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Cyril Höschl www.hoschl.cz National Institute of Mental Health Prague Psychiatric Centre & Charles University, 3 rd Medical Faculty, Prague QEEG-based.

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Presentation on theme: "Cyril Höschl www.hoschl.cz National Institute of Mental Health Prague Psychiatric Centre & Charles University, 3 rd Medical Faculty, Prague QEEG-based."— Presentation transcript:

1 Cyril Höschl www.hoschl.cz National Institute of Mental Health Prague Psychiatric Centre & Charles University, 3 rd Medical Faculty, Prague QEEG-based prediction of response to antidepressant modalities 6th International Congress on Psychopharmacology, Antalya 17 April 2014 I NI H M NATIONAL INSTITUTE OF MENTAL HEALTH

2 Treatment of Depression Only one-third of patients being treated with SSRIs for depression achieve remission on the first drug tried (Verqouwen AC et al. 2007; Bondolfi G et al. 2006; Trivedi et al.2006 ) 25-30% of non-responders to first-line treatment can achieve remission by switching to- or adding of a second medication (Papakostas GI et al. 2008; Ruhé HG et al.2006) Clinical improvement generally requires 4-12 weeks Over 30% of patients prescribed antidepressants discontinue treatment before 30 days of therapy ( (Sheehan et al.2004) All of this leads to prolonged suffering of depressed patients and to enormous expenses for society Every day in depression represents huge individual and social burden. Any intervention shortening period of disability saves a lot of suffering and expenses.

3 Predictor: QEEG Cordance Calculated from quantitative EEG power values; integrates absolute and relative power information Associated with regional cerebral perfusion Provides information interpretable in the context of PET and SPECT neuroimaging studies of depression Leuchter et al, Neuroimage 1994; Cook et al, EEG Clin Neurophysiol 1998; Leuchter et al, Psychiatry Res 1999

4 δ (0,5-4Hz) σ (4-8 Hz) α (8-12 Hz) β (12-30Hz)

5 lesion hypoperfusion ↓ absolute spectrum  relative spectrum

6 1.Absolute power values ɑ are reattributed to each individual electrode by averaging power from all bipolar electrode pairs sharing that electrode. (Fp2-F4)+(F8-F4)+(C4-F4)+(Fz-F4) F4 = 4 4. Cordance calculation: 2. Relative power values are calculated 3. calculation of normalized absolute (ANORM (s,f)) and normalized relative (RNORM (s,f)) power values (absolute and relative power values at each electrode site (s) and for each frequency band (f ) are divided by AMAXf and RMAXf respectively). www.cordance.com

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8 Design of our study Baseline MADRS, BDI, CGI, EEG I Onset of treatment Screening MADRS, BDI, CGI, Wash-out 5-7 days 1 st week treatment MADRS, BDI, CGI, EEG II 4 weeks treatment MADRS, BDI, CGI, EEG III Notes: BDI-Beck self-rating scale for depression; CGI-clinical global impression; MADRS-Montgomery & Asberg depression rating scale; EEG-electroencephalography Distribution of EEG electrodes: N=17 (8M;9F) MADRS>25 Stage I criteria for resistant depression (Thase and Rush, 1997) Response was defined as equal to or more than 50% reduction of MADRS scores and remission as MADRS scores less than 12 points after four weeks of treatment. 5 responded (4 achieved remission ) 5 responded (4 achieved remission ) Medication Responders: Venlafaxine 2 Escitalopram 1 Clomipramine 1 Bupropion 1 Non-responders: Venlafaxine 5 Escitalopram 2 Mirtazapine 1 Citalopram 1 Bupropion 1 Clomipramine 1 Milnacipran 1 Medication Responders: Venlafaxine 2 Escitalopram 1 Clomipramine 1 Bupropion 1 Non-responders: Venlafaxine 5 Escitalopram 2 Mirtazapine 1 Citalopram 1 Bupropion 1 Clomipramine 1 Milnacipran 1

9 Bareš M, Brunovský M, Kopeček M, Novák T, Stopková P, Kožený J, Höschl C. Journal of Psychiatric Research 2006 Results Positive predictive value (PPV) was 0.71 Negative predictive value (NPV) was 1.0 All responders showed decrease 10/12 non-responders showed increase

10 Results Condition PositiveNegative Test outcome Positive True positive False positive (Type I error; p- value) → Positive predictive value Negative False Negative (Type II error) True Negative → Negative predictive value ↓ sensitivity ↓ specificity Hamlet Othello

11 Results Depression ResponseNon-response Cordance Positive decrease 52→ PPV=0.71 Negative  increase 010→ NPV=1.00 Sensitivity 100% Specificity >83%

12 N=26 (8M;18F) MADRS>20 Stage I criteria for resistant depression (Thase and Rush, 1997) Response was defined as equal to or more than 50% reduction of MADRS scores after four weeks of treatment.

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14 Results Depression ResponseNon-response Cordance Positive decrease 115→ PPV=0.69 Negative  increase 18→ NPV=0.89 Sensitivity 92% Specificity >62%

15 cc Stereotaxis of Lexel’s knife Electrodes placed trans-cranially sCg25 on MRI DBS target DBS: Procedure and first results IPG implanted in thorax Fr Cg25 dF9 SCC25 dF9 Fr Cg25 Fr Cg25 Fr 6 months DBS Ham17=7.8+3 Depression-baseline Ham17=27+2 3 months DBS Ham17=9.3+6   Mayberg et al. Neuron, 45:651-660, 2005

16 Predictor: QEEG Cordance QEEG cordance integrates absolute and relative power information. Change of QEEG average cordance from 3 frontal electrodes (Fp1, Fp2 and Fz) in theta band (4-8Hz) predicts therapeutic response to antidepressants after one week of AD administration PPV≈0.7; NPV≈0.9; SENS 90-100%; SPEC 60-90% Any intervention shortening period of disability saves a lot of suffering and expenses. Conclusions

17 0 + - Predictor: QEEG Cordance QEEG cordance might reflect activity changes in CG25 Conclusions  

18 0 + - Predictor: QEEG Cordance QEEG cordance might reflect activity changes in CG25 Conclusions  

19 Comparison between AUCs obtained for MADRS reduction and cordance change at week 1 in the prediction of treatment response AUC=0.82 AUC = 0.65 Pairwise comparison of ROC curves, z-statistic=2.55, p=0.01 Bareš et al. 2011

20 Comparison of predictive power of cordance change in rTMS and VNF Tx AUC rTMS=0,75 (N=25) AUC VNF=0,89 (N=25) p=0,27 MADRS 0 26(24-30) 25(24-30) NS MADRS 28 18(12-25) 18(10-24) NS rTMS VNF Bareš et al., Clin EEG Neurosci (in press) NS

21 Martin Brunovský Psychiatric Centre Prague & Charles University, 3rd Medical Faculty, Prague Milan Kopeček Martin Bareš Jiří Kožený Pavla Stopková Tomáš Novák Thanks to the team of Peter Šóš Vladimír Krajča


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