Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
Abstract
:1. Introduction
2. Methods
2.1. Participants
2.2. Neuropsychological Assessment
2.2.1. Tower of London Test (TOLT)
2.2.2. Visual Span Test
2.3. Impulsivity Scores
2.4. MRI Data Acquisition
2.5. Image Processing
2.6. DMN Seed Regions and FC Calculations
2.7. Random Forest Classification Model and Parameters
3. Results
3.1. Random Forest Classification
3.1.1. Classification Accuracy and Top (Ranked) Significant Variables
3.1.2. Multi-Way Importance Plot
3.1.3. Distribution of Minimal Depth
3.1.4. Relations among Rankings of Different RF Parameters
3.1.5. Connectivity Mapping of Significant DMN Connections
3.2. Correlations between Top Significant Variables and Age
3.3. Correlations among the Top Significant Variables
4. Discussion
4.1. Aberrant FC in Individuals with AUD
4.1.1. Hyperconnectivity within Frontal and Parietal Regions in AUD
4.1.2. Hypoconnectivity across Anterior–Posterior and Interhemispheric Connections in AUD
4.2. Poor Neuropsychological Performance in AUD
4.3. Heightened Impulsivity in AUD
4.4. Associations among AUD, FC, Impulsivity, and Neurocognition
4.5. Potential Implications, Limitations, and Future Directions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Trees: Decision trees whose results are aggregated into one final result for classifying the factors or outcomes. Each tree is constructed based on a random (bootstrapped) subsample of the observations. |
Node: A point in a tree, where a split occurs as a result of a ‘test’ on an attribute leading to binary outcomes (e.g., whether a coin flip results in head or tail). A binary split at a node partitions the data from the parent node into two daughter nodes. |
Branch: The outcome of the test resulting in a split or two branches in a classification tree. |
Leaf: A terminal node that has no children or branches. |
Random Forest ensemble: Aggregation of individual decision trees in order to combine predictions (votes) from each tree. The class/group/outcome with most votes becomes the RF model’s prediction. |
Bagging: It’s the short form of ‘bootstrap aggregating’, which is a method to improve classification by combining classifications of randomly generated training sets. |
Out of bag (OOB) estimate: The observations that are not part of the bootstrap subsample are referred to as out-of-bag (OOB) observations. The OOB error refers to the classification error based on this subsample and serves as a validation of Random Forest model accuracy. |
Gini (mean) decrease: It represents the importance of a specific feature/predictor/variable (Vi) for the classification or prediction. It’s the mean decrease in node impurity (classification error) of Vi. A higher Gini decrease indicates higher variable importance for Vi. |
Accuracy decrease: Mean decrease in prediction accuracy after Vi is not taken into account. |
Mean minimal depth: It refers to the number of nodes along the shortest path from the root node down to the nearest leaf node. Smaller depth for the Vi indicates its higher importance. |
Mtry: A preset number of features/variables/predictors randomly selected (from the entire list) for splitting at each node in the construction of each decision tree. |
ntree: A preset total number of trees to grow for a given model. Larger ‘ntree’ normally produce more stable models and more reliable predictions. |
Number of nodes: Total number of nodes that use Vi for splitting (it is usually equal to number of trees if trees are shallow). |
Times a root: Total number of trees in which Vi is used for splitting the root node (i.e., the whole sample is divided into two based on the value of Vi). |
p value: probability value of hypothesis testing based on a one-sided binomial test that indicates whether the observed number of successes (number of nodes in which Vi was used for splitting) exceeds the theoretical number of successes if they were random. |
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Variable | AUD | CTL | ||||
---|---|---|---|---|---|---|
n * | Mean | SD | n * | Mean | SD | |
Age (in years) | 30 | 41.42 | 7.31 | 30 | 27.44 | 4.74 |
Education (in Years) | 30 | 11.93 | 2.35 | 30 | 15.77 | 1.87 |
Age of onset (regular alcohol use) | 30 | 15.77 | 2.58 | 12 | 20.50 | 3.80 |
Alcohol: Drinks/day (heavy alcohol use period) | 30 | 12.08 | 10.02 | 12 | 2.88 | 1.93 |
Alcohol: Days/month (heavy alcohol use period) | 30 | 20.30 | 9.01 | 12 | 3.35 | 3.64 |
Alcohol: Drinks (last 6 months) | 30 | 2.68 | 6.61 | 18 | 2.61 | 1.98 |
Alcohol: Days (last 6 months) | 30 | 3.97 | 8.02 | 18 | 2.94 | 3.62 |
Length of Abstinence (in months) | 30 | 22.43 | 28.16 | 18 | 1.9 | 4.99 |
Tobacco: Times/day (last 6 months) | 20 | 9.90 | 5.80 | 6 | 2.33 | 1.63 |
Tobacco: Days/month (last 6 months) | 20 | 28.35 | 4.83 | 6 | 14.17 | 13.82 |
Marijuana: Times in last 6 months | 10 | 98.80 | 91.38 | 4 | 18.75 | 27.61 |
Seed | Region Name | Region Code | BA | MNI (X) | MNI (Y) | MNI (Z) |
---|---|---|---|---|---|---|
s1 | Left posterior cingulate cortex | L.PCC | 23 | −10 | −45 | 25 |
s2 | Right posterior cingulate cortex | R.PCC | 23 | 10 | −45 | 25 |
s3 | Left anterior cingulate cortex | L.ACC | 32 | −10 | 45 | 10 |
s4 | Right anterior cingulate cortex | R.ACC | 32 | 10 | 45 | 10 |
s5 | Left inferior parietal lobule | L.IPL | 40 | −55 | −55 | 20 |
s6 | Right inferior parietal lobule | R.IPL | 40 | 55 | −55 | 20 |
s7 | Left prefrontal cortex | L.PFC | 46 | −45 | 25 | 25 |
s8 | Right prefrontal cortex | R.PFC | 46 | 45 | 25 | 25 |
s9 | Left lateral temporal cortex | L.LTC | 21 | −55 | −15 | −20 |
s10 | Right lateral temporal cortex | R.LTC | 21 | 55 | −15 | −20 |
s11 | Left parahippocampal gyrus | L.PHG | 36 | −25 | −30 | −20 |
s12 | Right parahippocampal gyrus | R.PHG | 36 | 25 | −30 | −20 |
Variable | Mean Minimal Depth | No. of Nodes | No. of Trees | Times a Root | Accuracy Decrease | Gini Decrease | p Value | Direction |
---|---|---|---|---|---|---|---|---|
BIS_Total | 1.0000 | 118 | 115 | 50 | 0.0265 | 2.0285 | <0.0000 | AUD > CTL |
BIS_NP | 1.4343 | 114 | 110 | 45 | 0.0156 | 1.4792 | <0.0000 | AUD > CTL |
BIS_MI | 1.6723 | 110 | 106 | 27 | 0.0117 | 1.4611 | <0.0000 | AUD > CTL |
s1–s5 (L.PCC–L.IPL) | 2.0023 | 103 | 101 | 27 | 0.0062 | 1.0920 | <0.0000 | AUD > CTL |
s4–s8 (R.ACC–R.PFC) | 1.7829 | 100 | 98 | 33 | 0.0051 | 1.2818 | <0.0000 | AUD > CTL |
s3–s7 (L.ACC–L.PFC) | 2.3930 | 75 | 74 | 25 | 0.0080 | 0.9634 | <0.0000 | AUD > CTL |
s2–s4 (R.PCC–R.ACC) | 2.9542 | 72 | 68 | 7 | 0.0030 | 0.6109 | <0.0000 | CTL > AUD |
BIS_AI | 2.9277 | 64 | 63 | 18 | 0.0042 | 0.6316 | 0.0019 | AUD > CTL |
s1–s8 (L.PCC–R.PFC) | 3.1205 | 63 | 61 | 8 | 0.0004 | 0.5023 | 0.0029 | CTL > AUD |
s6–s9 (R.IPL–L.LTC) | 3.0683 | 62 | 61 | 9 | 0.0039 | 0.5994 | 0.0044 | CTL > AUD |
Span_Fw (VST) | 2.7308 | 62 | 59 | 29 | 0.0059 | 0.7670 | 0.0044 | CTL > AUD |
TotCor_Fw (VST) | 2.7656 | 62 | 59 | 24 | 0.0083 | 0.7481 | 0.0044 | CTL > AUD |
s1–s4 (L.PCC–R.ACC) | 2.8648 | 61 | 60 | 16 | 0.0022 | 0.6903 | 0.0065 | CTL > AUD |
s2–s5 (R.PCC–L.IPL) | 3.1569 | 61 | 59 | 13 | -0.0006 | 0.5276 | 0.0065 | AUD > CTL |
s3–s9 (L.ACC–L.LTC) | 3.1359 | 61 | 58 | 12 | 0.0007 | 0.5459 | 0.0065 | CTL > AUD |
s4–s9 (R.ACC–L.LTC) | 3.1395 | 61 | 59 | 5 | 0.0032 | 0.4423 | 0.0065 | CTL > AUD |
s8–s10 (R.PFC–R.LTC) | 3.1983 | 57 | 56 | 13 | -0.0009 | 0.5901 | 0.0268 | CTL > AUD |
s3–s4 (L.ACC–R.ACC) | 3.3493 | 56 | 52 | 3 | 0.0007 | 0.3313 | 0.0368 | AUD > CTL |
s5–s8 (L.IPL–R.PFC) | 3.3390 | 56 | 54 | 3 | 0.0003 | 0.3792 | 0.0368 | CTL > AUD |
s7–s10 (L.PFC–R.LTC) | 3.2817 | 56 | 55 | 7 | 0.0022 | 0.4746 | 0.0368 | CTL > AUD |
Variable | AUD | CTL | ALL § | |||
---|---|---|---|---|---|---|
r | p | r | p | r | p | |
BIS_AI | 0.1006 | 0.5970 | −0.1371 | 0.4699 | 0.0196 | 0.8829 |
BIS_MI | 0.2346 | 0.2121 | 0.1156 | 0.5432 | 0.1993 | 0.1302 |
BIS_NP | 0.0255 | 0.8936 | 0.2104 | 0.2644 | 0.0926 | 0.4854 |
BIS_Tot | 0.1389 | 0.4643 | 0.1060 | 0.5772 | 0.1274 | 0.3363 |
s1–s4 (L.PCC–R.ACC) | −0.2429 | 0.1958 | −0.1915 | 0.3107 | −0.2262 | 0.0849 |
s1–s5 (L.PCC–L.IPL) | −0.1380 | 0.4669 | −0.0108 | 0.9547 | −0.0910 | 0.4932 |
s1–s8 (L.PCC–R.PFC) | −0.2273 | 0.2270 | 0.0488 | 0.7979 | −0.1245 | 0.3475 |
s2–s4 (R.PCC–R.ACC) | −0.2834 | 0.1291 | −0.1491 | 0.4315 | −0.2317 | 0.0774 |
s2–s5 (R.PCC–L.IPL) | −0.1836 | 0.3316 | −0.0677 | 0.7223 | −0.1314 | 0.3212 |
s3–s4 (L.ACC–R.ACC) | −0.2658 | 0.1557 | 0.3790 | 0.0389 * | −0.0914 | 0.4910 |
s3–s7 (L.ACC–L.PFC) | 0.0033 | 0.9860 | 0.0227 | 0.9051 | 0.0098 | 0.9415 |
s3–s9 (L.ACC–L.LTC) | 0.2421 | 0.1974 | 0.1860 | 0.3251 | 0.2232 | 0.0892 |
s4–s8 (R.ACC–R.PFC) | 0.0242 | 0.8991 | −0.0323 | 0.8655 | 0.0049 | 0.9703 |
s4–s9 (R.ACC–L.LTC) | −0.0495 | 0.7952 | 0.1276 | 0.5016 | 0.0072 | 0.9570 |
s5–s8 (L.IPL–R.PFC) | −0.2007 | 0.2876 | 0.1877 | 0.3206 | −0.0850 | 0.5222 |
s6–s9 (R.IPL–L.LTC) | −0.3233 | 0.0814 | −0.2604 | 0.1646 | −0.2956 | 0.0230 * |
s7–s10 (L.PFC–R.LTC) | −0.1732 | 0.3602 | 0.1974 | 0.2957 | −0.0629 | 0.6362 |
s8–s10 (R.PFC–R.LTC) | −0.0745 | 0.6957 | −0.1071 | 0.5732 | −0.0836 | 0.5290 |
Span_Fw (VST) | −0.3938 | 0.0313 * | −0.0429 | 0.8219 | −0.2393 | 0.0679 |
TotCor_Fw (VST) | −0.4178 | 0.0216 * | −0.0231 | 0.9038 | −0.2383 | 0.0692 |
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Kamarajan, C.; Ardekani, B.A.; Pandey, A.K.; Kinreich, S.; Pandey, G.; Chorlian, D.B.; Meyers, J.L.; Zhang, J.; Bermudez, E.; Stimus, A.T.; et al. Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sci. 2020, 10, 115. https://doi.org/10.3390/brainsci10020115
Kamarajan C, Ardekani BA, Pandey AK, Kinreich S, Pandey G, Chorlian DB, Meyers JL, Zhang J, Bermudez E, Stimus AT, et al. Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sciences. 2020; 10(2):115. https://doi.org/10.3390/brainsci10020115
Chicago/Turabian StyleKamarajan, Chella, Babak A. Ardekani, Ashwini K. Pandey, Sivan Kinreich, Gayathri Pandey, David B. Chorlian, Jacquelyn L. Meyers, Jian Zhang, Elaine Bermudez, Arthur T. Stimus, and et al. 2020. "Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures" Brain Sciences 10, no. 2: 115. https://doi.org/10.3390/brainsci10020115
APA StyleKamarajan, C., Ardekani, B. A., Pandey, A. K., Kinreich, S., Pandey, G., Chorlian, D. B., Meyers, J. L., Zhang, J., Bermudez, E., Stimus, A. T., & Porjesz, B. (2020). Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sciences, 10(2), 115. https://doi.org/10.3390/brainsci10020115