Hugo Cesar Baggio

Objective: Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting medical disorders that might compromise the normal course of aging. The aim of the current study of brain connectivity patterns was to examine whether adults with obesity would show signs of premature aging, such as lower segregation, in large-scale networks. Methods: Participants with obesity (n = 30, mean age = 32.8 ± 5.68 years) were compared with healthy-weight controls (n = 33, mean age = 30.9 ± 6.24 years) and senior participants who were stroke-free and without dementia (n = 30, mean age = 67.1 ± 6.65 years) using resting-state magnetic resonance imaging and graph theory metrics (i.e., small-world index, clustering coefficient, characteristic path length, and degree). Results: Contrary to our hypothesis, participants with obesity exhibited a higher clustering coefficient compared with senior participants (t = 5.06, p < .001, d = 1.23, 95% CI_bca = 0.64 to 1.88). Participants with obesity also showed lower global degree relative to seniors (t = −2.98, p = .014, d = −0.77, 95% CI_bca = −1.26 to −0.26) and healthy-weight controls (t = −2.92, p = .019, d = −0.72, 95% CI_bca = −1.19 to −0.25). Regional degree alterations in this group were present in several functional networks. Conclusions: Participants with obesity displayed greater network clustering than did seniors and also had lower degree compared with seniors and individuals with normal weight, which is not consistent with the notion that obesity is associated with premature aging of the brain. Although the cross-sectional nature of the study precludes causal inference, the overly clustered network patterns in obese participants could be relevant to age-related changes in brain function because regular networks might be less resilient and metabolically inefficient.

Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting biological deregulations that might compromise the normal course of aging. The aim of the current study was to test whether the network composition of young adults with obesity would show signs of premature aging. To this end, subjects with obesity (N = 30, mean age 32.8 ± 5.68), healthy-weight controls (N = 33, mean age 30.9 ± 6.24) as well as non-demented seniors (N = 30, mean age 67.1 ± 6.65) all underwent a resting-state MRI acquisition. Functional connectivity was studied by means of graph-theory measurements (i.e., small-world index, clustering coefficient, characteristic path length, and mean degree). Contrary to what expected, obesity in adults was related to disruptions in small-world properties driven by increases in network segregation (i.e., clustering coefficient) as compared to elders. Also, this group showed alterations in global and regional centrality metrics (i.e., degree) relative to controls and seniors. Despite not mimicking what was here shown by seniors, the topological organization linked to an obesity status may represent a flaw for cognitive functions depending on the rapid combination between different modular communities.

The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge‐wise group‐level statistical inference in brain graphs while controlling for multiple‐testing associated false‐positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold‐free network‐based statistics (TFNBS). The TFNBS combines threshold‐free cluster enhancement, a method commonly used in voxel‐wise statistical inference, and network‐based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge‐wise significance values and does not require the a priori definition of a hard cluster‐defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false‐positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.