Abstract

Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention
Original languageEnglish (US)
StatePublished - 2016

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Aptitude
Transfer (Psychology)
Auditory Cortex
Executive Function
Brain
Signal-To-Noise Ratio
Psychiatry
Young Adult
Machine Learning
Drive

Keywords

  • training, machine learning, cognitive training, transfer, functional connectivity, MRI, structural volume

Cite this

Neural correlates of training and transfer. / Nikolaidis, George Aki; Kramer, Arthur; Barbey, Aron; Sutton, Brad; Smaragdis, Paris.

2016, .

Research output: Other contribution

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abstract = "Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention",
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N2 - Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention

AB - Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention

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