Lars Kasper

Please wait… you will be redirected to shortly

SPM Course 2016

Zurich 2016 SPM Course for fMRI

Thank you all for a wonderful 10th Zurich SPM course! The talks and tutorials offered during the course can be accessed here.


BOLD Physiology (Jakob Heinzle) Pre-processing (Lars Kasper) The General Linear Model for fMRI analyses & Tutorial (Frederike Petzschner) Statistical inference and design efficiency (Andreea Diaconescu)

Multiple comparison correction in SPMs (Justin Chumbley) Experimental design (Sandra Iglesias) Event-related fMRI (Christian Ruff) Resting state” fMRI . . . → Read More: SPM Course 2016

PhysIO Toolbox

The PhysIO Toolbox can be downloaded as part of the TAPAS software collection of the TNU.

Example_FContrast_RETROICOR_SingleSubj The general purpose of this toolbox is the model-based physiological noise correction of fMRI data using peripheral measures of respiration and cardiac pulsation. A comprehensive description of the toolbox can be found in the remainder of this article and in the accompanying quick start manual and handbook.

Current Version: r671 (February 2nd, 2015)
(Code | Examples | GettingStarted | Handbook) New features include:

  • Compatibility tested for SPM12, small bugfixes Batch Dependencies
  • Cleaner Batch Interface with grouped sub-menus (cfg_choice)
  • New model: ‘none’ to just read out physiological raw data and preprocess, without noise modelling
  • Philips: Scan-timing via gradient log now automatized (gradient_log_auto)
  • Siemens: Tics-Logfile read-in (proprietary, needs Siemens-agreement)
  • All peak detections (cardiac/respiratory) now via auto_matched algorithm
  • Adapt plots/saving for Matlab R2014b

. . . → Read More: PhysIO Toolbox

SPM Course 2015

Thank you all for taking part in the SPM course! The SPM 2015 lecture slides are now available online: Basic Course SPM Overview  Preprocessing The General Linear Model for fMRI analyses Statistical inference and design efficiency Multiple comparison correction in SPMs  Experimental design Modelling of hemodynamic timeseries and 2nd-level statistics “Resting state” fMRI  Practicals Scripts  Advanced Course Group analyses Advanced issues in fMRI statistics (SnPM, power, meta-analysis) Practicals Slides & Worksheet Voxel-based morphometry Multivariate analyses Computational models for fMRI analyses Bayesian model selection . . . → Read More: SPM Course 2015

SPM Course 2014

11-14 February, 2014

An IBT Advanced Imaging Course

This four-day course, held annually since 2007, offers comprehensive coverage of all MRI-related aspects of SPM, including . . . → Read More: SPM Course 2014

SPM Course 2013 - Presentation Slides

Here are links to the presentation slides for the SPM Course 2013 (in PDF or Powerpoint format). . . . → Read More: SPM Course 2013 – Presentation Slides

Dr. Andreea Diaconescu

Post-doctoral Fellow, Translational Neuromodeling Unit (TNU),  Institute for Biomedical Engineering, University of Zürich & ETH Zürich Biography

Andreea Diaconescu obtained her PhD at the Rotman Research Institute affiliated with University of Toronto under the supervision of Dr. Randy McIntosh. During her graduate training, Andreea examined the temporal dynamics of multisensory processes and how auditory-visual interactions change with age using electro- and magneto-encephalography. Andreea has also collaborated with Shitij Kapur (Institute of Psychiatry, King’s College London) and Gwenn Smith (Johns Hopkins Bayview Medical Center), investigating pharmacological models of psychiatric disorders, including . . . → Read More: Dr. Andreea Diaconescu

Teaching - HS 2012

Fall Semester 2012

. . . → Read More: Teaching – HS 2012

SPM Course 2013

13-15 February, 2013

An IBT Advanced Imaging Course All lectures take place at the main University building (lecture hall KO2-F-180), whereas the practical sessions take place at the Gymnasium Rämibühl (Rämistrasse 58, 8001 Zurich) . . . → Read More: SPM Course 2013

Mixed-effects inference for classification group studies

Multivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. However, meaningful inference requires models that explicitly account for fixed-effects (within-subjects) and random-effects (across-subjects) variance components. While models of this sort are standard practice in mass-univariate analyses of fMRI data, they have not yet received much attention in the decoding literature.

This software closes this gap by providing full Bayesian mixed-effects inference for multivariate classification studies.

. . . → Read More: Mixed-effects inference for classification group studies