The MR center
The MR center is a core facility that handles the MR research capacity at the neuroradiology clinic. It is equipped with a 3 Tesla GE SIGNA Premier owned by Karolinska institutet thanks to a generous donation from the Knut and Alice Wallenberg Foundation in 2010.
This page will give you updates and information about MR center: organisation, wiki, courses, events, etc.
To all people scanning at the MR Center:
The recommendations from Region Stockholm applies: stay at home if you have disease symptoms, wear face mask, avoid close contact, wash hands often with soap and . At the MR center this means:
1 – Be sure that the participants are healthy and not presenting any symptoms for COVID-19 (fever, cough, fatigue, muscle and joint pain, sore throat, headache, loss of smell and taste, runny nose, nasal congestion) before they enter the MR centrum. If they or a member of their family have been tested positive for COVID-19 they should not participate in any study before 15 days from the test.
2 - Face masks are mandatory for the participant as well as anybody entering MR-centrum.
3 – One participant at a time in the MR-centrum, no group of people.
4 - Clean with disinfection solution everything that had been or will be in contact with the participants (MR scanner bed, button boxes, etc.).
On Wednesday (24/1) at 11.00, it is time for the next KI MR Research Center seminar (Zoom).
Seminar title: Potential pitfalls of using data dimensionality reduction methods in neuroimaging data analysis.
Datasets in neuroimaging have become increasingly larger and more complex. The task of interpreting high-dimensional datasets has become an important issue not only in functional neuroimaging but in neuroscience in general. To extract signal patterns and structure in big datasets that are of interest, data-dimensionality reduction methods such as Principal Component Analysis (PCA) has become an indispensable tool to the neuroscientist. However, data dimensionality reduction methods can sometimes give rise to artificial results because the underlying statistical assumptions of PCA is not met by the data. I will in this seminar give a detailed explanation how PCA works and why it may provide misleading information. I will use examples from fMRI studies to illustrate this problem.