fmri markers for Major Depressive Disorder (MDD) - diagnosis and prognosis -
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1 fmri markers for Major Depressive Disorder (MDD) - diagnosis and prognosis - Dr. H.G. (Eric) Ruhé, MD PhD Program for Mood and Anxiety Disorders University Medical Center Groningen H.G.Ruhe@UMCG.NL Co-authors: M.M. Rive, H. Geugies, L. Schmaal, M.J. van Tol, E.M. Opmeer, A.F. Marquand, N.A. van der Wee, B. Penninx, A.H. Schene and D.J. Veltman
2 Clinical Problems in MDD Prediction of long-term course Distinction MDD and Bipolar Disorder Indication of best treatment
3 proportion still in episode Clinical Problems in MDD Prediction of long-term course NEMESIS-study n= 7076 n= 273 MDD cases 50% recovery in 3 mth 20% chronic MDD Risk factors: severity & dysthymia Distinction MDD and Bipolar Disorder Indication of best treatment 1,2 1,0,8,6,4,2 0,0 censored 0 duration (months) 10 Spijker et al. Br J Psychiatry
4 Clinical Problems in MDD Prediction of long-term course Distinction MDD and Bipolar Disorder Depressive episode as 1 st manifestation Recurrences Similar symptoms Conversion rate 10% in 5 years Risk factors: Age, Family-history, psychotic features, hypomanic Syx Indication of best treatment
5 Clinical Problems in MDD Prediction of long-term course Distinction MDD and Bipolar Disorder Indication of best treatment Psychotherapy vs Pharmacotherpy Which therapy / antidepressant One-size-fits all / Trial & change Treatment Resistant Depression (TRD) or pseudo-trd
6 How can we individualize predictions? Group vs Individual level Possibilities of new approaches especially with (big) neuroimaging data
7 Big data analysis Artificial intelligence algorithms and techniques to automatically extract information from big data (Support Vector) Machine Learning (SVM) techniques Orru et al. Neurosci Biobehav Rev 2012
8 Big data analysis Artificial intelligence algorithms and techniques to automatically extract information from big data (Support Vector) Machine Learning (SVM) techniques Supervised OR unsupervised Orru et al. Neurosci Biobehav Rev 2012
9 Big data analysis Artificial intelligence algorithms and techniques to automatically extract information from big data (Support Vector) Machine Learning (SVM) techniques Supervised OR unsupervised Training -> Testing Validation Cross-validation New datasets Orru et al. Neurosci Biobehav Rev 2012
10 Big data analysis Artificial intelligence algorithms and techniques to automatically extract information from big data (Support Vector) Machine Learning (SVM) techniques Supervised OR unsupervised Training -> Testing Validation Cross-validation New datasets Orru et al. Neurosci Biobehav Rev 2012
11 Big data analysis Artificial intelligence algorithms and techniques to automatically extract information from big data (Support Vector) Machine Learning (SVM) techniques Supervised OR unsupervised Training -> Testing Validation Cross-validation New datasets Orru et al. Neurosci Biobehav Rev 2012
12 Test c+d a+b Information obtained - diagnostic testing - Outcome Yes No Total Yes No.. a b c D d Total.... a+c b+d a+b+c+d Sensitivity (%) a/a+c Specificity (%) d/b+d Accuracy (%) a+d/a+b+c+d
13 Prediction of long-term course/chronicity Clinical predictors: Symptom severity/duration, age, age of onset, comorbidity, childhood adversity, personality (Karsten et al 2013, Penninx et al. 2011) Neuroimaging predictors (group-level): Reduced hippocampus and ACC volume (e.g. Woudstra et al. under review) Abnormal activation in mpfc regions during emotional processing (Farb et al. 2011; Siegle et al. 2012) Abnormal DLPFC recruitment during visuospatial planning (Woudstra et al. under review)
14 NESDA 2 year longitudinal course prediction fmri - Design - MDD-Patients (n= 188; 6 months Dx) 1. A rapid remission trajectory (REM; N=59) 2. Gradual decline of symptoms (DEC; N=36) 3. Chronic trajectory (CHR; N=23) Schmaal et al. Submitted
15 NESDA 2 year longitudinal course prediction fmri - Design - MDD-Patients (n= 188; 6 months Dx) 1. A rapid remission trajectory (REM; N=59) 2. Gradual decline of symptoms (DEC; N=36) 3. Chronic trajectory (CHR; N=23) Schmaal et al. Submitted
16 NESDA 2 year longitudinal course prediction fmri - Design - Neuroimaging T1 smri fmri: Tower of London, Emotional Facial Expressions Clinical information Depression severity (IDS) Anxiety severity (BAI) Duration of symptoms prior to baseline (Life Chart) Age of onset Personality: neuroticism, extraversion, conscientiousness (NEO- FFI) Environmental factors Childhood maltreatment Schmaal et al. Submitted
17 ACCURACIES (Sens/Spec): Modality MDD-CHR (N=23) versus MDD-REM (N=59) MDD-CHR (N=23) versus MDD-DEC (N=36) MDD-DEC (N=36) versus MDD-REM (N=59) Faces Task Angry>baseline 64%* (67/62) 54% (53/55) 48% (42/54) Fear>baseline 62%* (67/56) 59% (60/58) 40% (35/45) Happy>baseline 64%* (73/54) 69%* (67/71) 53% (55/51) Sad>baseline 58% (60/56) 49% (47/52) 45% (39/51) Neutral>baseline 53% (47/59) 67%* (67/68) 37% (32/41) Overall emotion>baseline 73%** (80/67) 59% (53/65) 50% (48/51) Tower of London 51% (53/50) 38% (37/46) 48% (46/50) Grey matter images 43% (35/52) 53% (48/58) 43% (33/53) Clinical characteristics 69%* (70/68) 61% (61/61) 61% (69/53) Faces contrast images and clinical characteristics combined 65%* (52/78) 52% (35/69) 54% (14/93) All modalities combined c 62%* (74/49) 61% (65/57) 44% (43/44) Schmaal et al. Submitted
18 ACCURACIES (Sens/Spec): Modality MDD-CHR (N=23) versus MDD-REM (N=59) MDD-CHR (N=23) versus MDD-DEC (N=36) MDD-DEC (N=36) versus MDD-REM (N=59) Faces Task Angry>baseline 64%* (67/62) 54% (53/55) 48% (42/54) Fear>baseline 62%* (67/56) 59% (60/58) 40% (35/45) Happy>baseline 64%* (73/54) 69%* (67/71) 53% (55/51) Sad>baseline 58% (60/56) 49% (47/52) 45% (39/51) Neutral>baseline 53% (47/59) 67%* (67/68) 37% (32/41) Overall emotion>baseline 73%** (80/67) 59% (53/65) 50% (48/51) Tower of London 51% (53/50) 38% (37/46) 48% (46/50) Grey matter images 43% (35/52) 53% (48/58) 43% (33/53) Clinical characteristics 69%* (70/68) 61% (61/61) 61% (69/53) Faces contrast images and clinical characteristics combined 65%* (52/78) 52% (35/69) 54% (14/93) All modalities combined c 62%* (74/49) 61% (65/57) 44% (43/44) Schmaal et al. Submitted
19 ACCURACIES (Sens/Spec): Modality MDD-CHR (N=23) versus MDD-REM (N=59) MDD-CHR (N=23) versus MDD-DEC (N=36) MDD-DEC (N=36) versus MDD-REM (N=59) Faces Task Angry>baseline 64%* (67/62) 54% (53/55) 48% (42/54) Fear>baseline Happy>baseline Sad>baseline Neutral>baseline Conclusions: Overall emotion>baseline 62%* (67/56) 59% (60/58) 40% (35/45) 64%* (73/54) - Clinical < neuroimaging precdictors 58% (60/56) 53% (47/59) - Prediction by emotion recognition task 73%** (80/67) 69%* (67/71) 53% (55/51) 49% (47/52) 45% (39/51) 67%* (67/68) 59% (53/65) - No prediction by visuospatial planning task and structural neuroimaging 37% (32/41) 50% (48/51) Tower of London 51% (53/50) 38% (37/46) 48% (46/50) Grey matter images 43% (35/52) 53% (48/58) - A distributed network including many regions having low individual but strong aggregate prognostic value 43% (33/53) Clinical characteristics 69%* (70/68) 61% (61/61) 61% (69/53) Faces contrast images and clinical characteristics combined 65%* (52/78) 52% (35/69) 54% (14/93) All modalities combined c 62%* (74/49) 61% (65/57) 44% (43/44) Schmaal et al. Submitted
20 Distinction MDD and BD - Redlich et al. - 2 Sites: Pittsburg / Münster 2x3x29 depressed BD / depressed MDD / HC Mostly medicated patients 3T smri Univariate analyses & SVM/GPC Mask for regions emotion regulation Redlich et al. JAMA Psychiatry 2014 Online
21 Distinction MDD and BD - Redlich et al. - 2 Sites: Pittsburg / Münster 2x3x29 depressed BD / depressed MDD / HC Mostly medicated patients 3T smri Univariate analyses & SVM/GPC Mask for regions emotion regulation Redlich et al. JAMA Psychiatry 2014 Online
22 Distinction MDD and BD - Redlich et al. - 2 Sites: Pittsburg / Münster 2x3x29 depressed BD / depressed MDD / HC Mostly medicated patients 3T smri Univariate analyses & SVM/GPC Mask for regions emotion regulation Redlich et al. JAMA Psychiatry 2014 Online
23 Distinction MDD and BD - Redlich et al. - 2 Sites: Pittsburg / Münster 2x3x29 depressed BD / depressed MDD / HC Mostly medicated patients 3T smri Univariate analyses & SVM/GPC Mask for regions emotion regulation Redlich et al. JAMA Psychiatry 2014 Online
24 Distinction MDD and BD - design DIADE - Drug-free patients: SCID +ve; 2 episodes; 5 yrs of illness; age of onset 40yrs 32 BD (10 depressed (BDd), 22 remitted (BDr)) 32 MDD (10 MDDd and 22 MDDr) matched for age, gender, education and depression severity 32 healthy controls (HC) Resting state (RS) fmri (7 min) Independent components analysis (ICA) 11 RSN -> a priori selection of 3 RSN Default Mode Network (DMN), FrontoParietal Network (FPN), Salience Network (SN) Rive et al. Submitted
25 Distinction MDD and BD - results - ACCURACIES (Sens/Spec): Modality MDDd (N=10) versus BDd (N=10) MDDr (N=22) versus BDr (N=22) HC (N=32) versus MDD (N=32) HC (N=32) versus BD (N=32) DMN 85%** (90/80) 52% (46/59) 65%* (70/59) 74%** (76/72) FPN Left Right 45% (50/40) 40% (40/40) 50% (59/41) 55% (55/55) NA NA SN 30% (30/30) 48% (46/50) NA NA * p<0.05; **p<0.01 Rive et al. Submitted
26 Distinction MDD and BD - results - ACCURACIES (Sens/Spec): Modality MDDd (N=10) versus BDd (N=10) MDDr (N=22) versus BDr (N=22) HC (N=32) versus MDD (N=32) HC (N=32) versus BD (N=32) DMN 85%** (90/80) 52% (46/59) 65%* (70/59) 74%** (76/72) FPN Left Right 45% (50/40) 40% (40/40) 50% (59/41) 55% (55/55) NA NA SN 30% (30/30) 48% (46/50) NA NA * p<0.05; **p<0.01 Rive et al. Submitted
27 Distinction MDD and BD - results - ACCURACIES (Sens/Spec): Modality Conclusions: MDDd (N=10) versus BDd (N=10) MDDr (N=22) versus BDr (N=22) HC (N=32) versus MDD (N=32) - Distinction MDD vs BD (vs HC) based on DMN / smri HC (N=32) versus BD (N=32) DMN 85%** (90/80) 52% (46/59) 65%* (70/59) 74%** (76/72) FPN Left Right 45% (50/40) 40% (40/40) 50% (59/41) 55% (55/55) SN 30% (30/30) 48% (46/50) NA NA * p<0.05; **p< Especially in depressed state - Drug free or drug use in subjects might matter!! - A distributed network including many regions having low individual but strong aggregate diagnostic value NA NA Rive et al. Submitted
28 Best Treatment? Prognosis of treatment outcome Responders vs non-responders Pizzagalli. Neuropsychopharmacol. 2011
29 Best Treatment? Prognosis of treatment outcome Responders vs non-responders Pizzagalli. Neuropsychopharmacol. 2011
30 Best Treatment? Prognosis of treatment outcome Conclusions: - Group-level responders vs non-responders - racc hyperactivation associated with better treatment outcome - High effect-sizes - Important role of racc with - Emotion regulation - DMN (intermediating between Task positive and task negative activities) Responders vs non-responders Pizzagalli. Neuropsychopharmacol. 2011
31 NESDA Prediction of TRD Baseline scan in 112 current MDD 2yr follow-up TRD ( 2 antidepressants; n= 17) Responders (<2 antidepressants; n=32; matched age/sex/education) No differences in depression severity (IDS) anxiety severity (BAI) and illness duration (in months) between TRD and nontrd group RS fmri -> ICA-analysis Group-analyses Geugies et al. In preparation
32 Second level group differences HC vs TRD no difference HC vs nontrd no difference TRD vs nontrd connectivity of right insula in the salience network (P FWE = 0.006) Contrast estimates and 90% C.I. HC TRD nontrd Geugies et al. In preparation
33 Second level group differences HC Conclusions: vs TRD no difference HC vs nontrd no difference - Right insula hypo connectivity in SN associated with TRD TRD vs nontrd connectivity of right insula in the - Group-level TRD vs non-trd Contrast estimates and 90% C.I. salience network (P FWE = 0.006) - Important role of Insula - DMN (activity preceeding switch Task negative to Task positive) - Ongoing analyses of functional connectivity with insula as seed HC TRD nontrd Geugies et al. In preparation
34 Prognosis of treatment outcome Individualized approaches Psychotherapy & Pharmacotherapy Orru et al. Neurosci Biobehav Rev 2012
35 Prognosis of treatment outcome Individualized approaches Psychotherapy & Pharmacotherapy smri > fmri? Orru et al. Neurosci Biobehav Rev 2012
36 Prognosis of treatment outcome Individualized approaches Psychotherapy & Pharmacotherapy smri > fmri? Very small samples -> new studies Orru et al. Neurosci Biobehav Rev 2012
37 Individual prediction in TRD - Outcome of ECT - N=45 TRD-patients (25 final remitters) RS fmri -> ICA Van Waarde et al. Mol Psychiatry 2014
38 Individual prediction in TRD - Outcome of ECT - N=45 TRD-patients (25 final remitters) RS fmri -> ICA Van Waarde et al. Mol Psychiatry 2014
39 Individual prediction in TRD - Outcome of ECT - N=45 TRD-patients (25 final remitters) RS fmri -> ICA Accuracies: 84% (84 sens /85 spec) 77% (80 sens /75 spec) smri 61% (n.s.) Van Waarde et al. Mol Psychiatry 2014
40 Individual prediction in TRD - Outcome of ECT - Conclusions: - Connectivity in 2 cognitive/affective networks are associated with remission by ECT - Prediction fmri > smri - Role of DLPFC - Emotion regulation / Dorsal Nexus N=45 TRD-patients (25 final remitters) - A distributed RS fmri network -> ICA including many regions having low individual but strong aggregate diagnostic value Accuracies: 84% (84 sens /85 spec) 77% (80 sens /75 spec) smri 61% (n.s.) Van Waarde et al. Mol Psychiatry 2014
41 Summary Advantages of machine learning vs univariate approaches However Small samples Need for replications in independent samples and if so -> worse prediction??? Accuracies may/must improve Combinations are not always better Better combinations/other modalities
42 Early change in neural processing of fear predicts antidepressant response Godlewska et al submitted
43 Acknowledgement Maaike Rive Nic van der Wee Hanneke Geugies Brenda Penninx Marie Jose van Tol Aart Schene Lianne Schmaal Dick Veltman Esther Opmeer Cath Harmer Andre Marquand Questions?
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