IPEG 2012, New York

The IPEG 2012 conference will be held in New York (US) from Thursday October 18th to Sunday 21st and hosted by the Brain Research Laboratories of the New York University (550 1st Avenue) School of Medicine. On Thursday October 18th there will be a special symposium open to all registered IPEG attendees focused on 'EEG based Personalized Medicine in Psychiatry'.

The abstract book for the IPEG 2012 meeting in New York can be downloaded here.


Consider becoming an IPEG Member? See our membership application page.


Confirmed keynote speakers include:

  • Prof. Max Fink (NY, US),
  • Prof. Diego Pizzagalli (Belmont, US)
  • Prof. Ulrich Hegerl (Leipzig, Germany)
  • Dr. Roberto Pascual-Marqui (Zurich, Switzerland)
  • Prof. Leslie Prichep (NY, US)
  • Dr. Martijn Arns (Nijmegen, the Netherlands)



IPEG 2012 EEG Based Personalized Medicine Symposium


Traditionally, the focus of the IPEG conference has been on pharmaco-EEG research in pre-clinical and clinical work, using the EEG for pharmaco-kinetic and pharmaco-dynamic assessments (PK/PD modeling) and polysomnography. In both academia and industry as well as in regulatory bodies such as the FDA a strong focus on personalized medicine has emerged. In most cases genetics, or biomarkers, are proposed as key measures to achieve personalized medicine.


However, in psychiatry and neuropsychiatry, the use of neuroimaging methods, especially neurophysiological techniques such as the EEG have a long and rich research history, investigating predictors or moderators of treatment outcome. In 1957 the first studies were conducted on predicting treatment outcome to ECT using barbiturate-induced changes in the EEG (Roth et al., 1957). Several recent review articles in Nature and Science celebrated the 10th anniversary of the Human Genome project, however the contributions of genetic approaches have only been able to explain a few percent of the genetic variance (Lander, 2011), suggesting that a strictly genetic approach to personalized medicine for psychiatry will be not so fruitful. The notion of personalized medicine suggests heterogeneity within a given DSM-IV disorder, despite apparent symptom homogeneity, from a brain-based perspective. Based on the rich research history of EEG in this approach (also often referred to as Quantitative EEG or QEEG) and the fact that EEG is widely available, non-invasive and cost effective, makes this technique an exciting candidate for the future of personalized medicine in psychiatry.


Therefore, the IPEG meeting 2012 includes a special 1-day symposium at the first day of the IPEG meeting (October 18th 2012) where we have invited all internationally renowned speakers on this topic. They will present their most recent results and insights. The day will end with a round-table discussion where all developments and results are weighed and a ‘road-map’ for the future of EEG Based Personalized medicine in psychiatry is achieved, resulting in a scientific paper to be published in a peer-reviewed journal with all invited speakers as co-author.


Below a preliminary program can be found for this special 1-day event, together with a small biography for all speakers.


Program:EEG Based Personalized Medicine in Psychiatry (IPEG Conference)

An introduction: EEG Subtypes and Endophenotypes

9.00-9.45       Leslie Prichep: QEEG subtyping for Treatment Optimization
9.45-10.30     Martijn Arns: A review and integration of EEG based predictors for treatment outcome in ADHD and Depression.


10.30-11.00     Coffee break


‘EEG Cordance’ and the ATR (Antidepressant Treatment Response)

11.00 - 11.45   Andrew Leuchter, Ian Cook & Aimee Hunter:  Neurophysiologic Predictors of Treatment Response in Major Depression.
11.45 – 12.15  Martin Brunovsky: Prefrontal EEG Cordance as a predictor of response to antidepressive treatment in patients with MDD and Bipolar depression


12.15-13.30:   Lunch

ERP approaches to Personalized Medicine and EEG Vigilance

13.30-14.15   Gerard Bruder, Craig Tenke & Jürgen Kayser: Resting EEG and Evoked Potential Predictors of Clinical Response to Antidepressants.

14.15-15.00   Ulrich Hegerl: Can vigilance regulation be used to predict clinical response to antidepressants?


15.00-15.30: Break


EEG based Personalized Medicine: The Industry perspective

15.30-16.15   Evian Gordon & Chris Spooner: Biomarker Discovery: An integrative Neuroscience Approach
16.15-17.00   Daniel Hoffman: Pharmacologic treatment of major depressive disorder guided by QEEG..


17.00-18.00   Roundtable: Designing a roadmap for the future of EEG based Personalized medicine in Psychiatry.



Confirmed speakers


Andrew F. Leuchter (UCLA, LA): Andrew Leuchter has published extensively on research using several EEG metrics for predicting treatment outcome to antidepressants. He has focused on ‘treatment emergent biomarkers’ such as EEG Cordance and the ATR (Antidepressant Treatment Response). He recently published very promising results based on the BRITE-MD trial demonstrating the possibility of predicting treatment outcome to an SSRI (Leuchter et al. 2009a), but also differentially predicting treatment outcome to an atypical antidepressant with a different mode of action, namely Buproprion (Leuchter et al. 2009b).




Martin Brunovsky: (Prague Psychiatric Center, Czech Republic): Martin Brunovsky is an experienced neurophysiologist with a special interest in the pharmaco-EEG prediction of response to antidepressive treatment and evaluation of CNS effects of psychotomimetics. He and his co-workers independently replicated the usefulness of EEG Cordance in the prediction of response to various antidepressants as well as to rTMS, extending the application of this method to patients with treatment resistant depression and bipolar depression (Bares et al., 2010, 2011). The usefulness of this QEEG biomarker was also evaluated after ketamine administration, implicated its antidepressant effect (Horacek et al., 2010).







Gerard Bruder / Craig Tenke / Jürgen Kayser (Columbia University, NY): Gerald Bruder and his associates have conducted numerous studies beginning with research on hemispheric lateralization and EEG in relation to prediction of clinical response to antidepressants and most recently has extended this research to include ERP measures.





Ulrich Hegerl (Leipzig University, Germany): Traditionally, Ulrich Hegerl has been investigating the Loudness Dependent Auditory Evoked Potential (LDAEP) in predicting treatment response to SSRI’s given this measure very well reflected 5HT innervation of the underlying cortex. More recently, Hegerl and his group have been further investigating and elaborating on the EEG Vigilance model and also testing the implications for treatment prediction such as applying stimulant medication in Mania (Schoenknecht et al., 2010) which is now being further tested in an RCT.




Evian Gordon (Brain Resource Company, Australia): This group is currently conducting one of the largeststudies to-date investigating predictors of treatment response in ADHD and Depression in the iSPOT studies (International Study to Predict Optimized Treatment response) in over 2000 patients with Depression and 500 patients with ADHD. This study includes genetics, neuropsychological and neurophysiological data (EEG and ERPs). At this moment the first analysis on the first half of subjects for both studies are underway and it is expected that by October 2012 this group will be able to present more details on their first results.


Daniel Hoffman (CNS Response Inc., CA; Neuro-Therapy Clinic Inc., CO):

 CNS Response has been focusingfor a long time on the utilization of the EEG in offering peer information for treatment outcomes after the pioneering work of Suffin & Emory (1995). Their approach is called PEER Outcomes (previously known as Referenced-EEG or rEEG). Recently a large scale controlled study was published showing the promise of this approach in guiding treatment decisions in Depression, where, based on their EEG analysis, subjects either received medications optimized from the STAR*D Study or guided by the QEEG report (DeBattista et al., 2010). They have also been examining the ability of the report to caution prescribers about the possibility of serious adverse events based on such a personalized medication approach (Hoffman et. al., paper in progress).


Leslie Prichep (Brain Research Laboratories, NYU School of Medicine, NY):

Leslie Prichep togetherwith the late E. Roy John, have developed quantitative methods to aid in the diagnosis, subtyping and treatment evaluation of neuropsychiatric patients (John et al., 1988). Their main focus over the past decade has been on the identification of treatment responsive subtypes within diagnostic categories, (e.g., ADHD, OCD, psychosis, cocaine dependence, dementia). With the use of source localization methods, they have also demonstrated different underlying pathophysiology in the subtypes they identify, suggesting different targets for intervention. In addition, Prichep et al. (2006), reported high accuracy in prediction of future cognitive decline in normal elderly patients with subjective complaints only.


Martijn Arns (Research Institute Brainclinics, Nijmegen and Utrecht University,

 The Netherlands):

Martijn Arns is specialized in EEG based personalized medicine, which was also the focus of his PhD. He has conducted several studies on predicting treatment outcome to stimulant medication, antidepressants and rTMS with a focus on predicting non-response to treatment. He has recently suggested an endophenotype for non-response, which consist of a slow individual Alpha Peak Frequency predicting non-response to stimulant medication, antidepressants and rTMS (Arns 2011).




Arns, M. (2011). Personalized medicine in ADHD and depression: A quest for EEG treatment predictors. PhD thesis, Utrecht University.


Arns, M., Drinkenburg, W. H. I. M., Fitzgerald, P. B., & Kenemans, J. L. (2012). Neurophysiological predictors of non-response to rTMS in depression. Brain Stimulation. doi:10.1016/j.brs.2011.12.003


Bares M, Brunovsky M, Novak T, Kopecek M, Stopkova P, Sos P, Krajca V, Höschl C. The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments. Eur Neuropsychopharmacol. 2010 Jul;20(7):459-66.


Bares M, Novak T, Brunovsky M, Kopecek M, Stopkova P, Krajca V, Höschl C. The change of QEEG prefrontal cordance as a response predictor to antidepressive intervention in bipolar depression. A pilot study. J Psychiatr Res. 2011 Sep 19.


Debattista, C., Kinrys, G., Hoffman, D., Goldstein, C., Zajecka, J., Kocsis, J., . . . Fava, M. (2010). The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression. Journal of Psychiatric Research, 45(1), 64-75.


Fink, M. (2010). Remembering the lost neuroscience of pharmaco-EEG. Acta Psychiatrica Scandinavica, 121(3), 161-173.


Gordon, E. (2007). Integrating genomics and neuromarkers for the era of brain-related personalized medicine. Personalized Medicine, 4(2), 201-215.


Horacek J, Brunovsky M, Novak T, Tislerova B, Palenicek T, Bubenikova-Valesova V, Spaniel F, Koprivova J, Mohr P, Balikova M, Hoschl C. Subanesthetic dose of ketamine decreases prefrontal theta cordance in healthy volunteers: implications for antidepressant effect. Psychol Med. 2010 Sep;40(9):1443-51.


John, E. R., Prichep, L. S., Fridman, J., & Easton, P. (1988). Neurometrics: Computer-Assisted differential diagnosis of brain dysfunctions. Science, 239(4836), 162-9.


Lander, E. S. (2011). Initial impact of the sequencing of the human genome. Nature, 470(7333), 187-97.


Leuchter, A. F., Cook, I. A., Gilmer, W. S., Marangell, L. B., Burgoyne, K. S., Howland, R. H., . . . Greenwald, S. (2009a). Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Research, 169(2), 132-8.


Leuchter, A. F., Cook, I. A., Marangell, L. B., Gilmer, W. S., Burgoyne, K. S., Howland, R. H., . . . Greenwald, S. (2009b). Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in major depressive disorder: Results of the BRITE-MD study. Psychiatry Research, 169(2), 124-31.


Prichep, L. S., John, E. R., Ferris, S. H., Rausch, L., Fang, Z., Cancro, R., . . . Reisberg, B. (2006). Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiology of Aging, 27(3), 471-81.


Roth, M., Kay, D. W., Shaw, J., & Green, J. (1957). Prognosis and pentothal induced electroencephalographic changes in electro-convulsive treatment; an approach to the problem of regulation of convulsive therapy. Electroencephalogr Clin Neurophysiol, 9(2), 225-37.


Schoenknecht, P., Olbrich, S., Sander, C., Spindler, P., & Hegerl, U. (2010). Treatment of acute mania with modafinil monotherapy. Biological Psychiatry.


Suffin, S. C., & Emory, W. H. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clin Electroencephalogr, 26(2), 76-83.



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