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Neuroimage. 2018 Jun;173:421-433. doi: 10.1016/j.neuroimage.2018.02.025. Epub 2018 Feb 19.

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

Author information

1
Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA.
2
Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA; Department of Health and Exercise Science, Wake Forest University, Winston Salem, NC, USA; Department of Geriatric Medicine, Wake Forest University, Winston Salem, NC, USA.
3
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
4
Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston Salem, NC, USA.
5
Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA.
6
Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA. Electronic address: plaurien@wakehealth.edu.

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

PMID:
29471100
PMCID:
PMC5911254
[Available on 2019-06-01]
DOI:
10.1016/j.neuroimage.2018.02.025
[Indexed for MEDLINE]

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