Pa#ern recogni,on and neuroimaging in psychiatry

Transcription

1 Pa#ern recogni,on and neuroimaging in psychiatry Janaina Mourao-Miranda Machine Learning and Neuroimaging Lab Max Planck UCL Centre for Computa=onal Psychiatry and Ageing Research

2 Outline Supervised learning in clinical neuroimaging Limita=ons Associa=ve models A mul=ple hold-out framework for associa=ve models

3 Supervised Learning Framework: classifica,on Group 1: At Risk Predic=ve func=on Clinical ques,ons in psychiatry: Group 2: Low Risk Training ü Diagnosis across diseases New subject Tes=ng Predic=on: At Risk/Low Risk ü Predic=ng diseases outcome ü Iden=fying at risk subjects ü Iden=fying treatment responders

4 Supervised Learning Framework: regression Score Score Score Score Score Score Training Predic=ve func=on Clinical ques,ons in psychiatry: ü Predict symptom intensity from brain scans. New subject Tes=ng Predic=on: Score = 23 ü Predict personality traits from brain scans.

5 Limita,on Clinical assessments label: pa=ent/control +1/-1 Pa=ents groups are heterogeneous -> categorical labels are unreliable. Clinical/behavioral informa=on are not embedded in the model

6 NIMH Research Domain Criteria (RDoC) framework Diagnos=c categories based on clinical assessments fail to align with findings from clinical neuroscience, gene=cs and have not been predic=ve of treatment response. Develop new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.

7 Mul,-source learning Multisource predictive models Multivariate associative models Better diagnosis and prognosis Better characterization of mental health disorders

8 Classifica=on/regression model label label label label label label label label label label Predic=ve func=on (w) Output metric: accuracy/mse x* Predicted label Real label y* f(x*) =x*.w w error/accuracy measure

9 Associa=ve models PLS/CCA Associa=ve effects (u, v) Output metric: correla=on/covariance v x* y*.v Correla=on between the projec=ons y* x*.u u

10 Associa,ve models Par=al Least Squares (PLS) and Canonical Correla=on Analysis (CCA), find direc=ons (weight vectors) that maximize the covariance or correla=on between the projec=ons of two type of data (e.g. brain and behavioral). Partial Least Squre (PLS) max u,v Cov(Xu,Yv) = u T X T Yv subject to u 2 =1, v 2 =1 Canonical Correlation Analysis (CCA) max u,v Corr(Xu,Yv) = u T X T Yv subject to u T X T Xu =1 and v T Y T Yv =1 X : matrix containing neuroimaging informa=on Y : matrix containing behavioral/clinical informa=on

11 Mul,variate Associa,ve Effects A pair of weight vector u and v represents a mul=variate associa=ve effect between the two types of data. Once the first pair is found, the associated effect can be removed from the data (by matrix defla=on) and the same procedure can be applied to find addi=onal associa=ve effects. The associa=ve effects to be ranked, since each weight vector pair will explain more covariance/correla=on in the data than the following ones.

12 Weight vectors The weight vector u and v have the same dimensionality of the original data types. By looking at the paired vectors, one can iden=fy the features in each view are more related with each associa=ve effect. Sparse version of PLS/CCA enable a selec=on of the necessary features to describe each associa=ve effect. Image weight (u) Clinical weight (v) Scores for clinical variables Clinical Variable Clinical Variable 2 0 Behavioral Variable 1 0.4

13 Latent Space By projec=ng the each data set onto the latent space (i.e. u and v ) one can see how the rela=onship between the data sources varies across the sample. For example, how brain-behaviour rela=onship varies in health and disease samples. Neuroimaging projected onto u (Brain score) Latent Space Clinical data projected onto v (Clinical score)

14 Examples of SPLS/SCCA applica,ons to neuroimaging Le Floch et al. [2012]: associa=ons between Single Nucleo=de Polymorphisms (SNPs) and fmri Regions of Interest (ROIs) Avants et al. [2014]: associa=ons between sub-scores of the Philadelphia Brief Assessment of Cogni=on (PBAC) ques=onnaire from structural MRI data Rosa et al. [2015]: associa=ons between two Arterial Spin Labelling (ASL) datasets from the same subjects using different drugs

15 Challenges How to find the op=mal number of variables in which view to describe the mul=variate associa=ve effect? How to test the significance of the mul=variate associa=ve effect?

16 Contents lists available at ScienceDirect Journal of Neuroscience Methods jo ur nal home p age: A multiple hold-out framework for Sparse Partial Least Squares João M. Monteiro a,b,, Anil Rao a,b, John Shawe-Taylor a, Janaina Mourão-Miranda a,b, for the Alzheimer s Disease Initiative 1 Novel SPLS framework which: 1. Selects the adequate number variables to describe each associa=ve effect. 2. Tests their reliability by fieng the model to different splits of the data.

17 Sparse PLS formula=on (Wigen et al., 2009). max u,v u T X T Yv subject to u , v 2 1, u 1 c u, v 1 c v Nested cross-valida,on: Computa=onal expensive Large number of folds lead to high variance of the results Mul,ple hold-out framework: Computa=onally more efficient Check the reliability of the obtained solu=ons to data perturba=on. Needs large sample -> risk of FN

18 Hold-out framework Fig. 1. Hyper-parameter optimisation framework. Fig. 2. Permutation framework.

19 Fig. 1. Hyper-parameter optimisation framework. Fig. 2. Permutation framework. Repeat 10 =mes Correct for p-values mul=ple comparison. Combined/Omnibus hypothesis H omni is: All the null-hypothesis H s are true. If any of the 10 p-values is sta=s=cally significant, then, the omnibus hypothesis will be rejected.

20 Leave 10% of the data for holdout Repeat 100 Sample: 80% training and 20% test Greedy search for hyperparameter op=miza=on using correla=on as metric Repeat Permute one of the data matrices of the train/test set Train the model using the op=mal hyper-parameters Compute hold-out data correla=on Selected op=mal hyperparameter Compute p-value Train the model using the op=mal hyper-parameters Compute hold-out data correla=on p-value hold out correla,on

21 Brain data: smri 592 unique subjects from the ADNI: 309 males (average age ± 7.36) 283 females (average age ± 7.50) T1 weighted MRI scans preprocessed in SPM2 Segmented into grey mager probability maps Normalised using DARTEL Converted to MNI space (2x2x2 mm) Smoothed with a Gaussian filter with 2mm FWHM Mask to select voxels >10% gray mager probability

22 Clinical Data MMSE ques=onnaire items Domain Orientation Registration Att. & calc. Recall Language Question/task 1. What is today s date? 2. What year is it? 3. What month is it? 4. What day of the week is today? 5. What season is it? 6. What is the name of this hospital? 7. What floor are we on? 8. What town or city are we in? 9. What county (district) are we in? 10. What state are we in? 11. Name object (ball) 12. Name object (flag) 13. Name object (tree) 13a. Number of trials 14. D 15. L 16. R 17. O 18. W 19. Recall Ball 20. Recall Flag 21. Recall Tree 22. Show a wrist watch and ask What is this? 23. Show a pencil and ask What is this? 24. Repeat a sentence 25. Takes paper in right hand 26. Folds paper in half 27. Puts paper on floor 28. Read and obey a command ( Close your eyes ) 29. Write a sentence 30. Copy design

23 Results across different splits Split Proj. deflation Rej. H omni Yes Yes No

24 First associa,ve effect: memory related Clinical weights - v 1 Brain weights - u 1 Table 3 Top 10 atlas regions for the first image weight vector. Atlas region # voxels found Amygdala L 98 Amygdala R 90 Hippocampus R 175 Hippocampus L 152 ParaHippocampal R 92 ParaHippocampal L 44 Lingual L 9 Precuneus L 2 Precuneus R 1 Temporal Pole Sup L 1

25 Second associa,ve effect: seman,c related Clinical weights - v 2 Brain weights - u 2 Table 4 Top 10 atlas regions for the second weight vector. Atlas region # voxels found Amygdala L 36 Temporal Inf L 292 Hippocampus L 88 Amygdala R 11 ParaHippocampal L 53 Fusiform L 78 Temporal Inf R 64 Hippocampus R 22 Occipital Inf L 12 Temporal Mid L 76

26 Projec,ons across data sources 3421 B.5 Projections First effect Precentral_R 2 Insula_L 4 Parietal_Inf_L 1 Lingual_L 2 Parietal_Inf_R 1 Caudate_L 1 Thalamus_L 1 Second effect Clinical projected onto v (Clinical score) Yv Yv 2 Healthy Healthy MCI MCI Dementia Dementia Clinical projected onto v (Clinical score) Yv Healthy Healthy MCI MCI Dementia Dementia Xu Xu 1 Brain data projected onto u Brain data projected onto u (a) Projection (a) Projection of the (Brain of image the score) image data onto datathe ontofirst the(b) firstprojection (b) Projection of theofimage (Brain image score) data data onto onto the the weight weight vector vector pair {u pair 1,v{u 1 }. 1,v 1 }. second second weight weight vector vector pair {u pair 2,v{u 2 }. 2,v 2 }. Figure Figure B.8: B.8: Projection Projection of theofdata onto data the ontospls the SPLS weight weight vector vector pairs. pairs. Xu 2 Xu 2

27 Projec,ons within each data source Projec=ons onto the brain weights Projec=ons onto the clinical weights Brain data projected onto u 2 (Brain score 2) Clinical data projected onto v 2 (Clinical score 2) Brain data projected onto u 1 (Brain score 1) Clinical data projected onto v 1 (Clinical score 1)

28 Summary The supervised pagern recogni=on framework has limita=ons when applied to data with unreliable labels (e.g. categorical classifica=on in psychiatry). Alterna=ve associa=ve models that integrate mul=ple sources of informa=on might provide new insights about the psychiatric disorders and poten=ally help to beger characterize pa=ent groups.

29 Acknowledgements Colleagues and collaborators v Joao de Matos Monteiro, UCL, UK v Maria Joao Rosa, UCL, UK v Anil Rao, UCL, UK v Prof John Shawe-Taylor

30 References Le Floch et al, Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse partial least squares. Neuroimage 63 (1), Avants et al, Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. Neuroimage. Rosa et al Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging. Frontiers in neuroscience. Monteiro et al A multiple hold-out framework for sparse partial least squares. Journal of Neuroscience Methods. Code: github.com/jmmonteiro/spls