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Body mass dependent differences in functional connectivity
- 1. Body mass dependent differences in functional connectivity Jennifer R. Sadler Department of Nutrition University of North Carolina, Chapel Hill
- 2. The brain integrates appetitive signals & directs eating Background Appetitive hormones Social and emotional cues Reinforcement- driven eating habits Food environment Obesity
- 3. Network connectivity represents how the brain communicates Resting state network connectivity Background Beckmann et al (2005)
- 4. Network connectivity represents how the brain communicates Resting state network connectivity Background Greicius (2008) Disruptions in Alzheimer's Disease, depression, schizophrenia, and obesity?
- 5. Obesity is associated with altered resting state connectivity • Obese (compared to healthy weight) show: Background
- 6. Obesity is associated with altered resting state connectivity • Obese (compared to healthy weight) show: – Disrupted connectivity within the salience network Background Garcia-Garcia et al (2013)
- 7. Obesity is associated with altered resting state connectivity • Obese (compared to healthy weight) show: – Disrupted connectivity within the salience network – Decreased global connectivity of prefrontal and eating circuits Background Garcia-Garcia et al (2013) Geha et al (2016)
- 8. Obesity is associated with altered resting state connectivity • Obese (compared to healthy weight) show: – Disrupted connectivity within the salience network – Decreased global connectivity of prefrontal and eating circuits – Less efficient network organization Background Garcia-Garcia et al (2013) Geha et al (2016) Baek et al (2017)
- 9. Obesity is associated with altered resting state connectivity Background
- 10. Aim 1 1. Identify BMI dependent differences in resting state network connectivity Background BMI Discordant Twins
- 11. Aim 2 2. Examine if effects are driven by BMI Background BMI Discordant Twins
- 12. Aim 2 2. Examine if effects are driven by BMI Background Unrelated, BMI Discordant Pairs SAME CONNETIVITY BMI Discordant Twins compared to
- 13. Aim 2 2. Examine if effects are driven by BMI Background BMI Similar Twins Unrelated, BMI Discordant Pairs SAME CONNETIVITY DIFFERENT CONNECTIVITY BMI Discordant Twins compared to
- 14. Sample Methods Overall HCP sample: - Healthy, young adults (n = 820; aged 22-35)
- 15. Sample Methods Overall HCP sample: - Healthy, young adults (n = 820; aged 22-35) Analysis sample: - Total n = 282 - 3 samples of 94 individuals each (47 pairs) - BMI discordant twin sample - Unrelated BMI discordant sample - BMI similar twin sample
- 16. Sample (Aim 1) Methods 6.7 6.7 0.6 0 1 2 3 4 5 6 7 8 9 10 BMIDiscordance(kg/m2) BMI Discordant Twins (n=94) Sample Characteristics BMI Discordance (kg/m2) 6.7 3.1 Min: 3.4 Max: 16.2 Body Mass Index (kg/m2) 28.1 5.6 Min: 18.6 Max: 44.7 Zygosity Monozygotic 44 (47%) Dizygotic 50 (53%) Gender Male 32 (34%) Female 62 (66%)
- 17. Samples (Aim 2) Methods 6.7 6.7 0.6 0 1 2 3 4 5 6 7 8 9 10 BMIDiscordance(kg/m2) BMI Discordant Twins (n=94) Unrelated BMI Discordant Pairs (n=94) BMI Similar Twins (n=94) Matched on BMI and gender
- 18. Samples (Aim 2) Methods 6.7 6.7 0.6 0 1 2 3 4 5 6 7 8 9 10 BMIDiscordance(kg/m2) BMI Discordant Twins (n=94) Unrelated BMI Discordant Pairs (n=94) BMI Similar Twins (n=94)
- 19. Analyses Methods Full HCP sample (n=820) - 25 ICA-derived brain networks (Smith et al, 2013) - Time-series of resting state activity within each network
- 20. Analyses • Between network connectivity analyses - FSLNets Methods FSLNets v0.6.3, FMRIB Analysis Group
- 21. Analyses • Between network connectivity analyses - FSLNets – Paired sample T-tests Methods vs HeavyLight BMI Discordant, unrelated pairs BMI Discordant Twins
- 22. Analyses • Between network connectivity analyses - FSLNets – Paired sample T-tests Methods vs vs Heavy ‘Heavy’Light ‘Light’ BMI Discordant, unrelated pairs BMI Similar Twins BMI Discordant Twins
- 23. Analyses • Results corrected for multiple comparisons – Nonparametric null distribution created via 10,000 permutations of data – Corrected significance threshold of family- wise error pFWE < 0.05 Methods
- 24. Results Medial orbitofrontal cortex, posterior cingulate cortex z = -3 z = 33z = 7 Stronger Connectivity in Higher BMI BMI Discordant Twins z = 20 z = 40
- 25. Results z = 30 pFWE = 0.0001 Medial orbitofrontal cortex, posterior cingulate cortex Occipital pole z = 9 z = -3 z = 33z = 7 z = 17 Stronger Connectivity in Higher BMI BMI Discordant Twins z = 20 z = 40
- 26. Results z = 40 Insula, oral-somatosensory cortex, dorsal anterior cingulate cortex z = 11 Stronger Connectivity in Higher BMI z = 23 BMI Discordant Twins z = 20 z = 40
- 27. Results Cerebellum: right crus I z = -38 z = -23 z = 40 Insula, oral-somatosensory cortex, dorsal anterior cingulate cortex pFWE = 0.0377 z = 11 z = -32 Stronger Connectivity in Higher BMI z = 23 z = 20 z = 40 BMI Discordant Twins
- 28. Stronger Connectivity in Lower BMI Results z = -11 z = 8 striatum, thalamus z = -1 BMI Discordant Twins z = 20 z = 40
- 29. Results z = -11 z = 8 striatum, thalamus Dorsolateral prefrontal cortex pFWE = 0.0351 z = 22 z = 36 z = -1 z = 1 Stronger Connectivity in Lower BMI z = 20 z = 40 BMI Discordant Twins
- 30. z = 20 z = 40 mOFC, PCC Occipital poleright crus I Insula, oral-somatosensory cortex, dACC striatum, thalamus dlPFC Higher BMI Lower BMI Results BMI Discordant Twins
- 31. z = 20 z = 40 mOFC, PCC Occipital poleright crus I Insula, oral-somatosensory cortex, dACC striatum, thalamus dlPFC Higher BMI Lower BMI Results Unrelated, BMI discordant pairs
- 32. Unrelated, BMI discordant pairs Results z = 20 z = 40 mOFC, PCC Occipital poleright crus I Insula, oral-somatosensory cortex, dACC BMI Discordant Twins striatum, thalamus dlPFC Higher BMI Lower BMI These BMI related differences in network connectivity were the same in the twin and un- related BMI discordant groups
- 33. Unrelated, BMI discordant pairs Results z = 20 z = 40 mOFC, PCC Occipital poleright crus I Insula, oral-somatosensory cortex, dACC BMI Discordant Twins striatum, thalamus dlPFC Higher BMI Lower BMI Similar results in network connectivity were NOT found in BMI Similar Twins
- 34. Summary Higher BMI associated with: – stronger integration of visual processing with taste/gustatory processing, as well as with hedonic evaluation Discussion BMI Discordant Twins BMI Discordant, unrelated pairs
- 35. Summary Higher BMI associated with: – stronger integration of visual processing with taste/gustatory processing, as well as with hedonic evaluation Lower BMI associated with: – stronger connectivity between motivated behavior & executive functioning Discussion BMI Discordant Twins BMI Discordant, unrelated pairs
- 36. Stronger frontostriatal connectivity associated with lower BMI Discussion Striatum Hedonically motivated behavior
- 37. Stronger frontostriatal connectivity associated with lower BMI Discussion dlPFC Executive functioning Striatum Hedonically motivated behavior
- 38. Stronger frontostriatal connectivity associated with lower BMI Discussion Theoretically stronger connectivity = better behavior regulation dlPFC Executive functioning Striatum Hedonically motivated behavior
- 39. Summary Higher BMI associated with: – stronger integration of visual processing with taste/gustatory processing, as well as with hedonic evaluation Lower BMI associated with: – stronger connectivity between motivated behavior & executive functioning No overlap with BMI Similar Twins indicates BMI dependent differences in functional connectivity BMI Similar Twins Discussion BMI Discordant Twins BMI Discordant, unrelated pairs
- 40. Dr. Kyle S. Burger Dr. Grace Shearrer Lily Jones https://github.com/niblunc https://openfmri.org/dataset/ ClinicalTrials.gov Open Science Framework Commitment to Open and Reproducible Science niblunc.org jen_sadler@unc.edu @NIBL_unc
- 41. BMI categories of BMI discordant twins* Healthy Weight – Overweight: 16 pairs Healthy Weight – Obese: 9 pairs Overweight – Obese: 11 pairs Healthy Weight – Healthy Weight: 6 pairs Overweight – Overweight: 1 pair Obese – Obese: 7 pairs *BMI discordant unrelated group were matched by BMI, so categories are the same
- 42. Sample Characteristics BMI discordant twins (n=94) Unrelated BMI discordant pairs (n=94) BMI similar twins (n=94) BMI Discordance (kg/m2) 6.7 3.1 Min: 3.4 Max: 16.2 6.7 3.1 Min: 3.5 Max: 16.1 0.6 0.4 Min: 0.03 Max: 1.3 Body Mass Index (kg/m2) 28.1 5.6 Min: 18.6 Max: 44.7 28.1 5.7 Min: 18.6 Max: 45.2 25.8 4.3 Min 19.6 Max: 36.5 Zygosity Monozygotic 44 (47%) N/A 56 (60%) Dizygotic 50 (53%) N/A 38 (40%) Gender Male 32 (34%) 32 (34%) 48 (51%) Female 62 (66%) 62 (66%) 46 (49%) Results * * Significant difference in proportion of males/female
- 43. References 1. Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1457), 1001-1013. 2. Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current opinion in neurology, 21(4), 424- 430. 3. García‐García, I. et al. Alterations of the salience network in obesity: a resting‐state fMRI study. Hum. Brain Mapp. 34, 2786–2797 (2013). 4. Geha, P., Cecchi, G., Todd Constable, R., Abdallah, C., & Small, D. M. (2017). Reorganization of brain connectivity in obesity. Human brain mapping, 38(3), 1403-1420. 5. Baek, K., Morris, L. S., Kundu, P., & Voon, V. (2017). Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency. Psychological medicine, 47(4), 585-596. 6. Yang, Z. et al. Genetic and environmental contributions to functional connectivity architecture of the human brain. Cereb. Cortex 26, 2341– 2352 (2016). 7. FSLNets v0.6.3, FMRIB Analysis Group, 2016, University of Oxford
Editor's Notes
- Good morning.
- Eating behavior is influenced by a variety of factors, including appetitive hormones, learned eating habits, social and emotional cues, and cues from the environment. All these factors are processed and integrated in the brain, making it a point of interest for eating behavior and obesity research.
- One way we can study the brain is through resting state network connectivity. which represents how brain networks, such as the default mode network or salience network, co-activate and communicate at rest.
- Network connectivity is disrupted in a number of neuropsychiatric disorders and possibly in obesity.
- Research shows that obesity is associated with disruptions in resting state activity including…
- …Altered connectivity within the salience network, including the putamen
- …Decreased connectivity of prefrontal and feeding networks to other parts of the brain
- …and less efficient of network organization across the brain. Together, these results indicate that obesity is associated with aberrant changes in network connectivity.
- Importantly, we know resting state connectivity is influenced by environmental and genetic factors, which supports the need for study designs to address these confounds. So in this analysis, we used a twin study approach, to look connectivity differences associated with weight.
- To account for the influence of genetic and environmental factors, we used a sample of BMI-discordant twins
- So we selected two comparison groups. The first was unrelated BMI discordant pairs. Similar connectivity in this group would suggest BMI-dependent effects.
- So we selected two comparison groups. The first was unrelated BMI discordant pairs. Similar connectivity in this group would suggest BMI-dependent effects.
- and one comprised of BMI similar twins. We didn’t expect to see overlapping connectivity with this group, since within pairs, there should be weak BMI effects.
- To answer these questions, we used publicly available data from the Human Connectome Project. The HCP sample includes young adults, and purposefully recruited a high proportion of pairs of twins and sets of siblings.
- To answer these questions, we used publicly available data from the Human Connectome Project. The HCP sample includes young adults, and purposefully recruited a high proportion of pairs of twins and sets of siblings.
- Using the same HCP data, we selected an unrelated BMI discordant pair group that was matched by BMI and gender to the weight discordant twins.
- Using the same HCP data, we selected an unrelated BMI discordant pair group that was matched by BMI and gender to the weight discordant twins.
- The BMI similar twins were selected to have low BMI discordance. The mean difference within pairs was less than one BMI unit, with a maximum discordance of 1.3.
- The data we used was part of the parcellation, timeseries, and netmat release. HCP team generated 25 resting state networks identified using data driven independent components analysis. For each participant, they modeled activity in those networks over time.
- To examine between network connectivity, we used FSLNets, which models the activity over time in a network, and correlates it with activity over time in another network Connectivity is identified as significant covariance of resting state activity over time.
- Using FSLNets, we compared network connectivity using a paired sample t-test design that, within pairs, compares connectivity of the higher BMI individual to the lower BMI individual and vice versa. for the impact of BMI on between network connectivity
- Using FSLNets, we compared network connectivity using a paired sample t-test design that, within pairs, compares connectivity of the higher BMI individual to the lower BMI individual and vice versa.
- We corrected for multiple comparisons by completing 10K permutations to create a nonparametric null distribution based on our data. Significance was considered family wise error rate corrected p <0.05
- When comparing higher BMI twins to lower BMI twins, we found significant stronger connectivity of a network including the medial OFC and posterior cingulate cortex network, which are associated with reward valuation, decision making, internal awareness, and attention…
- and a network including the occipital pole, which is part of the primary visual cortex.
- We also found stronger connectivity between a network including the insula, oral somatosensory cortex, and dorsal ACC, all of which are associated with taste processing
- with a network including the right crus I of the cerebellum, which is implicated in motor control and higher cognitive processing like executive control (language, spatial awareness).
- When comparing the light twins to heavy twins, we found stronger connectivity of a network containing the striatum and thalamus, which are involved in motivated behavior and sensory relay
- with a network including the dorsolateral prefrontal cortex, which is implicated in decision making and value prediction. This result is really interesting, considering these regions are part of the established frontostriatal, dopaminergic circuit, that controls most motivated behaviors, like eating.
- When we compared the connectivity of the BMI discordant twins to our unrelated discordant sample….
- …..those networks were all significantly connected in the unrelated, discordant pairs.
- So, we found network connectivity that was the same in the two BMI discordant groups.
- And these three networks were not connected in the BMI similar twins.
- In summary, we found in a sample of weight discordant twins and unrelated individuals, that higher BMI was associated with stronger network connectivity that may confer: stronger visual response to foods during evaluation of those foods and greater integration of areas involved in taste processing and executive function
- Lower BMI was associated with stronger connectivity of networks involved in motivated behavior and executive functioning, and these regions are part of the established frontostriatal dopamine circuitry .
- Stronger connectivity of the striatum, which controls hedonically motivated behavior, driving us to repeat actions that are rewarding…..
- With the dlPFC, a region important for executive function and exercising restraint
- Would theoretically, confer better behavioral regulation for individuals at a lower BMI.
- To conclude, the results observed were not seen in the BMI similar twin group, suggesting that these changes in connectivity reflect BMI-dependent effect on connectivity.
- I’d like to recognize my lab, Dr. Burger, Dr. Shearrer greatly helped with this project, and thank you to the Obesity Society for letting we share our finding with you all today. Cole et al (2014) in Neuron: “These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes.” --- Questions Twins (WD or WS) have 1in difference in height on avg. Unrelated have more within pair variability (avg. 3). If you used adiposity as measure instead of BMI, you might see slightly difference results. Home environment is not the same Genetics are not the same for all twins (MZ versus DZ) That’s right, this will introduce between person variability. Results are the same in twins and unrelated individuals, showing that with high variability, effects hold
- About ¾ of the weight discordant pairs compared between BMI categories ¼ of the WD samples included individuals who were in the same BMI category.
- However, we find a significant difference between the proportion of men and women in each group.