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Towards fMRI informed EEG Neurofeedback for ADHD

Ilana Klovatch-Podlipsky1*, Ayelet Or-Borichev1, Roy Sar-El1, Nitzan Lubianiker1 and Talma Hendler1, 2, 3, 4
  • 1 Tel Aviv Sourasky Medical Center, Whol Institute for Advanced Imaging, Israel
  • 2 Tel Aviv University, The School of Psychological Sciences, Israel
  • 3 Tel Aviv University, Sagol School of Neuroscience, Israel
  • 4 Tel Aviv University, Sackler Faculty of Medicine, Israel

The Right Inferior Frontal gyrus (rIFG) has a pivotal role in attention deficit disorders (Rubia et al., 2014), hence gaining control over its activity could facilitate better treatment and recovery. Learning to volitionally regulate IFG activity was thus far possible only via real-time fMRI. In the present study a novel fMRI enriched EEG model (herby, "EEG-Finger-Print", EFP) of the rIFG, was developed to enable the prediction of its fMRI-BOLD activity using only EEG. Simultaneous EEG/fMRI neurofeedback (NF) was conducted in the current study to test whether the rIFG-EFG reliably predicts IFG fMRI-BOLD activity; and weather feedback about this EFP activity can be used by subjects to regulate IFG activity. A model of the rIFG activity was constructed using simultaneous EEG/fMRI data of 10 subjects from a different study (Kinreich et al., 2014) based on a previously described method (Meir-Hasson et al., 2014; Keynan et al., 2016) using EEG data extracted from electrode F4. The resulting estimated model correlates well with the rIFG BOLD activity (r=0.6, p<0.5). 14 healthy subjects performed rIFG EEG-NF training simultaneously with fMRI acquisition in the scanner. The training included two test runs and one sham run. The EFP-NF training was implemented as a game where a skateboard rider and speedometer above the rider head were displayed on the screen. The game included 5 blocks of three conditions: 1.'Rest' condition (60 seconds), subjects instructed to passively view the skateboard rider which was moving at a constant speed; 2. 'Play' condition (60 seconds), the speed represented the corresponding level of EEG-EFP activity. Subjects were instructed to increase the speed of the skateboard as much as possible by practicing mental strategies of their choosing; 3. At the end of each NF block a bar indicating the average speed during the current block was presented. In the EFP-Sham runs subjects had the same instructions but received visual feedback driven by their rIFG-EFP signal from a previous EFP-NF run that was randomly assigned. Success rate index which represents the percentage of the time in which the mean EFP value was significantly higher than mean baseline value during all runs was defined. As expected this index was significantly positive (one sample ttest, t(26)=18.92, p<0.001; Mean=66.59±18.29). Whole-brain random-effects (RFX-GLM) analysis using rIFP-EEG signal as a regressor, revealed correlation with the right IFG-BOLD activity with full respect to the region originally used to develop the model (Figure 1A, p<0.017, FDR p<0.1, n=11). Block design whole brain (RFX-GLM) group analysis overall NF runs vs baseline (n=14 subject; 27 NF runs) revealed significant BOLD activations in the rIFG which was originally used to develop the model within a network of functionally relevant areas. Analysis of weighted beta values extracted from rIFG-EFP during NF relative to baseline indicated that as hypothesized the EFP-NF runs responded differently from the the EFP-Sham runs (Figure 1B). Paired samples T-test of betta values fom the rIFG region of interest revealed a significant difference between EFP-NF runs (NF=0.73±0.45, n=10) and EFP-Sham runs (Sham=0.43±0.37, n=10); (t(9)=2.46, p<0.03). In order to evaluate the neural impact of successful rIFG-EFP modulation, parametric analisys of block design RFX-GLM was used. NF blocks amplitude was parametrized according to success rate index of each block. A network of regions relevant to sensori-motor functions and intoceptive awareness. The results obtained by the simultaneous recording of EEG and fMRI show that the rIFG-EFP model reliably predicts fMRI-BOLD activity in the rIFG ROI, for which it was originally developed. Remarkably the rIFG-EFP NF training elicited distributed activation in the rIFG ROI that used to develop the model. Additionally, as evidenced by the results, subjects manage to up-regulate the activation in the rIFG during neurofeedback relative to baseline, a significantly higher during rIFG-EFP-NF runs compared to rIFG-EFP-Sham runs. The current work demonstrated the potential of the EFP imaging approach to enhance the spatial resolution of EEG alone and to be used as a monitor for specific regional activation. Furthermore, our results suggest that implementing the EFP approach in NF training could be used by subjects to facilitate up-regulation of rIFG activity without the use of fMRI.

Figure 1
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Acknowledgements

The authors would like to acknowledge the following programs for funding this study: the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 602186. The European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project) and the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation (grant no. 51/11)

References

Keynan, J.N., Meir-Hasson, Y., Gilam, G., Cohen, A., Jackont, G., Kinreich, S., et al. (2016). Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation. Biol Psychiatry. doi: 10.1016/j.biopsych.2015.12.024.
Kinreich, S., Podlipsky, I., Jamshy, S., Intrator, N., and Hendler, T. (2014). Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG NeuroFeedback. Neuroimage 97, 19-28.
Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., and Intrator, N. (2014). An EEG Finger-Print of fMRI deep regional activation. Neuroimage 102 Pt 1, 128-141. doi: 10.1016/j.neuroimage.2013.11.004.
Rubia, K., Alegria, A.A., Cubillo, A.I., Smith, A.B., Brammer, M.J., and Radua, J. (2014). Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: a systematic review and meta-analysis. Biol Psychiatry 76(8), 616-628. doi: 10.1016/j.biopsych.2013.10.016.

Keywords: EEG-neurofeedback (EEG-NF), EEG-fMRI integration, machine learning, inferior frontal gyrus, Brain computer interface (BCI)

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in SAN 2016 Conference

Topic: Oral Presentations

Citation: Klovatch-Podlipsky I, Or-Borichev A, Sar-El R, Lubianiker N and Hendler T (2016). Towards fMRI informed EEG Neurofeedback for ADHD. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00018

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Received: 29 Jul 2016; Published Online: 30 Jul 2016.

* Correspondence: Mrs. Ilana Klovatch-Podlipsky, Tel Aviv Sourasky Medical Center, Whol Institute for Advanced Imaging, Tel Aviv, Israel, ilanap@gmail.com