PhysIO – An open-source toolbox to model physiological noise in fMRI

In a collaborative project led by Lars Kasper from the Translational Neuromodeling Unit (TNU) and the Institute for Biomedical Engineering at the University of Zurich and ETH Zurich together with National Imaging Facility (NIF) Fellow Steffen Bollmann from the Centre for Advanced Imaging (CAI), The University of Queensland node, a new open source toolbox for improving fMRI data analysis has been developed.

The physIO simplifies the workflow of incorporating physiological noise correction in fMRI analyses by providing robust pre-processing, fully automated modelling and a user-friendly performance assessment for group studies. All of this is tightly integrated in the open source toolbox SPM, the most widely used software for analysing fMRI data.

The project was started by Lars Kasper to access physiological data, i.e. breathing and heartrate from a Philips MRI scanner. Soon, he extended the toolbox to perform analysis of the physiological data and implemented various noise modelling methods. Steffen Bollmann joined the project in 2013 when he wanted to apply the algorithms to data collected during his PhD on a GE MRI scanner. The toolbox was not compatible with the GE data format and Steffen contributed routines for reading in data from this vendor and developed an algorithm for detecting ECG peaks more robustly. This was required due to poor data quality at times, as Steffen worked with children with Attention Deficit Hyperactivity Disorder (ADHD), who had a hard time lying still in the MRI scanner.

“New features for the toolbox were often developed on weekends when we met and discussed problems and possible solutions. The toolbox and our understanding of the problems we had solved grew during this time and we had more and more people testing it on their data and reporting problems they faced. Now, more than five years after the first lines of code, we have read-in routines for all major MRI vendors and a large repertoire of noise correction methods has been tested and implemented”, Steffen said.

Encouraged by its successful application in several studies including hundreds of volunteers, the team hopes that this open source toolbox will find useful application in future neuroimaging studies of health and disease, particularly in areas strongly affected by physiological noise such as the brainstem.

To download the toolbox, refer to https://www.tnu.ethz.ch/en/software/tapas.html and for more information, contact Dr. Steffen Bollmann (steffen.bollmann@cai.uq.edu.au).

 

Collaborators
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland
Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Switzerland
Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
Wellcome Trust Centre for Neuroimaging, University College London, London, UK
Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK
Department of Psychiatry and Psychotherapy, Campus Mitte, Charité Universitätsmedizin, Berlin, Germany
Department of Neurology, Schulthess Clinic, Zurich, Switzerland
Max Planck Institute for Metabolism Research, Cologne, Germany

Novel Tractography to Detect Mild Traumatic Brain Injury

A mild traumatic brain injury (mTBI), often referred to as a concussion, rarely has lasting effects and is often presumed to cause only transient disturbances to brain function. However, repeated mTBIs, particularly those occurring in the sports and military settings, have been associated with cumulative and chronic neurological impairments, and the development of neurodegenerative diseases such as chronic traumatic encephalopathy (CTE).

There is evidence that these long-term adverse effects of repeated mTBIs are in part due to the recurring insults occurring before the brain has recovered from the initial mTBI and is in a period of increased cerebral vulnerability (ICV). There is increasing evidence that mTBI triggers complex biological changes including inflammatory, metabolic, neuronal, vascular and axonal abnormalities. It is believed that such changes are responsible for ICV and therefore, the identification of reliable markers that indicate when the brain is no longer in a state of ICV might allow them to be used to guide medical decisions.

The current clinical management of mTBI is largely guided by the presence or absence of neuropsychological symptoms, and typically evaluated by subjective and/or self-reported methods. Symptoms may include physical, cognitive, co-ordination, emotional, and sleep abnormalities. The onset of symptoms, although typically rapid, can take minutes or hours to occur, and symptoms are usually mild, or may even go unrecognized.

Recovery is determined to have occurred after all post-injury symptoms have resolved, at which point patients are commonly cleared to return to pre-injury activity. However, there is now evidence that the resolution of symptoms might not accurately indicate that the brain has recovered from the neuropathophysiological changes induced by mTBI. Therefore, research is required to guide and facilitate more informed medical decisions pertaining to return to pre-injury activity. In particular, it is critical that objective markers sensitive to the brain’s changes and recovery after an mTBI are identified.

Magnetic Resonance Imaging (MRI) and blood proteomics might provide objective measures of pathophysiological changes in mTBI, and indicate when the brain is no longer in a state of ICV. In a collaborative study, the use of MRI, blood proteomics, and behavioral methods as markers to detect changes and estimate recovery after experimental mTBI in rat models was investigated. Rats were given a sham or mild fluid percussion injury (mFPI), and behavioral testing, MRI, and blood collections were conducted up to 30 days post-injury.

There were cognitive impairments for three days post-mFPI, before normalizing by day 5 post-injury. In contrast, advanced MRI (i.e., tractography) and blood proteomics (i.e., vascular endothelial growth factor) detected a number of abnormalities, some of which were still present 30 days post-mFPI.

These findings suggest that MRI and blood proteomics are sensitive measures of the molecular and subtle structural changes following mTBI. Of particular significance, this study identified novel tractography measures that are able to detect mTBI and may be more sensitive than traditional diffusion-tensor measures. Furthermore, the blood and MRI findings may have important implications in understanding ICV and are translatable to the clinical setting.

For more information on this project, contact Mr. David Wright (wrightd@unimelb.edu.au).

 

Collaborators
Anatomy and Neuroscience, The University of Melbourne
The Florey Institute of Neuroscience and Mental Health
Department of Medicine, The Royal Melbourne Hospital
Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, USA
Department of Electrical and Electronic Engineering, The University of Melbourne
Centre for Stroke and Brain Injury, The University of Newcastle
School of Health Sciences, The University of Newcastle

Privacy Settings
Youtube
Vimeo
Google Maps