This groups documentation according to year, category, author and keyword.If you're looking for help on a particular topic you can find the relevant papers from the Online Bibliography. Alternatively, you can search the SPM pages using the search facility that appears at the bottom of every page. Also browse and search the SPM WikiBook and please feel free to edit it if you can. If you still can't find what you need, you could send an email to the SPM Email list, which gives you access to our community of experts.You should also be aware of the many courses on SPM. If there isn't one in your country this year then there's always the annual short course in London. Finally, once you've mastered SPM you can learn about the various extensions provided by experts in the wider community.Good luck !Latest newsSPM Course for M/EEG VideosJuly 2012: Videos recorded at the May 2012 SPM Course for M/EEG are now freely available online.SPM Course for fMRI/PET/VBM VideosNovember 2011: Videos recorded at the May 2011 SPM Course for fMRI/PET/VBM are now freely available online.SPM8 releasedApril 2009: SPM8 is a major update to the SPM software, containing substantial theoretical, algorithmic, structural and interface enhancements over previous versions (more info).New Book: Statistical Parametric Mapping: TheAnalysis of Functional Brain ImagesNovember 2006: This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography (more info).The SPM approach in briefThe Statistical Parametric Mapping approach is voxel based:∙Images are realigned, spatially normalised into a standard space, and smoothed.∙Parametric statistical models are assumed at each voxel, using the General Linear Model GLM to describe the data in terms of experimental and confounding effects, and residual variability.∙For fMRI the GLM is used in combination with a temporal convolution model.∙Classical statistical inference is used to test hypotheses that are expressed in terms of GLM parameters. This uses an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{Z}, SPM{F})∙For such classical inferences, the multiple comparisons problem is addressed using continuous random field theory RFT, assuming the statistic image to be a good lattice representation of an underlying continuous stationary random field. This results in inference based on corrected p-values.∙Bayesian inference can be used in place of classical inference resulting in Posterior Probability Maps PPM s.For fMRI, analyses of effective connectivity can be implemented using Dynamic Causal Modelling DCM.Last modified $Date: 2013/06/24 19:14:39 $ by $Author: guillaume $ home | search | sitemapCopyright © 1991,1994-2013 FILT SoftwareIntroductionSPM is made freely available to the [neuro]imaging community, to promote collaboration and a common analysis scheme across laboratories. The software represents the implementation of the theoretical concepts of Statistical Parametric Mapping in a complete analysis package.The SPM software is a suite of MATLAB (The MathWorks, Inc) functions and subroutines with some externally compiled C routines.SPM was written to organise and interpret our functional neuroimaging data. The distributed version is the same as that we use ourselves.LicenceSPM is free but copyright software, distributed under the terms of the GNU General Public Licence as published by the Free Software Foundation (either version 2, as given in file spm_LICENCE.man, or at your option, any later version). Further details on "copyleft" can be found at /copyleft/. In particular, SPM is supplied as is. No formal support or maintenance is provided or implied.Beta version: SPM12bA beta version of SPM12, SPM12b, was released 21st December 2012 and is available for beta-testing. Please assist us by reporting bugs to <fil.spm@>.Current version: SPM8The current version is SPM8and was released inApril 2009. This provides a major update to theSPMsoftware, containing substantial theoretical,algorithmic, structural and interface enhancementsover previous versions.SPM8 uses the NIfTI-1 file format.Previous versionsWe still provide access to previous versions of SPM, but recommend you use the current version wherever possible.SPM5SPM5 was released in December 2005.SPM2SPM2 was released in 2003.SPM99SPM99was released in January 2000.SPM96 and earlier versions are no longer available.NITRCSPM is listed in the Neuroimaging Informatics Tools andResources Clearinghouse (NITRC ). See /projects/spm/. Funded by the NationalInstitutes of Health Blueprint for Neuroscience Research,NITRC facilitates finding and comparing neuroimagingresources for fMRI and related structural analyses.Last modified $Date: 2014/02/27 12:08:57 $ by $Author: guillaume $ home | search | sitemapCopyright © 1991,1994-2014 FILThe FIL Methods group <fil.spm@ >he FIL Methods group <fil.spm@ > Documentation IntroductionSPM is an academic software toolkit for the analysis of functional imaging data, for users familiar with the underlying statistical, mathematical and image processing concepts. It is essential to understand these underlying concepts in order to use the software effectively. A good starting point is the Introduction to SPM, by Karl Friston.Peer reviewed literatureThe primary reference for the theories underlying SPM are the academic papers in the peer reviewed literature. These are available via the Online Bibliography which organises books, papers and technical reports by year, category, author and keyword. An Annotated Bibliography provides a guide to the various papers.BooksThe book Statistical Parametric Mapping: The Analysis ofFunctional Brain Images (2007) provides the backgroundand methodology for the analysis of all types of brainimaging data, from functional magnetic resonanceimaging to magnetoencephalography. Critically,"Statistical Parametric Mapping"provides a widelyaccepted conceptual framework which allows treatmentof all these different modalities. The book takes thereader from the basic concepts underlying the analysis ofneuroimaging data to cutting edge approaches thatwould be difficult to find in any other source. The materialis presented in an incremental way so that the reader canunderstand the precedents for each new development.This book will be particularly useful to neuroscientistsengaged in any form of brain mapping; who have tocontend with the real-world problems of data analysisand understanding the techniques they are using. It isprimarily a scientific treatment and a didactic introductionto the analysis of brain imaging data. It can be used asboth a textbook for students and scientists starting to usethe techniques, as well as a reference for practicingneuroscientists.The book can be ordered online via Elsevier or, e.g., Amazon.Introductory articles are also provided in the earlier book Human Brain Function. PDFs of draft versions of book chapters are available online: ∙First Edition(1997)∙Second Edition(2003)ManualThere is an SPM8 manual distributed with the software (spm8/man/manual.pdf), which is written by the developers. Much of the manual's contents are also available via the SPM8 user-interface.The release notes accompanying SPM8 contain some useful information.The SPM5 distribution also contains a manual in (spm5/man/manual.pdf). There is no manual for SPM2, but the SPM99 manual is roughly applicable. Methods for DummiesEach year our department runs a series of methods talks by non-experts. Powerpoint versions are available for 2003, organised by Alexa Morcom, 2004, organised by Lucy Lee, 2005, orgainsed by Julia Hocking, 2006, organised by Davina Bristow and Marcus Gray,2007, organised by Justin Chumbley and Hanneke den Ouden,2008 organised by Maria Joao, Hanneke den Ouden and Justin Chumbley,2009organised by Antoinette Nicolle and Maria Joao,2010 organised by Christian Lambert, Suz Prejawa and Maria Joao, and 2011 organised by Rumana Chowdhury, Peter Smittenaar and Suz Prejawa. This years meetings are organised by Mona Gavert, Peter Smittenaar and Giles Story.Third party documentationThere is a SPM WikiBook, which is written and maintained by the international SPM community. The wiki allows users to read and edit the content for free. The end results will be mature, up-to-date and broad repositories for information on SPM. Please do take a look at the SPM wiki and feel free to contribute to it if you can.SPM WikiBooks/wiki/SPMBeginners guides & overviews of SPM :Cambridge Imaging Home Page (MRC Cognition and Brain Sciences Unit) /imagingHistoryThe SPM suite and associated theory was originally developed by Karl Friston for the routine statistical analysis of functional neuroimaging data from Positron Emission Tomography (PET), while at the Medical Research Council Cyclotron Unit. Now known as SPM classic, this software was made available to the emerging functional imaging community in 1991, to promote collaboration and a common analysis scheme across laboratories.SPM'94 was the first major revision of the SPM software. SPM'94 was written primarily by Karl Friston during the summer of 1994, with invaluable conceptual and technical help from John Ashburner, Jon Heather, Andrew Holmes and Jean-Baptiste Poline. SPM'95, SPM'96, SPM'99, SPM2, SPM5 and SPM8are based on SPM'94, and represent the ongoing theoretical advances and technical improvements.Tell me more about SPM history.Last modified $Date: 2013/01/22 11:55:27 $ by $Author: spm $home | search | sitemapCopyright © 1991,1994-2013 FILThe FIL Methods group <fil.spm@>SPM CoursesLondon, May & October 2014The FIL SPM course on using Statistical Parametric Mapping for neuroimaging is held each May and October as part of the Institute of Neurology's short course programme.Since 2010, we provide two separate SPM courses reflecting the different imaging modalities. There is a new three-day course on SPM for EEG/MEG followed by the long-established three-day course on SPM for fMRI/VBM/PET. Both courses are suitable for beginners and more advanced users. We advise students to gain at least some minimal familiarity with the methodology, for example, from reading introductory articles available from the SPM web pageor by following data analysis examples in the SPM manual.The next SPM courses in London will take place on 12-14 May 2014 (MEG/EEG) and 15-17 May 2014 (MRI/VBM/fMRI).The courses are organised through the Institute of Neurology(contact: Miss Jean Reynolds at the IoN to register).Externally organised SPM coursesOther SPM courses are organised around the world at different times of year and in different languages. If you do organise one, please let us know so that it can be listed here.Lausanne SPM Course, Switzerland, 22-25 April 2014This 6th edition of the Lausanne SPM course will focus on how to conduct neuroimaging studies using structural and functional MRI data within the framework of Statistical Parametric Mapping (SPM). This 4 days course is divided into two modules with theoretical and practical sessions covering all aspects of imaging data analysis from spatial pre-processing to statistical inference and reporting results. The course is suitable both for beginners and for those with previous experience of SPM.http://www3.unil.ch/wpmu/lren-spm/Zurich SPM course, Switzerland, 2015This three-day course, which is held annually since 2007, offers a comprehensive coverage of all MRI-related aspects of SPM./spm-course-2014/Course material for 2008, 2009, 2010, 2011, 2012, 2013, 2014Hannover SPM course, Germany, 3-4 November 2014The 7th SPM workshop for Beginners in Hannover, Germany, will be held on November 03. and 04. 2014. (teaching language: german)More information and program:http://www.spmworkshop-hannover.de/SPM Workshops at the Martinos Center, Boston, Massachusetts, USA: /martinos/training/fMRI-Extension/SPM8.phpMRN fMRI Image Acquisition and Analyses Course, University of Colorado, Boulder, USA:/courses/Slides of previous coursesThe latest course material are from 2013 (fMRI/VBM) and 2013 (MEEG).Archive:2002, 2003, 2004, 2005, 2005 (USA), 2006, 2007, 2008 (May), 2008 (October), 2009 (May), 2009 (October), 2009 (MEEG), 2010 (fMRI), 2010 (MEEG), 2010 (Vancouver), 2010 (October), 2011 (Brussels)May 2011 (fMRI), May 2011 (MEEG), Oct 2011 (fMRI), 2012 (MEEG, Lyon), May 2012 (fMRI/VBM), 2012 (MEEG), Oct 2012 (fMRI/VBM), May 2013 (fMRI/VBM).These can be viewed using the Microsoft PowerPoint Viewer for Windows or the multi-platform Open Office.RecordingsOnline videos are now available, recorded at the May 2011 SPM for fMRI/PET/VBM course and at the May 2012 SPM for MEG/EEG course. Higher quality versions of the videos can be obtained on a DVD available for purchase.Last modified $Date: 2014/02/26 16:06:03 $ by $Author: guillaume $ home | search | sitemapCopyright © 1991,1994-2014 FILThe FIL Methods group <fil.spm@>Discussion ListIntroductionThe SPM discussion list is an electronic mailing list for discussion and help with the methodology, implementation and use of Statistical Parametric Mapping and the SPM package. The list is not moderated, but is monitoredand owned by members of the Wellcome Trust Centre for Neuroimaging. Much of the approach and use of SPM is described in the documentation. Subscribers are urged to consult these sources, and any local expertise, before using the discussion list as a helpline. A response from one of the SPM authors cannot be guaranteed, due to resource limitations. Experienced list members are therefore encouraged to address issues within their experience, posting their responses & discussion to the list for general enlightenment.Searching archives∙Archives of SPM messages.∙Search SPMarchives.SubscriptionThe list is run at the UK's automated JISCmail service.To subscribe or unsubscribe to the SPM list:Enter your details at /cgi-bin/webadmin?SUBED1=spm&A=1To send a message to all the people currently subscribed to the SPM list: Just send a mail to <spm@> after subscription.If you have difficulties, then have a look to the frequently asked questions about JISCmail or email the list owner at <fil.spm@>, for assistance. Last modified $Date: 2013/01/22 11:55:31 $ by $Author: spm $home | search | sitemapCopyright © 1991,1994-2013 FILThe FIL Methods group <fil.spm@>Data sets and tutorialsIntroductionThe following data sets are being made available for training and personal education and evaluation purposes. Those wishing to use these data for other purposes, including illustrations or evaluations of methods, should contact the Methods group at the Wellcome Trust Centre for Neuroimaging.A set of instructions showing how SPM can be used to analyse each data set are also provided. These tutorials show how one can use SPM to implement analyses of PET data, epoch or event-related fMRI data, and data from a group of subjects using Random effects analyses (RFX). They also cover more advanced topics such as Psychophysiological Interactions (PPI s) and Dynamic Causal Modelling (DCM).fMRI: epochThe instructions accompanying these data sets show you how to implement a block-design fMRI analysis in SPM. They are both single-subject or 'first-level' analyses.∙Auditory - single subject∙Attention to Visual Motion - single subjectThe instructions accompanying the Attention to Visual Motion data also show you how to use SPM to implement, for example, Psychophysiological Interactions (PPI s) and Dynamic Causal Modelling (DCM).fMRI: event-relatedThe instructions accompanying these data sets show you how to use SPM to analyse data for an event-related fMRI experiment. This includes, for example, selection of different temporal basis sets. They are both single-subject or 'first-level' analyses.∙Repetition primingfMRI: multi-subject (random effects) analyses These data sets comprise contrast images from single-subject fMRI analyses or 'first-level' analyses from the repetition priming experiment described here.In the summary statistic approach to Random Effects Analysis (RFX) these contrast images are then used in a 'second-level' analysis allowing you to make inferences about the population from which the subjects were drawn.∙fMRI: multi-subject (random effects) analysesfMRI: Bayesian comparison of DCMsDynamic Causal Models from an event related fMRI study of the language system are available here∙fMRI: Bayesian comparison of DCMsEEG Single Subject Mismatch Negativity datasetThis is an 128-channel EEG single subject example data set which is used for demonstrating the usage of scripts in M/EEG pre-processing and DCM for evoked responses.Anaesthesia Depth in Rodent DataThis 2-channel Local Field Potential (LFPs) dataset demonstrates the use of DCM for Cross Spectral Densities.Multi-modal facesThis archive contains EEG, MEG and fMRI data on the same subject within the same paradigm.It can be used to examine how various measures of face perception, such as the "N170" ERP (EEG), the "M170" ERF (MEG) and fusiform activation (fMRI), are related. For example, the localisation of the generator(s) of the N170 and/or M170 can be constrained by the fMRI activations.It also includes a high resolution anatomical MRI image (aMRI) for construction of a head-model for the EEG and MEG data, together with data from a Polhemus digitizer that can be used to coregister the EEG and MEG data with the aMRI.FieldMapHere are two example data sets that can be used to demonstrate the usage of the FieldMap toolbox.PETThe instructions accompanying these data sets show you how to use SPM to analyse PET data.∙Verbal fluency - multiple subjects∙Motor activation - single subjectLast modified $Date: 2014/04/30 17:48:34 $ by $Author: guillaume $ home | search | sitemapCopyright © 1991,1994-2014 FILThe FIL Methods group <fil.spm@>SPM ExtensionsIntroductionMany SPM users have created tools for neuroimaging analyses that are based on SPM. You will find here a list of these tools classified between Toolboxes, Utilities, Batch Systems and Templates. The distinction between Toolboxes and Utilities can be blurry, but for the purposes of this page we define a toolbox to be a utility that can be completely operated via a graphical user interface.If you notice inaccuracies or out of date links, please email the SPM manager. Likewise, to have your SPM extension linked here, send an email including a URL, a contact email, and a brief summary (please note if any MATLABtoolboxes are required, or if it is platform-specific).The SPM Developers take no responsibility for the usability of the extensions listed here. In particular, some extensions may be mutually incompatible. Please contact the respective extension authors with questions and problems (though CC'ing answered questions to the Email list will be appreciated).Extensions compatible with SPM8, SPM5, SPM2, SPM99.The list of SPM extensions is also available as an RSS feed.Note: All email addresses in this page have their "@" replaced with "_at_" to minimize spam. Please reverse this change before emailing.Quick LinksToolboxes:AQuA | AAL | ACID | ALVIN | ArtRepair | AMAT | AnalyzeMovie | Anatomy | aslm | ASLtbx | at4fmri | aws4SPM | BrainNetViewer | BredeQuery | CCAfMRI | CLASS| Clinical| Complexity| conn| CPCA| DAiSS| DICOMCD_Import| Diffusion_II | Distance | DPARSF | DRIFTER | EMS | ExtractVals | FASL | FAST | FDR | FieldMap | fieldmap_undistort | FieldTrip | fMRIPower | fOSA | Grocer | HRF | HV | IBASPM | iBrainAT | iBrainLT | INRIAlign | IBZM_tool | ISAS | LI | LogTransform | MARINA | MarsBar | MASCOI | mfBox | Masking | Masks | MM | multifocal | NIRS-SPM | NS | Ortho | PSPM | REST | rfxplot | RobustWLS | SCRalyze| SDM| SimpleROIBuilder| SnPM| spm_wavelet| SPMd| SPMMouse| SSM| SUIT| SurfRend| TOM| UF2C| Unwarp2| Volumes| WBM | WSPM | WFU_PickAtlas | xjView | XMLToolsUtilities:AveLI| BrainMagix| Bruker2Analyze| CBMG-Tools| Design_Magic| dicom2nifti| DynPET| Easy_ROI| Easy_Volumes| FDRill| Fluctuation| fToolbelt| FAD| GA| GE2SPM| GIFT| JG| L2S| log_roi_batch| LMGS| Motion| mri_toolbox| MSU| Orth1| PCT| pvconv| r2agui| SEM| slice_overlay | TSDiffAna | TSU | iTT | VBMtools | VIS | visionToSPM | VoiTool Batch Utilities:aa| AutoSPET| BatchAAL| KULscripts| parallelize_matlabbatch| spm_segment | spm2-batch | spm2batch | spm2Batch | spmbatch | spmjob | X_batch | zephyrTemplates:Baboon| BrainDev_Atlas| Cerebellum| CCHMC_Pediatric| Macaca_Fascicularis| Macaca_Fascicularis_PET| Macaca_Mulatta| Macaca_Nemestrina | Rat | SPMtemplates | UNC_Pediatric | Zebra_FinchToolboxesAQuA SPM5Summary: AQuA is a tool that helps you in the assessment process for the quality of the acquired fMRI data, identifying images with movement and other artefacts, so that they do not compromise the experimental analysis. Author: Antonios Antoniou and Juan J. LullURL: http://www.ibime.upv.es/mi/AAL - Anatomical Automatic Labeling SPM99 SPM2 SPM5 SPM8Summary: Automated parcellation method, as described in Tzourio-Mazoyer et al. NI 2002.Author: CyceronURL: rs.fr/spip.php?article217ACID - Artefact correction in diffusion MRI SPM8 Summary: The Artefact correction in diffusion MRI (ACID) toolbox is an academic software toolkit for pre-processing of diffusion MRI data, estimation of DTI indices and normalisation of DTI index maps, which fully integrates into the batch system of SPM8.Author: Siawoosh MohammadiURL: /ALVIN - Automatic Lateral Ventricle delIneatioNSPM8Summary: Segmentation of the lateral ventricles validated in infants, adults and patients with Alzheimer's disease. Works with both T1 and T2 images. Author: Matthew KemptonURL: /site/mrilateralventricle/ArtRepair - Artifact Repair toolbox SPM2 SPM5 SPM8Summary: Special methods to improve the fMRI analysis of high motion pediatric and clinical subjects. The methods correct for large motions, and automatically detect and remove artifact noise in the data. Viewing tools allow quality checking at every step in the analysis.Author: Paul MazaikaURL:/tools/human-brain-project/artrepair-software.html AMAT: A meta-analysis toolbox SPM2Summary: AMAT is a matlab program which lets you search through the coordinates reported in lots of fMRI papers. It is designed to answer the frequently asked question: what the *?%! does that bit do?Author: Antonia HamiltonURL: /amat.htmlAnalyzeMovie - AVI movies from Analyze images SPM99Summary: Create AVI movies from Analyze images, of axial, coronal, sagittal or ortho slices. Requires Matlab6 or greater.Author: Robert WelshURL: /ni-stat/AnalyzeMovie.tarAnatomy - SPM Anatomy toolbox SPM99 SPM2 SPM5 SPM8Summary: This toolbox integrates probabilistic cytoarchitectonic maps derived from human post-mortem studies into the SPM environment and provides a wide range of different approaches to analyse structure / function correlations.Author: Simon EickhoffURL:http://www.fz-juelich.de/inm/inm-1/DE/Forschung/_docs/SPMAnatomyToolbox /SPMAnatomyToolbox_node.htmlaslm - a slightly modified asl-module SPM8 Summary: aslm is an object orientated toolbox for common tasks associated with the analysis of arterial spin labeling (ASL) and other MRI data.Author: Philipp HomanURL: ASLtbx SPM2 SPM5 SPM8Summary: ASLtbx is a Matlab and SPM based toolkit for processing arterial spin labeling (ASL) perfusion MRI data. It's basically a collection of a bunch of batch scripts. I'm currently only distributing the SPM5-based version, but the SPM2(or 8)-based version can be obtained through email. The function for quantifying cerebral blood flow should be SPM independent except the image reading and writing functions from SPM.Author: Ze WangURL: /~zewang/ASLtbx.phpat4fmri - adaptive thresholding of fMRI maps SPM8 Summary: Allow to obtain a threshold for cluster FDR - the method fits a Gamma-Gaussian mixture model to the SPM-T and finds the optimal threshold(crossing between noise and activation). Optionally write the thresholded maps.Author: Chris Filo Gorgolewski and Cyril PernetURL: /projects/at-4-fmri/aws4SPM SPM2 SPM5 SPM8Summary: Structural adaptive smoothing fMRI data as described in Tabelow et al. NI (2006).Author: Devy Hoffmann and Karsten TabelowURL: http://www.wias-berlin.de/software/aws4SPM/BrainNet ViewerSummary: BrainNet Viewer is a brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.Author: Mingrui XiaURL: /projects/bnv/BredeQuery SPM5Summary: BredeQuery plugin for SPM5 - enables coordinate-based meta-analytic search of related literature for brain regions directly from SPM5 environment. The coordinate-based search is performed using Finn Aarup Nielsen's Brede Database. Works with coordinates in Talairach and MNI space, MNI-to-Talairach transformations are available (Brett and Lancaster transformations). Moreover, query results can be exported automatically to the suitable bibliographic file format (BibTeX, Reference Manager, RefWorks, EndNote).Author: Bartlomiej WilkowskiURL: http://neuro.imm.dtu.dk/wiki/BredeQueryCCA-fMRI SPM2 SPM5Summary: The CCA-fMRI utilizes canonical correlation analysis in combination with the Balloon model and adaptive filtering of fMRI data to detect areas of brain activation. The CCA-fMRI toolbox provides its own user interface and can also be used as stand alone scripts, e.g. for batch processing.Author: Magnus BorgaURL: /CLASS - Classifier Learning for Asymmetrically Small Samples SPM8Summary: Multivariate kernel-based pattern classification using support vector machines (SVM) with a novel modification to obtain more balanced sensitivity and specificity on unbalanced data-sets (i.e. those with very different numbers of cases in each group).Author: Ged RidgwayURL: Contact email aboveClinical Toolbox SPM8Summary: Aids normalization of clinical scans, providing an easy way to include lesion cost function masking. Allows unified segmentation-normalization (USN) or traditional normalization. Includes custom age adjusted templates for CT/CAT scans and USN. Includes FLAIR template provided by the Glahn group.Author: Rorden, Bonilha, Fridriksson, Bender and KarnathURL: /CRNL/clinical-toolbox Complexity SPM8Summary: Complexity is a toolkit used to analyze the complexity of resting state fMRI (rs-fMRI) data. Pre-processing module is included to realize low pass filtering and linear detrending. Five methods, Approximate Entropy, Sample Entropy, Cross Approximate Entropy, Multiscale Sample Entropy and Wavelet MSE, are available to calculate the entropy. Matlab toolbox, "Tools for NIfTI and ANALYZE image", is needed.。
fmri技术的原理及应用1. 引言功能性磁共振成像（functional magnetic resonance imaging，以下简称fmri）是一种非侵入性的神经影像学方法，用于研究大脑在特定任务中的功能活动。
本文将介绍fmri技术的原理以及其在神经科学研究和临床应用中的重要性。
2. 原理fmri基于血液供应和代谢的相关性，通过测量血液中氧气含量变化来推断大脑活动的区域和程度。
具体而言，fmri利用磁共振成像（magnetic resonance imaging，MRI）技术，测量血氧水平依赖（blood oxygenation level dependent，BOLD）信号来间接反映神经元的活动。
当神经元活跃时，细胞对氧气的需求增加，导致血液流动增加，血液中含氧量增加。
这种激活效应通过fmri技术可被探测到，并转化为图像显示。
3. 应用领域fmri技术在神经科学研究和临床应用中具有广泛的应用价值。
以下是一些主要的应用领域：• 3.1 认知神经科学fmri可以帮助研究者了解不同认知过程中的大脑活动模式。
通过比较在特定任务下不同个体或者不同条件下的fmri图像，研究者可以揭示大脑的功能连接及其变化，进一步研究记忆、学习、决策等认知过程。
• 3.2 精神疾病研究fmri技术在研究精神疾病中的应用发挥着重要作用。
例如，研究者可以通过比较患者与健康对照组的fmri数据，来发现精神疾病患者的大脑活动模式的差异，有助于理解疾病的发生机制，并为临床诊断和治疗提供参考。
• 3.3 神经反馈训练fmri技术还可以应用于神经反馈训练，通过让个体观察自身大脑活动的实时变化，在训练过程中调节注意力和自我调节能力。
这种技术可以被用于焦虑症、注意力缺陷多动障碍等疾病的治疗。
• 3.4 意识状态评估在一些疑似昏迷或意识障碍的患者中，fmri技术可以帮助评估其意识状态。
通过分析患者的fmri数据，研究者可以了解患者的脑活动是否存在与自发意识相关的特征，以辅助临床决策。
fmri的原理与应用1. 介绍功能磁共振成像（functional magnetic resonance imaging, fMRI）是一种用于观察人脑活动的非侵入性成像技术。
它利用磁共振成像（MRI）技术测量脑部血氧水平的变化，从而间接反映出神经活动的强度和区域。
本文将介绍fmri的原理和应用。
2. 原理•fMRI利用血氧水平依赖性信号（blood oxygenation level dependent, BOLD）来观测脑部活动。
当神经元活跃时，局部血流量增加，血红蛋白中的氧含量也增加。
这种血氧水平的变化可以通过MRI技术进行测量和成像。
•BOLD信号是在基于磁场的MRI仪器中通过磁共振检测到的。
MRI 仪器用于测量脑部不同区域的氧含量和血液流动情况，并生成相应的图像。
3. 应用3.1 神经科学研究fMRI广泛用于神经科学研究，可以帮助研究者理解人脑的功能和结构。
以下是一些常见的应用： - 神经功能定位：fMRI被用于确定特定的脑区在特定功能过程中的参与程度，如语言和视觉处理等。
- 神经网络建模：通过观察大脑在特定任务下的活动，研究者可以构建神经网络模型，来解释和预测人脑的功能。
- 研究脑部疾病：fMRI可以帮助研究人员了解脑部疾病的发病机制和病理生理。
3.2 临床诊断与治疗fMRI也有一些临床应用，可以帮助医生进行诊断和治疗。
- 神经影像诊断：fMRI可以提供更详细、准确的脑部图像，用于辅助医生对脑部疾病的诊断。
- 神经可塑性训练：通过观察患者在特定任务下的脑活动，医生可以利用fMRI来指导和监测神经可塑性训练，促进康复。
- 术前计划：对于需要进行脑部手术的患者，fMRI可以提供精确的脑图像和功能定位信息，帮助医生进行术前计划。
4. 优缺点4.1 优点•非侵入性：fMRI是一种非侵入性的成像技术，不需要对患者进行手术或注射药物。
•高时空分辨率：fMRI可以提供高时空分辨率的脑图像，可以监测到神经活动的细微变化，并定位到特定的脑区。
fMRI实验刺激系统解决方案一、概述磁共振成像（MRI）使用无线电波和一个比x-射线能量更大的强磁场为人们提供人体内部器官和组织的清晰且详细的图片。
FMRI是使用MRI来测量在大脑的活跃的部分发生的迅速、极微小新陈代谢变化的一个相对的新方法。
内科医师们知道，人的说话、感觉、记忆以及其它功能行为的产生是分别由大脑的哪些区域控制的。
然而，对每个人的大脑结构内部的精确定位却各不相同。
创伤和功能性疾病，例如中风或者脑瘤，甚至能导致大脑其它部分的功能障碍。
FMRI不仅能帮助放射科医师细致地查看大脑的解剖图，而且能帮助他们精确地确定大脑的哪个部分正在处理着比如思考、说话、运动和感觉等关键性的行为。
这些信息对外科手术计划、辐射治疗、治疗中风或者治疗其它的大脑紊乱性障碍都起着关键性的作用。
二、视觉刺激系统（SA-9800,VisualStimulationPackage）SAMRTECSA-9800视觉刺激系统源于美国著名的德克萨斯大学脑功能成像研究中心（ResearchImagingCenterofTexasUniversity）的专业技术，为用户提供了独一无二的、完整的fMRI视觉刺激系统解决方案，让研究者和医生能够运用fMRI对健康人和病人进行视觉、语言、注意、记忆等认知功能的研究以及大量的临床应用。
SAMRTEC fMRI 刺激系统解决方案示意图国外大多数实验室采用直接背投式视觉刺激装置，投影系统为完全无磁射频屏蔽，可以安放在磁共振扫描室中，并有风冷散热装置，保证投影的稳定性。
与一般在扫描控制室放置投影仪相比，背投方式能够避免亮度衰减，视觉信号的衰减，保持亮度均匀，保证高分辨率的视觉信号，保证实验环境的一致性，并可方便地与其他刺激装置相匹配，具有无限的升级空间。
按照人体工程学设计的无射频按键式响应控制盒通过CREATOR光纤传输系统把受试者的反应直接传送到计算机，可利用事件相关设计对不同类别的认知任务进行统计比较。
生理心理学的研究内容有哪些生理心理学是心理科学体系中的重要基础学科，它除用人为研究对象外还用各种实验动物为对象，研究心理行为活动的生理学机制。
随着心理科学、生物学、神经科学和新技术的发展，生理心理学超越了传统生理心理学的视野和方法，越来越明显地表现出自身多学科交叉的发展特点和趋势。
科学家们延伸了这个领域，给这个领域起了很多名称，如生物心理学(Biopsychology)，行为神经科学(BehavioralNeuroscience)，行为脑科学(BehavioralandBrainSciences)等，这些名称也都反映出揭示行为的脑机制的基本目标。
这一学科的发展促进了将行为水平的研究方法渗透到神经生物学微观领域，同时将神经生物学研究方法渗透到心理学领域。
从多学科、多方面、多角度、多层次对心理行为现象展开研究。
中国社会正处于快速转型期，各类心理疾病(如抑郁症、自杀、社会适应障碍、行为问题)和心身疾病(如冠心病、癌症)的发病率持续升高，已成为21世纪中国最令人关注的心理卫生课题。
我国生理心理学的研究也正密切地关注心身健康领域的基础研究。
与此相配合，该专辑收录了19篇文章，内容涉及应激、精神疾病、抑郁、成瘾、认知神经科学、和学习记忆等多学交叉性研究，从不同侧面概述了国内外生理心理学研究进展。
行为的动机和情绪，睡眠和觉醒，学习和记忆，语言和思考的心理过程，感觉和知觉过程，以及心理障碍等问题的生理机制。
心理生理学方法（一）电生理测量方法神经系统活动时，我们可以在体表或者神经元上记录到电信号，这种电信号为我们推知个体心理话动的生理机制提供了直接的证据。
脑电某某某、脑磁某某某及多导生理记录仪是记录电生理信号的有效技术。
1.脑电某某某和脑磁某某某脑电某某某(electroencephalography,简称EEG )通过在头皮表面记录大脑内的电生理活动情况获得。
神经元处于静息状态时，细胞膜内为负电位，膜外为正电位，由此形成极化状态。
fmri的名词解释fmri技术（Functional Magnetic Resonance Imaging，功能性磁共振成像）是一种用于探索大脑活动的非侵入性方法。
它结合了MRI（Magnetic Resonance Imaging，磁共振成像）和神经科学，能够通过测量血液氧合水平的变化，反映出大脑各个区域的功能活动。
本文将从fmri的原理、应用范围、数据分析方法以及局限性等方面进行详细解释。
fmri的原理是基于血液氧合水平依赖效应（BOLD，Blood Oxygenation Level Dependent）的测量。
当大脑某个区域活跃时，该区域的神经细胞会消耗氧气，并引起周边血液流量的增加。
增加的血流导致血液中的氧含量增加，进而改变血液的磁性质。
为了获取fmri数据，研究者需要将被试者放置在磁共振设备中，该设备利用强磁场和无害的无线电频率来获取图像。
在进行fmri扫描时，被试者通常会执行一系列特定的任务，或者在休息状态下进行观察。
通过监测被试者大脑不同区域的BOLD信号变化，研究者可以推断哪些区域与特定任务相关联，进而研究脑功能和大脑结构之间的关系。
fmri的应用范围非常广泛。
在认知心理学领域，它被用来研究不同认知过程如记忆、学习、决策等的脑机制。
在神经病学和精神病学领域，fmri可以帮助研究人员了解各类神经疾病的潜在机制，例如阿尔茨海默病、帕金森病、精神分裂症等。
此外，fmri还在神经工程学、人机交互以及脑机接口等领域得到广泛应用。
在fmri数据分析方面，研究者常常使用统计学方法来识别与特定任务或条件相关的脑活动模式。
研究者会首先预处理数据，包括去除噪声、校正头部运动等。
然后，使用特定的统计模型对数据进行分析，以确定哪些区域在特定任务下显示出显著的激活。
常用的统计测试方法有单样本t检验、多样本t检验、方差分析等。
此外，数据分析还可以使用机器学习方法，如支持向量机、深度学习等，以提高脑活动模式分类的精确度。
