Dr. Ahlfor’s research focuses on non-invasive neuroimaging — in particular, the analysis and interpretation of magnetoencephalography (MEG) signals. He has developed methodologies for multimodal integration of MEG, electroencephalography (EEG) and structural and functional magnetic resonance imaging (MRI) data. He and colleagues, in the David Cohen MEG Laboratory and elsewhere, are currently using effective connectivity analysis of combined MEG, EEG and MRI data to identify neural mechanisms that regulate phonological structure during human language processing, and MEG and 7T-fMRI to identify feedforward and feedback influences in cortical activation associated with multisensory processing. He also collaborates with clinical and cognitive scientists in applications of MEG to studies of normal as well as various clinical populations, including patients with epilepsy, aphasia, obsessive compulsive disorder, dyslexia and autism.


PhD in Technical Physics and Biomedical Engineering, Helsinki University of Technology

Select Publications

1. Ahlfors SP, Jones SR, Ahveninen J, Hamalainen MS, Belliveau JW, Bar M. Direction of magnetoencephalography sources associated with feedback and feedforward contributions in a visual object recognition task. Neurosci Lett. 2015;585:149-54.

2. Ahlfors SP, Wreh C, 2nd. Modeling the effect of dendritic input location on MEG and EEG source dipoles. Med Biol Eng Comput. 2015;53(9):879-87.

3. Ahlfors SP, Han J, Belliveau JW, Hämäläinen MS. Sensitivity of MEG and EEG to source orientation. Brain Topogr. 2010;23(3):227-32.


In his research, Dr. Ahlfors has demonstrated novel types of complementary properties of MEG and EEG, specifically by showing how signal cancellation and source orientation sensitivity leads to systematic differences between MEG and EEG signals for focal versus extended regions of cortical activation. Building on cognitive neuroscience theories and biophysical computational modeling, he has proposed a relationship between the direction of MEG and EEG source currents and the underlying feedforward or feedback type of activity between cortical areas.


The David Cohen MEG Laboratory