Browsing by Author "Hassel, Stefanie"

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    A randomized, crossover comparison of ketamine and electroconvulsive therapy for treatment of major depressive episodes: a Canadian biomarker integration network in depression (CAN-BIND) study protocol
    (2020-06-02) Phillips, Jennifer L; Jaworska, Natalia; Kamler, Elizabeth; Bhat, Venkat; Blier, Jean; Foster, Jane A; Hassel, Stefanie; Ho, Keith; McMurray, Lisa; Milev, Roumen; Moazamigoudarzi, Zahra; Placenza, Franca M; Richard-Devantoy, Stéphane; Rotzinger, Susan; Turecki, Gustavo; Vazquez, Gustavo H; Kennedy, Sidney H; Blier, Pierre
    Abstract Background Recent evidence underscores the utility of rapid-acting antidepressant interventions, such as ketamine, in alleviating symptoms of major depressive episodes (MDE). However, to date, there have been limited head-to-head comparisons of intravenous (IV) ketamine infusions with other antidepressant treatment strategies in large randomized trials. This study protocol describes an ongoing multi-centre, prospective, randomized, crossover, non-inferiority trial comparing acute treatment of individuals meeting diagnostic criteria for a major depressive episode (MDE) with ketamine and electroconvulsive therapy (ECT) on efficacy, speed of therapeutic effects, side effects, and health care resource utilization. A secondary aim is to compare a 6-month maintenance strategy for ketamine responders to standard of care ECT maintenance. Finally, through the measurement of clinical, cognitive, neuroimaging, and molecular markers we aim to establish predictors and moderators of treatment response as well as treatment-elicited effects on these outcomes. Methods Across four participating Canadian institutions, 240 patients with major depressive disorder or bipolar disorder experiencing a MDE are randomized (1:1) to a course of ECT or racemic IV ketamine (0.5 mg/kg) administered 3 times/week for 3 or 4 weeks. Non-responders (< 50% improvement in Montgomery-Åsberg Depression Rating Scale [MADRS] scores) crossover to receive the alternate treatment. Responders during the randomization or crossover phases then enter the 6-month maintenance phase during which time they receive clinical assessments at identical intervals regardless of treatment arm. ECT maintenance follows standard of care while ketamine maintenance involves: weekly infusions for 1 month, then bi-weekly infusions for 2 months, and finally monthly infusions for 3 months (returning to bi-weekly in case of relapse). The primary outcome measure is change in MADRS scores after randomized treatment as assessed by raters blind to treatment modality. Discussion This multi-centre study will help identify molecular, imaging, and clinical characteristics of patients with treatment-resistant and/or severe MDEs who would benefit most from either type of therapeutic strategy. In addition to informing clinical practice and influencing health care delivery, this trial will add to the robust platform and database of CAN-BIND studies for future research and biomarker discovery. Trial registration ClinicalTrials.gov identifier NCT03674671. Registered September 17, 2018.
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    ItemOpen Access
    AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale
    (2023-01-23) Fu, Cynthia H. Y.; Erus, Guray; Fan, Yong; Antoniades, Mathilde; Arnone, Danilo; Arnott, Stephen R.; Chen, Taolin; Choi, Ki S.; Fatt, Cherise C.; Frey, Benicio N.; Frokjaer, Vibe G.; Ganz, Melanie; Garcia, Jose; Godlewska, Beata R.; Hassel, Stefanie; Ho, Keith; McIntosh, Andrew M.; Qin, Kun; Rotzinger, Susan; Sacchet, Matthew D.; Savitz, Jonathan; Shou, Haochang; Singh, Ashish; Stolicyn, Aleks; Strigo, Irina; Strother, Stephen C.; Tosun, Duygu; Victor, Teresa A.; Wei, Dongtao; Wise, Toby; Woodham, Rachel D.; Zahn, Roland; Anderson, Ian M.; Deakin, J. F. W.; Dunlop, Boadie W.; Elliott, Rebecca; Gong, Qiyong; Gotlib, Ian H.; Harmer, Catherine J.; Kennedy, Sidney H.; Knudsen, Gitte M.; Mayberg, Helen S.; Paulus, Martin P.; Qiu, Jiang; Trivedi, Madhukar H.; Whalley, Heather C.; Yan, Chao-Gan; Young, Allan H.; Davatzikos, Christos
    Abstract 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.
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    Erratum to: Online information seeking by patients with bipolar disorder: results from an international multisite survey
    (2017-03-31) Conell, Jörn; Bauer, Rita; Glenn, Tasha; Alda, Martin; Ardau, Raffaella; Baune, Bernhard T; Berk, Michael; Bersudsky, Yuly; Bilderbeck, Amy; Bocchetta, Alberto; Bossini, Letizia; Paredes Castro, Angela M; Cheung, Eric Y W; Chillotti, Caterina; Choppin, Sabine; Del Zompo, Maria; Dias, Rodrigo; Dodd, Seetal; Duffy, Anne; Etain, Bruno; Fagiolini, Andrea; Garnham, Julie; Geddes, John; Gildebro, Jonas; Gonzalez-Pinto, Ana; Goodwin, Guy M; Grof, Paul; Harima, Hirohiko; Hassel, Stefanie; Henry, Chantal; Hidalgo-Mazzei, Diego; Kapur, Vaisnvy; Kunigiri, Girish; Lafer, Beny; Lam, Chun; Larsen, Erik R; Lewitzka, Ute; Licht, Rasmus W; Lund, Anne H; Misiak, Blazej; Piotrowski, Patryk; Monteith, Scott; Munoz, Rodrigo; Nakanotani, Takako; Nielsen, René E; O’Donovan, Claire; Okamura, Yasushi; Osher, Yamima; Reif, Andreas; Ritter, Philipp; Rybakowski, Janusz K; Sagduyu, Kemal; Sawchuk, Brett; Schwartz, Elon; Scippa, Ângela M; Slaney, Claire; Sulaiman, Ahmad H; Suominen, Kirsi; Suwalska, Aleksandra; Tam, Peter; Tatebayashi, Yoshitaka; Tondo, Leonardo; Vieta, Eduard; Vinberg, Maj; Viswanath, Biju; Volkert, Julia; Zetin, Mark; Zorrilla, Iñaki; Whybrow, Peter C; Bauer, Michael
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    Internet use by older adults with bipolar disorder: international survey results
    (2018-09-04) Bauer, Rita; Glenn, Tasha; Strejilevich, Sergio; Conell, Jörn; Alda, Martin; Ardau, Raffaella; Baune, Bernhard T; Berk, Michael; Bersudsky, Yuly; Bilderbeck, Amy; Bocchetta, Alberto; Castro, Angela M P; Cheung, Eric Y W; Chillotti, Caterina; Choppin, Sabine; Cuomo, Alessandro; Del Zompo, Maria; Dias, Rodrigo; Dodd, Seetal; Duffy, Anne; Etain, Bruno; Fagiolini, Andrea; Fernández Hernandez, Miryam; Garnham, Julie; Geddes, John; Gildebro, Jonas; Gitlin, Michael J; Gonzalez-Pinto, Ana; Goodwin, Guy M; Grof, Paul; Harima, Hirohiko; Hassel, Stefanie; Henry, Chantal; Hidalgo-Mazzei, Diego; Lund, Anne H; Kapur, Vaisnvy; Kunigiri, Girish; Lafer, Beny; Larsen, Erik R; Lewitzka, Ute; Licht, Rasmus W; Misiak, Blazej; Piotrowski, Patryk; Miranda-Scippa, Ângela; Monteith, Scott; Munoz, Rodrigo; Nakanotani, Takako; Nielsen, René E; O’Donovan, Claire; Okamura, Yasushi; Osher, Yamima; Reif, Andreas; Ritter, Philipp; Rybakowski, Janusz K; Sagduyu, Kemal; Sawchuk, Brett; Schwartz, Elon; Slaney, Claire; Sulaiman, Ahmad H; Suominen, Kirsi; Suwalska, Aleksandra; Tam, Peter; Tatebayashi, Yoshitaka; Tondo, Leonardo; Veeh, Julia; Vieta, Eduard; Vinberg, Maj; Viswanath, Biju; Zetin, Mark; Whybrow, Peter C; Bauer, Michael
    Abstract Background The world population is aging and the number of older adults with bipolar disorder is increasing. Digital technologies are viewed as a framework to improve care of older adults with bipolar disorder. This analysis quantifies Internet use by older adults with bipolar disorder as part of a larger survey project about information seeking. Methods A paper-based survey about information seeking by patients with bipolar disorder was developed and translated into 12 languages. The survey was anonymous and completed between March 2014 and January 2016 by 1222 patients in 17 countries. All patients were diagnosed by a psychiatrist. General estimating equations were used to account for correlated data. Results Overall, 47% of older adults (age 60 years or older) used the Internet versus 87% of younger adults (less than 60 years). More education and having symptoms that interfered with regular activities increased the odds of using the Internet, while being age 60 years or older decreased the odds. Data from 187 older adults and 1021 younger adults were included in the analysis excluding missing values. Conclusions Older adults with bipolar disorder use the Internet much less frequently than younger adults. Many older adults do not use the Internet, and technology tools are suitable for some but not all older adults. As more health services are only available online, and more digital tools are developed, there is concern about growing health disparities based on age. Mental health experts should participate in determining the appropriate role for digital tools for older adults with bipolar disorder.
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    Youth at-risk for serious mental illness: methods of the PROCAN study
    (2018-07-05) Addington, Jean; Goldstein, Benjamin I; Wang, Jian L; Kennedy, Sidney H; Bray, Signe; Lebel, Catherine; Hassel, Stefanie; Marshall, Catherine; MacQueen, Glenda
    Abstract Background Most mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses. Method This 2-site longitudinal study will recruit 240 youth, ages 12–25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction. Discussion Outcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.