Affiliation: Harvard University
- A Bayesian approach for stochastic white matter tractographyOla Friman
Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, MA 02115, USA
IEEE Trans Med Imaging 25:965-78. 2006..Theory for estimating global connectivity is also presented, as well as a theorem that facilitates the estimation of the parameters in a constrained tensor model of the local water diffusion profile...
- Detection and detrending in fMRI data analysisOla Friman
Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, MA, USA
Neuroimage 22:645-55. 2004..The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series...
- Resampling fMRI time seriesOla Friman
Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Thorn 323, 75 Francis Street, Boston, MA 02115, USA
Neuroimage 25:859-67. 2005..While blocked designs can induce large biases, event-related designs generate significantly smaller biases. Results supporting these claims are provided...
- Uncertainty in white matter fiber tractographyOla Friman
Laboratory of Mathematics in Imaging, Department of Radiology Brigham and Women's Hospital, Harvard Medical School, USA
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 8:107-14. 2005..We also provide a theorem that facilitates the estimation of the parameters in this diffusion model, making the presented method simple to implement...
- Geometrically constrained two-tensor model for crossing tracts in DWISharon Peled
Harvard Center for Neurodegeneration and Repair, Boston, MA 02115, USA
Magn Reson Imaging 24:1263-70. 2006..Upon evaluation in simulations and application to in vivo human brain DTI data, the method appears to be robust and practical and, if correctly applied, could elucidate tract directions at critical points of uncertainty...
- Multiple channel detection of steady-state visual evoked potentials for brain-computer interfacesOla Friman
Institute of Automation, University of Bremen, Otto Hahn Allee 1, 28359 Bremen, Germany
IEEE Trans Biomed Eng 54:742-50. 2007..An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed...
- Adaptive analysis of fMRI dataOla Friman
Department of Biomedical Engineering, Linkoping University, Linkoping, Sweden
Neuroimage 19:837-45. 2003..Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided...
- Exploratory fMRI analysis by autocorrelation maximizationOla Friman
Department of Biomedical Engineering, , University Hospital, , Sweden
Neuroimage 16:454-64. 2002..The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data...
- CellProfiler: image analysis software for identifying and quantifying cell phenotypesAnne E Carpenter
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
Genome Biol 7:R100. 2006....
- Detection of neural activity in fMRI using maximum correlation modelingOla Friman
Department of Biomedical Engineering, , , Sweden
Neuroimage 15:386-95. 2002..Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data...