Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior
Abstract
:1. Introduction
2. The Triadic Neural Systems Model
2.1. Conceptual Definition
2.2. Neural Substrates
2.2.1. Approach System
2.2.2. Avoidance System
2.2.3. Control System
2.3. Triadic Model in Adolescence
3. Testing the Triadic Neural Systems Model
3.1. Introduction to Machine Learning Tools
3.2. Overall Strategy for Testing the Triadic Neural Systems Model
3.3. Question 1: Functional Architecture of Each System
3.3.1. Data Organization/Reduction
3.3.2. The Predictive Model
3.3.3. Output of the Predictive Model of the Characterization of the Three Neural Systems
3.4. Question 2: Dynamic Interactions among the Three Systems
3.4.1. Question 2, Step 1
3.4.2. Question 2, Step 2
3.5. Question 3: Maturation of the Triadic Neural System Dynamics
- (a)
- Step 1: Behavioral characterization. The predicted behavior outcome measures are computed as in Question 1 by conducting a factor analysis of all available behavioral data, at each follow-up.
- (b)
- Step 2: Brain-behavior classification: This step gives rise to two types of results: trees that depict the hierarchical organization of predictors at each follow-up point and equations that quantify the contribution of each cluster to a given behavioral domain, respectively.
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Ernst, M.; Gowin, J.L.; Gaillard, C.; Philips, R.T.; Grillon, C. Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior. Brain Sci. 2019, 9, 67. https://doi.org/10.3390/brainsci9030067
Ernst M, Gowin JL, Gaillard C, Philips RT, Grillon C. Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior. Brain Sciences. 2019; 9(3):67. https://doi.org/10.3390/brainsci9030067
Chicago/Turabian StyleErnst, Monique, Joshua L. Gowin, Claudie Gaillard, Ryan T. Philips, and Christian Grillon. 2019. "Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior" Brain Sciences 9, no. 3: 67. https://doi.org/10.3390/brainsci9030067