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Personalized cognitive training : protocol for individual-level meta-analysis implementing machine learning methods

Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, Shimrit Daches, Alishia D. Williams, Jenny Yiend, et al.
(2021) JOURNAL OF PSYCHIATRIC RESEARCH. 138. p.342-348
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Abstract
Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
Keywords
Biological Psychiatry, Psychiatry and Mental health, Cognitive training, Cognitive remediation, Machine learning, Meta-analysis, Personalized treatment, ATTENTION BIAS MODIFICATION, RANDOMIZED CONTROLLED-TRIAL, MAJOR DEPRESSIVE DISORDER, WORKING-MEMORY CAPACITY, SOCIAL ANXIETY, INTELLIGENCE, INHIBIT, PTSD

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MLA
Shani, Reut, et al. “Personalized Cognitive Training : Protocol for Individual-Level Meta-Analysis Implementing Machine Learning Methods.” JOURNAL OF PSYCHIATRIC RESEARCH, vol. 138, 2021, pp. 342–48, doi:10.1016/j.jpsychires.2021.03.043.
APA
Shani, R., Tal, S., Derakshan, N., Cohen, N., Enock, P. M., McNally, R. J., … Okon-Singer, H. (2021). Personalized cognitive training : protocol for individual-level meta-analysis implementing machine learning methods. JOURNAL OF PSYCHIATRIC RESEARCH, 138, 342–348. https://doi.org/10.1016/j.jpsychires.2021.03.043
Chicago author-date
Shani, Reut, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, et al. 2021. “Personalized Cognitive Training : Protocol for Individual-Level Meta-Analysis Implementing Machine Learning Methods.” JOURNAL OF PSYCHIATRIC RESEARCH 138: 342–48. https://doi.org/10.1016/j.jpsychires.2021.03.043.
Chicago author-date (all authors)
Shani, Reut, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, Shimrit Daches, Alishia D. Williams, Jenny Yiend, Per Carlbring, Jennie M. Kuckertz, Wenhui Yang, Andrea Reinecke, Christopher G. Beevers, Brian E. Bunnell, Ernst Koster, Sigal Zilcha-Mano, and Hadas Okon-Singer. 2021. “Personalized Cognitive Training : Protocol for Individual-Level Meta-Analysis Implementing Machine Learning Methods.” JOURNAL OF PSYCHIATRIC RESEARCH 138: 342–348. doi:10.1016/j.jpsychires.2021.03.043.
Vancouver
1.
Shani R, Tal S, Derakshan N, Cohen N, Enock PM, McNally RJ, et al. Personalized cognitive training : protocol for individual-level meta-analysis implementing machine learning methods. JOURNAL OF PSYCHIATRIC RESEARCH. 2021;138:342–8.
IEEE
[1]
R. Shani et al., “Personalized cognitive training : protocol for individual-level meta-analysis implementing machine learning methods,” JOURNAL OF PSYCHIATRIC RESEARCH, vol. 138, pp. 342–348, 2021.
@article{8712419,
  abstract     = {{Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.}},
  author       = {{Shani, Reut and Tal, Shachaf and Derakshan, Nazanin and Cohen, Noga and Enock, Philip M. and McNally, Richard J. and Mor, Nilly and Daches, Shimrit and Williams, Alishia D. and Yiend, Jenny and Carlbring, Per and Kuckertz, Jennie M. and Yang, Wenhui and Reinecke, Andrea and Beevers, Christopher G. and Bunnell, Brian E. and Koster, Ernst and Zilcha-Mano, Sigal and Okon-Singer, Hadas}},
  issn         = {{0022-3956}},
  journal      = {{JOURNAL OF PSYCHIATRIC RESEARCH}},
  keywords     = {{Biological Psychiatry,Psychiatry and Mental health,Cognitive training,Cognitive remediation,Machine learning,Meta-analysis,Personalized treatment,ATTENTION BIAS MODIFICATION,RANDOMIZED CONTROLLED-TRIAL,MAJOR DEPRESSIVE DISORDER,WORKING-MEMORY CAPACITY,SOCIAL ANXIETY,INTELLIGENCE,INHIBIT,PTSD}},
  language     = {{eng}},
  pages        = {{342--348}},
  title        = {{Personalized cognitive training : protocol for individual-level meta-analysis implementing machine learning methods}},
  url          = {{http://doi.org/10.1016/j.jpsychires.2021.03.043}},
  volume       = {{138}},
  year         = {{2021}},
}
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