AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

doi:10.21203/rs.3.rs-1608215/v1

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Research Article

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

https://doi.org/10.21203/rs.3.rs-1608215/v1

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published 23 Jan, 2023

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Background: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

Methods: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level.

International datasets are being shared from multi-ethnic community populations, first-episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

Results: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

Conclusion: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

classification

biomarkers

deep learning

neuroimaging

depression

harmonization

predictors

MRI

Depression has been recognized for millennia as a distinct illness, included in what Hippocrates termed ‘melancholia’ and posited to be caused by black bile. Current diagnostic criteria for major depressive disorder (MDD) are based solely on a set of symptoms and observable behaviors. MDD is characterised by a persistent low mood and/or an inability to experience usual feelings of enjoyment, associated with disturbances in sleep, appetite and psychomotor functioning, low energy, poor concentration, guilt or worthlessness, and, for some, suicidal ideation and behaviors (1, 2). MDD is highly prevalent and has significant personal, familial and socioeconomic impacts (3, 4). At the present time, MDD remains a syndrome without an identified etiology, rather than a disease with a demonstrable pathology. There are no neurobiological markers that can identify the diseases which comprise a clinical MDD diagnosis. We lack reproducible neurobiological markers to improve the etiological and prognostic homogeneity as well as predict response to treatment.

Not only can heterogeneous combinations of symptoms fulfil diagnostic criteria, but current criteria do not fully capture the range of symptoms. For example, high levels of anxiety and comorbid anxiety disorders are present in 50–75% of patients, which are further linked with impaired treatment response and chronic longitudinal course (5, 6). Heterogeneity among clinical profiles can lead to less predictable responses to a given treatment, an inability to predict the longitudinal course for individual patients, and the symptom variability in ‘gold standard’ scales designated to evaluate efficacy of therapeutic interventions. Further, treatment outcomes are frequently unsatisfactory in MDD with 30–40% of patients failing to achieve an adequate treatment response or remission even after several trials of pharmacotherapy or psychotherapy over a year (7, 8).

It is unlikely that MDD is caused by a single etiological factor. MDD heritability estimates range from 28–44% (9) and are considerably lower than estimates for bipolar disorder and schizophrenia, which range from 60–90% (10, 11). Genome-wide association and candidate gene studies have powerfully demonstrated polygenic heritability, consisting of hundreds of variants and genes with each having a small genetic contribution. Moreover, genetic risk variants though are not clinically useful at the level of the individual for diagnosis, and it is unclear how genetic risk ultimately translates into an acute depressive episode (12).

Neuroimaging-based biomarkers can help to identify the various disease components that comprise MDD. Genetic and environmental factors that lead to MDD are expressed as subtle and widespread alterations in brain structure and brain function. Research over two decades provides convincing evidence of morphometric and neural alterations in MDD, despite limitations in diagnosis and treatment selection. An overarching aim has been to delineate the neurobiological features that comprise MDD and to develop imaging markers of this disorder (13–15). It is important to recognize, however, that to search for a neural signature that wholly replicates current diagnostic criteria would be circular, petitio principii, because current diagnostic criteria are based on clinical features and different mechanisms could lead to the same clinical presentation. Efforts have been further hindered by neural measures which have been confounded by a mixture of depressive states, multiple and longstanding treatments, as well as comorbid disorders.

In recent years, machine learning based methods for MRI heterogeneity analysis have been developed which detect and characterize neuroanatomical heterogeneity of disease using a data-driven approach that generates quantifiable, replicable and neurobiologically-based metrics of disease subtypes (16). Our consortium aims to identify imaging signatures of disease heterogeneity in MDD using structural and resting state functional MRI. This will generate a neuroanatomical-neurofunctional (NA-NF) dimensional coordinate system (COORDINATE-MDD) in which each dimension captures a distinct pattern of brain alterations.

Our aim is to identify the multivariate dimensions that define disease-related phenotypes in MDD and the distinct dimensions that predict treatment response at the individual level. Importantly, this is an iterative process to: (i) define neural dimensions in deeply phenotyped participants who are medication-free and in a current depressive episode in order to delineate state and trait status; and (ii) test dimensions in novel samples to assess specificity and reproducibility. With this aim, our consortium combines extensive datasets of ‘raw’ individual-level neuroimaging and deeply phenotyped clinical data, using state-of-the-art analytic methods for big data and semi-supervised clustering. The present sample consists of richly phenotyped, individual-level data from participants with first-episode or recurrent MDD, that is not treatment-resistant depression, antidepressant medication-free, with prospective longitudinal treatment outcomes, and healthy controls. The current focus is on structural and resting-state functional MRI.

Sample size directly influences the capacity of machine learning methods to reliably identify imaging signatures of disease from MRI data and machine learning-based signature will need to be replicated in independent cohorts. Through international collaborations, we are bringing together a large and integrated sample. MRI images are processed using image processing methods that leverage robust and fully automated pipelines for extracting structural and functional imaging features. In large multi-site datasets, harmonization of imaging features from each site is a critical requirement. We will apply a statistical harmonization methodology, Combat-GAM (17), developed for pooling neuroimaging data across multiple scanners and cohorts with diverse age ranges and with the presence of nonlinear age-related differences in brain images. Combat-GAM has been shown to remove unwanted sources of variability, specifically site differences, while preserving variations due to biologically-relevant covariates in the data. Harmonized data from different scanners and sites are analyzed using machine learning and deep learning methods for imaging pattern analysis. These methods integrate small yet coordinated brain effects into signatures that may yield high sensitivity and specificity in characterizing disease effects in individuals.

The dimensional approach naturally extends to other modalities, including diffusion tensor imaging, task-based functional MRI, and to molecular brain imaging with PET. We will extend these findings to studies of at-risk individuals and to clinical trials, in which the position and trajectory of a new individual in a broader dimensional system can be interrogated along with any number of clinical phenotypes, with the potential to study such cohorts over the course of illness and with treatment.

To establish a NA-NF dimensional system of brain imaging biomarkers and predictors of clinical outcome, we apply the following steps:

1) Inter-site harmonization and image analysis to create a methodological platform for constructive integration of structural imaging and neurofunctional connectivity data from multiple sites.

2) To investigate heterogeneity in neuroanatomy and functional connectivity as a collection of NA-NF patterns or dimensions, we will use semi-supervised AI methods, instead of commonly used clustering (unsupervised) approaches, in order to delineate disease-related effects rather than variations in brain morphology and physiology that might be caused by a number of disease-irrelevant factors. Validity and relevance of identified dimensions will be verified by cross-validation and replication in independent datasets. Critically, we will derive the presence of each NA-NF signature in each patient.

3) To evaluate individual NA-NF signatures for their ability to predict or to moderate treatment response, two complementary approaches will be applied. The first approach is wholly data-driven and will assess the degree of expression of each signature to evaluate whether the position in the NA-NF dimensional system predicts treatment response. The second approach incorporates knowledge of treatment type and clinical response to enrich the NA-NF system by dimensions that are strong predictors in order to will assess whether accuracy improves with the interaction of NA-NF dimension and clinical knowledge of treatment type and response.

1. Participating studies and datasets

An invitation for participation was made to research groups to share ‘raw’ neuroimaging data. The present consortium provided medication-free participants with first episode or recurrent MDD, that is not treatment-resistant depression, and healthy controls.

Collaborations have been established with centers worldwide from Canada, China, European Union, United Kingdom and USA. Each center has completed a Data Sharing Agreement to provide de-identified data in accordance with institutional policies and applicable federal, state or local laws and regulations, including ethical approvals. Summary descriptions are below (Tables 1–2):

Table 1

Demographic, clinical and neuroimaging data
Clinical assessments
	Demographic data (age, sex, ethnicity, handedness, IQ, years of education)
	Standardised diagnostic criteria (DSM or ICD)
	Psychiatric history (including comorbid disorders)
	Form of depression (first episode, recurrent, treatment resistant)
	Treatment (current, history)
	Depression rating scales (HRSD, MADRS, QIDS)
	Depressive severity (mild, moderate, severe)
	Treatment outcome (baseline and post-treatment rating scale scores, remission, response)
Neuroimaging
	Structural MRI
	Resting-state fMRI Diffusion tensor imaging PET EEG
Neuropsychological assessments

Table 2
Study	Number	Mean age	Age range	MRI	MRI scanner model
CAN-BIND	309	34	18–61	3T	GE Discovery MR750
				3T	GE Signa HDx
				3T	Philips Intera
				3T	Siemens TrioTim
EMBARC	336	37	18–65	3T	GE Discovery MR750
				3T	GE Signa HDx
				3T	Philips Achieva
				3T	Philips Ingenia
				3T	Siemens TrioTim
KCL	40	30	18–45	3T	GE Discovery MR750
LIBR	296	32	18–59	3T	GE Discovery MR750
Manchester	70	35	20–56	1.5T	Philips Intera
NeuroPharm1	207	28	18–59	3T	Siemens Prisma
Oxford	70	30	19–61	3T	Siemens TrioTim
PReDICT	344	40	18–64	3T	Siemens TrioTim
SCU	269	31	18–60	3T	GE Excite
				3T	Siemens TrioTim
Stanford	110	33	19–58	3T	GE Signa-Excite
STRADL	1189	60	26–84	3T	Philips Achieva-TX
				3T	Philips Prisma-FIT

Canadian Biomarker Integration Network in Depression (CAN-BIND) is a national depression program with recruitment from 7 centers (18, 19). The CAN-BIND-1 treatment protocol was an 8-week trial with a selective serotonin reuptake inhibitor (SSRI) antidepressant, escitalopram, followed by an 8-week augmentation with aripiprazole if there was poor treatment response (i.e., less than 50% improvement in depressive symptoms). MRI scans were acquired at baseline, weeks 2 and 8 in both MDD and healthy participants.

Copenhagen University (NeuroPharm 1) cohort consists of data from the Center for Integrated Molecular Brain Imaging (Cimbi) and Center for Experimental Medicine Neuropharmacology prospective longitudinal treatment study with the SSRI escitalopram. The patients are followed up for a total of 12 weeks. MRI scans are acquired at baseline and week 8 in MDD and healthy participants (20–26).

Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) is a multisite, randomized, placebo-controlled clinical trial with recruitment from 4 centers (27). Treatment protocol was an 8-week, double-blind, randomized allocation to placebo medication or to an SSRI, sertraline. At week 8, participants who achieved a score of “much improved” in the Clinical Global Improvement scale (CGI) continued their treatment for an additional 8 weeks, while participants who did not, then received an additional 8 weeks of treatment in a double-blind, cross over allocation. Participants initially in the placebo treatment arm, then received sertraline; and participants initially in the sertraline treatment arm, then received a norepinephrine–dopamine reuptake inhibitor antidepressant, bupropion. MRI scans were acquired in medication-free MDD and healthy participants at baseline.

Huaxi MR Research Center (HMRRC) cohort consists of medication-naïve first episode MDD and matched healthy participants with single session MRI scans (28–30).

King’s College London cohort consists of 4 studies (31–37). Treatment protocol was an 8-week trial of serotonin-norepinephrine reuptake inhibitor (SNRI), duloxetine. MRI scans were acquired at baseline, weeks 2 and 8 in both MDD and healthy participants (34).

Laureate Institute for Brain Research (LIBR) cohort consists of 2 studies with MRI data in first episode and recurrent MDD and matched healthy controls (38–40).

University of Manchester cohort consists of 3 studies (41–43). Treatment protocol was an 8-week trial of SSRI, citalopram, with MRI scans acquired at baseline and week 8 in both MDD and healthy participants (41).

University of Oxford cohort consists of an 6-week trial of SSRI, escitalopram, with MRI scans acquired at baseline and week 6 (44, 45).

Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study is a 12-week randomized clinical trial of treatment-naïve MDD with 3 treatment arms: an SSRI, escitalopram; an SNRI, duloxetine; or CBT, and a 12-week second phase if remission was not achieved with monotherapy, with the addition of CBT to the medication treatment arms or augmentation with escitalopram to the CBT treatment arm. MRI scans were acquired at baseline and week 12 (46, 47).

REST-meta-MDD study consists of resting state fMRI data in medication-naïve first episode and recurrent MDD from 17 sites in China (48, 49).

Southwest University (SWU) cohort consists of a community-based recruitment which includes first episode and recurrent MDD and healthy control participants (50–52).

Stanford University cohort consists of MRI data in first episode and recurrent MDD and healthy participants (53–55).

STratifying Resilience and Depression Longitudinally (STRADL) is a community-based cohort from the Generation Scotland Scottish Family Health Study with detailed clinical, cognitive and neuroimaging assessments. Single session MRI scans were acquired (56, 57).

University of California at San Francisco (UCSF) cohort consists of first episode and recurrent MDD and matched healthy participants (58, 59).

2. Assessments

MDD diagnosis is based on standardised diagnostic criteria, DSM or ICD: DSM-IV (HMRRC, Manchester, Oxford, PReDICT, SWU), DSM-IV-TR (CAN-BIND, EMBARC, KCL, LIBR, Stanford, STRADL) and DSM-5 (Copenhagen). Structured clinical interview assessments were performed: Structured Clinical Interview for DSM (SCID) (EMBARC, HMMRC, KCL, LIBR, Manchester, Oxford, PReDICT, Stanford, SWU, STRADL) or Mini International Neuropsychiatric Interview (MINI) (CAN-BIND, Copenhagen, KCL).

Symptom severity was measured using standardised clinician-rated symptom scales: 17-item Hamilton Rating Scale for Depression (HRSD) (60) (Copenhagen, EMBARC, HMRRC, KCL, LIBR, Oxford, Stanford, SWU), 24-item HRSD (PReDICT), Montgomery-Åsberg Depression Rating Scale (MADRS) (61) (CAN-BIND, LIBR, Manchester; PReDICT), and Quick Inventory for Depressive Symptomatology (QIDS-SR) (62, 63) (EMBARC, PReDICT, STRADL).

Clinical measures include illness history (e.g., age onset, number of previous episodes) and medication records. Demographic information (e.g., years of education) and neuropsychological assessments, including memory and executive function, where available are included (CAN-BIND, Copenhagen, EMBARC, KCL, LIBR, Manchester, PReDICT, STRADL).

Prospective longitudinal treatment studies have provided clinical outcome data (CAN-BIND, Copenhagen, EMBARC, KCL, Manchester, Oxford, PReDICT). We have additional information in MDD participants taking antidepressant medication (CAN-BIND, Copenhagen, KCL, Manchester, Oxford, PReDICT, Stanford, STRADL, SWU).

3. Clinical data harmonization

High reliability, internal consistency and correspondence have been demonstrated for HRSD (64), between HRSD and QIDS (65, 66), HRSD and MADRS (67), and QIDS and MADRS (68) in outpatient non-psychotic MDD. Standardized conversions will be applied to generate comparable scores (65, 66, 69). Clinical remission is defined as 17-item HRSD score of less than or equal to 7 and the equivalent in MADRS and QIDS (8). Clinical response is defined as having an improvement of at least 50% relative to baseline depressive severity.

It is possible that derived NA-NF dimensions could be transdiagnostic. We will also incorporate clinical knowledge using a Research Domain Criteria (RDoC)-informed approach to specify transdiagnostic dimensions, using individual items as well as symptom clusters scores from rating scales (70–73). For example, within the Arousal and Regulatory System domain, we can specify fine-grained disturbances in sleep (i.e., initial, middle and late insomnia, as well as hypersomnia) and vegetative disturbances (i.e., appetite increase or decrease, weight increase or decrease), and in the Negative Valence Systems domain, a dimension based on items assessing anxiety reflecting sensitivity to potential threat.

While all patients have a diagnosis based on standardised criteria, we cannot rule out the possibility of site differences reflecting true cultural or ethnic differences in MDD phenomenology. Potential systemic differences in symptom profiles across sites due to different socio-cultural contexts will be examined. Common factor structures have been demonstrated across cultures (74, 75). Principal component analyses will be performed to examine factor structures that characterize symptom profiles by site. We will control for any site differences in the magnitude of symptoms by deriving within-center standardized scores using the residuals. If necessary, we will apply factorization methods to the standardized scores to obtain illness dimensions, which we will validate by analyzing their associations with demographic, diagnostic, neurocognitive and other clinical data. We will empirically test the brain-to-symptom mappings by altering the balance between inter-site pooling and within-site normalization.

4. MRI imaging characteristics

MRI data include structural MPRAGE or equivalent structural MRI scans and resting state functional MRI scans acquired on 1.5 Tesla (T) or 3T MRI systems. Different models of scanner have been used, including Discovery MR750 3T (GE Healthcare, Little Chalfont, Buckinghamshire, UK), Signa HDxt 3T (GE Healthcare, Little Chalfont, Buckinghamshire, UK), MAGNETOM TrioTim or MAGNETOM Prisma Fit (Siemens Healthcare, Erlangen, Germany), and Achieva 3T (Philips Healthcare, Best, Netherlands) (Table 2).

5. MRI data integration and harmonization

Harmonization is a critical process as it enables the constructive pooling and integration of all datasets within the consortium. Although imaging protocols among all included studies are comparable, scanner and minor acquisition protocol variations nonetheless introduce inter-study differences in imaging characteristics which render direct pooling of the data very difficult. In the present consortium, we have harmonized image processing pipelines, statistical harmonization of derived measures, and deep learning based harmonization of raw images (76).

5.1. Image-level extraction of structural anatomy and functional connective measures

Imaging variables are extracted from the MRI scans to provide a multi-scale representation of structural and connectomic characteristics. Some features are defined a priori (e.g., multi atlas-based parcellations) and some are data-driven (e.g., structural covariance and functional connectivity networks).

5.1.1. MUSE region of interest segmentation

Volumetric MRI features are extracted via established and validated methods using a fully automated processing pipeline. The T1-weighted scan of each participant is first preprocessed for correction of intensity inhomogeneities. A multi-atlas skull stripping algorithm is applied for the removal of extra-cranial material. Each scan is segmented into a set of 153 anatomical regions of interest (ROIs) using a multi-atlas label fusion method, MUSE (77). Volumes in larger anatomical regions are calculated by grouping single ROIs within a hierarchical representation whereby brain anatomy can be quantified at multiple levels of resolutions. MUSE utilizes state-of-the-art multi-atlas, multi-warp algorithms, with a very rich set of atlases spanning several protocols from 1.5T SPGR images to 3T MPRAGE images. The ensemble of atlases used to segment each scan contains sufficient diversity which renders it robust to scanner variations (77). Critically, we have leveraged several studies which have scanned the same individuals in multiple scanners and hence have built procedures that render the multi-scanner atlases mutually consistent (77). Voxel-based, tissue-specific (grey matter, white matter, cerebrospinal fluid) regional volumetric maps are obtained using MUSE segmentation and inverse atlas warping is applied for voxel-based analyses.

5.1.2. Structural covariance networks

Our research has highlighted the potential role of structural covariance networks as a flexible and biologically interpretable method to encapsulate complex anatomical images within a set of measurements (78, 79). This approach uses orthogonally-projective non-negative matrix factorizations to parcellate the brain into regions that show consistent trends across individuals, potentially because they are influenced by common underlying neurobiological factors (79).

5.1.3. Deep learning generative adversarial networks (GAN)

A second approach utilizes deep learning CycleGAN and STAR-GAN methods (76), which synthesize images drawn from certain distributions. This method has been used extensively in numerous applications seeking to transform images from one style to another. We have used data from numerous scanners to learn the mapping of brain MRI scans to a canonical reference domain, in which inter-scanner variations are minimized (76) (Fig. 1). By making the characteristics of images very similar, while preserving important information, subsequent image analysis steps, including segmentation and parcellation, become significantly more consistent across studies.

5.1.4. Personalized functional networks

For resting state fMRI, personalized functional networks using a spatially-regularized nonnegative matrix factorization (NMF) method align well with functional activation patterns and have improved functional homogeneity (80) (Fig. 2). Functional homogeneity quantifies the degree to which a functional network represents one signal, rather than mixing multiple neurophysiological signals. Measures of functional connectivity, derived from brain parcellations and functional networks show promise in predicting brain maturity and distinguishing disease from healthy brain states (81–84) (Fig. 3). Furthermore, there are significant correlations between elements of the connectivity matrices, which may lead to unreliable classification (85).

We are also employing a complementary method based on sparse connectivity pattern (SCP) learning, leveraging the effective non-linearity of sparse dictionary learning (86–89) as a means for describing the functional connectivity patterns of brain networks (90). SCPs also minimize the negative impact of correlated features on the robustness of prediction models (85) (Fig. 4). A hierarchical extension of this approach extracts these functional networks at multiple scales (91). Adopting these complementary techniques will provide a rich set of imaging features to characterize the functional brain connectome.

6. Harmonization of derived measures

We have developed a statistical harmonization approach (17), building upon the COMBAT method that has been successfully used for over a decade to remove batch effects in genomic studies and recently adapted in neuroimaging research (92–95). This approach is fully multivariate, utilizing hyper-parameters to define over-arching statistical priors, and has been successfully adopted in imaging. In order to model nonlinear effects of covariates (e.g., age), we have combined COMBAT with generalized additive models (GAMs) using spline functions. The resultant COMBAT-GAM general tool for harmonization can be applied to various forms of data, including ROIs and coefficients of structural covariance and functional connectivity networks. Preliminary results from statistical harmonization are shown in Fig. 5.

Harmonization of resting state fMRI data is typically performed at the correlation matrix level. In particular, we have developed a functional connectivity covariance batch effect correction (FC-CovBAT) (96) that models second order moments of the upper-triangular elements of individual correlation matrices derived from the fMRI data. FC-CovBAT is an extension of COMBAT and CovBat methods (97) for structural imaging data. These methods statistically model the site/scanner differences not only in the means and variances of the multivariate correlation values, but also in the covariance structures between the multivariate correlation values from the FC data. Preliminary results are shown in Figs. 6–7.

7. NA-NF dimensional neurobiological representation of MDD

Direct clustering of patient data, especially high-dimensional images, is challenged by effects of confounding variations in the data, namely variations in healthy individuals that are not related to disease effects. We have developed semi-supervised clustering methods to account for these effects, effectively clustering differences between patients and healthy controls, rather than directly clustering patient data. The methods assume that patient data have been derived from healthy control data via one of several, to be estimated, transformations that reflect disease effects (Fig. 8). Whatever variations are present in the healthy control data and are unrelated to disease effects will follow the disease-specific transformations. Within this approach, we acknowledge that some control samples might reflect a “super-healthy” control group without any chronic diseases that may represent resilience (98), though healthy control participants have also been recruited from the general community in the present consortium.

Two semi-supervised methods will be used: 1) HYDRA, a conventional machine learning approach that is largely a discriminative method (16), with its multi-scale extension (99); and 2) Generative Adversarial Networks (GANs), a complementary method which leverages the power of a class of generative deep learning methods (100). These methods have complementary strengths, which will be combined in seeking robust and reproducible MDD dimensions.

7.1. HYDRA

HYDRA uses the concept of a convex polytope, in conjunction with support vector machine principles (maximum margin cost function), to simultaneously classify patients from healthy controls and to determine a number of hyperplanes that represent disease dimensions. An iterative process determines the disease dimensions, as well as memberships in them. HYDRA has recently helped discover two distinct neuroanatomical dimensions in schizophrenia with evidence of differences in cognitive and clinical profiles between dimensions, in polygenic risk scores for schizophrenia and autism, as well as in brain development and aging studies (101). We will apply this method to the imaging feature panels (ROI volumes, structural covariance network coefficients derived from voxel-based maps, and functional connectivity network coefficients), in order to identify MDD dimensions and to obtain positions of each patient within each dimension. It is likely that some patients will express multiple NA-NF signatures concurrently.

7.2. Deep learning and generative adversarial networks (GANs)

The second approach relies on a generative methodology for semi-supervised clustering, termed Smile-GAN, which utilizes state-of-the-art deep learning CycleGAN architectures, with latent variables representing neurobiological dimensions (100). The model simultaneously learns the mapping and clustering functions using the discriminator function and the data from the patient domain. Representations of the patient clustering structure and the depression-related NA-NF dimensions are stored respectively in the network weights of functions after learning, which are the main outcomes of the model training (Fig. 9).

While these approaches can be very powerful, they require large training datasets. The availability of a sufficiently large and well-harmonized dataset in the present consortium will allow us to harness the power of these deep learning methods. We will seek experimental confirmation via split sample and sensitivity analyses.

We use cross-validation, split-sample, and random permutation experiments to determine the optimal number of reproducible and statistically significant clusters corresponding to NA-NF dimensions of MDD, by measuring the adjusted rand index (ARI) (101). The most reproducible clusters, according to two main criteria, both high and statistically significant ARI, will be used to derive the NA-NF dimensions. As part of the method, cluster memberships of each individual will be readily calculated (16, 100). These memberships will effectively be the coordinates of an individual in the NA-NF coordinate system, reflecting the dominant NA-NF MDD patterns.

8. Multidimensional predictors of treatment responses

A main goal is to investigate whether position in the NA-NF dimensional system captures disease effect in predicting treatment response at the individual level based on semi-supervised approaches. Treatment response will be defined as the harmonized change in clinician-rated depressive severity scales. Changes of scores after the treatment period will be modelled both as continuous values and binary variables. We recognise that these measures are often insufficient for capturing treatment efficacy and might not reflect patient experience. We will further investigate whether NA-NF dimensions might map onto subfacets of the scales with greater clinimetric properties (102).

We will further enrich NA-NF dimensions by directly and specifically looking for patterns that predict treatment response via supervised classification. Such imaging signatures might or might not capture broad and treatment-oblivious NA-NF variations, however they capture NA-NF measures that are tailored to prediction of treatment response in which baseline clinical variables will be part of these treatment-specific predictive models.

In particular, specificity to particular categories of treatment will be investigated. Prediction of differential response between placebo and antidepressant medication is highly important, yet difficult to assess. Prediction of placebo response is uniquely accessible in the present sample, in which treatment outcome data are available for both active antidepressant and placebo arms of randomized controlled trials.

To identify pre-treatment moderators of differential treatment response, the specificity of NA-NF predictors to different categories of antidepressant medication will be investigated. Treatment outcome data are available for SSRI and SNRI medication classes. Specificity of NA-NF predictors of response to psychotherapy or to antidepressant medication will also be investigated.

A key weakness of multivariate pattern analysis studies has been the frequent lack of generalizability and validation on new datasets. Given the size of the consortium, we will test the NA-NF dimensional system in wholly independent prospective longitudinal treatment cohorts, namely Copenhagen. We emphasize that cross-validation methods are engrained in all our analyses to reduce overfitting and improve generalizability.

COORDINATE-MDD brings together deeply phenotyped clinical data, multi-site ‘raw’ individual-level structural and functional neuroimaging data, and state-of-the-art AI-based methods in order to identify the multimodal dimensions that comprise MDD and which inform treatment response. Our consortium data are derived from adults with first episode or recurrent MDD, medication-free, in a current major depressive episode, that is not treatment-resistant depression, and healthy controls. We leverage statistical harmonization, machine learning and deep learning methods and large integrated and harmonized sample of highly-phenotyped, individual-level patient data with prospective longitudinal treatment outcomes. Our objectives are to delineate the robust and reproducible neurobiological NA-NF signatures which comprise MDD and which predict or moderate treatment response. We will follow an iterative process by first defining the multivariate neural dimensions that characterise deeply phenotyped samples and then testing the utility of these dimensions in novel samples.

MDD

major depressive disorder

NA-NF

neuroanatomical-neurofunctional

SSRI

selective serotonin reuptake inhibitor

CAN-BIND

Canadian Biomarker Integration Network in Depression

Cimbi

Center for Integrated Molecular Brain Imaging

EMBARC

Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care

CGI

Clinical Global Improvement scale

HMRRC

Huaxi MR Research Center

SNRI

serotonin-norepinephrine reuptake inhibitor

LIBR

Laureate Institute for Brain Research

PReDICT

Predictors of Remission in Depression to Individual and Combined Treatments

SWU

Southwest University

STRADL

STratifying Resilience and Depression Longitudinally

UCSF

University of California at San Francisco

MINI

Mini International Neuropsychiatric Interview

HRSD

Hamilton Rating Scale for Depression

MADRS

Montgomery-Åsberg Depression Rating Scale

QIDS-SR

Quick Inventory for Depressive Symptomatology

RDoC

Research Domain Criteria

Tesla

ROIs

regions of interest

GAN

generative adversarial networks

NMF

nonnegative matrix factorization

SCP

sparse connectivity pattern

GAMs

generalized additive models

FC-CovBAT

functional connectivity covariance batch effect correction

GANs

Generative Adversarial Networks

ARI

adjusted rand index

Ethics approval and consent to participate:

All methods were carried out in accordance with relevant guidelines and regulations.

All participating sites had obtained permission from their local institutional review boards or ethics committees and all study participants provided written informed consent. Research analyses specific to this study only used de-identified data.

Consent for publication:

Not Applicable.

Availability of data and materials:

Original data can be requested from each consortium member. Derived data in the present consortium can be made available from the corresponding author on reasonable request and with permission of each consortium member.

Competing Interests:

SRA has consulted for Indoc Research Canada.

AMM has received research support from Eli Lilly, Janssen, and The Sackler Trust. AMM has also received speaker fees from Illumina and Janssen.

HS has received grant funding from NIH.

SCS received research support from Brain Canada, CIHR (Canadian Institutes of Health Research), Ontario Brain Institute and CFI (canadian Foundation for Innovation). SCS is a founder and share holder of ADMdx, Inc.

DT has received grant funding from NIH.

BWD has received research support from Boehringer-Ingelheim, Compass, Pathways, NIMH, Otsuka, Usona. Honoraria for Consulting: Aya Biosciences, Myriad Neuroscience, Otsuka, Sophren Therapeutics, Cerebral Therapeutics, Sage.

CJH serves as a consultant for P1vital, Lundbeck, Servier and Compass Pathways. She holds grant income from Zogenix and J&J.

SHK has received funding for Consulting or Speaking engagements from Abbvie, Boehringer-Ingelheim, Janssen, Lundbeck, Lundbeck Institute, Merck, Otsuka Pfizer, Sunovion and Servier. He has received Research Support from Abbott, Brain Canada, CIHR (Canadian Institutes of Health Research), Janssen, Lundbeck, Ontario Brain Institute, Otsuka, Pfizer, SPOR (Canada's Strategy for Patient-Oriented Research). He has stock/stock options in Field Trip Health.

GMK has received honoraria as a speaker for Sage Biogen and as a consultant for Sanos.

HSM has received grant funding from NIH; Consulting and IP Licenses Fees from Abbott Labs.

MHT received Research Support from: NIH, PCORI, AFSP; Consultant: Alkermes Inc., Alto Neuroscience Inc, Axsome Therapeutics, Boegringer Ingelheim, GH Research, GreenLight VitalSign6 Inc, Heading Health, inc., Janssen Pharmaceutical, Legion Health, Merck Sharp & Dohme Corp., Mind Medicine Inc., Navitor, Neurocrine Biosciences Inc., Noema Pharma AG, Orexo US Inc., Otsuka Canada Pharmaceutical Inc, Otsuka Pharmaceutical Development & Commercialization, Inc. (MDD Section Advisor), SAGE Therapeutics, Signant Health, Takeda Pharmaceuticals Inc; Editorial compensation from Oxford University Press.

AHY reports declaration of interests: Paid lectures and advisory boards for the following companies: Astrazenaca, Eli Lilly, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allegan, Bionomics, Sumitomo Dainippon Pharma, COMPASS, Sage, Novartis. Consultant to Johnson & Johnson and to Livanova. Received honoraria for attending advisory boards and presenting talks at meetings organised by LivaNova. Principal Investigator in the Restore-Life VNS registry study funded by LivaNova. UK Chief Investigator for Novartis MDD study MIJ821A12201. Principal Investigator on ESKETINTRD3004: “An Open-label, Long-term, Safety and Efficacy Study of Intranasal Esketamine in Treatment-resistant Depression”. Principal Investigator on “The Effects of Psilocybin on Cognitive Function in Healthy Participants”. Principal Investigator on “The Safety and Efficacy of Psilocybin in Participants with Treatment-Resistant Depression (P-TRD)”. No shareholdings in pharmaceutical companies. Deputy Editor of BJPsych Open. Grant funding (past and present): NIMH (USA); CIHR (Canada); NARSAD (USA); Stanley Medical Research Institute (USA); MRC (UK); Wellcome Trust (UK); Royal College of Physicians (Edin); BMA (UK); UBC-VGH Foundation (Canada); WEDC (Canada); CCS Depression Research Fund (Canada); MSFHR (Canada); NIHR (UK). Janssen (UK).

Authors AS, AS, BRG, BN, CCF, C-GY, CD, CHYF, DA, DTW, GE, HCW, IHG, IMA, IS, JFWD, JQ, JS, JG, KH, KSC, KQ, MPP, MA, MDS, MG, QG, RDW, RE, SH, SR, TAV, TC, TW, VGF and YF declare that they have no competing interests to declare.

Funding

IMA received funding from the Medical Research Council (MRC) (G0601526).

BNF, SHK, SH, SCS, SRA, and SR received funding from Ontario Brain Institute and Canadian Institutes of Health Research (CIHR) (for CANBIND01 data).

CD received funding from the National Institute of Health (R01 MH112070), HC (RF1-AG054409, R01-MH123550, U01-AG068057), IHG (R37MH101495) and YF (R01 AG066650 and R01EB022573).

EMBARC study (NCT01407094) was supported by the National Institute of Mental Health of the National Institutes of Health under award numbers U01MH092221 (Trivedi, M.H.) and U01MH092250 (McGrath, P.J., Parsey, R.V., Weissman, M.M.), and in part by the Hersh Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Valeant Pharmaceuticals donated the Wellbutrin XL used in the study. This work was supported by the EMBARC National Coordinating Center at UT Southwestern Medical Center, Madhukar H. Trivedi, M.D., Coordinating PI, and the Data Center at Columbia and Stony Brook Universities. In addition, this work was funded in part by the Center for Depression Research and Clinical Care (PI: Madhukar Trivedi).

RE received funding from the National Institute for Health and Care Research (NIHR), and the Medical Research Council (MRC).

CHYF received funding from the Medical Research Council (MRC) (G0802594) and Brain and Behaviour NARSAD Young Investigators Award.

QG received funding from the National Natural Science Foundation of China (81820108018; 81621003).

CJH received support from the Medical Research Council (G0701421) and is supported by the Oxford Health NIHR Biomedical Research Centre.

GMK, MG & VGF received funding from the Lundbeck Foundation (R279-2018-1145 (BrainDrugs)).

HSM & BWD received funding from the National Institute of Mental Health (P50MH077083, 1RO1MH080880).

MPP received funding from The William K. Warren Foundation, National Institute on Drug Abuse (U01 DA041089), and National Institute of General Medical Sciences Center Grant Award Number (1P20GM121312).

MDS received funding from the National Institute of Mental Health (R01MH125850).

JS received funding from the National Institute of Mental Health (K01MH096077; R01MH098099).

STRADL study was supported and funded by the Wellcome Trust Strategic Award, Stratifying Resilience and Depression Longitudinally (ref. 104036/Z/14/Z), and the Medical Research Council (MRC-MC/PC/17209). AMM received funding from the Wellcome Trust (220857/Z/20/Z, 216767/Z/19/Z). AS was funded as part of the STRADL study and indirectly through the Lister Institute of Preventive Medicine award ref. 173096. Data processing used the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/).

SWU dataset was supported by the following: National Natural Science Foundation of China (31571137; 31500885), the National Outstanding young people plan, the Program for the Top Young Talents by Chongqing, the Fundamental Research Funds for the Central Universities (SWU1509383; SWU1509451; SWU1609177), Natural Science Foundation of Chongqing (cstc2015jcyjA10106), the Fok Ying Tung Education Foundation (151023) to JQ and DTW.

DT received funding from the National Institute of Mental Health (5R01MH101472).

TW received funding from the Anthony and Elizabeth Mellows Charitable Foundation.

TC received funding from the National Natural Science Foundation of China (81401398).

CGY was supported by the National Natural Science Foundation of China (82122035) and Beijing Nova Program of Science and Technology (Z191100001119104).

AHY independent research is funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.

RZ received funding from the Medical Research Council (MRC) (MR/T017538/1).

Author Contributions:

Authors made substantial contributions to the conception of the work (CHYF, CD), design (all authors), data acquisition (all authors), analysis (CD, CHYF, GE, YF, MA, JG, HS). All authors have contributed to drafting and revising the work. All authors have approved the submitted version. All authors agree both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Acknowledgements:

MT, CCF, CHYF and all authors wish to thank the participants, families, staff, and colleagues who made this project possible. BWD acknowledges W. Edward Craighead, PhD. The views expressed are those of the author(s) and not necessarily those of the funding agencies.

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Competing interest reported. SRA has consulted for Indoc Research Canada.

AMM has received research support from Eli Lilly, Janssen, and The Sackler Trust. AMM has also received speaker fees from Illumina and Janssen.

HS has received grant funding from NIH.

DT has received grant funding from NIH.

RZ is a private psychiatrist service provider at The London Depression Institute and co-investigator on a Livanova-funded observational study of Vagus Nerve Stimulation for Depression. RZ has received honoraria for talks at medical symposia sponsored by Lundbeck as well as Janssen. He has collaborated with EMIS PLC and advises Depsee Ltd. He is affiliated with the D’Or Institute of Research and Education, Rio de Janeiro and advises the Scients Institute, USA. BWD has received research support from Boehringer-Ingelheim, Compass, Pathways, NIMH, Otsuka, Usona. Honoraria for Consulting: Aya Biosciences, Myriad Neuroscience, Otsuka, Sophren Therapeutics, Cerebral Therapeutics, Sage. CJH serves as a consultant for P1vital, Lundbeck, Servier and Compass Pathways. She holds grant income from Zogenix and J&J.

GMK has received honoraria as a speaker for Sage Biogen and as a consultant for Sanos.

HSM has received grant funding from NIH; Consulting and IP Licenses Fees from Abbott Labs.

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published 23 Jan, 2023

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Editorial decision: Major revision
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You are reading this latest preprint version

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

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Journal Publication

Version 1

Abstract

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Background

Methods And Preliminary Results

1. Participating studies and datasets

2. Assessments

3. Clinical data harmonization

4. MRI imaging characteristics

5. MRI data integration and harmonization

5.1. Image-level extraction of structural anatomy and functional connective measures

5.1.1. MUSE region of interest segmentation

5.1.2. Structural covariance networks

5.1.3. Deep learning generative adversarial networks (GAN)

5.1.4. Personalized functional networks

6. Harmonization of derived measures

7. NA-NF dimensional neurobiological representation of MDD

7.1. HYDRA

7.2. Deep learning and generative adversarial networks (GANs)

8. Multidimensional predictors of treatment responses

Summary

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1