LISA: Scientists introduce a new method of statistical inference in neuroimaging (fMRI)

16.10.2018 10:00
By: Beate Fülle

New method detects brain activations with improved sensitivity and accuracy

Tuebingen, Germany. 16 October 2018. One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local neuronal activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. The group of scientists of the Max-Planck-Institute of the Biological Cybernetics and the University Hospital in Tuebingen came up with a new software framework called “LISA” to address these problems.

The most widely used statistical inference procedures were invented more than 10 years ago and are not well suited for handling state-of-the art high-resolution neuroimaging data. MRI technology improved considerably in recent years due to the advent of ultra-high field scanners (>= 7 Tesla) that offer greatly improved spatial resolution. However, standard algorithms were not designed to handle such high-precision data so that some of the main advantages of ultrahigh-field scanning were lost due to inadequate software. Furthermore, a recent publication by Eklund et al. (PNAS, 2016) showed that some of the most widely used statistical methods sometimes produced incorrect results.

Reasons enough for our scientists to invent better approaches for statistical inference in fMRI. Our scientist Dr. Gabriele Lohmann explains:” Sophisticated mathematical methods are needed in order to make sense of neuroimaging data. The colored 'blobs' that are often depicted in articles on neuroimaging are computed using complicated statistical methods. Without those tools we would not be able to see anything in these data.”

The scientists now introduced a new method of statistical inference in fMRI, which they call LISA (Local Indicators of Spatial Association). It is inspired by a concept otherwise used in geographical information systems. In conclusion, the scientist hope that because of its improved sensitivity and better spatial specificity, LISA will help in developing novel and more realistic models of human brain function. Lohmann further elaborates: “In our first tests, we found that our method is much more sensitive and can detect brain activity more accurately than previous methods. We are convinced that our method will help to provide a more complete understanding of the brain function.” She is convinced that for the future, the insights gained from this basic research may benefit patients with neurological diseases.


Original Publication:
"LISA improves statistical analysis for fMRI", Gabriele Lohmann, Johannes Stelzer, Eric Lacosse, Vinod J. Kumar, Karsten Mueller, Esther Kuehn, Wolfgang Grodd & Klaus Scheffler, Nature Communications 9:4014 (2018)



PD. Dr. Gabriel Lohmann works at the Max Planck Institute for Biological Cybernetics at the Magnetic Resonance Centre (MRZ) and the University Hospital in Tuebingen. Her research focuses on the development of new mathematical methods for the analysis of data obtained from the human brain using MRI. She is particularly interested in methods of static inference and the development of network models.

Interview with Gabriele Lohmann


Press Contact:
Beate Fülle
Head of Public Relations and Communication
Phone: +49 7071 601- 777
E-mail: presse-kyb[at]tuebingen.mpg.de



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The Max Planck Institute for Biological Cybernetics is studying signal and information processing in the brain. The scientists aim to determine which signals and processes are responsible for creating a coherent percept of our environment and for eliciting the appropriate behavior. Scientists of three departments and several research groups are working towards answering fundamental questions about processing in the brain, using different approaches and methods.

The Max Planck Institute for Biological Cybernetics is one of 84 Max Planck Institutes and facilities that make up the Max Planck Society, Germany's most successful research organization. Since its establishment in 1948, no fewer than 18 Nobel laureates have emerged from the ranks of its scientists, putting it on a par with the best and most prestigious research institutions worldwide. All Institutes conduct basic research in the service of the general public in the natural sciences, life sciences, social sciences, and the humanities.
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Founded in 1805, the University Hospital Tuebingen is one of the leading centres of German university medicine. As one of 33 University Hospitals in Germany, it contributes to a successful combination of top-level medicine, research, and teaching.
More than 400,000 in- and outpatients from around the world benefit from this connection of science and practice each year, since the clinics, institutes, and centres unite specialists from all fields under one roof. Its experts collaborate across disciplines and offer state-of-the-art research-based treatment to all patients. The University Hospital does research to improve diagnostics, therapies, and healing processes. Many new cutting-edge treatments are clinically tested and applied in Tuebingen.

Neurosciences, Oncology and Immunology, Infection Biology, Vascular Medicine and Diabetes are focus areas of research at the University Hospital Tuebingen. It is a reliable partner in four of the six German Centres for Health Research (DZG) created by the Federal Government. www.medizin.uni-tuebingen.de/en/


This image shows a comparison of LISA against the most commonly used other statistical inference algorithms. Brain activations detected by LISA are better reproducible and are thus more reliable. Source: Gabriele Lohmann/MPI for Biological Cybernetics (cited in Nature Communications 9:4014 (2018) https://www.nature.com/articles/s41467-018-06304-z

This image shows a comparison of LISA against the most commonly used other statistical inference algorithms. Brain activations detected by LISA are better reproducible and are thus more reliable. Source: Gabriele Lohmann/MPI for Biological Cybernetics (cited in Nature Communications 9:4014 (2018) https://www.nature.com/articles/s41467-018-06304-z