Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.
- Authors
- Kamarajan, Chella; Ardekani, Babak A; Pandey, Ashwini K; Kinreich, Sivan; Pandey, Gayathri; Chorlian, David B; Meyers, Jacquelyn L; Zhang, Jian; Bermudez, Elaine; Stimus, Arthur T; Porjesz, Bernice
- Year
- 2020
- Journal
- Brain sciences
- PMID
- 32093319
- DOI
- 10.3390/brainsci10020115
- PMCID
- PMC7071377
Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD ( = 30) from healthy controls (CTL, = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.
Seed regions of the DMN consisting of 6 regions in the left hemisphere (blue beads) and 6 homologous regions in the right hemisphere (red beads), as listed in Table 1. Axial (top), coronal (front), and sagittal (left side) views are shown. (PCC–Posterior cingulate cortex; ACC–Anterior cingulate cortex; IPL–Inferior parietal lobule; PFC–Prefrontal cortex; LTC–Lateral temporal cortex; PHG–Parahippocampal gyrus; L–Left; R–Right; A–Anterior; P–Posterior; S–Superior; I–Inferior).
The multi-way importance plot showing the top significant variables (labelled and marked with black circles) that contributed to the classification of AUD from CTL individuals based on the following measures: the Gini decrease, the number of trees, and the p-values. Fourteen DMN connections, two neuropsychological variables, and all four impulsivity scores were significant (circled and labelled red and green dots). BIS–Barratt Impulsivity Scale; MI–Motor impulsivity; NP–Non-planning; AI–Attentional impulsivity; Tot–Total; Span_Fw–Span forward; TotCor_Fw–Total correct forward; s1-s12–DMN seeds 1-12 as listed in Table 2.
The distribution of minimal depth among the trees of the forest for the significant variables is shown in different colors for each level of minimal depth. The mean minimal depth in the distribution for each variable is marked by a vertical black bar overlapped by its value inside a box. A lower mean minimal depth of a functional connectivity (FC) variable represents a higher number of observations (participants) categorized in a specific group on the basis of the variable. The top significant variables (14 DMN connections, two neuropsychological scores, and all four BIS scores) followed the same rank in the plot as in Table 3, which is ordered based on p-values.
Illustration of rankings of variables based on any of the two RF parameters of importance (inside the left and bottom panels showing distribution of the rankings of all predictor variables marked with black dots along a blue trend line) as well as the respective correlation coefficients across rankings of any two parameters (inside the right and top panels). It is shown that all RF parameters of importance were found to have very high correlations among each other, suggesting that these parameters are highly reliable at ranking the importance of variables in group classification.
Significant DMN connections that contributed to the Random Forest classification of AUD from CTL individuals based on importance parameters including the p-value (p < 0.01), as listed in Table 3. These connections (i.e., edges) across the seed regions (i.e., nodes) within an anatomical brain template are shown: (A) axial (top) view; (B) coronal (front) view; and (C) sagittal (left side) view. The blue and red beads represent left and right-sided nodes, respectively, and the edges in orange and cyan lines represent hyper- and hypo-connectivity, respectively, in the AUD compared to the CTL group.
Correlation matrix showing associations among the top significant variables. The values within each cell represent the bivariate Pearson correlation between the variable on its vertical axis and the variable on its horizontal axis. The correlation values are color coded (red/pink shades represent negative r-values, blue/cyan shades indicate positive r-values, darker shades represent relatively higher magnitudes) and significant correlations have been marked with asterisks (*p < 0.05; **p < 0.01; and ***p < 0.001).
| # | Section | Preview |
|---|---|---|
| 20 | 2. Methods — 2.7. Random Forest Classification Model and Parameters | Random forest classification analysis was performed using R-packages “randomForest”… |
| 21 | 2. Methods — 2.7. Random Forest Classification Model and Parameters | first, the model trains itself using a training data for creating each tree based on bootstrap… |
| 22 | 2. Methods — 2.7. Random Forest Classification Model and Parameters | The Random Forest classification model included 66 DMN connections, 13 neuropsychological scores,… |
| 23 | 2. Methods — 2.7. Random Forest Classification Model and Parameters | to estimate the prediction accuracy of the RF model. While classification trees are grown for each… |
| 24 | 3. Results — 3.1. Random Forest Classification — 3.1.1. Classification Accuracy and Top (Ranked) Significant Variables | The classification accuracy was 76.67% as the RF algorithm correctly classified 23 out of 30… |
| 25 | 3. Results — 3.1. Random Forest Classification — 3.1.2. Multi-Way Importance Plot | The top significant variables were also shown in a multi-way importance plot based on the… |
| 26 | 3. Results — 3.1. Random Forest Classification — 3.1.3. Distribution of Minimal Depth | The distribution of minimal depth among the trees of the forest for the top significant variables is… |
| 27 | 3. Results — 3.1. Random Forest Classification — 3.1.4. Relations among Rankings of Different RF Parameters | The relations among rankings of different RF parameters are shown in Figure 4. The correlations… |
| 28 | 3. Results — 3.1. Random Forest Classification — 3.1.5. Connectivity Mapping of Significant DMN Connections | The 3-D brain connectivity map of significant DMN connections, which contributed to group… |
| 29 | 3. Results — 3.2. Correlations between Top Significant Variables and Age | Since age difference across the groups was statistically significant (p < 0.001), the association of… |
| 30 | 3. Results — 3.3. Correlations among the Top Significant Variables | Correlations among the top significant variables are shown in Figure 6. It was found that BIS… |
| 31 | 3. Results — 3.3. Correlations among the Top Significant Variables | significant correlations with two visual memory scores (i.e., the memory span and total correct… |
| 32 | 4. Discussion | The current study aimed to identify specific features of FC, neuropsychological, and impulsivity to… |
| 33 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.1. Hyperconnectivity within Frontal and Parietal Regions in AUD | The observed hyperconnectivity across prefrontal regions in AUD is an important finding, given the… |
| 34 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.1. Hyperconnectivity within Frontal and Parietal Regions in AUD | had smaller volumes in frontal cortices (left pars orbitalis, right medial orbitofrontal, right… |
| 35 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.1. Hyperconnectivity within Frontal and Parietal Regions in AUD | to long-range hypoconnectivity across multiple regions during the alcoholic beverage condition… |
| 36 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.2. Hypoconnectivity across Anterior–Posterior and Interhemispheric Connections in AUD | In tandem with the local hyper-connectivity in prefrontal and parietal regions (as discussed in the… |
| 37 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.2. Hypoconnectivity across Anterior–Posterior and Interhemispheric Connections in AUD | individuals showed hypoconnectivity in the long-range anterior–posterior connections, viz.,… |
| 38 | 4. Discussion — 4.1. Aberrant FC in Individuals with AUD — 4.1.2. Hypoconnectivity across Anterior–Posterior and Interhemispheric Connections in AUD | found in abstinent AUD individuals [113]. Furthermore, diffusion tensor imaging (DTI) studies have… |
| 39 | 4. Discussion — 4.2. Poor Neuropsychological Performance in AUD | The RF model also identified two scores in the VST forward condition as important features to… |
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