Correlation between brain circuit segregation and obesity

Chao, Seh-Huang; Liao, Yin-To; Chen, Vincent Chin‐Hung; Li, Cheng-Jui; McIntyre, Roger S.; Lee, Yena; Weng, Jun‐Cheng

doi:10.1016/j.bbr.2017.09.017

Cited by 36 publications

(36 citation statements)

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“…These findings were predominantly observed in uni-and heteromodal association cortices that encompass integrative default mode and frontoparietal networks and are reflective of an increased differentiation of these areas to other brain networks. Prior fMRI studies reported atypical intrinsic functional connectivity in individuals with obesity, at both local node and global network levels, relative to individuals with a healthy weight (Chao et al, 2018;Chen et al, 2018;García-García et al, 2013Park et al, 2016Park et al, , 2018. Our findings complement these previous reports focusing on the analysis of connectivity patterns of specific areas Lips et al, 2014;Park et al, 2015) alongside prior graph theoretical analyses (García-García et al, 2015;Park et al, 2016Park et al, , 2018 in the context of person-to-person variations in BMI.…”

Section: Discussionsupporting

confidence: 83%

“…Our findings complement these previous reports focusing on the analysis of connectivity patterns of specific areas Lips et al, 2014;Park et al, 2015) alongside prior graph theoretical analyses (García-García et al, 2015;Park et al, 2016Park et al, , 2018 in the context of person-to-person variations in BMI. Seed-based and graph theoretical functional connectivity studies found that individuals with obesity showed increased connectivity in nodes belonging to frontoparietal and default mode networks, relative to individuals with healthy weight (Chao et al, 2018;García-García et al, 2013. These findings are complemented by studies reporting positive associations between overall connectivity degree and broad variability in BMI, again with frequent findings in transmodal areas (Park et al, 2016(Park et al, , 2018.…”

Section: Discussionmentioning

confidence: 94%

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Body mass variations relate to fractionated functional brain hierarchies

Park

Morys

et al. 2020

Preprint

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Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined association between functional connectome organization and BMI variations. We capitalized on connectome manifold learning techniques, which represent macroscale functional connectivity patterns along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of an increasingly segregated modular architecture and a disruption in the hierarchical integration of different brain system. Transcriptomic decoding and subsequent gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings provide novel insights for functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 94%

Body mass variations relate to fractionated functional brain hierarchies

Park

Morys

et al. 2020

Preprint

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“…For MRI-DTI studies, 14 age-and sex-matched healthy controls (HC-3) were selected from a historical cohort of diffusion MRI studies at the FIMR. As the majority of HC subjects (23 of 25) were selected from an imaging historical healthy control cohort established at the Center for Neurosciences in the FIMR, we were able to match for sex and age but not for ethnicity, race, BMI, and socioeconomic status even though race, BMI, and socioeconomic status have been reported to influence structural brain development (54,55). The HC subjects also did not perform the cognitive and behavioral testing.…”

Section: Subject Selectionmentioning

confidence: 99%

Metabolic and microstructural alterations in the SLE brain correlate with cognitive impairment

Mackay¹,

Tang

et al. 2019

JCI Insight

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“…Obesity and addiction have many shared neural circuits such as the mesolimbic “reward” neural system and the prefrontal “control” neural system ( Chao et al, 2018 ; Gearhardt et al, 2011 ; Volkow et al, 2012 , 2013 ; Volkow & Wise, 2005 ; Wang et al, 2004 ). Addiction is considered a disease of the brain’s reward system.…”

Section: Introductionmentioning

confidence: 99%

Neural correlates of executive functions in patients with obesity

Chen

Chao

et al. 2018

PeerJ

Self Cite

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Obesity is one of the most challenging problems in human health and is recognized as an important risk factor for many chronic diseases. It remains unclear how the neural systems (e.g., the mesolimbic “reward” and the prefrontal “control” neural systems) are correlated with patients’ executive function (EF), conceptualized as the integration of “cool” EF and “hot” EF. “Cool” EF refers to relatively abstract, non-affective operations such as inhibitory control and mental flexibility. “Hot” EF refers to motivationally significant affective operations such as affective decision-making. We tried to find the correlation between structural and functional neuroimaging indices and EF in obese patients. The study population comprised seventeen patients with obesity (seven males and 10 females, BMI = 37.99 ± 5.40, age = 31.82 ± 8.75 year-old) preparing to undergo bariatric surgery. We used noninvasive diffusion tensor imaging, generalized q-sampling imaging, and resting-state functional magnetic resonance imaging to examine the neural correlations between structural and functional neuroimaging indices and EF performances in patients with obesity. We reported that many brain areas are correlated to the patients’ EF performances. More interestingly, some correlations may implicate the possible associations of EF and the incentive motivational effects of food. The neural correlation between the left precuneus and middle occipital gyrus and inhibitory control may suggest that patients with a better ability to detect appetitive food may have worse inhibitory control. Also, the neural correlation between the superior frontal blade and affective decision-making may suggest that patients’ affective decision-making may be associated with the incentive motivational effects of food. Our results provide evidence suggesting neural correlates of EF in patients with obesity.

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Correlation between brain circuit segregation and obesity

Cited by 36 publications

References 50 publications

Body mass variations relate to fractionated functional brain hierarchies

Body mass variations relate to fractionated functional brain hierarchies

Metabolic and microstructural alterations in the SLE brain correlate with cognitive impairment

Neural correlates of executive functions in patients with obesity

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