Dynamic fMRI networks predict success in a behavioral weight loss program among older adults

Abstract

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment.

Keywords: Behavioral weight loss interventions; Dynamic fMRI networks; Machine learning; Obesity; Older adults; Prediction.

Fig. 1

Dynamic connectivity tensor creation procedure using sliding window technique, for which a window of fixed length is used to split the fMRI time series. For each split, a connectivity matrix is then constructed using pairwise Pearson correlation analysis. By concatenating the resulting matrices, a 3^rd-order connectivity tensor is created for each participant. A thresholding value is chosen to remove weak connections, such that for each individual and at each time window a fixed percentage (e.g. 10%) of the total connections is maintained.

Fig. 2

Prediction performance, quanitified using accuracy (rate of correct prediction), sensitivity (rate of correct prediction for low-weight loss group), specificity (rate of correct prediction for high-weight loss group) and CE (a measure of prediction error defined as the difference between the prediction grouping probabilities and the real grouping labels, see section 2.7 for detailed definitions) for the dynamic and static connectivity and random grouping analysis. Dynamic connectivity tensors were constructed using a sliding window length of 61 time points and theresholded at connectivity density of 10%. No sliding window was used to make the static netowork; the entire fMRI time series was used to compute a single pairwise correlation matrix for the static networks. For the random grouping analysis, the particpants’ labels were randomly permuted. HOSVD was used to reduce the networks’ rank, HOSVD linearly decomposes the dynamic connectivity tensors to a set of linearly independent (equivalently orthogonal) dynamic connectivity components. To identify the components, HOSVD maximizes the amount of variance captured by the components.

Fig. 3

Prediction performance measures [mean with error bars reflecting ±SD across 100 validation folds] of the SVM model estimated for different window sizes and different connectivity density thresholds. The numbers on the left vertical axis of each figure illustrate the prediction performance values, and the numbers on the right side indicate connectivity density thresholding values.

Fig. 4

The connectivity principal components revealed by HOSVD. For each component, the left column displays a network-based representation and the right column shows the individual nodes in their anatomical locations. Each network component is a collection of nodes and edges that captures the greatest amount of variance across time and within individuals. The size of each node is directly related to its number of connections. The variance explained by each component, v_i, where i indicates the component index, is noted on the top of the same component’s graph. For each component, the variance was computed as the square of the corresponding singular value normalized by the first singular value, that was finally averaged across 100 validation folds.

Fig. 5

(a) Strength of SVM weight matrix, |W| ∈ ℝ^21×21, averaged over 100 validation folds, (b) average weights on the diagonal entries of the SVM weight matrix |W| ∈ ℝ^21×21, (c) average of row-wise sum of the matrix |W| ∈ ℝ^21×21.