Sidney H. Kennedy, MD

FROM THE NATIONAL NETWORK OF DEPRESSION CENTERS

One of the biggest challenges in treating depression is the ability to select the most appropriate and effective treatment for a particular individual from among the many available options. This is a significant public health issue, since the first treatment selected is effective only about 

Although there are excellent clinical guidelines to address the management of a major depressive episode, the evidence is based on average rates of response in group data and not on individual characteristics or objective biological measures.

While the use of biomarkers in psychiatry holds great potential, large multimodal studies with standardized methods of data collection, longitudinal assessments, and collaboration among groups are necessary for discovery and subsequent validation of findings. Researchers have adopted this approach in an effort to stratify populations of depressed individuals based on their response to various treatments including pharmacotherapy, cognitive behavioral therapy (CBT), and neurostimulation.

Furthermore, standardized data collection using common measures within and across studies is required to help tease apart the heterogeneity of depression and thus identify treatment response subtypes. These measures can range from cognitive tests to blood-based protein assays as well as functional and structural MRI brain scans. Once these rich multidimensional datasets are integrated, the hope is that big data analytics will uncover clinically meaningful biological differences that can be translated into biomarkers.

Canadian Biomarker Integration Network in Depression

 (CAN-BIND) is one of several multisite initiatives generating rich integrated datasets designed to identify biosignatures to inform treatment selection.

 CAN-BIND’s standardized multimodality platform approach to data collection began with a 16-week open label standardized antidepressant treatment study called CAN-BIND-1. This internationally recognized research and education program has since expanded to include more than 10 clinical studies spanning 8 clinical research sites across Canada.

CAN-BIND researchers and study participants are working together to generate a large harmonized dataset that will span diverse interventions and specific populations, including adolescents at risk for developing mental health disorders and those who have attempted suicide. Collection of high quality data across CAN-BIND sites and studies is enabled by the Ontario Brain Institute’s (OBI) large-scale web-based neuroinformatics platform known as the Brain-Centre for Ontario Data Exploration (

OBI, a provincially funded, not-for-profit research organization, created Brain-CODE to support collection, centralized storage, federation, sharing, and analysis of 

 (eg, structured interview and self-report measures, MRI scans, and molecular assays) across several brain disorders.

 In partnership with the Indoc Consortium,

 OBI created Brain-CODE to breakdown silos, promote team science, address commonalities across diseases and disorders, and harness expertise across diverse disciplines; their combined goal is to improve the lives of patients diagnosed with neurological and mental health disorders. In this article, we highlight how CAN-BIND and its major sponsor, OBI, are working together through Brain-CODE to drive forward the discovery of depression biomarkers (

CAN-BIND is one of 5 OBI-sponsored Integrated Discovery Programs mandated to take a truly transdisciplinary research approach to advancing brain health.

 To maximize discovery from multimodal data collection, harmonized data points and established quality control processes are necessary to ensure sustained collection of high quality data and data integration. To facilitate this, CAN-BIND established platforms led by experts in each of the domains of interest: clinical outcomes, neuroimaging scans, electroencephalography (EEG) recordings, molecular assays involving standardized biospecimen collection, and mobile-health (m-health) technologies.

Within this piece, we briefly describe standardization challenges, solutions provided by Brain-CODE, and potential effects within and across platforms. The hope is that these rich multidisciplinary datasets will contribute to the development and validation of tools and technology to help with self-management, real-time monitoring, and early detection and prevention of depression and relapses.

 While depression severity scales, such as the Montgomery-Asberg Depression Rating Scale (MADRS) and Hamilton Depression Rating Scale (HAM-D), are commonly used by research clinicians to assess patients, more nuanced behavioral and self-report tools to assess such variables as sleep and alertness, cognitive deficits, and anhedonia provide additional clinical information that could inform treatment selection. As such, 10 clinician-administered and 19 self-report questionnaires, all of which are based on theoretical and clinical utility, are part of the standardized battery of clinical assessments in the CAN-BIND-1 study to help identify predictors of response to standard antidepressant treatment regimen.

Data collection is enabled by electronic data capture tools, including Research Electronic Data Capture (REDCap)

 on Brain-CODE. This minimizes the need for paper forms and potential error related to data entry. The data gathered from this large battery of questionnaires can help identify key clinical characteristics that predict treatment outcomes and allow evidence-based decisions to effectively guide treatment selection and monitor progress. In addition to reducing the number of measures moving forward, these results also can be used to inform the development of user-friendly mobile health applications that will enable remote collection and real-time monitoring capabilities.

Multisite neuroimaging standardized data collection.

 In comparison to collecting clinical outcomes, collecting and standardizing brain scans is more costly and more technically difficult, especially when accounting for different MRI machines across sites. While this poses significant challenges for data integration, the additional biological information gained holds great promise for new discoveries and is representative of real world clinical practice settings. CAN-BIND has taken on this challenge and implemented multiple quality control steps, including regular phantom scans that serve as a common reference to facilitate downstream analysis from different machines. In addition, semi-automated quality control procedures delivered through Brain-CODE’s imaging informatics software platform called Stroke Patient Research Recovery Database (SPReD),

 which is a part of the Extensible Neuroimaging Analysis Toolkit (XNAT), provides additional regular checks to ensure collection of high quality brain imaging data across CAN-BIND sites and studies.

CAN-BIND is collecting structural and functional brain scans, both resting state and during various tasks, in an attempt to identify different depression subgroups based on brain connectivity and structure.

 These neuroimaging findings will contribute to a better understanding of the structural and/or functional changes associated with depression and treatment response, which in turn may inform treatment selection and facilitate a more targeted delivery. Hopefully this also will lead to the development of more cost-effective and time-efficient proxy tests to shorten the path to recovery.

 Brain activity measured by EEG may serve as a proxy biomarker for neuroimaging. With recent technological advances, portable EEG devices make it possible for assessments to be completed quickly and in community settings. Further research and development is needed to define measurements and create algorithms to predict and/or monitor treatment response.

As a first step, CAN-BIND has developed standardized methodology to collect and analyze EEG data from dedicated research machines across sites and studies.

 This has yielded encouraging results as pilot EEG data from the CAN-BIND-1 study at baseline and 2 weeks post-antidepressant treatment have shown significant utility toward predicting treatment response.

 To investigate the possibility of enhancing sensitivity and specificity of predicting treatment outcome, follow-up analyses involving integration with MRI, clinical, and molecular data are being conducted.

Molecular and mobile health technology data.

 has the capacity to handle collection and storage of a wide array of molecular data ranging from inflammatory makers to functional genomic and genetic data. LabKey serves as a file repository and allows for secure transfer and storage of CAN-BIND’s m-health data collected from wearables and mobile applications.

While individual platforms within CAN-BIND will contribute to expanding our knowledge of depression, the real strength and goal of the network is to carry out integrative data analyses across data collection platforms. To help prepare data for complex large-scale analytics, harmonized data are aggregated and interactive visual dashboards are created to query metadata and generate data packages on Brain-CODE.

Having completed platform specific data quality control and assurance, cleaning, and curation processes for the CAN-BIND-1 study, CAN-BIND is currently conducting integrative analyses across data platforms. These analyses involve collaborations to increase analytics capabilities in machine learning, network biology, computational psychiatry, and other data science methodologies. This further highlights the multidisciplinary team effort and vast array of expertise required to advance depression biomarker discovery. Brain-CODE is able to support these collaborative analyses by providing personalized virtual group workspaces with readily available access to such analytic packages as R Studio and MATLAB.

For the CAN-BIND-1 study, CAN-BIND’s major goal is to identify biomarkers that predict treatment response to standard pharmacotherapy. A longer term goal is to expand this precision-treatment approach to other interventions. To this end, CAN-BIND is generating a large harmonized dataset across treatments like CBT and neurostimulation therapies such as repetitive transcranial magnetic stimulation (rTMS).

In addition to integration across CAN-BIND studies, there are opportunities to collaborate and federate data with other international depression initiatives, such as the Establishing Moderators and Biosignatures of Antidepressants Response for Clinical Care for Depression (EMBARC) initiative. This should expedite discovery and validation of findings. Hopefully, this collaborative team effort will lead to readily accessible biological-based tests that can accurately and efficiently guide treatment selection for depression.

CAN-BIND, through Brain-CODE, will also have the opportunity to form linkages with other databases to both expand and augment datasets for further discovery. For example, Brain-CODE has developed a customized subject registry tool that collects an encrypted version of study participants’ Ontario Health Insurance Plan number; this enables linkage with the Institute for Clinical Evaluative Sciences health administrative database. In turn, this will allow CAN-BIND to link research and health care utilization data to conduct health economic analyses, including evaluation of the effects of new biomarkers and interventions and, ultimately, generate evidence to influence policy.

CAN-BIND, through its unique partnership with OBI and Brain-CODE, is also poised to advance our understanding of depression in other neurological disorders. For instance, in addition to CAN-BIND, OBI has supported the development of research programs in epilepsy, cerebral palsy, neurodevelopmental disorders (eg, autism), and neurodegenerative diseases (eg, Alzheimer disease, Parkinson disease).

To facilitate collaboration among the five existing OBI research programs, several common data elements (CDEs) were established using a Delphi consensus process with participation from all research programs. These CDEs reflect existing international standards established by such organizations as the National Institute of Neurological Disorders and Stroke and Clinical Data Interchange Standards Consortium. They include standardized instruments to collect demographic, depression, sleep, anxiety, quality of life, and other clinical questionnaire-based data. This paves the road for analysis of comorbid symptoms and common pathways related to depression and mental health (

To maximize outcomes, Brain-CODE is designed with data sharing, collaborative promotion, and open science in mind. Much thought and careful planning has gone into ensuring appropriate Brain-CODE governance, privacy, and security measures are in place to allow data from Brain-CODE to be shared, not only within and across OBI’s research programs, but also eventually with third parties to support data mining, discovery, and innovation.

Big data holds great potential for multidisciplinary innovations toward precision treatment and improving brain health. From CAN-BIND, this includes the use of portable brain activity monitoring devices, mobile applications, and blood based molecular assays as biomarker readouts, likely in combination with novel analytic algorithms, to improve management and treatment of depression.

This article is based on Drs Brendan Behan’s and Joanna Yu’s presentation at the 2017 Annual National Network of Depression Center (NNDC) Conference.

NNDC’s goal is to foster collaborative research to advance scientific discovery and drive forward improved care for patients with depression and mood disorders. NNDC is an American-based initiative and is a content partner of 

is Neuroinformatics Manager, Centre for Depression and Suicide Studies, St. Michael’s Hospital; 

 is Program Lead, Informatics and Analytics, Ontario Brain Institute; 

 is Manager, Informatics and Analytics, Ontario Brain Institute and Chief Operating Officer and Director of Clinical Research, Indoc Research; 

is Vice President, Research and Informatics, Ontario Brain Institute; 

 is Professor of Psychiatry, University of Michigan, Ann Arbor; 

is Program Manager, Centre for Depression and Suicide Studies, St. Michael’s Hospital and Assistant Professor of Psychiatry, University of Toronto; 

 is Director, Centre for Depression and Suicide Studies, St. Michael’s and Professor of Psychiatry, University of Toronto.

Rush AJ. Star-D: Lessons learned and future implications. 

 Vaccarino AL, Dharsee M, Strother SC, et al. Brain-CODE: a secure neuroinformatics platform for management, federation, sharing and analysis of multi-dimensional neuroscience data. 

Stuss D. The Ontario Brain Institute: completing the circle. 

Lam RW, Milev R, Rotzinger S, et al. Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort. 

 Fonseka TM, MacQueen GM, Kennedy SH. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. 

 Faranak F, Atluri S, Frehlich M, et al. Standardization of electroencephalography for multi-site, mutli-platform and multi-investigator studies: insights from the Canadian Biomarker Integration Network in Depression. 

 Baskaran A, Farzan F, Milev R, et al. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: a pilot study. 

Articles

One of the biggest challenges in treating depression is the ability to select the best treatment for a particular individual from among the many available options.

Big Data for Depression

Psychiatric disorders are heterogeneous and of multifactorial origin.

 The illness course is often chronic and associated with high rates of nonrecovery, episode recurrence, interepisodic dysfunction, and morbidity as well as premature mortality.

It is therefore not surprising that MDD, bipolar disorder, and schizophrenia are among the primary leading causes of disability and economic burden worldwide. One major contributor to the economic burden associated with these conditions is the limited capability of available treatment options to effectively modify the disease process, fully restore function, halt illness progression, and treat cognitive deficits.

Since the original description of lithium’s efficacy for mania in 1948, followed by chlorpromazine and iproniazid shortly thereafter, no substantial pharmacological advances have been made in the field of psychiatry. Although advances in safety, tolerability, and simplicity of use have been made, the long-term outcomes for most patients with MDD, bipolar disorder, or schizophrenia remain largely the same as they did several decades ago, ie, many of these patients do not achieve symptomatic, syndromal, and functional recovery with conventional pharmacotherapy. This underscores the need for restructuring the current disease model.

The prevailing theory for the past 50 years has been that affective and psychotic disorders are subserved by disturbances in monoamines. Yet, despite intensified research efforts, monoamines have not proved to be reliable biomarkers of psychiatric disorders and no disease-modifying agents have been discovered on the basis of this model. A more comprehensive disease model that encompasses other physiological systems may accelerate progress toward novel treatment development.

Results from preclinical and clinical translational research indicate that disturbances in inflammation may play a role in the pathophysiology of psychiatric disorders, including MDD, bipolar disorder, and schizophrenia. Hence, it could be hypothesized that inflammatory mediators may constitute novel targets for drug development.

Cytokines are pleiotropic and often redundant soluble proteins that regulate survival, differentiation, proliferation, and effector functions of tissues and cells. While these proteins are well-known mediators of innate and adaptive immunity, more recently they have been implicated in a variety of other systems. For example, inflammatory cytokines have been shown to interact with neurotransmitter metabolism, neuroendocrine function, and neural as well as glial plasticity. Peripherally and centrally derived inflammatory cytokines exert a bidirectional influence across the blood-brain barrier or via peripheral afferent nerve fibers (ie, vagus nerve).

The increased incidence of inflammatory diseases in individuals with psychiatric disorders (eg, cardiovascular disease, obesity, autoimmune diseases) provides conjectural evidence that inflammatory mediators may be involved in the pathophysiology of these conditions. The obverse association is also reported, wherein in individuals with rheumatic diseases, the incidence of subsequent psychiatric disorders is higher.

In a population-based study that evaluated the entire Swedish population, the standardized incidence ratio of subsequent psychiatric disorders was highest for individuals with systemic lupus erythematosus followed by ankylosing spondylitis and rheumatoid arthritis.

 Further analysis revealed a higher incidence of subsequent affective, psychotic, and personality disorders, as well as dementia and delirium, although the risk of psychotic disorders was significant only in women with rheumatoid arthritis and systemic lupus erythematosus.

Other population-based studies, however, have reported a lower incidence of schizophrenia in individuals with rheumatoid arthritis.

Higher rates of depression are also evident with various other medical conditions, including obesity and cardiovascular disease.

 The bidirectional co-occurrence of psychiatric and autoimmune or inflammatory diseases suggests the involvement of common networks central to the pathophysiology of these conditions.


Triggers of inflammation in psychiatric disorders

The hypothesis that inflammatory cytokines play a role in the pathophysiology of psychiatric disorders stems from the observation that sickness behavior is characterized by signs and symptoms that are common to major depression. Sickness behavior is a nonspecific reaction to inflammation that is mediated by proinflammatory cytokines (eg, interleukin (IL)-1, IL-6, tumor necrosis factor Î± [TNF-Î±], and interferon-Î³ [IFN-Î³]) and is characterized by decreased appetite, amotivation, cognitive impairment, and neurovegetative symptoms.

During the past 2 decades, numerous studies have documented abnormal inflammatory cytokine concentrations in patients with MDD, bipolar disorder, or schizophrenia.

 Most replicated and common to all 3 conditions are elevated concentrations of circulating TNF-Î±, IL-6, and IL-1Î². Preliminary evidence also documents abnormalities in concentrations of proinflammatory cytokines in the cerebrospinal fluid of patients with MDD, bipolar disorder, or schizophrenia.

 It has been hypothesized that inflammation in mood disorders may arise from an imbalance between proinflammatory and anti-inflammatory cytokines. This is supported by evidence of increased proinflammatory to anti-inflammatory cytokine ratios in these populations.

There is a pressing need for studies of inflammatory markers in children and adolescents at risk for or in the early stages of a psychiatric disorder. At this point, it is not known if inflammatory cytokine abnormalities are a cause or consequence of the illness. Preliminary evidence suggests that manic but not depressive symptom severity in adolescents with bipolar disorder is significantly associated with high-sensitivity C-reactive protein (CRP) levels, an acute phase reactant that is elevated in response to inflammation, but not with IL-6 levels.

 A preliminary proinflammatory gene expression signature has been reported in offspring of individuals with bipolar disorder, and elevated messenger RNA (mRNA) levels as well as protein expression levels of IL-1Î², IL-6, and TNF-Î± have been reported in the prefrontal cortex of adolescent suicide victims.

 Longitudinal studies in children at genetic risk for major psychiatric disorders may aid identification of biomarkers that allow for earlier detection of candidates for prophylactic treatment.

Although the triggers of inflammation in psychiatric disorders are unknown, they are likely multifactorial in origin and encompass environmental and genetic factors (

). For example, maternal infections as well as maternal stress have been reported to pose a risk for the onset of schizophrenia later in life.

 Early childhood stress has also been shown to trigger an inflammatory response and may render individuals more vulnerable to psychiatric disorders.

Among female adolescents at high risk for depression, transition to depression was associated with increases in CRP and IL-6 levels; the magnitude of these changes was greater among females with multiple types of childhood adversity.

 Lower socioeconomic status in early childhood (1 to 2 years of age) has also been associated with elevated circulating IL-6 levels in adulthood.

 Environmental factors during the prenatal period or early childhood may activate the inflammatory response, which in combination with a genetic susceptibility or epigenetic modification, may increase the risk of psychiatric disorders.

Inflammation is highly interconnected with oxidative stress and results in a widespread influence on protein expression and cell survival. Oxidative stress exerts effects on DNA methylation patterns and produces unique epigenetic profiles that may increase vulnerability to psychiatric disorders. For example, decreased IL-6 CpG methylation is associated with elevated serum IL-6 and CRP levels in individuals with lifetime depression.

 Moreover, inflammation and oxidative stress are associated with increased apoptosis and decreased neural and glial plasticity.

 Abnormalities in these processes have also been reported in persons with MDD, bipolar disorder, and schizophrenia.

Anti-inflammatory agents have been shown to mitigate psychiatric symptoms. For example, adjunctive celecoxib has been shown to exert antidepressant effects in MDD and bipolar disorder as well as ameliorate symptoms of schizophrenia.

 Minocycline, a tetracycline antibiotic with anti-inflammatory properties, has also been shown to mitigate negative symptoms and improve cognitive function in patients with schizophrenia.

 Moreover, evidence from animal studies and a pilot open-label clinical trial in patients who had MDD with psychotic symptoms suggests that minocycline exerts antidepressant effects.

 TNF antagonists may also have antidepressant effects, as suggested by improvements in secondary measures of depression and quality of life in individuals with inflammatory disorders.

Conventional antidepressants, mood stabilizers, and antipsychotics, in varying capacities, down-regulate proinflammatory cytokine mRNA and protein levels. Moreover, for patients who are candidates for IFN-Î± therapy, antidepressant pretreatment has been shown to reduce the risk of cytokine-induced depression.

 Interestingly, preliminary preclinical evidence suggests that SSRIs and antipsychotics suppress inflammatory cytokine production and inhibit disease progression in an animal model of rheumatoid arthritis.

A plethora of studies support the hypothesis that inflammation plays a role in the pathophysiology of major psychiatric disorders. A unique inflammatory signature capable of distinguishing between each condition, however, remains to be elucidated. Although differences between the psychiatric disorders have been reported, no consistent disorder-specific profiles have emerged from these efforts. The heterogeneous presentation of each psychiatric disorder and the pleiotropic nature of inflammatory cytokines support the hypothesis that a combination of biomarkers along with genetic and environmental risk factors may prove to be a more reliable predictor of disease.

Considering that not all individuals will manifest inflammatory abnormalities, identifying subtypes of mood disorders (perhaps informed by biomarkers) may help identify a subgroup of individuals with mood disorders wherein inflammatory disturbances are a critical component of the pathoetiology and/or treatment. With technological advancement, it is now possible to conduct microarrays and evaluate over 1000 analytes simultaneously. Preliminary studies using this method have begun to delineate common and distinct biological processes in MDD, bipolar disorder, and schizophrenia, bringing us one step closer to a more comprehensive model of disease and pharmacological revolution.

Acknowledgment-The authors would like to thank Marena Manierka for her invaluable input and comments during the writing of this article.

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Prog Neuropsychopharmacol Biol Psychiatry

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A plethora of studies support the hypothesis that inflammation plays a role in the pathophysiology of major psychiatric disorders.

Sidney H. Kennedy, MD

Articles

Big Data for Depression

Are Psychiatric Disorders Inflammatory-Based Conditions?