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1. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis

Author

Wannan, CMJ, Nelson, B, Addington, J, Allott, K, Anticevic, A, Arango, C, Baker, JT, Bearden, CE, Billah, T, Bouix, S, Broome, MR, Buccilli, K, Cadenhead, KS, Calkins, ME, Cannon, TD, Cecci, G, Chen, EYH, Cho, KIK, Choi, J, Clark, SR, Coleman, MJ, Conus, P, Corcoran, CM, Cornblatt, BA, Diaz-Caneja, CM, Dwyer, D, Ebdrup, BH, Ellman, LM, Fusar-Poli, P, Galindo, L, Gaspar, PA, Gerber, C, Glenthoj, LB, Glynn, R, Harms, MP, Horton, LE, Kahn, RS, Kambeitz, J, Kambeitz-Ilankovic, L, Kane, JM, Kapur, T, Keshavan, MS, Kim, S-W, Koutsouleris, N, Kubicki, M, Kwon, JS, Langbein, K, Lewandowski, KE, Light, GA, Mamah, D, Marcy, PJ, Mathalon, DH, McGorry, PD, Mittal, VA, Nordentoft, M, Nunez, A, Pasternak, O, Pearlson, GD, Perez, J, Perkins, DO, Powers, AR, Roalf, DR, Sabb, FW, Schiffman, J, Shah, JL, Smesny, S, Spark, J, Stone, WS, Strauss, GP, Tamayo, Z, Torous, J, Upthegrove, R, Vangel, M, Verma, S, Wang, J, Winter-van Rossum, I, Wolf, DH, Wolff, P, Wood, SJ, Yung, AR, Agurto, C, Alvarez-Jimenez, M, Amminger, P, Armando, M, Asgari-Targhi, A, Cahill, J, Carrion, RE, Castro, E, Cetin-Karayumak, S, Chakravarty, MM, Cho, YT, Cotter, D, D'Alfonso, S, Ennis, M, Fadnavis, S, Fonteneau, C, Gao, C, Gupta, T, Gur, RE, Gur, RC, Hamilton, HK, Hoftman, GD, Jacobs, GR, Jarcho, J, Ji, JL, Kohler, CG, Lalousis, PA, Lavoie, S, Lepage, M, Liebenthal, E, Mervis, J, Murty, V, Nicholas, SC, Ning, L, Penzel, N, Poldrack, R, Polosecki, P, Pratt, DN, Rabin, R, Eichi, HR, Rathi, Y, Reichenberg, A, Reinen, J, Rogers, J, Ruiz-Yu, B, Scott, I, Seitz-Holland, J, Srihari, VH, Srivastava, A, Thompson, A, Turetsky, BI, Walsh, BC, Whitford, T, Wigman, JTW, Yao, B, Yuen, HP, Ahmed, U, Byun, AJS, Chung, Y, Do, K, Hendricks, L, Huynh, K, Jeffries, C, Lane, E, Langholm, C, Lin, E, Mantua, V, Santorelli, G, Ruparel, K, Zoupou, E, Adasme, T, Addamo, L, Adery, L, Ali, M, Auther, A, Aversa, S, Baek, S-H, Bates, K, Bathery, A, Bayer, JMM, Beedham, R, Bilgrami, Z, Birch, S, Bonoldi, I, Borders, O, Borgatti, R, Brown, L, Bruna, A, Carrington, H, Castillo-Passi, RI, Chen, J, Cheng, N, Ching, AE, Clifford, C, Colton, B-L, Contreras, P, Corral, S, Damiani, S, Done, M, Estrade, A, Etuka, BA, Formica, M, Furlan, R, Geljic, M, Germano, C, Getachew, R, Goncalves, M, Haidar, A, Hartmann, J, Jo, A, John, O, Kerins, S, Kerr, M, Kesselring, I, Kim, H, Kim, N, Kinney, K, Krcmar, M, Kotler, E, Lafanechere, M, Lee, C, Llerena, J, Markiewicz, C, Matnejl, P, Maturana, A, Mavambu, A, Mayol-Troncoso, R, McDonnell, A, McGowan, A, McLaughlin, D, McIlhenny, R, McQueen, B, Mebrahtu, Y, Mensi, M, Hui, CLM, Suen, YN, Wong, SMY, Morrell, N, Omar, M, Partridge, A, Phassouliotis, C, Pichiecchio, A, Politi, P, Porter, C, Provenzani, U, Prunier, N, Raj, J, Ray, S, Rayner, V, Reyes, M, Reynolds, K, Rush, S, Salinas, C, Shetty, J, Snowball, C, Tod, S, Turra-Farina, G, Valle, D, Veale, S, Whitson, S, Wickham, A, Youn, S, Zamorano, F, Zavaglia, E, Zinberg, J, Woods, SW, Shenton, ME, Wannan, CMJ, Nelson, B, Addington, J, Allott, K, Anticevic, A, Arango, C, Baker, JT, Bearden, CE, Billah, T, Bouix, S, Broome, MR, Buccilli, K, Cadenhead, KS, Calkins, ME, Cannon, TD, Cecci, G, Chen, EYH, Cho, KIK, Choi, J, Clark, SR, Coleman, MJ, Conus, P, Corcoran, CM, Cornblatt, BA, Diaz-Caneja, CM, Dwyer, D, Ebdrup, BH, Ellman, LM, Fusar-Poli, P, Galindo, L, Gaspar, PA, Gerber, C, Glenthoj, LB, Glynn, R, Harms, MP, Horton, LE, Kahn, RS, Kambeitz, J, Kambeitz-Ilankovic, L, Kane, JM, Kapur, T, Keshavan, MS, Kim, S-W, Koutsouleris, N, Kubicki, M, Kwon, JS, Langbein, K, Lewandowski, KE, Light, GA, Mamah, D, Marcy, PJ, Mathalon, DH, McGorry, PD, Mittal, VA, Nordentoft, M, Nunez, A, Pasternak, O, Pearlson, GD, Perez, J, Perkins, DO, Powers, AR, Roalf, DR, Sabb, FW, Schiffman, J, Shah, JL, Smesny, S, Spark, J, Stone, WS, Strauss, GP, Tamayo, Z, Torous, J, Upthegrove, R, Vangel, M, Verma, S, Wang, J, Winter-van Rossum, I, Wolf, DH, Wolff, P, Wood, SJ, Yung, AR, Agurto, C, Alvarez-Jimenez, M, Amminger, P, Armando, M, Asgari-Targhi, A, Cahill, J, Carrion, RE, Castro, E, Cetin-Karayumak, S, Chakravarty, MM, Cho, YT, Cotter, D, D'Alfonso, S, Ennis, M, Fadnavis, S, Fonteneau, C, Gao, C, Gupta, T, Gur, RE, Gur, RC, Hamilton, HK, Hoftman, GD, Jacobs, GR, Jarcho, J, Ji, JL, Kohler, CG, Lalousis, PA, Lavoie, S, Lepage, M, Liebenthal, E, Mervis, J, Murty, V, Nicholas, SC, Ning, L, Penzel, N, Poldrack, R, Polosecki, P, Pratt, DN, Rabin, R, Eichi, HR, Rathi, Y, Reichenberg, A, Reinen, J, Rogers, J, Ruiz-Yu, B, Scott, I, Seitz-Holland, J, Srihari, VH, Srivastava, A, Thompson, A, Turetsky, BI, Walsh, BC, Whitford, T, Wigman, JTW, Yao, B, Yuen, HP, Ahmed, U, Byun, AJS, Chung, Y, Do, K, Hendricks, L, Huynh, K, Jeffries, C, Lane, E, Langholm, C, Lin, E, Mantua, V, Santorelli, G, Ruparel, K, Zoupou, E, Adasme, T, Addamo, L, Adery, L, Ali, M, Auther, A, Aversa, S, Baek, S-H, Bates, K, Bathery, A, Bayer, JMM, Beedham, R, Bilgrami, Z, Birch, S, Bonoldi, I, Borders, O, Borgatti, R, Brown, L, Bruna, A, Carrington, H, Castillo-Passi, RI, Chen, J, Cheng, N, Ching, AE, Clifford, C, Colton, B-L, Contreras, P, Corral, S, Damiani, S, Done, M, Estrade, A, Etuka, BA, Formica, M, Furlan, R, Geljic, M, Germano, C, Getachew, R, Goncalves, M, Haidar, A, Hartmann, J, Jo, A, John, O, Kerins, S, Kerr, M, Kesselring, I, Kim, H, Kim, N, Kinney, K, Krcmar, M, Kotler, E, Lafanechere, M, Lee, C, Llerena, J, Markiewicz, C, Matnejl, P, Maturana, A, Mavambu, A, Mayol-Troncoso, R, McDonnell, A, McGowan, A, McLaughlin, D, McIlhenny, R, McQueen, B, Mebrahtu, Y, Mensi, M, Hui, CLM, Suen, YN, Wong, SMY, Morrell, N, Omar, M, Partridge, A, Phassouliotis, C, Pichiecchio, A, Politi, P, Porter, C, Provenzani, U, Prunier, N, Raj, J, Ray, S, Rayner, V, Reyes, M, Reynolds, K, Rush, S, Salinas, C, Shetty, J, Snowball, C, Tod, S, Turra-Farina, G, Valle, D, Veale, S, Whitson, S, Wickham, A, Youn, S, Zamorano, F, Zavaglia, E, Zinberg, J, Woods, SW, and Shenton, ME

Abstract

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Published
2024

2. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Author

Bayer, JMM, Thompson, PM, Ching, CRK, Liu, M, Chen, A, Panzenhagen, AC, Jahanshad, N, Marquand, A, Schmaal, L, Samann, PG, Bayer, JMM, Thompson, PM, Ching, CRK, Liu, M, Chen, A, Panzenhagen, AC, Jahanshad, N, Marquand, A, Schmaal, L, and Samann, PG

Abstract

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

Published
2022

3. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

Author

Bayer, JMM, Dinga, R, Kia, SM, Kottaram, AR, Wolfers, T, Lv, J, Zalesky, A, Schmaal, L, Marquand, A, Bayer, JMM, Dinga, R, Kia, SM, Kottaram, AR, Wolfers, T, Lv, J, Zalesky, A, Schmaal, L, and Marquand, A

Abstract

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the v

Published
2022

4. Normative modeling of neuroimaging data using generalized additive models of location scale and shape

Author

Seyed Mostafa Kia, Richard Dinga, Christian F. Beckmann, Charlotte Fraza, Bayer Jmm, and Andre F. Marquand

Subjects
Structure (mathematical logic), Heteroscedasticity, Scale (ratio), Computer science, business.industry, Generalized additive model, Overfitting, Machine learning, computer.software_genre, Study heterogeneity, Neuroimaging, Normative, Artificial intelligence, business, computer
Abstract

Normative modeling aims to quantify the degree to which an individual’s brain deviates from a reference sample with respect to one or more variables, which can be used as a potential biomarker of a healthy brain and as a tool to study heterogeneity of psychiatric disorders. The application of normative models is hindered by methodological challenges and lacks standards for the usage and evaluation of normative models. In this paper, we present generalized additive models for location scale and shape (GAMLSS) for normative modeling of neuroimaging data, a flexible modeling framework that can model heteroskedasticity, non-linear effects of variables, and hierarchical structure of the data. It can model non-Gaussian distributions, and it allows for an automatic model order selection, thus improving the accuracy of normative models while mitigating problems of overfitting. Furthermore, we describe measures and diagnostic tools suitable for evaluating normative models and step-by-step examples of normative modeling, including fitting several candidate models, selecting the best models, and transferring them to new scan sites.

Published
2021
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5. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Gau, R, Noble, S, Heuer, K, Bottenhorn, KL, Bilgin, IP, Yang, YF, Huntenburg, JM, Bayer, JMM, Bethlehem, RAI, Rhoads, SA, Vogelbacher, C, Borghesani, V, Levitis, E, Wang, HT, Van Den Bossche, S, Kobeleva, X, Legarreta, JH, Guay, S, Atay, SM, Varoquaux, GP, Huijser, DC, Sandström, MS, Herholz, P, Nastase, SA, Badhwar, AP, Dumas, G, Schwab, S, Moia, S, Dayan, M, Bassil, Y, Brooks, PP, Mancini, M, Shine, JM, O'Connor, D, Xie, X, Poggiali, D, Friedrich, P, Heinsfeld, AS, Riedl, L, Toro, R, Caballero-Gaudes, C, Eklund, A, Garner, KG, Nolan, CR, Demeter, DV, Barrios, FA, Merchant, JS, McDevitt, EA, Oostenveld, R, Craddock, RC, Rokem, A, Doyle, A, Ghosh, SS, Nikolaidis, A, Stanley, OW, Uruñuela, E, Anousheh, N, Arnatkeviciute, A, Auzias, G, Bachar, D, Bannier, E, Basanisi, R, Basavaraj, A, Bedini, M, Bellec, P, Benn, RA, Berluti, K, Bollmann, S, Bradley, C, Brown, J, Buchweitz, A, Callahan, P, Chan, MY, Chandio, BQ, Cheng, T, Chopra, S, Chung, AW, Close, TG, Combrisson, E, Cona, G, Constable, RT, Cury, C, Dadi, K, Damasceno, PF, Das, S, De Vico Fallani, F, DeStasio, K, Dickie, EW, Dorfschmidt, L, Duff, EP, DuPre, E, Dziura, S, Esper, NB, Esteban, O, Fadnavis, S, Flandin, G, Flannery, JE, Flournoy, J, Forkel, SJ, Gau, R, Noble, S, Heuer, K, Bottenhorn, KL, Bilgin, IP, Yang, YF, Huntenburg, JM, Bayer, JMM, Bethlehem, RAI, Rhoads, SA, Vogelbacher, C, Borghesani, V, Levitis, E, Wang, HT, Van Den Bossche, S, Kobeleva, X, Legarreta, JH, Guay, S, Atay, SM, Varoquaux, GP, Huijser, DC, Sandström, MS, Herholz, P, Nastase, SA, Badhwar, AP, Dumas, G, Schwab, S, Moia, S, Dayan, M, Bassil, Y, Brooks, PP, Mancini, M, Shine, JM, O'Connor, D, Xie, X, Poggiali, D, Friedrich, P, Heinsfeld, AS, Riedl, L, Toro, R, Caballero-Gaudes, C, Eklund, A, Garner, KG, Nolan, CR, Demeter, DV, Barrios, FA, Merchant, JS, McDevitt, EA, Oostenveld, R, Craddock, RC, Rokem, A, Doyle, A, Ghosh, SS, Nikolaidis, A, Stanley, OW, Uruñuela, E, Anousheh, N, Arnatkeviciute, A, Auzias, G, Bachar, D, Bannier, E, Basanisi, R, Basavaraj, A, Bedini, M, Bellec, P, Benn, RA, Berluti, K, Bollmann, S, Bradley, C, Brown, J, Buchweitz, A, Callahan, P, Chan, MY, Chandio, BQ, Cheng, T, Chopra, S, Chung, AW, Close, TG, Combrisson, E, Cona, G, Constable, RT, Cury, C, Dadi, K, Damasceno, PF, Das, S, De Vico Fallani, F, DeStasio, K, Dickie, EW, Dorfschmidt, L, Duff, EP, DuPre, E, Dziura, S, Esper, NB, Esteban, O, Fadnavis, S, Flandin, G, Flannery, JE, Flournoy, J, and Forkel, SJ

Published
2021

6. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective

Author

Levitis, E, van Praag, CDG, Gau, R, Heunis, S, DuPre, E, Kiar, G, Bottenhorn, KL, Glatard, T, Nikolaidis, A, Whitaker, KJ, Mancini, M, Niso, G, Afyouni, S, Alonso-Ortiz, E, Appelhoff, S, Arnatkeviciute, A, Atay, SM, Auer, T, Baracchini, G, Bayer, JMM, Beauvais, MJS, Bijsterbosch, JD, Bilgin, IP, Bollmann, S, Botvinik-Nezer, R, Bright, MG, Calhoun, VD, Chen, X, Chopra, S, Hu, C-P, Close, TG, Cookson, SL, Craddock, RC, De la Vega, A, De Leener, B, Demeter, D, Di Maio, P, Dickie, EW, Eickhoff, SB, Esteban, O, Finc, K, Frigo, M, Ganesan, S, Ganz, M, Garner, KG, Garza-Villarreal, EA, Gonzalez-Escamilla, G, Goswami, R, Griffiths, JD, Grootswagers, T, Guay, S, Guest, O, Handwerker, DA, Herholz, P, Heuer, K, Huijser, DC, Iacovella, V, Joseph, MJE, Karakuzu, A, Keator, DB, Kobeleva, X, Kumar, M, Laird, AR, Larson-Prior, LJ, Lautarescu, A, Lazari, A, Legarreta, JH, Li, X-Y, Lv, J, Mansour, SL, Meunier, D, Moraczewski, D, Nandi, T, Nastase, SA, Nau, M, Noble, S, Norgaard, M, Obungoloch, J, Oostenveld, R, Orchard, ER, Poldrack, RA, Qiu, A, Raamana, PR, Rokem, A, Rutherford, S, Sharan, M, Shaw, TB, Syeda, WT, Testerman, MM, Toro, R, Valk, SL, Van den Bossche, S, Varoquaux, G, Vasa, F, Veldsman, M, Vohryzek, J, Wagner, AS, Walsh, RJ, White, T, Wong, F-T, Xie, X, Yan, C-G, Yang, Y-F, Yee, Y, Zanitti, GE, Van Gulick, AE, Duff, E, Maumet, C, Levitis, E, van Praag, CDG, Gau, R, Heunis, S, DuPre, E, Kiar, G, Bottenhorn, KL, Glatard, T, Nikolaidis, A, Whitaker, KJ, Mancini, M, Niso, G, Afyouni, S, Alonso-Ortiz, E, Appelhoff, S, Arnatkeviciute, A, Atay, SM, Auer, T, Baracchini, G, Bayer, JMM, Beauvais, MJS, Bijsterbosch, JD, Bilgin, IP, Bollmann, S, Botvinik-Nezer, R, Bright, MG, Calhoun, VD, Chen, X, Chopra, S, Hu, C-P, Close, TG, Cookson, SL, Craddock, RC, De la Vega, A, De Leener, B, Demeter, D, Di Maio, P, Dickie, EW, Eickhoff, SB, Esteban, O, Finc, K, Frigo, M, Ganesan, S, Ganz, M, Garner, KG, Garza-Villarreal, EA, Gonzalez-Escamilla, G, Goswami, R, Griffiths, JD, Grootswagers, T, Guay, S, Guest, O, Handwerker, DA, Herholz, P, Heuer, K, Huijser, DC, Iacovella, V, Joseph, MJE, Karakuzu, A, Keator, DB, Kobeleva, X, Kumar, M, Laird, AR, Larson-Prior, LJ, Lautarescu, A, Lazari, A, Legarreta, JH, Li, X-Y, Lv, J, Mansour, SL, Meunier, D, Moraczewski, D, Nandi, T, Nastase, SA, Nau, M, Noble, S, Norgaard, M, Obungoloch, J, Oostenveld, R, Orchard, ER, Poldrack, RA, Qiu, A, Raamana, PR, Rokem, A, Rutherford, S, Sharan, M, Shaw, TB, Syeda, WT, Testerman, MM, Toro, R, Valk, SL, Van den Bossche, S, Varoquaux, G, Vasa, F, Veldsman, M, Vohryzek, J, Wagner, AS, Walsh, RJ, White, T, Wong, F-T, Xie, X, Yan, C-G, Yang, Y-F, Yee, Y, Zanitti, GE, Van Gulick, AE, Duff, E, and Maumet, C

Abstract

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Published
2021

7. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

Author

Wannan CMJ, Nelson B, Addington J, Allott K, Anticevic A, Arango C, Baker JT, Bearden CE, Billah T, Bouix S, Broome MR, Buccilli K, Cadenhead KS, Calkins ME, Cannon TD, Cecci G, Chen EYH, Cho KIK, Choi J, Clark SR, Coleman MJ, Conus P, Corcoran CM, Cornblatt BA, Diaz-Caneja CM, Dwyer D, Ebdrup BH, Ellman LM, Fusar-Poli P, Galindo L, Gaspar PA, Gerber C, Glenthøj LB, Glynn R, Harms MP, Horton LE, Kahn RS, Kambeitz J, Kambeitz-Ilankovic L, Kane JM, Kapur T, Keshavan MS, Kim SW, Koutsouleris N, Kubicki M, Kwon JS, Langbein K, Lewandowski KE, Light GA, Mamah D, Marcy PJ, Mathalon DH, McGorry PD, Mittal VA, Nordentoft M, Nunez A, Pasternak O, Pearlson GD, Perez J, Perkins DO, Powers AR 3rd, Roalf DR, Sabb FW, Schiffman J, Shah JL, Smesny S, Spark J, Stone WS, Strauss GP, Tamayo Z, Torous J, Upthegrove R, Vangel M, Verma S, Wang J, Rossum IW, Wolf DH, Wolff P, Wood SJ, Yung AR, Agurto C, Alvarez-Jimenez M, Amminger P, Armando M, Asgari-Targhi A, Cahill J, Carrión RE, Castro E, Cetin-Karayumak S, Mallar Chakravarty M, Cho YT, Cotter D, D'Alfonso S, Ennis M, Fadnavis S, Fonteneau C, Gao C, Gupta T, Gur RE, Gur RC, Hamilton HK, Hoftman GD, Jacobs GR, Jarcho J, Ji JL, Kohler CG, Lalousis PA, Lavoie S, Lepage M, Liebenthal E, Mervis J, Murty V, Nicholas SC, Ning L, Penzel N, Poldrack R, Polosecki P, Pratt DN, Rabin R, Rahimi Eichi H, Rathi Y, Reichenberg A, Reinen J, Rogers J, Ruiz-Yu B, Scott I, Seitz-Holland J, Srihari VH, Srivastava A, Thompson A, Turetsky BI, Walsh BC, Whitford T, Wigman JTW, Yao B, Yuen HP, Ahmed U, Byun AJS, Chung Y, Do K, Hendricks L, Huynh K, Jeffries C, Lane E, Langholm C, Lin E, Mantua V, Santorelli G, Ruparel K, Zoupou E, Adasme T, Addamo L, Adery L, Ali M, Auther A, Aversa S, Baek SH, Bates K, Bathery A, Bayer JMM, Beedham R, Bilgrami Z, Birch S, Bonoldi I, Borders O, Borgatti R, Brown L, Bruna A, Carrington H, Castillo-Passi RI, Chen J, Cheng N, Ching AE, Clifford C, Colton BL, Contreras P, Corral S, Damiani S, Done M, Estradé A, Etuka BA, Formica M, Furlan R, Geljic M, Germano C, Getachew R, Goncalves M, Haidar A, Hartmann J, Jo A, John O, Kerins S, Kerr M, Kesselring I, Kim H, Kim N, Kinney K, Krcmar M, Kotler E, Lafanechere M, Lee C, Llerena J, Markiewicz C, Matnejl P, Maturana A, Mavambu A, Mayol-Troncoso R, McDonnell A, McGowan A, McLaughlin D, McIlhenny R, McQueen B, Mebrahtu Y, Mensi M, Hui CLM, Suen YN, Wong SMY, Morrell N, Omar M, Partridge A, Phassouliotis C, Pichiecchio A, Politi P, Porter C, Provenzani U, Prunier N, Raj J, Ray S, Rayner V, Reyes M, Reynolds K, Rush S, Salinas C, Shetty J, Snowball C, Tod S, Turra-Fariña G, Valle D, Veale S, Whitson S, Wickham A, Youn S, Zamorano F, Zavaglia E, Zinberg J, Woods SW, and Shenton ME

Subjects
Humans, Prospective Studies, Adult, Prodromal Symptoms, Young Adult, International Cooperation, Adolescent, Research Design standards, Male, Female, Psychotic Disorders, Schizophrenia
Abstract

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Published
2024
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8. The SPEAK study rationale and design: A linguistic corpus-based approach to understanding thought disorder.

Author

Bayer JMM, Spark J, Krcmar M, Formica M, Gwyther K, Srivastava A, Selloni A, Cotter M, Hartmann J, Polari A, Bilgrami ZR, Sarac C, Lu A, Yung AR, McGowan A, McGorry P, Shah JL, Cecchi GA, Mizrahi R, Nelson B, and Corcoran CM

Subjects
Adolescent, Humans, Prospective Studies, Linguistics, Language, Speech, Psychotic Disorders complications, Psychotic Disorders diagnosis
Abstract

Aim: Psychotic symptoms are typically measured using clinical ratings, but more objective and sensitive metrics are needed. Hence, we will assess thought disorder using the Research Domain Criteria (RDoC) heuristic for language production, and its recommended paradigm of "linguistic corpus-based analyses of language output". Positive thought disorder (e.g., tangentiality and derailment) can be assessed using word-embedding approaches that assess semantic coherence, whereas negative thought disorder (e.g., concreteness, poverty of speech) can be assessed using part-of-speech (POS) tagging to assess syntactic complexity. We aim to establish convergent validity of automated linguistic metrics with clinical ratings, assess normative demographic variance, determine cognitive and functional correlates, and replicate their predictive power for psychosis transition among at-risk youths., Methods: This study will assess language production in 450 English-speaking individuals in Australia and Canada, who have recent onset psychosis, are at clinical high risk (CHR) for psychosis, or who are healthy volunteers, all well-characterized for cognition, function and symptoms. Speech will be elicited using open-ended interviews. Audio files will be transcribed and preprocessed for automated natural language processing (NLP) analyses of coherence and complexity. Data analyses include canonical correlation, multivariate linear regression with regularization, and machine-learning classification of group status and psychosis outcome., Conclusions: This prospective study aims to characterize language disturbance across stages of psychosis using computational approaches, including psychometric properties, normative variance and clinical correlates, important for biomarker development. SPEAK will create a large archive of language data available to other investigators, a rich resource for the field., Competing Interests: Declaration of competing interest The authors declare no real or potential conflict of interest. All authors have reviewed and approved the manuscript before submission., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Published
2023
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9. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.

Author

Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, and Marquand A

Subjects
Humans, Bayes Theorem, Brain diagnostic imaging, Models, Statistical, Neuroimaging
Abstract

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Published
2022
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10. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.

Author

Bayer JMM, Thompson PM, Ching CRK, Liu M, Chen A, Panzenhagen AC, Jahanshad N, Marquand A, Schmaal L, and Sämann PG

Abstract

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bayer, Thompson, Ching, Liu, Chen, Panzenhagen, Jahanshad, Marquand, Schmaal and Sämann.)

Published
2022
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11. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.

Author

Levitis E, van Praag CDG, Gau R, Heunis S, DuPre E, Kiar G, Bottenhorn KL, Glatard T, Nikolaidis A, Whitaker KJ, Mancini M, Niso G, Afyouni S, Alonso-Ortiz E, Appelhoff S, Arnatkeviciute A, Atay SM, Auer T, Baracchini G, Bayer JMM, Beauvais MJS, Bijsterbosch JD, Bilgin IP, Bollmann S, Bollmann S, Botvinik-Nezer R, Bright MG, Calhoun VD, Chen X, Chopra S, Chuan-Peng H, Close TG, Cookson SL, Craddock RC, De La Vega A, De Leener B, Demeter DV, Di Maio P, Dickie EW, Eickhoff SB, Esteban O, Finc K, Frigo M, Ganesan S, Ganz M, Garner KG, Garza-Villarreal EA, Gonzalez-Escamilla G, Goswami R, Griffiths JD, Grootswagers T, Guay S, Guest O, Handwerker DA, Herholz P, Heuer K, Huijser DC, Iacovella V, Joseph MJE, Karakuzu A, Keator DB, Kobeleva X, Kumar M, Laird AR, Larson-Prior LJ, Lautarescu A, Lazari A, Legarreta JH, Li XY, Lv J, Mansour L S, Meunier D, Moraczewski D, Nandi T, Nastase SA, Nau M, Noble S, Norgaard M, Obungoloch J, Oostenveld R, Orchard ER, Pinho AL, Poldrack RA, Qiu A, Raamana PR, Rokem A, Rutherford S, Sharan M, Shaw TB, Syeda WT, Testerman MM, Toro R, Valk SL, Van Den Bossche S, Varoquaux G, Váša F, Veldsman M, Vohryzek J, Wagner AS, Walsh RJ, White T, Wong FT, Xie X, Yan CG, Yang YF, Yee Y, Zanitti GE, Van Gulick AE, Duff E, and Maumet C

Abstract

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume., (© The Author(s) 2021. Published by Oxford University Press on behalf of GigaScience. 2021.)

Published
2021
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12. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Author

Gau R, Noble S, Heuer K, Bottenhorn KL, Bilgin IP, Yang YF, Huntenburg JM, Bayer JMM, Bethlehem RAI, Rhoads SA, Vogelbacher C, Borghesani V, Levitis E, Wang HT, Van Den Bossche S, Kobeleva X, Legarreta JH, Guay S, Atay SM, Varoquaux GP, Huijser DC, Sandström MS, Herholz P, Nastase SA, Badhwar A, Dumas G, Schwab S, Moia S, Dayan M, Bassil Y, Brooks PP, Mancini M, Shine JM, O'Connor D, Xie X, Poggiali D, Friedrich P, Heinsfeld AS, Riedl L, Toro R, Caballero-Gaudes C, Eklund A, Garner KG, Nolan CR, Demeter DV, Barrios FA, Merchant JS, McDevitt EA, Oostenveld R, Craddock RC, Rokem A, Doyle A, Ghosh SS, Nikolaidis A, Stanley OW, and Uruñuela E

Subjects
Congresses as Topic, Practice Guidelines as Topic, Communication, Internet, Neurosciences organization & administration
Abstract

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress., Competing Interests: Declaration of interests Anisha Keshavan is an employee of Octave Bioscience., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Published
2021
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