Functional Brain Imaging Unit
Functional Brain Imaging Unit
Sadeh B.,Tel Aviv University |
Podlipsky I.,Functional Brain Imaging Unit |
Podlipsky I.,Tel Aviv University |
Zhdanov A.,Functional Brain Imaging Unit |
Yovel G.,Tel Aviv University
Human Brain Mapping | Year: 2010
A face-selective neural signal is reliably found in humans with functional MRI and event-related potential (ERP) measures, which provide complementary information about the spatial and temporal properties of the neural response. However, because most neuroimaging studies so far have studied ERP and fMRI face-selective markers separately, the relationship between them is still unknown. Here we simultaneously recorded fMRI and ERP responses to faces and chairs to examine the correlations across subjects between the magnitudes of fMRI and ERP face-selectivity measures. Findings show that the face-selective responses in the temporal lobe (i.e., fusiform gyrus-FFA) and superior temporal sulcus (fSTS), but not the face-selective response in the occipital cortex (OFA), were highly correlated with the face-selective N170 component. In contrast, the OFA was correlated with earlier ERPs at about 110 ms after stimulus-onset. Importantly, these correlations reveal a temporal dissociation between the face-selective area in the occipital lobe and face-selective areas in the temporal lobe. Despite the very different time-scale of the fMRI and EEG signals, our data show that a correlation analysis across subjects may be informative with respect to the latency in which different brain regions process information. © 2010 Wiley-Liss, Inc.
Meir-Hasson Y.,Tel Aviv University |
Keynan J.N.,Functional Brain Imaging Unit |
Keynan J.N.,Tel Aviv University |
Kinreich S.,Functional Brain Imaging Unit |
And 6 more authors.
PLoS ONE | Year: 2016
Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations. © 2016 Meir-Hasson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.