Representation learning for improved interpretability and classification accuracy of clinical factors from EEG

1 INTRODUCTION . Mental health disorders make up one of the main causes of the overall disease burden worldwide ( Vos et al. , 2013 ) , with depression ( e.g. , Major Depressive Disorder , MDD ) believed to be the second leading cause of disability ( Lozano et al. , 2013 ; Whiteford et al. , 2013 ) , and around 17 % of the population experiencing its symptoms at some point throughout their lifetime ( McManus et al. , 2016 ; 2009 ; Kessler et al. , 1993 ; Lim et al. , 2018 ) . At the same time diagnosing mental health disorders has many well-identified limitations ( Insel et al. , 2010 ) . Despite the existence of diagnostic manuals ∗Equal contribution † { ghonk , irinah , nthigpen , vmiskovic , katielink , sunnyd , pramodg } @ google.com ‡JK is now at Humboldt-Universität zu Berlin , Berlin , Germany ; julia.klawohn @ hu-berlin.de §greg.hajcak @ med.fsu.edu like Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders ( SCID ) ( DSM-V , 2013 ) , diagnostic consistency between expert psychiatrists and psychologists with decades of professional training can be low , resulting in different diagnoses in upwards of 30 % of the cases ( Cohen ’ s Kappa = 0.66 ) ( Lobbestael et al. , 2011 ) . Even if higher inter-rater reliability was achieved , many psychological disorders do not have a fixed symptom profile , with depression alone having many hundreds of possible symptom combinations ( Fried & Nesse , 2015 ) . This means that any two people with the same SCID diagnosis can exhibit entirely different symptom expressions . This is a core challenge for developing an objective , symptom-driven diagnostic tool in this domain . Electroencephalography ( EEG ) is a measurement of post-synaptic electrical potentials that can be taken non-invasively at the scalp . EEG signals can function as important biomarkers of clinical disorders ( Hajcak et al. , 2019 ) but they are difficult to clean and interpret at scale . For example , components of the EEG signal can often significantly overlap or interfere with each other . Furthermore , nearby electronics , line noise , hardware quality , signal drift and other variations in the electrode–scalp connection can all distort the recorded EEG signal . Hence , the extraction of EEG data of sufficient quality is usually a laborious , semi-automated process executed by lab technicians with extensive training . A typical EEG analysis pipeline consists of collecting EEG recordings evoked from a large number of stimulus presentations ( trials ) in order to have sufficient data to average out the noise . Independent Components Analysis ( ICA ) is often used to visually identify and remove the component that corresponds to eye blinks ( Delorme & Makeig , 2004 ; Makeig et al. , 2004 ; Jung et al. , 2000 ) ( although see Weber et al . ( 2020 ) ; Nolan et al . ( 2010 ) as examples of fully automated artifact removal pipelines ) . This can be followed by a trial rejecton stage where anomalous trials are identified and removed from the EEG data scroll , sometimes also through visual examination . The cleaned up EEG recordings from a large number of trials are then averaged to produce an Event Related Potential ( ERP ) ( Luck , 2012 ) . This allows a clinician to extract specific ERP components relevant to the clinical factor of interest , average out the event-locked activity within them , and then either perform a statistical group comparison , or—in the case of the diagnosis classification goal—apply an off-the-shelf classifier , like Logistic Regression ( LR ) to obtain the final diagnostic results . Some more advanced classification approaches might include Support Vector Machines ( SVM ) , Linear Discriminant Analysis ( LDA ) , or Random Forest ( RF ) ( Parvar et al. , 2015 ; Güler & Übeyli , 2007 ; Subasi & Gursoy , 2010 ; Tomioka et al. , 2007 ; Bashivan et al. , 2016 ) . To summarise , EEG recordings are noisy measures of electric activity from across the brain . There is evidence that these signals are useful as markers of depression , but we lack understanding of what aspects of depression they index . Furthermore , the field of clinical psychopathology still lacks consensus on the etiopathogenesis of mental health disorders , which means that there is no such thing as the “ ground truth ” diagnostic labels . Hence , while EEG is routinely used to diagnose conditions like epilepsy ( Smith , 2005 ) , memory ( Stam et al. , 1994 ) or sleep disorders ( Karakis et al. , 2012 ) , its promise for being a reliable diagnostic tool for clinical conditions like depression has not been fully realised so far . In order to make EEG a viable diagnostic tool for a broader set of clinical conditions it is important to have an automated pipeline for extracting the relevant interpretable biomarker correlates from the ( preferably individual trial ) EEG data in a robust manner . Furthermore , this process should not depend fully on diagnostic labels which are often subjective and at best represent highly heterogeneous classes . Recent advances in deep learning have prompted research into end-to-end classification of EEG signal using convolutional and/or recurrent neural networks ( Bashivan et al. , 2016 ; Mirowski et al. , 2009 ; Cecotti & Graser , 2011 ; Güler et al. , 2005 ; Wang et al. , 2018 ; Farahat et al. , 2019 ; Solon et al. , 2019 ; Cecotti et al. , 2014 ) , holding the promise of automated extraction of relevant biomarker correlates . However , deep classifiers operate best in the big data regime with clean , well-balanced ground truth classification targets . In contrast , even the largest of EEG datasets typically contain only a few hundred datapoints , and the classification labels are subjective , noisy and unbalanced , with the majority of the data coming from healthy control participants . Hence , in order to utilise the benefits of deep learning but avoid the pitfalls of over-reliance on the classification labels , we propose a two-step pipeline consisting of unsupervised representation learning , followed by supervised mapping of the pre-trained representation to the latest version of the available diagnostic labels . The hope is that the unsupervised learning step would denoise the input signal and extract the broad statistical regularities hidden in it thus serving as an alternative for the existing automatic EEG pre-processing pipelines ( Weber et al. , 2020 ; Nolan et al. , 2010 ) while minimising the need for a priori knowledge , resulting in a representation that can continue to be useful even if the label taxonomy evolves . Recently great progress has been made in the field of deep unsupervised representation learning ( Roy et al. , 2019 ; Devlin et al. , 2018 ; Brown et al. , 2020 ; Chen et al. , 2020b ; Grill et al. , 2020 ; Chen et al. , 2020a ; Higgins et al. , 2017 ; Burgess et al. , 2019 ) . Disentangled representation learning is a branch of deep unsupervised learning that produces interpretable factorised low-dimensional representations of the training data ( Bengio et al. , 2013 ; Higgins et al. , 2017 ) . Given the requirement for model interpretability in our use-case , we use Beta Variational Autoencoders ( β-VAE ) ( Higgins et al. , 2017 ) —one of the state of the art unsupervised disentangled representation learning methods—to discover low-dimensional disentangled representations of the EEG data . We then train the recently proposed Symbol–Concept Association Network ( SCAN ) ( Higgins et al. , 2018 ) to map the available classification labels to the representations learnt by β-VAE ( see Fig . 1 ) . We demonstrate that our proposed pipeline results in better classification accuracy than the typical approach for extracting a known ERP pattern for use as a biomarker—a process that is often heavily influenced by a priori knowledge . This holds true when predicting a number of factors , including age , gender , and depression diagnosis . Furthermore , SCAN is able to produce arguably interpretable classification recommendations , whereby its decisions on different clinical factors are grounded in a small number ( often single ) latent dimensions of the β-VAE , allowing the clinicians an opportunity to interpret the recommendations produced by SCAN , and visualise what aspects of the EEG signal are associated with the classification decision post-hoc . This opens up the opportunity to use our proposed pipeline as a tool for discovering new EEG biomarkers . We validate this by “ re-discovering ” a known biomarker for depression . Finally , we demonstrate that once a β-VAE is pre-trained on ERP signals , it can often produce ERP-like reconstructions even when presented with single noisy EEG trajectories . Furthermore , the representations inferred from single EEG trials produce good classification results , still outperforming the canonical baseline method . This suggests that once a good disentangled representation is learnt , the model can be used online as new EEG data is being recorded , thus lowering the burden of keeping potentially vulnerable participants in the lab for extended recording sessions . 2 METHODS . 2.1 DATA . Anhedonia . This work targets one of the two cardinal symptoms of depression—anhedonia . Anhedonia is the lack of pleasure and/or interest in previously pleasurable stimuli and activities ( DSM-V , 2013 ) . One established approach for objectively quantifying this symptom is the use of EEG to measure neural responses elicited by emotionally salient visual stimuli . Research in this domain has uncovered a stereotyped neural activation pattern in healthy control participants , where emotionally-salient stimuli evoke a Late Positive Potential ( LPP ) in ERPs—the averaged timeseries of stimulus time-locked EEG recordings . This pattern has been identified as a potential biomarker for depression because ( on average ) this positive deflection in amplitude is attenuated or absent in individuals who exhibit symptoms of anhedonia ( Brush et al. , 2018 ; Foti et al. , 2010 ; Klawohn et al. , 2020 ; MacNamara et al. , 2016 ; Weinberg et al. , 2016 ; Weinberg & Shankman , 2017 ) . Participants . The data were collected as part of a mental health study across multiple laboratory sites . The multi-site aspect of the study meant that more data could be pooled together , however , it also meant that the data was noisier . Participants ( N=758 , ageX̄=16.7 , agerange= [ 11.0 , 59.8 ] , 398 female ) were samples of healthy controls ( nHC=485 ) and people diagnosed with depression ( among other mental illnesses ) ( see Sec . A.1.1 and Tbl . A1 for further breakdown ) . Stimuli and Experimental Design . Participants were shown a series of 80 images from the International Affective Picture System ( IAPS ) ( Lang et al. , 2008 ) presented in random order up to 40 times each . The images varied in valence : either positive ( affiliative scenes or cute animals designed to elicit the LPP ERP component ) , or neutral ( objects or scenes with people ) . Each image trial consisted of a white fixation cross presented for a random duration between 1000-2000 ms ( square window ) followed by a black and white image presented for 1000 ms. EEG Preprocessing . While participants completed the picture viewing task , EEG was continuously recorded . Each picture trial was then segmented to contain a 200 ms pre-stimulus baseline and a 1200 ms post-stimulus interval . The raw EEG signal was digitized , bandpass filtered and cleared of the eye movement artifacts and anomalous trials as described in Sec . A.1.2 . Classification labels . The following classification labels were used in this study : age ( adult or child ) , gender ( male or female ) , study site , and the presence or absence of two clinical conditions : depression diagnosis and a broader Axis 1 disorder diagnosis . All classification labels were binary , apart from study site , which contained four possible values corresponding to four different sites where the data were collected . Participants 18 years of age and older were classified as adults . Gender was classified based on self-reported values . Positive depression labels ( n=110 ) include all participants that were diagnosed with Major Depressive Disorder ( MDD ) , Persistent Depressive Disorder ( PDD ) , and depressive disorder NOS ( not-otherwise-specified ) by expert clinicians through a clinical interview ( e.g. , SCID for adults , KSADS for children ) . Axis 1 is a broad category consisting of the most prevalent psychological disorders in the population ( now discontinued in DSM-V ) that excludes intellectual disabilities and personality disorder ( DSM-V , 2013 ) . Positive Axis 1 labels ( n=273 ) encompassed all participants with positive depression labels plus individuals diagnosed with Cyclothemia , Dysthemia , anxiety disorders , mood disorders ( e.g. , Bipolar Disorder ) , panic disorders , and substance and eating disorders ( all of which are sparse ) . The Axis 1 class is provided to compare model behavior on a transdiagnostic measurement of psychopathology.1 While recruitment for the study was primarily focused on depression , the SCID produces a large set of diagnostic decisions , and we collapsed this sparser set of positive diagnoses into the existing Axis 1 DSM-IV superordinate category . We include this label in modeling and analysis to maximize the number of positive labels for training and evaluation and to give the reader a sense of what the algorithm may have learned that is generalizable across disorders—akin to the P factor ( Caspi et al. , 2014 ) . 1Disorders in this class that are present in the data include Major Depressive Disorder , Persistent Depressive Disorder , Depression NOS , Cyclothemia , Dysthemia , Bipolar I , Bipolar II , Bipolar NOS , Mania , Hypomania , Agoraphobia , Social Phobia , Separation Anxiety , Generalized Anxiety Disorder , Panic Disorder , Panic Disorder with Agoraphobia , Anorexia , Bulimia , Eating disorder NOS , Alcohol Abuse , Alcohol Dependence , and Substance Abuse and Substance Dependence disorders .

Summary: The authors propose a beta-VAE network to learn EEG representation as biomarkers for diagnosing depression from EEG data. They show improved performance compared to an off-the shelf linear classifier. The paper is well-written but lacks a description of related work in the field and also a detailed analysis of the results to support the claims.