… a better similarity measure to drive … time series and actual data stemming from a 3T magnet.
It is shown that these methods are more robust than the usual difference of squares measure…

… similarities in activation between voxels. We employ a novel metric that measures the similarity
… The aim of this contribution is to focus on the application of clustering to fMRI time series …

… (inter-subject correlations of aggregate time-courses) (Hasson et al., 2004)… fMRI time series
recorded from 24 viewers during the first viewing of the movie clips. To measure the similarity …

… We found that the initial number of clusters (k) did not have a clear effect on the similarity
between the atlas and the rest fMRI clusters and therefore we set the number of clusters k to 12 (…

… In this paper, a different point of view to solve the fMRI problem is addressed. The proposed
approach is intended to find the voxels that are necessary to rebuild the stimulus signals of …

… and the temporal one), slices of figures or multivariated time series [4, 5]… fMRI time series.
The most popular form of unsupervised machine learning is clustering, which uses the similarity …

… of fMRI clustering. By defining a similarity measure between different time series, we hope
to group voxels with similar time series. The underlying assumption is that voxels with similar …

… signal time series at each pixel (pixel time course, PTC). Cluster analysis for both fMRI and …
pixels together based on the similarity of their intensity profile in time. In the clustering process…

… voxels in the fMRI context), c ≥ 2 is the number of clusters in the set and d(x k ;v i ) is the
similarity measure between a datum and a centroid. The measure d is any metric induced by an …

… Since we want to distinguish between positive and negative correlations, we use a signed
similarity measure defined as s ij = r ij + 1 2 and then the soft power adjacency function is …