The SIPBA group use computational and mathematical approaches based on the statistical learning theory to develop computer-aided diagnosis systems in the field of neuroscience. SiPBA aim to provide supporting tools to physicians in the early diagnosis of neuropathological diseases, such as Alzheimer or Parkinson diseases, that will influence treatment and patient management. In that sense, from the perspective of data analysis, SiPBA propose a number of statistical image analysis methods that compare an individual?s data with reference images of control subjects for the in-vivo assessment of brain functional/structural parameters in neurodegenerative diseases.
Statistical Agnositic Mapping
SAM is an alternative method to classic statistical inference in neuroimaging. Specifically, it tries to address a few thorny points about multiple comparison correction through family-wise error rate (FWER) control and the link between sample size and sensitivity of the test. The approach proposed relies on a region-wise machine learning technique and the calculation of error bounds on classification accuracy.
