No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Functional network centrality in obesity: A resting-state and task fMRI study
... Multiple task-based functional MRI studies have also shown associations between BMI and brain activations in impulse control and reward processing paradigms [22][23][24][25][26][27][28][29] . During resting conditions, studies reported associations between BMI and connectivity of specific regions [30][31][32][33] and larger networks involved in cognitive control and reward systems [34][35][36] . A recent study suggested regional functional connectivity patterns related to inter-individual variations in obesity phenotypes using machine learning 37,38 . ...
... Further contextualization with manifold eccentricity and graph theoretical parameters indicated segregation of association cortices in individuals with higher BMI. Prior fMRI studies reported atypical intrinsic functional connectivity in individuals with obesity, at both nodal and global network levels, relative to individuals with healthy weight 30,34,35,[101][102][103] . Our findings complement these previous reports focusing on the analysis of connectivity patterns of specific areas [30][31][32]35 alongside prior graph-theoretical analyses 30,34,103 in the context of person-to-person variations in BMI. ...
... Prior fMRI studies reported atypical intrinsic functional connectivity in individuals with obesity, at both nodal and global network levels, relative to individuals with healthy weight 30,34,35,[101][102][103] . Our findings complement these previous reports focusing on the analysis of connectivity patterns of specific areas [30][31][32]35 alongside prior graph-theoretical analyses 30,34,103 in the context of person-to-person variations in BMI. Prior functional connectivity studies found that individuals with obesity showed increased connectivity in nodes belonging to frontoparietal and default mode networks 30,35,101 . ...
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 associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization 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 a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.
... The brain voxels with higher EC values are usually strongly connected with more nodes with a higher weighting, indicating the central roles of these voxels in the global functional integrity of whole-brain networks. These metrics could capture the functional relationships of a given voxel (node) within the entire connectivity matrix of the brain (connectome) and have been successfully applied in AD (Li et al., 2018a;Luo et al., 2016;Qiu et al., 2016), obesity (García-García et al., 2015) and autism (Martino et al., 2013) research. Thus, the combination of DC and EC may work as a useful approach to reflect the pathogenesis of AD. ...
... In detail, DCM was computed with DPARSFA by counting the number Brain Imaging and Behavior of voxels that each voxel was correlated to at a threshold of r ≥ 0.25. Details regarding DCM processing are available in the literature (Zuo et al., 2012;García-García et al., 2015;Buckner et al., 2009;Li et al., 2016;Takeuchi et al., 2015). On the other hand, ECM was calculated by counting the weighted number of correlations with the Fast ECM (fECM) toolbox (https://www.github.com/amwink/bias/tree/master/ ...
... On the other hand, ECM was calculated by counting the weighted number of correlations with the Fast ECM (fECM) toolbox (https://www.github.com/amwink/bias/tree/master/ matlab/fastECM) Each voxel was weighted based on their connection with the whole brain (García-García et al., 2015;Lohmann et al., 2010). We subsequently Z-transformed centrality metrics so that centrality maps across participants were comparable and closer to a normal distribution. ...
Early-onset Alzheimer’s disease (EOAD) involves multiple cognitive domains and shows more rapid progression than late-onset Alzheimer’s disease (LOAD). However, the difference in pathogenesis between EOAD and LOAD is still unclear. Accordingly, we applied intrinsic network analysis to explore the potential neuropathological mechanism underlying distinct clinical phenotypes. According to the cut-off age of 65, we included 20 EOAD patients, 20 LOAD patients, and 36 age-matched controls (19 young and 17 old controls). We employed resting-state functional MRI and network centrality analysis to explore the local (degree centrality (DC)) and global (eigenvector centrality (EC)) functional integrity. Two-sample t-test analysis was performed, with gray matter volume, age, gender, and education as covariates. Furthermore, we performed a correlation analysis between network metrics and cognition. Compared to young controls, EOAD patients exhibited lower DC in the middle temporal gyrus (MTG), parahippocampal gyrus (PHG), superior temporal gyrus (STG), and lower EC in the MTG, PHG, and postcentral gyrus. In contrast, LOAD patients exhibited lower DC in the STG and anterior cingulum gyrus and higher DC in the middle frontal gyrus compared to old controls. No significant difference in EC was observed in LOAD patients. Furthermore, both DC and EC correlated with cognitive performance. Our study demonstrated divergent functional network impairments in EOAD and LOAD patients. EOAD patients showed more complex network damage involving both local and global centrality properties, while LOAD patients mainly featured local functional connectivity changes. Such centrality impairments are related to poor cognition, especially regarding memory performance.
... This approach aims to identify taskindependent and more fundamental functional patterns underlying different states and/or disorders (Fox & Greicius, 2010). RsfMRI has been adopted to describe functional connectivity in relation to eating and weight disorders (García-García et al., 2015;Stopyra et al., 2019), and preliminary evidence converges in identifying altered functional connectivity patterns in regions mainly implicated in impulsivity-related aspects (such as, prefrontal, subcortical and parietal regions) in overeating conditions (García-García et al., 2015;Moreno-Lopez, Contreras-Rodriguez, Soriano-Mas, Stamatakis, & Verdejo-Garcia, 2016;Park, Seo, & Park, 2016). For example, functional connectivity changes within the frontoparietal circuit have been linked to disinhibited eating behavior (as assessed by Three Factor Eating Questionnaire, TFEQ; Stunkard & Messick, 1985) and body mass index (BMI) in normal-and overweight individuals (Park et al., 2016). ...
... This approach aims to identify taskindependent and more fundamental functional patterns underlying different states and/or disorders (Fox & Greicius, 2010). RsfMRI has been adopted to describe functional connectivity in relation to eating and weight disorders (García-García et al., 2015;Stopyra et al., 2019), and preliminary evidence converges in identifying altered functional connectivity patterns in regions mainly implicated in impulsivity-related aspects (such as, prefrontal, subcortical and parietal regions) in overeating conditions (García-García et al., 2015;Moreno-Lopez, Contreras-Rodriguez, Soriano-Mas, Stamatakis, & Verdejo-Garcia, 2016;Park, Seo, & Park, 2016). For example, functional connectivity changes within the frontoparietal circuit have been linked to disinhibited eating behavior (as assessed by Three Factor Eating Questionnaire, TFEQ; Stunkard & Messick, 1985) and body mass index (BMI) in normal-and overweight individuals (Park et al., 2016). ...
... For example, functional connectivity changes within the frontoparietal circuit have been linked to disinhibited eating behavior (as assessed by Three Factor Eating Questionnaire, TFEQ; Stunkard & Messick, 1985) and body mass index (BMI) in normal-and overweight individuals (Park et al., 2016). Additionally, using a graph theory approach (Bullmore & Sporns, 2009), García-García et al. (2015) report that obese individuals-compared to healthy-weight controls-are characterized by a lower degree centrality (see Section 3.2 of this article for details on degree centrality) within the right middle frontal gyrus (MFG), a region part of the dorsolateral PFC known to be involved in inhibitory control and monitoring of behavior (Bari & Robbins, 2013). ...
Objective:
Binge eating is characterized by episodes of uncontrolled eating, within discrete periods of time. Although it is usually described in obese individuals or as a symptom of Binge Eating Disorder (BED), this behavior can also occur in the normal-weight (NW) population. An interesting premise suggests that impulsivity might contribute to the onset of binge eating and the progression toward weight gain. Drawing upon this evidence, here we explored impulsivity in NW individuals reporting binge-eating episodes through a functional connectivity approach. We hypothesized that, even in the absence of an eating disorder, NW binge eaters would be characterized by connectivity pattern changes in corticostriatal regions implicated in impulsivity, similarly to the results described in BED individuals.
Methods:
A resting-state functional magnetic resonance imaging study tested 39 NW men and women, with and without binge eating (binge eaters, BE and non-BE). Brain functional connectivity was explored by means of graph theoretic centrality measures and traditional seed-based analysis; trait impulsivity was assessed with self-report questionnaires.
Results:
The BE group was characterized by a higher degree of trait impulsivity. Brain functional connectivity measures revealed lower degree centrality within the right middle frontal gyrus, left insula/putamen and left temporoparietal regions and a lower functional connectivity between the right middle frontal gyrus and right insula in the BE group.
Discussion:
The results support previous evidence on BED of altered functional connectivity and higher impulsivity at the roots of overeating behavior, but further extend this concept excluding any potential confounding effect exerted by the weight status.
... These results indicate a weakened control system combined with hypersensitivity to satiety and discomfort signals after eating in persons who are prone to overeating (Brooks et al., 2013). Restingstate functional magnetic resonance imaging (fMRI) studies have revealed that obese individuals have lower functional connectivity (FC) in the middle frontal gyrus (a cortical region associated with attention, executive control, and movement) than normal-weight individuals (García-García et al., 2015). Parsons et al. (2022) concluded that altered FC of the OFC may indicate a shift in the valuation of food-based rewards, and dysfunctional insular FC likely contributes to altered homeostatic signal processing. ...
... Recently, resting-state fMRI (rsfMRI) has been used to investigate functional alterations in obesity, and this method has unique advantages in clinical research (García-García et al., 2015;Zhang et al., 2015;Ding et al., 2020;Zhang et al., 2020;Parsons et al., 2022). ReHo is a data-driven method for rsfMRI that reflects spontaneous neuronal activity from different perspectives and demonstrates excellent performance in depicting clinical traits (Van Opstal et al., 2019;Zeighami et al., 2021;Li et al., 2022). ...
Background
Intermittent energy restriction (IER) is an effective weight loss strategy. However, the accompanying changes in spontaneous neural activity are unclear, and the relationship among anthropometric measurements, biochemical indicators, and adipokines remains ambiguous.
Methods
Thirty-five obese adults were recruited and received a 2-month IER intervention. Data were collected from anthropometric measurements, blood samples, and resting-state functional magnetic resonance imaging at four time points. The regional homogeneity (ReHo) method was used to explore the effects of the IER intervention. The relationships between the ReHo values of altered brain regions and changes in anthropometric measurements, biochemical indicators, and adipokines (leptin and adiponectin) were analyzed.
Results
Results showed that IER significantly improved anthropometric measurements, biochemical indicators, and adipokine levels in the successful weight loss group. The IER intervention for weight loss was associated with a significant increase in ReHo in the bilateral lingual gyrus, left calcarine, and left postcentral gyrus and a significant decrease in the right middle temporal gyrus and right cerebellum (VIII). Follow-up analyses showed that the increase in ReHo values in the right LG had a significant positive correlation with a reduction in Three-factor Eating Questionnaire (TFEQ)-disinhibition and a significant negative correlation with an increase in TFEQ-cognitive control. Furthermore, the increase in ReHo values in the left calcarine had a significant positive correlation with the reduction in TFEQ-disinhibition. However, no significant difference in ReHo was observed in the failed weight loss group.
Conclusion
Our study provides objective evidence that the IER intervention reshaped the ReHo of some brain regions in obese individuals, accompanied with improved anthropometric measurements, biochemical indicators, and adipokines. These results illustrated that the IER intervention for weight loss may act by decreasing the motivational drive to eat, reducing reward responses to food cues, and repairing damaged food-related self-control processes. These findings enhance our understanding of the neurobiological basis of IER for weight loss in obesity.
... Magnetic resonance imaging (MRI) is a powerful neuroimaging technique evaluating brain structure and function in vivo. It has been widely used for assessing whole-brain morphology and functional response, as well as connectivity, and resulted in various correlates of obesity [12][13][14][15][16][17][18][19] . Previous studies observed that individuals with obesity showed differences in gray matter volume in the sensorimotor and transmodal regions of frontal and temporal cortices 19 , as well as decreases in cortical thickness in reward systems, including the orbitofrontal cortex, ventral diencephalon, and brainstem 14 . ...
... Previous studies observed that individuals with obesity showed differences in gray matter volume in the sensorimotor and transmodal regions of frontal and temporal cortices 19 , as well as decreases in cortical thickness in reward systems, including the orbitofrontal cortex, ventral diencephalon, and brainstem 14 . In addition to morphological alterations, functional connectivity perturbation, particularly in the regions related to reward systems, has been observed in previous neuroimaging studies based on resting-state functional MRI (rs-fMRI) [15][16][17] . Task-based fMRI studies have shown enhanced food-related responses during reward processing in reward and default-mode networks 12,13 . ...
Functional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations.
... This is especially the case for anorexia, to which many other factors may contribute (Kaye, 2008;Kaye et al., 2020). Much previous research on brain connectivity and obesity has focussed more on reduced functionality of brain systems related to executive function, rather than on enhanced sensitivity of brain systems to food reward (Tregellas et al., 2011;Kullmann et al., 2012;Lips et al., 2014;Garcia-Garcia et al., 2015;Lowe et al., 2019;Donofry et al., 2020;Legget et al., 2021). Social and cognitive factors can operate by top-down biassing of the reward-related representations in the orbitofrontal cortex, with their origin in the dorsolateral prefrontal cortex (de Araujo et al., 2005;Deco and Rolls, 2005;Rolls et al., 2008;Ge et al., 2012;Luo et al., 2013;Rolls, 2013Rolls, , 2021a. ...
... In fact, this may be the largest neuroimaging investigation yet performed of food reward systems in the brain, and their relation to body weight / BMI. For example, activation studies to the food presentation have typically involved fewer than 30 participants (Tang et al., 2012;Masterson et al., 2019), and previous functional connectivity studies related to eating or obesity fewer than 100 participants (Tregellas et al., 2011;Kullmann et al., 2012;Lips et al., 2014;Garcia-Garcia et al., 2015;Donofry et al., 2020;Legget et al., 2021). Further, the results were cross-validated with Human Connectome Project data. ...
The aim was to investigate with very large scale analyses whether there are underlying functional connectivity differences between humans that relate to food reward; and whether these in turn are associated with being overweight. In 37,286 humans from the UK Biobank resting state functional connectivities of the orbitofrontal cortex, especially with the anterior cingulate cortex, were positively correlated with the liking for sweet foods (FDR p < 0.05). They were also positively correlated with the body mass index (BMI) (FDR p < 0.05). Moreover, in a sample of 502,492 people, the ‘liking for sweet foods’ was correlated with their BMI (r=0.06, p<10-125). In a cross-validation with 545 participants from the Human Connectome Project, higher functional connectivity involving the orbitofrontal cortex relative to other brain areas was associated with high BMI (≥30) compared to a mid-BMI group (22-25; p=6x10-5); and low orbitofrontal cortex functional connectivity was associated with low BMI (≤20.5; p<0.024). It is proposed that high BMI relates to increased efficacy of orbitofrontal cortex food reward systems, and low BMI to decreased efficacy. This was found with no stimulation by food, so may be an underlying individual difference in brain connectivity that is related to food reward and BMI.
... On the other hand, functional signatures of BMI at macroscale during resting conditions remain underexplored. Indeed, despite reports exploring associations between BMI and the connectivity of specific regions (García-García et al., 2015;Lips et al., 2014;Park et al., 2015) and larger networks Park et al., 2016), whole-brain functional network configurations associated with BMI are less well established. We aim to close this gap in the current work by applying connectome manifold learning techniques to identify functional substrates of BMI in a large population of healthy adults. ...
... 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 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. 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 García-García et al., 2013. ...
A bstract
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.
... Lastly, several neuroimaging studies demonstrated a marked difference in resting-state functional connectivity (RSFC) between obese and normal-weight individuals. This is indicative of lower functional connectivity in the middle frontal gyrus (Garcia-Garcia et al., 2015). On the other hand, increased activity synchronicity was found in the left putamen of obese men before consumption of a meal. ...
... A multitude of cognitive measures have been found to be altered in the obese brain, such as stimulus and reward processing, as well as memory, learning and cognitive control (Garcia-Garcia et al., 2013;Wijngaarden et al., 2015;Stingl et al., 2012;Cheke et al., 2017;Skoranski et al., 2013). Furthermore, changes in resting-state activity are seen in obese individuals (Garcia-Garcia et al., 2015;Zhang et al., 2015). ...
Obesity has a major impact on metabolic health thereby negatively affecting brain function and structure, however mechanisms involved are not entirely understood. The increasing prevalence of obesity is accompanied by a growing number of bariatric surgeries (BS). Weight loss after BS appears to improve cognitive function in patients. Therefore, unraveling mechanisms how BS influences brain function may be helpful to develop novel treatments or treatments in combination with BS preventing/inhibiting neurodegenerative disorders like Alzheimer's disease. This review shows the relation between obesity and impaired circulation to and in the brain, brain atrophy, and decreased cognitive functioning. Weight loss seems to recover some of these brain abnormalities as greater white matter and gray matter integrity, functional brain changes and increased cognitive functioning is seen after BS. This relation of body weight and the brain is partly mediated by changes in adipokines, gut hormones and gut microbiota. However, the exact underlying mechanisms remain unknown and further research should be performed.
... homeostatic and cognitive state) and external factors (i.e. social environment) and have been found altered in obese/ overweight individuals [25][26][27]. In particular, dysfunctions in the connectivity between these regions may reflect obesity-related defects in inhibitory control and attention processes involved in food intake behavior, and an increased motivation to internal signals, such as appetite or food-related reward [28]. ...
... In all subjects, the glycaemia at the time of PET scan was less than 160 mg/dl, as recommended by the international guidelines [40]. Cognitive status, as evaluated by means of the MMSE, was reported normal in all subjects (mean ± SD=29.03±1.23 [26][27][28][29][30] Table 3. All subjects provided written informed consent; the protocols conformed to the Ethical standards of the declaration of Helsinki for protection of human subjects. ...
There are reported gender differences in brain connectivity associated with obesity. In the elderlies, the neural endophenotypes of obesity are yet to be elucidated. We aim at exploring the brain metabolic and connectivity correlates to different BMI levels in elderly individuals, taking into account gender as variable of interest.
We evaluated the association between BMI, brain metabolism and connectivity, in elderly females and males, by retrospectively collecting a large cohort of healthy elderly subjects (N=222; age=74.03±5.88 [61.2-85.9] years; M/F=115/107; BMI=27.00±4.02 [19.21-38.79] kg/m²). Subjects underwent positron emission tomography with [18F]FDG. We found that, in females, high BMI was associated with increased brain metabolism in the orbitofrontal cortex (R=0.44; p<0.001). A significant BMI-by-gender interaction was present (F=7.024, p=0.009). We also revealed an altered connectivity seeding from these orbitofrontal regions, namely expressing as a decreased connectivity in crucial control/decision making circuits, and as an abnormally elevated connectivity in reward circuits, only in females. Our findings support a link between high BMI and altered brain metabolism and neural connectivity, only in elderly females. These findings indicate a strong gender effect of high BMI and obesity that brings to considerations for medical practice and health policy.
... In previous studies, EC has been found to change as a function of stimulus (Koelsch & Skouras, 2014), task (García- García et al., 2015), and condition (Binnewijzend et al., 2013;Lou et al., 2015). While functional connectivity studies traditionally have assumed connectivity patterns that are static over time, dynamic functional connectivity studies have become increasingly common (for a review, see Preti et al., 2017). ...
... Indeed, moments of high beat salience can be assumed not to require attentive beat finding and hence be considered as low load conditions, implying increased mind-wandering. García-García et al. (2015) observed increase in centrality of the precuneus and angular gyrus of the DMN in rest conditions versus task-based conditions (visual) which were associated with increased centrality in visual regions. This result is in accordance with the present findings, assuming that beat inference is considered to be a task which requires attentive listening, and beat maintenance, in contrast to beat inference, to be a rest condition. ...
Keeping time is fundamental for our everyday existence. Various isochronous activities, such as locomotion, require us to use internal timekeeping. This phenomenon comes into play also in other human pursuits such as dance and music. When listening to music, we spontaneously perceive and predict its beat. The process of beat perception comprises both beat inference and beat maintenance, their relative importance depending on the salience of beat in the music. To study functional connectivity associated with these processes in a naturalistic situation, we used functional magnetic resonance imaging to measure brain responses of participants while they were listening to a piece of music containing strong contrasts in beat salience. Subsequently, we utilized dynamic graph analysis and psychophysiological interactions (PPI) analysis in connection with computational modelling of beat salience to investigate how functional connectivity manifests these processes. As the main effect, correlation analyses between the obtained dynamic graph measures and the beat salience measure revealed increased centrality in auditory-motor cortices, cerebellum, and extrastriate visual areas during low beat salience, whereas regions of the default mode- and central executive networks displayed high centrality during high beat salience. PPI analyses revealed partial dissociation of functional networks belonging to this pathway indicating complementary neural mechanisms crucial in beat inference and maintenance, processes pivotal for extracting and predicting temporal regularities in our environment.
... In detail, we calculated DC by counting, for each voxel, the number of voxels it was connected to at a threshold of r ≥ 0.25. More details regarding DC processing are available in the literature [18,[31][32][33][34]. On the other hand, we calculated EC by counting the weighted number of correlations based on fast ECM (fECM) toolbox [31,35,36]. ...
... In detail, we calculated DC by counting, for each voxel, the number of voxels it was connected to at a threshold of r ≥ 0.25. More details regarding DC processing are available in the literature [18,[31][32][33][34]. On the other hand, we calculated EC by counting the weighted number of correlations based on fast ECM (fECM) toolbox [31,35,36]. Then, all DC and EC maps underwent smoothing with full width at half maximum with a Gaussian kernel of 6 mm × 6 mm × 6 mm and Fisher's Z transformation. ...
Background
Individuals with subjective memory complaints (SMC) feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease (AD). However, the pathological mechanism underlying SMC remains unclear. We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.
Methods
We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography (PET). Based on graph theory approaches, we detected local and global functional connectivity across the whole brain by using degree centrality (DC) and eigenvector centrality (EC) respectively. Additionally, we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid (CSF) data. The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition. Then, we correlated these network metrics with pathological results.
Results
The SMC individuals showed higher DC in the bilateral hippocampus (HP) and left fusiform gyrus and lower DC in the inferior parietal region than controls. Across all subjects, the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181. However, no significant between-group difference existed in EC and cerebral amyloid deposition.
Conclusion
We found impaired local, but not global, intrinsic connectivity networks in SMC individuals. Given the relationships between DC value and tau level, we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.
Electronic supplementary material
The online version of this article (10.1186/s40035-018-0130-z) contains supplementary material, which is available to authorized users.
... In detail, we calculated DC by counting, for each voxel, the number of voxels it was connected to at a threshold of r ≥ 0.25. More details regarding DC processing are available in the literature [18,[31][32][33][34]. On the other hand, we calculated EC by counting the weighted number of correlations based on fast ECM (fECM) toolbox [31,35,36]. ...
... In detail, we calculated DC by counting, for each voxel, the number of voxels it was connected to at a threshold of r ≥ 0.25. More details regarding DC processing are available in the literature [18,[31][32][33][34]. On the other hand, we calculated EC by counting the weighted number of correlations based on fast ECM (fECM) toolbox [31,35,36]. Then, all DC and EC maps underwent smoothing with full width at half maximum with a Gaussian kernel of 6 mm × 6 mm × 6 mm and Fisher's Z transformation. ...
Background: Individuals with subjective memory complaints (SMC) feature a higher risk of cognitive decline and
clinical progression of Alzheimer’s disease (AD). However, the pathological mechanism underlying SMC remains
unclear. We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in
SMC individuals.
Methods: We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional
MRI and positron emission tomography (PET). Based on graph theory approaches, we detected local and global
functional connectivity across the whole brain by using degree centrality (DC) and eigenvector centrality (EC)
respectively. Additionally, we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and
cerebrospinal fluid (CSF) data. The voxel-wise two-sample T-test analysis was used to examine between-group
differences in the intrinsic functional network and cerebral amyloid deposition. Then, we correlated these network
metrics with pathological results.
Results: The SMC individuals showed higher DC in the bilateral hippocampus (HP) and left fusiform gyrus and
lower DC in the inferior parietal region than controls. Across all subjects, the DC of the bilateral HP and left fusiform
gyrus was positively associated with total tau and phosphorylated tau 181 . However, no significant between-group
difference existed in EC and cerebral amyloid deposition.
Conclusion: We found impaired local, but not global, intrinsic connectivity networks in SMC individuals. Given the
relationships between DC value and tau level, we hypothesized that functional changes in SMC individuals might
relate to pathological biomarkers.
... Importantly, this relationship emerged only when the model accounted for BMI, indicating that BMI influences responses in higher-order regions, including LOC. This same region demonstrates cortical thinning [12,13,15] and impaired taskdependent functional connectivity [41] in individuals with higher BMI values. Therefore, we suggest that reduced sensitivity in the V1 leads to functional changes in higher-order visual regions, which results in deficits in the perception of line orientation. ...
Objective
Recent work has reported a negative association between BMI and performance on the Penn Line Orientation Task. To determine the reliability of this effect, a comprehensive assessment of visual function in individuals with healthy weight (HW) and those with overweight/obesity (OW/OB) was performed.
Methods
Visual acuity/contrast, Penn Line Orientation Task, and higher-order visuospatial function were measured in 80 (40 with HW, 40 with OW/OB) case-control study participants. Adiposity, fasting glucose, hemoglobin A1c, diet, physical activity, and heart rate variability were also assessed. A subgroup of 22 participants plus 5 additional participants (n = 27) underwent functional magnetic resonance imaging scanning.
Results
Compared with those with HW, individuals with OW/OB performed worse on tasks requiring judgments of line orientation. This effect was mediated by body fat percentage and was unrelated to other measures. Functional magnetic resonance imaging revealed a negative association between BMI and response in the primary visual cortex (V1) during line orientation judgment. Performance was unrelated to V1 response but positively correlated with response in a network of regions, including the lateral occipital cortex, when BMI was accounted for in the model.
Conclusions
These results demonstrate a selective deficit in line orientation perception associated with adiposity and blunted activation in the V1 that cannot be attributed to visual acuity and does not generalize to other visuospatial tasks.
... DC provides data regarding functional connectivity within the human cerebrum network instead of low-frequency fluctuation in regional homogeneity and amplitude [10]. Thus far, DC method has often been used for the investigation of the neuropathologic mechanisms of numerous diseases, including Parkinson's disease, obesity, and autism [11][12][13]. Similarly, the DC method has been used to study many eye diseases so far, such as in [14]. ...
Background:
Numerous anterior neuroimaging researches have revealed that corneal ulcers (CU) are related to changes in cerebral anatomic structure and functional area. Nonetheless, functional characteristics of the brain's network organization still show no definite research results. The study was designed to confirm CU-associated spatial centrality distribution functional network of the whole cerebrum and explore the mechanism through which the larvaceous changed the intrinsic functional hubs.
Material and methods:
In this study, 40 patients with CU and 40 normal controls (matched in sex, age, and education level) were enrolled in this study to undergo resting-state functional magnetic resonance imaging (fMRI) scans. The differences between the groups were determined by measuring the voxel-wise degree centrality (DC) throughout the whole cerebrum. For the purpose of assessing the correlation between abnormal DC value and clinical variables, the Linear correlation analysis was used.
Results:
Compared with normal controls (NCs), CU patients revealed high DC values in the frontal lobe, precuneus, inferior parietal lobule, posterior cingulate, occipital lobe, and temporal lobe in the brain functional connectivity maps throughout the brain. The intergroup differences also had high similarity on account of different thresholds. In addition, DC values were positively related to the duration of CU in the left middle frontal gyrus.
Conclusions:
The experimental results revealed that patients with CU showed spatially unnatural intrinsic functional hubs whether DC values increased or decreased. This brings us to a new level of comprehending the functional features of CU and may offer useful information to make us obtain a clear understanding of the dysfunction of CU.
... As a potentially powerful fusion between localizationism and holism, graph theory and TDA concepts have already been applied in brain research. Starting with graph theory, all the metrics mentioned above have been used in the investigation of brain networks in both normal or pathological states (Eijlers et al. 2017;Garcia-Garcia et al. 2015;Wang et al. 2017;Wink 2019;Breedt et al. 2021;DeSalvo et al. 2020;Liu et al. 2012;dos Santos Siqueira et al. 2014;Yu et al. 2012;Davis et al. 2013;Suo et al. 2015). As one can identify by reading these articles, researchers often use different graph-theoretical metrics in the same study, which helps them look for alterations that data. ...
The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.
... Besides the effects of obese mother on the brain development of baby, there were studies for the obese subjects at other ages. It was found that the degree centrality of functional connectivity (FC) for obese people (aged 20 to 40 years old) was lower than that of normal-weight people in the middle frontal gyrus (a cortical region related with attention, executive control and motor) both during the resting and task states (García-García et al., 2015). With an emotion task, the neural responses in amygdala of obese people (age M = 20.8, ...
Obesity was found to be related with the changes of brain functions in human beings. There were several brain areas that were verified to be correlated with the obesity, including the parietal cortex, frontal cortex and so on. However, the cortical regions found from different studies were discrepant due to the different ages, gender distribution and satiation degree of participants. We found that the regional homogeneity of right angular gyrus were smaller in obese undergraduates than that in normal-weight undergraduates. Moreover, functional connectivity of the left middle temporal cortex and the right angular gyrus were found to be smaller in obese group than that in normal-weight group by setting the right angular gyrus as seed region. In addition, multiple regression analysis suggested that the right superior frontal gyrus and left middle temporal gyrus were significantly correlated with their body mass index for normal-weight undergraduates, but no significant correlation was found for obese group. In summary, these findings indicated the functional changes of the cortex in obese undergraduates, which might be significant for providing imaging-based biomarkers for intervention and therapy of obesity.
... Other studies have observed higher internetwork rsFC between DMN and sensorimotor network in obesity [7], and speculated that this might relate to altered internal states and external information processing, which in turn could contribute to weight gain. Graphtheory studies indicated that global resting-state FC is lower in the right middle frontal gyrus (MFG) [10,11], ventromedial and ventrolateral prefrontal cortex (vmPFC and vlPFC) [12], insula [11,12], left middle temporal cortex [11], caudate [12], putamen [13], thalamus [13] and pallidum [13] in obese individuals, perhaps reflective of lower efficiency of brain functional architecture. Seedbased studies show that participants with excess weight have altered interregional rsFC between hypothalamus and areas in prefrontal cortex, including medial prefrontal cortex (mPFC), dorsal anterior cingulate cortex (ACC), and inferior frontal gyrus (IFG) [14]. ...
Obesity is associated with brain intrinsic functional reorganization. However, little is known about the BMI-related interhemispheric functional connectivity (IHFC) alterations, and their link with executive function in young healthy adults.
We examined voxel-mirrored homotopic connectivity (VMHC) patterns in 417 young adults from the Human Connectome Project. Brain regions with significant association between BMI and VMHC were identified using multiple linear regression. Results from these analyses were then used to determine regions for seed-voxel FC analysis, and multiple linear regression was used to explore the brain regions showing significant association between BMI and FC. The correlations between BMI-related executive function measurements and VMHC, as well as seed-voxel FC, were further examined.
BMI was negatively associated with scores of Dimensional Change Card Sort Test (DCST) assessing cognitive flexibility (r = −0.14, p = 0.006) and with VMHC of bilateral inferior parietal lobule, insula and dorsal caudate. The dorsal caudate emerged as a nexus for BMI-related findings: greater BMI was associated with greater FC between caudate and hippocampus and lower FC between caudate and several prefrontal nodes (right inferior frontal gyrus, anterior cingulate cortex, and middle frontal gyrus). The FC between right caudate and left hippocampus was negatively associated with scores of DCST (r = −0.15, p = 0.0018).
Higher BMI is associated with poorer cognitive flexibility performance and IHFC in an extensive set of brain regions implicated in cognitive control. Larger BMI was associated with higher caudate-medial temporal lobe FC and lower caudate-dorsolateral prefrontal cortex FC. These findings may have relevance for executive function associated with weight gain among otherwise healthy young adults.
... Using rs-fMRI and FC, previous studies have identified abnormal functional patterns in a number of resting-state networks (RSNs) in individuals with obesity (OB) including default-mode network (DMN), salience network (SN), temporal lobe network, frontoparietal network (FPN) and basal ganglia (BG) (McFadden KL et al., 2013;Park BY et al., 2016;Tregellas JR et al., 2011;Wijngaarden MA et al., 2015). OB showed greater FC strength in precuneus and decreased FC strength in right anterior cingulate cortex (ACC) compared with lean subjects (Garcia-Garcia I et al., 2015). ...
Previous fMRI studies have showed obesity-related alterations in intrinsic functional connectivity (FC) within and between different resting-state networks (RSNs). However, few studies have examined dynamic functional connectivity (DFC). Thus, we employed resting-state fMRI with independent component analysis (ICA) and DFC analysis to investigate alterations in FC within and between RSNs in 56 individuals with obesity (OB) and 46 normal-weight controls (NW). ICA identified 6 RSNs including basal ganglia (BG), salience-(SN), right-(rECN)/left-(lECN) executive-control and anterior-(aDMN)/posterior-(pDMN) default-mode network. The DFC analysis identified four FC states. OB compared to NW had more occurrences and a longer mean dwell time (MDT) in state 2 (positive connectivity of BG with other RSN) and also had higher FC of BG-SN in other states. Body mass index (BMI) was positively correlated with MDT and FCs of BG-aDMN (state 2) and BG-SN (state 4). DFC analysis within more refined nodes of RSNs showed that OB had more occurrences and a longer MDT in state 1 in which caudate had positive connections with the other network nodes. The findings suggest an association between caudate-related and BG-related positive FC in obesity, which was not revealed by traditional FC analysis, highlighting the utility of adding DFC to more conventional methods.
... The first main finding of this study was that in comparison to the sham control, HF-rTMS-F3 was associated with a significant decrease in BMI and was ranked to be associated with the second-largest decrease in BMI and the largest decrease in total energy intake and craving severity. In a previous study using resting-state fMRI, obese participants were found to have decreased functional connectivity in their DLPFC, which is part of the frontoparietal network, and its deficiency indicated a top-down deficiency in inhibitory control [50,51]. This deficient inhibitory control could serve as one of the possible links between obesity and self-control, which is decreased in obese participants [51]. ...
Obesity has recently been recognized as a neurocognitive disorder involving circuits associated with the reward system and the dorsolateral prefrontal cortex (DLPFC). Noninvasive brain stimulation (NIBS) has been proposed as a strategy for the management of obesity. However, the results have been inconclusive. The aim of the current network meta-analysis (NMA) was to evaluate the efficacy and acceptability of different NIBS modalities for weight reduction in participants with obesity.
Randomized controlled trials (RCTs) examining NIBS interventions in patients with obesity were analyzed using the frequentist model of NMA. The coprimary outcome was change in body mass index (BMI) and acceptability, which was calculated using the dropout rate.
Overall, the current NMA, consisting of eight RCTs, revealed that the high-frequency repetitive transcranial magnetic stimulation (TMS) over the left DLPFC was ranked to be associated with the second-largest decrease in BMI and the largest decrease in total energy intake and craving severity, whereas the high-frequency deep TMS over bilateral DLPFC and the insula was ranked to be associated with the largest decrease in BMI.
This pilot study provided a “signal” for the design of more methodologically robust and larger RCTs based on the findings of the potentially beneficial effect on weight reduction in participants with obesity by different NIBS interventions.
... Magnetic resonance imaging (MRI) studies have also demonstrated relationships between obesity and white matter (WM) disruption in several tracts, including the corpus callosum, internal capsule and thalamic radiation (7,14). Several functional magnetic resonance imaging (fMRI) studies have reported differences in brain activity levels between participants who are lean and those with obesity in various resting-state networks, including the default-mode network, salience network, prefrontal and temporal lobe networks (15)(16)(17)(18)(19). However, the exact mechanisms underlying the links between obesity and structural and functional brain alterations remain largely unknown. ...
Background: Metabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables.
Methods: The study included 57 participants with severe obesity who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N=36), 12 months (N=29), and 24 months (N=14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N=46) or severe obesity (N=46).
Results: We found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as insulin resistance index and blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks.
Conclusions: Bariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.
... Interestingly, in our study higher BMI also predicted lower connectivity between the ventral DC and low-and high-level visual areas: the left lingual gyrus, the bilateral calcarine cortex, and the left cuneal cortex, and a smaller cluster located in the left temporal occipital fusiform gyrus. Various studies have reported abnormalities in the connectivity of the ventral DC in individuals with overweight/obesity (Baek et al., 2017;Contreras-Rodríguez et al., 2017;Nummenmaa et al., 2012;Olivo et al., 2016;Park et al., 2015;Steward et al., 2016), as well as reduced connectivity of the fusiform gyri and temporal visual areas (García-García et al., 2015;Kullmann et al., 2013). Lower connectivity between these areas may be linked to a dissociation between perceptive and reward mechanisms, which can be one of the factors leading to overeating in obesity. ...
Previous studies have shown that individuals with overweight and obesity may experience attentional biases and reduced inhibition toward food stimuli. However, evidence is scarce as to whether the attentional bias is present even before stimuli are consciously recognized. Moreover, it is not known whether or not differences in the underlying brain morphometry and connectivity may co-occur with attentional bias and impulsivity towards food in individuals with different BMIs. To address these questions, we asked fifty-three participants (age M = 23.2, SD = 2.9, 13 males) to perform a breaking Continuous Flash Suppression (bCFS) task to measure the speed of subliminal processing, and a Go/No-Go task to measure inhibition, using food and nonfood stimuli. We collected whole-brain structural magnetic resonance images and functional resting-state activity. A higher BMI predicted slower subliminal processing of images independently of the type of stimulus (food or nonfood, p = 0.001, εp2 = 0.17). This higher threshold of awareness is linked to lower grey matter (GM) density of key areas involved in awareness, high-level sensory integration, and reward, such as the orbitofrontal cortex [t = 4.55, p = 0.003], the right temporal areas [t = 4.18, p = 0.002], the operculum and insula [t = 4.14, p = 0.005] only in individuals with a higher BMI. In addition, individuals with a higher BMI exhibit a specific reduced inhibition to food in the Go/No-Go task [p = 0.02, εp2 = 0.02], which is associated with lower GM density in reward brain regions [orbital gyrus, t = 4.97, p = 0.005, and parietal operculum, t = 5.14, p < 0.001] and lower resting-state connectivity of the orbital gyrus to visual areas [fusiform gyrus, t = -4.64, p < 0.001 and bilateral occipital cortex, t = -4.51, p < 0.001 and t = -4.34, p < 0.001]. Therefore, a higher BMI is predictive of non food-specific slower visual subliminal processing, which is linked to morphological alterations of key areas involved in awareness, high-level sensory integration, and reward. At a late, conscious stage of visual processing a higher BMI is associated with a specific bias towards food and with lower GM density in reward brain regions. Finally, independently of BMI, volumetric variations and connectivity patterns in different brain regions are associated with variability in bCFS and Go/No-Go performances.
... Degree centrality (DC) is a measurement index used to describe the importance of nodes in different brain networks [21]; it can be used to analyze the energy network and identify the important nodes in information transmission. The voxelbased DC analysis method regards each voxel in the brain as a brain network node and calculates the correlation between each node and other nodes in the whole brain [22]. The size reflects the functional connection characteristics at the brain voxel level, and the voxel DC value is positively correlated with its importance in the functional network. ...
Background
The aim of this study was to explore potential changes in brain function network activity in patients with adult strabismus with amblyopia (SA) using the voxel-wise degree centrality (DC) method.
Material/Methods
We enrolled 15 patients with SA (6 males, 9 females) and 15 sex-matched healthy controls (HCs). All subjects completed resting functional magnetic resonance imaging scans. Independent-sample t tests and receiver operating characteristic (ROC) curves were used to assess DC value differences between groups, and Pearson correlation analysis was performed to evaluate correlations between DC-changed brain regions and clinical data of patients with SA.
Results
Compared with the HC group, DC values that were lower in patients with SA included the left middle frontal gyrus and bilateral angular gyri. Increases were observed in the left fusiform gyrus, right lingual gyrus, right middle occipital gyrus, right postcentral gyrus, and left paracentral lobule. However, DC values were not correlated with clinical manifestations. ROC curve analysis showed high accuracy.
Conclusions
We found abnormal neural activity in specific brain regions in patients with SA. Specifically, we observed significant changes in DC values compared to HCs. These changes may be useful to identify the specific mechanisms involved in brain dysfunction in SA.
... It is well established that sex/gender differences exist in human brain functional connectivity [39,40]. Elevated BMI, the most common marker of obesity (BMI > 30 kg/m 2 ), has also been associated with brain connectivity [41,42]. However, the interaction between sex/gender and obesity remains unclear. ...
While the global prevalence of obesity has risen among both men and women over the past 40 years, obesity has consistently been more prevalent among women relative to men. Neuroimaging studies have highlighted several potential mechanisms underlying an individual’s propensity to become obese, including sex/gender differences. Obesity has been associated with structural, functional, and chemical alterations throughout the brain. Whereas changes in somatosensory regions appear to be associated with obesity in men, reward regions appear to have greater involvement in obesity among women than men. Sex/gender differences have also been observed in the neural response to taste among people with obesity. A more thorough understanding of these neural and behavioral differences will allow for more tailored interventions, including diet suggestions, for the prevention and treatment of obesity.
... A recent meta-analysis also reported a moderate inverse effect size for inhibitory control deficits among overweight and obese individuals (Yang et al., 2018). Atrophy of prefrontal cortical regions implicated in cognitive control has been found in individuals with obesity, providing evidence of obesity-impaired cognitive inhibition through both structural and functional brain imaging data (García-García et al., 2015). ...
Objective
The aim of the present study was to examine the influence of acute high-intensity interval exercise (HIIE) on neural and behavioral measures of inhibitory control in young male adults with obesity.DesignThe present study employed a within-subjects design.Methods
Sixteen male adults with obesity [body mass index (BMI) > 28 kg/m2] were recruited. Reaction time and response accuracy of the Flanker task as well as P3 and late positive potential (LPP) components of the event-related potential (ERP) were measured following HIIE and a sedentary control, in counterbalanced order. The HIIE session consisted of 30 min of stationary cycle exercise (5-min warm-up, 20-min HIIE, and a 5-min cool-down), whereas the control condition consisted of a time and attention-matched sedentary resting session.ResultsFaster response times were observed following HIIE regardless of Flanker task condition. Faster and more accurate responses were also observed for congruent relative to incongruent conditions across both sessions. Relative to the neuroelectric data, acute HIIE resulted in increased LPP amplitude but did not affect P3 amplitude.Conclusion
Collectively, a single bout of HIIE has a general beneficial effect on basic information processing and inhibitory control among young adult males with obesity. Acute HIIE was found to impact LPP amplitude, but not the P3, which may suggest a modulation in the ability to successfully maintain attention and filter irrelevant information to achieve successful cognitive inhibition. Future research is warranted to extend these findings to a larger sample size that includes both genders, other cognitive functions, and a comparison of different modes of exercise.
... In addition to the increased risk for Alzheimer's disease, other cognitive deficits (pertaining to i.e. learning, flexibility, attention and inhibitory control) have been associated with obesity [49][50][51][52] . Not surprisingly, the cognitive deficits are associated with abnormal brain functioning 49,53,54 . For some of the aforementioned cognitive deficits it may be difficult to disentangle whether these deficits precipitate the onset of obesity, or whether they are a result of obesity. ...
This chapter provides an overview of the main and most consistently reported psychological factors that play a role in the onset and persistence of obesity. Taking psychological factors into account is crucial, as a pure biomedical model does not explain sufficiently the sizeable individual variability of weight gain, and persistence of abnormal weight. From a biopsychosocial perspective, we focus on eating behaviour, how eating behaviour is affected by psychological factors and consequences of eating behaviour. We discuss the role of emotional and cognitive factors, mood and emotional regulation, stigma and discrimination and personality traits in relation to obesity. Studies show that individuals with obesity have a stronger sensitivity (attentional bias) for, and motivational drive ("wanting") towards foods rich in fat and sugar (palatable food) coupled with deficient impulse control in contexts of anticipated palatable food. Negative mood has been shown to be implicated in obesity and to induce compensatory (excessive) intake of palatable food. It should be noted that negative mood has a bidirectional relation with obesity; obesity has also been shown to result in negative mood. Certainly, adding to this is the fact that obesity is associated with stigma, discrimination, bullying and stereotypical media portrayals. Importantly, it should be emphasized that there is a considerable overlap of the condition of obesity with addiction, both in terms of phenomenology as well as with respect to the brain mechanism that drives maladaptive behaviour. Indeed, it has been suggested that obesity should be characterized a mental disorder. Currently, obesity is not classified as a mental disorder mainly because of the heterogeneity and uncertainty with respect to its etiology. This may be surprising as this is the case with several other included disorders, and the debate continues. At least part of the issue of current suboptimal treatment approaches is a lack of understanding of the key mechanism implicated in obesity. Hence, increased insight into the main psychological mechanisms could assist in future treatment directions.
... PFC modulating responses to high-calorie foods has strong linkage to executive-attention [20], inhibitory-control [21] as well as emotional-regulation [22]. Besides reports of brain responses to food cue stimulation, a number of resting-state fMRI studies have been performed to examine abnormal functional connectivity (FC) in brain regions within resting-state networks (RSNs) including the default-mode network (DMN), salience network (SN) and frontoparietal network (FPN) which are involved in self-referential, food reward and executive control processing [23][24][25]. Obese subjects showed increased FC strength in the precuneus and decreased FC strength in the right anterior cingulate cortex [25]; a seed-based correlation analysis revealed increased FC between the posterior cingulate cortex (PCC) and precuneus, and between PCC and PFC [26]. In addition, one newly published paper from our group not only investigated differences in FC in the DMN, SN and FPN, but also examined alteration in inter-network connectivity which showed increased connectivity between the SN and FPN in obesity [27]. ...
Obese subjects show enhanced brain responses in motivation and reward neurocircuitry encompassing sensory and somatic integration-interception, motivation–reward (striatal), emotion, and memory processes, which attenuate frontal region activation during food cues. Bariatric surgery (BS) is the only reliable treatment for morbid obesity. Unfortunately, it is unknown how BS affects neurocircuitry after weight loss. We aimed to examine effects of BS on the basal activity of brain areas involved in reward and motivation processing, emotion, memory, and gut–brain interaction. We combined resting-state fMRI with amplitude of low-frequency fluctuation (ALFF) and Granger causality analysis (GCA) to assess interactions between regions within the frontal-mesolimbic circuitry in 16 obese subjects (OB) and 22 normal-weight (NW) subjects. The OB group was studied at baseline and 1 month post BS. Comparisons between OB and NW, and pre- and post BS, showed significant differences in ALFF in areas involved in drive (caudate, orbitofrontal cortex [OFC]), arousal (thalamus), and conditioning/memory (amygdala, hippocampus) (P < 0.05, FDR correction). GCA revealed that in the OB group, the OFC had greater connectivity to limbic regions (amygdala, hippocampus, and medial thalamus) and the caudate. Post BS, the connectivity of the OFC to limbic regions decreased, whereas the connectivity from the amygdala and hippocampus to the caudate and thalamus was enhanced, particularly in subjects with lower body mass index (BMI). OFC activation in the OB group was associated with BMI prior to surgery, and changes in OFC post surgery were associated with alterations in BMI. Overall, the functional connectivity of the OFC was significantly decreased. As it is important for salience attribution and connected to limbic brain regions involved with emotional reactivity and conditioning after BS, its significant association with BMI changes indicates the contribution of OFC changes to the improved control of eating behavior after surgery.
... Inhibitory control refers to the ability to suppress inappropriate actions or override the prepotent processing of task-irrelevant or distracting information (Bari & Robbins, 2013). Unfortunately, the atrophy in the prefrontal regions (Garcia-Garcia et al., 2015) and decreased connectivity in the brain regions involving cognitive control (Moreno-Lopez et al., 2016) in obese individuals imply deficits in inhibitory control. Indeed, obese/overweight individuals generally perform more poorly than normal-weight individuals on behavioural tasks requiring individuals to override the interference caused by irrelevant distracting information (e.g., the Stroop test and the Flanker test), indicating that obese/overweight individuals are less effective in exerting inhibitory control (Kamijo et al., 2014;Reyes et al., 2015). ...
Whether the acute coordinative exercise could affect the inhibitory control and food-cue related attention in obese adolescents remains understudied. Therefore, this study used the Stroop test and the food-cue related Stroop test to explore the impacts of 20 min of acute coordinative exercise on the cognitive tests involving inhibitory control and attentional bias towards food-cue related stimuli, respectively, in obese adolescents. Thirty-eight obese adolescents (mean age = 14.63 ± 0.69 years) were equally divided into exercise and control groups. The cognitive tests (i.e., the Stroop test and the food-cue related Stroop test) and hunger scores were conducted and assessed before and after an intervention. The exercise group had significantly larger negative pre-post response time difference in the congruent (−1.04 ± 0.29 ms) and incongruent (−5.76 ± 1.66 ms) conditions of the Stroop test than the control group (ps < 0.01), and a smaller post-interference (1.13 ± 0.14) than the pre-interference (1.31 ± 0.14, p = 0.04). Moreover, a significantly larger negative pre-post response time difference on the food-cue related Stroop test was observed in the exercise group (−4.42 ± 7.20 ms) than the control group (1.76 ± 8.37 ms, p = 0.02). Collectively, an acute coordinative exercise session could induce superior inhibitory control and less attentional bias towards food-cue related stimuli in obese adolescents.
... Moderation of impulsive behavior has been previously associated with activation in cognitive control regions of the brain which include the prefrontal, dorsolateral prefrontal, orbitofrontal, posterior parietal, and anterior cingulate cortices and hippocampus (Bruce et al. 2010;Bruce et al. 2013;Chaddock-Heyman et al. 2013;Davids et al. 2010;Davis et al. 2011;Garcia-Garcia et al. 2013;Sokunbi et al. 2014;Van Leijenhorst et al. 2010;Voss et al. 2011). Studies that have specifically used food-related stimuli to investigate impulse control have shown activation differences in the prefrontal, orbitofrontal and anterior cingulate cortices, as well as connectivity with regions associated with memory, emotion, and reward (Garcia-Garcia et al. 2015;Maayan et al. 2011;Szabo-Reed et al. 2015;Yau et al. 2014). Functional MRI (fMRI) studies of food-related impulse control have also shown connectivity between cognitive control regions and regions associated with memory, emotion, and reward (Davis et al. 2011;Gupta et al. 2015;Nederkoorn et al. 2006). ...
The purpose of this study was to examine the effects of after-school sedentary screen time on children’s brain activation in reward and cognitive control regions in response to pictures of high- and low-calorie foods. Thirty-two children participated in a randomized crossover study with counterbalanced treatment conditions. Conditions took place on separate days after school and included three hours of active or sedentary play. After each condition, neural activation was assessed using functional magnetic resonance imaging (fMRI) while participants completed a go/no-go task involving pictures of high- and low-calorie foods. General response inhibition was also measured using the Stroop task. Hunger was measured upon arrival to the testing facility and just prior to fMRI scans. Mixed effects models were used to evaluate main effects and interactions. Significant stimulus by condition interactions were found in the right superior parietal cortex, and left anterior cingulate cortex (Ps ≤ 0.05). High-calorie pictures elicited significantly more activation bilaterally in the orbitofrontal cortex compared to low-calorie pictures (Ps ≤ 0.05). Stroop task performance diminished significantly following the sedentary condition compared to the active (P ≤ 0.05). Subjective feelings of hunger were not different between conditions at any point. Sedentary screen time was associated with significantly decreased response inhibition and a reversed brain activation pattern to pictures of high- and low-calorie foods compared to active play, in areas of the brain important to the modulation of food intake. Decreased attention, and impulse control following sedentary screen time may contribute to disinhibited eating that can lead to overweight and obesity.
... Growing evidence suggests that adiposity also has an adverse effect on the brain in terms of functional and structural alterations (García-García et al., 2015;Gustafson, Lissner, Bengtsson, Björkelund, & Skoog, 2004;Kullmann, Schweizer, Veit, Fritsche, & Preissl, 2015;Kurth et al., 2013;Marqués-Iturria et al., 2013;Stanek et al., 2011). Cognitive alterations, such as executive dysfunction (e.g. ...
... For example, obesity has also been associated with decreased global and local efficiency, as well as modularity of functional networks throughout the brain (Baek et al., 2017;Geha et al., 2017;Chao et al., 2018), suggesting that network architecture in obesity is characterized by reduced efficiency of information transfer both within and between networks and reduced functional segregation of networks. Moreover, there is evidence that some network hubs have reduced influence on neighboring regions in obesity, including the medial frontal gyrus (MFG; García-García et al., 2015). The MFG is a region that has been implicated in cognitive processes disrupted in obesity, including motor planning, inhibitory control and conflict monitoring (Rushworth et al., 2004). ...
Obesity is a major public health issue affecting nearly 40% of American adults and is associated with increased mortality and elevated risk for a number of physical and psychological illnesses. Obesity is associated with impairments in executive functions such as decision making and inhibitory control, as well as in reward valuation, which is thought to contribute to difficulty sustaining healthy lifestyle behaviors, including adhering to a healthy diet. Growing evidence indicates that these impairments are accompanied by disruptions in functional brain networks, particularly those that support self-regulation, reward valuation, self-directed thinking, and homeostatic control. Weight-related differences in task-evoked and resting state connectivity have most frequently been noted in the executive control network (ECN), salience network (SN), and default mode network (DMN), with obesity generally being associated with weakened connectivity in the ECN and enhanced connectivity in the SN and DMN. Similar disruptions have been observed in the much smaller literature examining the relationship between diet and disordered eating behaviors on functional network organization. The purpose of this narrative review was to summarize what is currently known about how obesity and eating behavior relate to functional brain networks, describe common patterns and to provide recommendations for future research based on the identified gaps in knowledge.
... Resting-state studies allow for the mapping of networks between distant regions that can be linked to certain processes or mental states. A growing body of research has demonstrated the existence of hyperconnected networks in obesity that are related to impulsivity [52]. For instance, one study has identified reduced cohesiveness in the sensorimotor network (SMN) and the visual network in individuals with obesity, suggesting an unbalanced integration between sensory characteristics of external stimuli (e.g. ...
Impulsivity and compulsivity are multidimensional constructs that are increasingly considered determinants of obesity. Studies using functional magnetic resonance imaging (fMRI) have provided insight on how differences in brain response during tasks exploring facets of impulsivity and compulsivity relate to the ingestive behaviors that support the etiology and maintenance of obesity. In this narrative review, we provide an overview of neuroimaging studies exploring impulsivity and compulsivity factors as they relate to weight status. Special focus will be placed on studies examining the impulsivity-related dimensions of attentional bias, delayed gratification and emotion regulation. Discussions of compulsivity within the context of obesity will be restricted to fMRI studies investigating habit formation and response flexibility under shifting contingencies. Further, we will highlight neuroimaging research demonstrating how alterations in neuroendocrine functioning are linked to excessive food intake and may serve as a driver of the impulsive and compulsive behaviors observed in obesity. Research on the associations between brain response with neuroendocrine factors, such as insulin, peptide YY (PYY), leptin, ghrelin and glucagon-like peptide 1 (GLP-1), will be reviewed.
... This finding may indicate that changes in brain structure precede deviations in function. Other studies have opted to combine both intrinsic resting-state and task-based activations, identifying alterations in the middle frontal gyrus and occipital areas during perceptual processes that may be explained by diminished functional integration 22 . Overall, these results uphold the benefits of utilizing multimodal imaging approaches to reveal the neurobiological abnormalities underpinning the behaviors found in OB. ...
Maladaptive emotion regulation contributes to overeating and impedes weight loss. Our study aimed to compare the voluntary downregulation of negative emotions by means of cognitive reappraisal in adult women with obesity (OB) and female healthy controls (HC) using a data-driven, multimodal magnetic resonance imaging (MRI) approach. Women with OB (n = 24) and HC (n = 25) carried out an emotion regulation task during functional MRI scanning. Seed-to-voxel resting-state connectivity patterns derived from activation peaks identified by this task were compared between groups. Diffusion tensor imaging (DTI) was used to examine white matter microstructure integrity between regions exhibiting group differences in resting-state functional connectivity. Participants in the OB group presented reduced activation in the ventromedial prefrontal (vmPFC) cortex in comparison to the HC group when downregulating negative emotions, along with heightened activation in the extrastriate visual cortex (p < 0.05, AlphaSim-corrected). Moreover, vmPFC peak activity levels during cognitive reappraisal were negatively correlated with self-reported difficulties in emotion regulation. OB patients exhibited decreased functional connectivity between the vmPFC and the temporal pole during rest (peak-pFWE = 0.039). Decreased fractional white-matter track volume in the uncinate fasciculus, which links these two regions, was also found in participants with OB. Taken together, our findings are indicative of emotion regulation deficits in OB being underpinned by dysfunctional hypoactivity in the vmPFC and hyperactivity in the extrastriate visual cortex. Our results provide a potential target circuit for neuromodulatory interventions to improve emotion regulation skills and weight-loss intervention outcomes.
This study aims to investigate the effect of increasing body mass index (BMI) on cognitive functions such as reaction time, sustained attention, and visuospatial working memory in children and adolescents. The research method is causal-comparative, and is a basic study in terms of purpose. The statistical population of the present study consists of all high school students (12-15 years old) in Tehran in 2019, among which 75 students (36 girls and 39 boys) were selected by convenience sampling method and classified into three groups of obese, overweight and normal weight based on BMI. All participants completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) to assess cognitive performance and BMI was also measured. Data were analyzed using SPSS software (version 26). The results of multivariate analysis of variance showed that obese students performed worse on the tests of reaction time and visuospatial working memory than the normal and overweight groups, and their difference is significant (p 0.01). Accordingly, one can say that increasing fat and BMI levels lead to a decrease in some cognitive functions and this may delay or decrease the development of students' academic and social skills.
Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.
Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist & hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets to implement the stated algorithms. Different theoretical literature from experts in ML & Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.
The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.
Background:
Cerebral microinfarcts (CMIs) might cause measurable disruption to brain connections and are associated with cognitive decline, but the association between CMIs and motor impairment is still unclear.
Objective:
To assess the CMIs effect on motor function in vivo and explore the potential neuropathological mechanism based on graph-based network method.
Methods:
We identified 133 non-demented middle-aged and elderly participants who underwent MRI scanning, cognitive, and motor assessment. The short physical performance battery (SPPB) assessed motor function, including balance, walking speed, and chair stand. We grouped participants into 34 incident CMIs carriers and 99 non-CMIs carriers as controls, depending on diffusion-weighted imaging. Then we assessed the independent CMIs effects on motor function and explored neural mechanisms of CMIs on motor impairment via mapping of degree centrality (DC) and eigenvector centrality (EC).
Results:
CMIs carriers had worse motor function than non-carriers. Linear regression analyses showed that CMIs independently contributed to motor function. CMIs carriers had decreased EC in the precuneus, while increased DC and EC in the middle temporal gyrus and increased DC in the inferior frontal gyrus compared to controls (p < 0.05, corrected). Correlation analyses showed that EC of precuneus was related to SPPB (r = 0.25) and balance (r = 0.27); however, DC (r = -0.25) and EC (r = -0.25) of middle temporal gyrus was related with SPPB in all participants (p < 0.05, corrected).
Conclusion:
CMIs represent an independent risk factor for motor dysfunction. The relationship between CMIs and motor function may be attributed to suppression of functional hub region and compensatory activation of motor-related regions.
Background
Metabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables.
Methods
The study included 57 participants with severe obesity (mean BMI=43.1 ± 4.3 kg/m²) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N = 36), 12 months (N = 29), and 24 months (N = 14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N = 46) or severe obesity (N = 46).
Results
We found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks.
Conclusions
Bariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.
Obesity is the second most common cause of preventable morbidity worldwide. Resting-state functional magnetic resonance imaging (fMRI) has been used extensively to characterise altered communication between brain regions in individuals with obesity, though findings from this research have not yet been systematically evaluated within the context of prominent neurobiological frameworks. This systematic review aggregated resting-state fMRI findings in individuals with obesity and evaluated the contribution of these findings to current neurobiological models. Findings were considered in relation to a triadic model of problematic eating, outlining disrupted communication between reward, inhibitory, and homeostatic systems. We identified a pattern of consistently increased orbitofrontal and decreased insula cortex resting-state functional connectivity in individuals with obesity in comparison to healthy weight controls. BOLD signal amplitude was also increased in people with obesity across studies, predominantly confined to subcortical regions, including the hippocampus, amygdala, and putamen. We posit that altered orbitofrontal cortex connectivity may be indicative of a shift in the valuation of food-based rewards and that dysfunctional insula connectivity likely contributes to altered homeostatic signal processing. Homeostatic violation signals in obesity may be maintained despite satiety, thereby ‘hijacking’ the executive system and promoting further food intake. Moving forward, we provide a roadmap for more reliable resting-state and task-based functional connectivity experiments, which must be reconciled within a common framework if we are to uncover the interplay between psychological and biological factors within current theoretical frameworks.
The current study examined the effects of an 11-week exercise intervention on brain activity during a working memory (WM) task and resting-state functional network connectivity in deaf children. Twenty-six deaf children were randomly assigned to either an 11-week exercise intervention or control conditions. Before and after the exercise intervention, all participants were scanned with functional magnetic resonance imaging (fMRI) during N-back task performance and a resting state. The behavioural results showed that the exercise intervention improved WM performance. Task activation analyses showed an increase in the parietal, occipital, and temporal gyri and hippocampus and hippocampus (HIP). In addition, WM performance improvements were associated with greater activation in the left HIP region. Resting-state functional connectivity (Rs-FC) between HIP and certain other brain areas shown a significant interaction of group (exercise versus no exercise) and time (pre- and postintervention). Moreover, connectivity between the left HIP and left middle frontal gyrus was related to improved WM performance. These data extend current knowledge by indicating that an exercise intervention can improve WM in deaf children, and these enhancements may be related to the WM network plasticity changes induced by exercise.
Weight gain is often associated with the pleasure of eating food rich in calories. This idea is based on findings that people with obesity showed increased neural activity in the reward and motivation systems of the brain in response to food cues. Such correlations, however, overlook the possibility that obesity may be associated with a metabolic state that impacts the functioning of reward and motivation systems, which in turn could be linked to reactivity to food and eating behavior and weight gain. In a study involving 44 female participants (14 patients with obesity, aged 20–63 years (mean: 42, sem: 3.2 years), and 30 matched lean controls, aged 22–60 years (mean: 37, sem: 1.8 years), we investigated how ventromedial prefrontal cortex seed-to-voxel resting-state connectivity distinguished between lean and obese participants at baseline. We used the results of this first step of our analyses to examine whether changes in ventromedial prefrontal cortex resting-state connectivity over 8 months could formally predict weight gain or loss. Important to note, participants with obesity underwent bariatric surgery at the beginning of our investigation period. We found that ventromedial prefrontal cortex–ventral striatum resting-state connectivity and ventromedial–dorsolateral prefrontal cortex resting-state connectivity were sensitive to obesity at baseline. However, only the ventromedial prefrontal cortex–ventral striatum resting-state connectivity predicted weight changes over time using cross-validation, out-of-sample prediction analysis. Such an out-of-sample prediction analysis uses the data of all participants of a training set to predict the actually observed data in one independent participant in the hold-out validation sample and is then repeated for all participants. In seeking to explain why ventromedial prefrontal cortex–ventral striatum resting-state connectivity as the central hub of the brain’s reward and motivational system may predict weight change over time, we linked weight loss surgery-induced changes in ventromedial prefrontal cortex–ventral striatum resting-state connectivity to surgery-induced changes in homeostatic hormone regulation. More specifically we focused on changes in fasting state systemic leptin, a homeostatic hormone signaling satiety, and inhibiting reward-related dopamine signaling. We found that the surgery-induced increase in ventromedial prefrontal cortex–ventral striatum resting-state connectivity was correlated with a decrease in fasting-state systemic leptin. These findings establish the first link between individual differences in brain connectivity in reward circuits in a more tonic state at rest, weight change over time, and homeostatic hormone regulation.
Abnormal activities in reward-related regions are associated with overeating or obesity. Preliminary studies have shown that changes in neural activity in obesity include not only regional reward regions abnormalities but also impairments in the communication between reward-related regions and multiple functional areas. A recent study has shown that the transitions between different neural networks are nonrandom and hierarchical, and that activation of particular brain networks is more likely to occur after other brain networks. The aims of this study were to investigate the key nodes of reward-related regions in obese males and explore the hierarchical integrated processing of key nodes. Twenty-four obese males and 24 normal-weight male controls of similar ages were recruited. The fMRI data were acquired using 3.0 T MRI. The fMRI data preprocessing was performed in DPABI and SPM 12. Degree centrality analyses were conducted using GRETNA toolkit, and Granger causality analyses were calculated using DynamicBC toolbox. Decreased degree centrality was observed in left ventral medial prefrontal cortex (vmPFC) and right parahippocampal/hippocampal gyrus in group with obesity. The group with obesity demonstrated increased effective connectivity between left vmPFC and several regions (left inferior temporal gyrus, left supplementary motor area, right insular cortex, right postcentral gyrus, right paracentral lobule and bilateral fusiform gyrus). Increased effective connectivity was observed between right parahippocampal/hippocampal gyrus and left precentral/postcentral gyrus. Decreased effective connectivity was found between right parahippocampal/hippocampal gyrus and left inferior parietal lobule. This study identified the features of hierarchical interactions between the key reward nodes and multiple function networks. These findings may provide more evidence for the existing view of hierarchical organization in reward processing.
Previous research has shown that empathy, a fundamental component of human social functioning, is engaged when listening to music. Neuroimaging studies of empathy processing in music have, however, been limited. fMRI analysis methods based on graph theory have recently gained popularity as they are capable of illustrating global patterns of functional connectivity, which could be very useful in studying complex traits such as empathy. The current study examines the role of trait empathy, including cognitive and affective facets, on whole-brain functional network centrality in 36 participants listening to music in a naturalistic setting. Voxel-wise eigenvector centrality mapping was calculated as it provides us with an understanding of globally distributed centres of coordination associated with the processing of empathy. Partial correlation between Eigenvector centrality and measures of empathy showed that cognitive empathy is associated with higher centrality in the sensorimotor regions responsible for motor mimicry while affective empathy showed higher centrality in regions related to auditory affect processing. Results are discussed in relation to various theoretical models of empathy and music cognition.
Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community.
The brain plays a central role in the pathophysiology of overweight and obesity. Connectome-based Predictive Modeling (CPM) is a newly developed, data-driven approach that exploits whole-brain functional connectivity to predict a behavior or trait that varies across individuals. We used CPM to determine whether brain “fingerprints” evoked during milkshake consumption could be isolated for common measures of adiposity in 67 adults with overweight and obesity. We found that CPM captures more variance in waist circumference than either percent body fat or BMI, the most frequently used measures to assess brain correlates of obesity. In a post-hoc analysis, we were also able to derive a largely separable functional connectivity network predicting fasting blood insulin. These findings suggest that, in individuals with overweight and obesity, brain network patterns may be more tightly coupled to waist circumference than BMI or percent body fat and that adiposity and glucose tolerance are associated with distinct maps, pointing to dissociable central pathophysiological phenotypes for obesity and diabetes.
Weight gain is often associated with the pleasure of eating foods rich in calories and lack of willpower to reduce such food cravings, but empirical evidence is sparse. Here we investigated the role that connectivity within the brain’s hedonic valuation system (BVS, the ventral striatum and the ventromedial prefrontal cortex) at rest plays (1) to predict weight gain or loss over time and (2) for homeostatic hormone regulation. We found that intrinsic connectivity within the BVS at rest (RSC) predicted out-of-sample weight changes over time in lean and obese participants. Counterintuitively, such BVS RSC was higher in lean versus obese participants before the obese participants underwent a drastic weight loss intervention (Roux-en-Y gastric bypass surgery, RYGB). The RYGB surgery increased BVS RSC in the obese after surgery. The obese participants’ increase in BVS RSC correlated with decreases in fasting state systemic leptin, a homeostatic hormone signalling satiety that has been previously linked to dopamine functioning. Taken together, our results indicate a first link between brain connectivity in reward circuits in a more tonic state at rest, homeostatic hormone regulation involved in dopamine functioning and ability to lose weight.
Significance statement
With obesity rates on the rise, advancing our understanding of what factors drive people’s ability to lose and gain weight is crucial. This research is the first to link what we know about the brain’s hedonic valuation system (BVS) to weight loss and homeostatic hormone regulation. We found that connectivity at rest (RSC) within the BVS system predicted changes in weight, differentiated between lean and obese participants, and increased after a weight loss intervention (gastric bypass surgery). Interestingly, the extent to which BVS RSC improved after surgery correlated to decreases in circulating levels of the satiety hormone leptin. These findings are the first to reveal the neural and hormonal determinants of weight loss, combining hedonic and homeostatic drivers of (over-)eating.
Objective: To investigate alterations in functional-connectivity (FC) within and interactions between between resting-state- networks (RSNs) involved in salience, executive-control and interoception in subjects with obesity (OB).
Methods: Using RS-fMRI with independent component analysis and FC, we investigated alterations within and interactions between RSNs in 35 OB and 35 normal weight (NW) controls.
Results: Compared to NW, OB showed reduced FC strength in the ventromedial prefrontal cortex (VMPFC) and posterior cingulate cortex (PCC)/precuneus within the default-mode network (DMN), dorsal anterior cingulate cortex (dACC) within the salience network (SN), bilateral dorsolateral prefrontal cortex (DLPFC)/angular gyrus (ANG) within the frontoparietal network (FPN), and increased FC strength in the insula (INS, PFWE<0.0125). The dACC FC strength was negatively correlated with craving for food-cues, left DLPFC FC strength was negatively correlated with Yale-Food-Addiction-Scale scores, and right INS FC strength was positively correlated with craving for high-calorie food-cues. Compared to NW, OB also showed increased FC between SN and FPN driven by altered FC of bilateral INS and ACC-ANG.
Conclusion: Alterations in FC within and between the SN, DMN and FPN might contribute to the high incentive value of food (craving), lack of control of over eating (compulsive overeating) and increased awareness of hunger (impaired interoception) in obesity.
Overweight and stress interact in complex ways. Excess weight promotes chronic low-grade inflammatory states that can mobilise the hypothalamic-pituitary-adrenal (HPA) axis. HPA axis activation resulting from frequent stress situations can modify energy uptake and expenditure. Separately, both conditions have been linked to changes in brain integrity and executive performance. The organism adapts to situations of caloric surplus through boosting immune, neuroendocrine and cardiometabolic systems to restore energy homeostasis. The allostatic load model establishes that the cumulative effects of adapting to challenging scenarios may result in adverse health situations in the future. There is sufficient evidence to consider that a state of overweight is inherently linked to a higher chronic physiological stress, or allostatic load. Our hypothesis was that, independently of the effects of visceral adiposity, the aggregated effects of the biological alterations related to overweight would be enough detrimental to brain structure and executive functioning. Lean-to-obese volunteers aged 21 to 40 years were recruited from primary health care centres belonging to the Consorci Sanitari de Terrassa. Subjects underwent a medical and neuropsychological examination, as well as a magnetic resonance imaging acquisition at the Hospital Clínic de Barcelona. The allostatic load index consisted of the sum of several biomarkers representing physiological stress. Overweight subjects had a greater allostatic load than healthy weight participants. The allostatic load escalation was negatively correlated with the morphology of cortical areas and tracts known to be ascribed to circuits involved in cognitive control, reward-processing and the integration of visceral-sensory signalling. Finally, the intensification in this index correlated with worse cognitive flexibility.
Background/Objectives
Excessive body mass index (BMI) has been linked to a low-grade chronic inflammation state. Unhealthy BMI has also been related to neuroanatomical changes in adults. However, research in adolescents is relatively limited and has produced conflicting results. This study aims to address the relationship between BMI and adolescents’ brain structure as well as to test the role that inflammatory adipose-related agents might have over this putative link.
Methods
We studied structural MRI and serum levels of interleukin-6, tumor necrosis factor alpha (TNF-α), C-reactive protein and fibrinogen in 65 adolescents (aged 12-21 years). Relationships between BMI, cortical thickness and surface area were tested with a vertex-wise analysis. Subsequently, we used backward multiple linear regression models to explore the influence of inflammatory parameters in each brain-altered area.
Results
We found a negative association between cortical thickness and BMI in the left lateral occipital cortex (LOC), the left fusiform gyrus and the right precentral gyrus as well as a positive relationship between surface area and BMI in the left rostral middle frontal gyrus and the right superior frontal gyrus. In addition, we found that higher fibrinogen serum concentrations were related to thinning within the left LOC (β = −0.45, p < 0.001) and the left fusiform gyrus (β = - 0.33, p = 0.035), while higher serum levels of TNF-α were associated to a greater surface area in the right superior frontal gyrus (β = 0.32, p = 0.045).
Conclusions
These results suggest that adolescents’ body mass increases are related with brain abnormalities in areas that could play a relevant role in some aspects of feeding behavior. Likewise, we have evidenced that these cortical changes were partially driven by inflammatory agents such as fibrinogen and TNF-α.
Background
Substantial efforts have been made to investigate the neurobiological underpinnings of human obesity with a number of studies indicating a profound influence of increased body weight on brain structure. Although body weight is known to be highly heritable, uncertainty remains regarding the respective contribution of genetic and environmental influences.
Methods
In this study we used structural magnetic resonance imaging (MRI) data from the Human Connectome Project (HCP). Voxel-based morphometry (VBM) was applied to study BMI-associated differences in gray matter volume (GMV) within monozygotic (MZ) twin pairs discordant for BMI (ΔBMI > 2.5 kg*m−2, n = 68 pairs). In addition, we investigated the relationship of ΔBMI (entire range) with GMV differences within the entire sample of MZ twin pairs (n = 153 pairs).
Results
Analyses of BMI discordant twin pairs yielded less GMV in heavier twin siblings (p < 0.05 FWETFCE; paired t-Test) within the occipital and cerebellar cortex, the prefrontal cortex and the bilateral striatum including the nucleus accumbens. A highly converging pattern was found in regression analyses across the entire sample of MZ twin pairs, withΔBMI being associated with less GMV in heavier MZ twins.
Conclusion
While MZ twins share the same genetic background, our findings indicate that non-genetic influences and the mere presence of a higher BMI constitute relevant factors in the context of body weight related structural brain alterations.
In this chapter, the executive functions of the prefrontal cortex are discussed against the background of its position in the neocortical map of cognitive representations. Lateral prefrontal areas constitute the highest stage in the cortical hierarchy of executive memories. Their neuronal networks represent schemas of sequential action, past or planned. The enactment of a goal-directed sequence of actions is a continuous process of temporal integration. At the root of this process is the mediation of cross-temporal contigencies between the action plan, the goal, and the acts leading to the goal. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Background: Alterations in the resting-state functional connectivity (rs-FC) of several brain networks have been demonstrated in eating disorders. However, very few studies are currently available on brain network dysfunctions in bulimia nervosa (BN). The somatosensory network is central in processing body-related stimuli and it may be altered in BN. The present study therefore aimed to investigate rs-FC in the somatosensory network in bulimic women.
Methods: Sixteen medication-free women with BN (age = 23 ± 5 years) and 18 matched controls (age = 23 ± 3 years) underwent a functional magnetic resonance resting-state scan and assessment of eating disorder symptoms. Within-network and seed-based functional connectivity analyses were conducted to assess rs-FC within the somatosensory network and to other areas of the brain.
Results: Bulimia nervosa patients showed a decreased rs-FC both within the somatosensory network (t = 9.0, df = 1, P = 0.005) and with posterior cingulate cortex and two visual areas (the right middle occipital gyrus and the right cuneus) (P = 0.05 corrected for multiple comparison). The rs-FC of the left paracentral lobule with the right middle occipital gyrus correlated with psychopathology measures like bulimia (r = −0.4; P = 0.02) and interoceptive awareness (r = −0.4; P = 0.01). Analyses were conducted using age, BMI (body mass index), and depressive symptoms as covariates.
Conclusion: Our findings show a specific alteration of the rs-FC of the somatosensory cortex in BN patients, which correlates with eating disorder symptoms. The region in the right middle occipital gyrus is implicated in body processing and is known as extrastriate body area (EBA). The connectivity between the somatosensory cortex and the EBA might be related to dysfunctions in body image processing. The results should be considered preliminary due to the small sample size.
Neuroimaging studies investigating the neural profile of anorexia nervosa (AN) have revealed a predominant imbalance between the reward and inhibition systems of the brain, which are also hallmark characteristics of the disorder. However, little is known whether these changes can also be determined independent of task condition, using resting-state functional magnetic resonance imaging, in currently ill AN patients.
Therefore the aim of our study was to investigate resting-state connectivity in AN patients (n = 12) compared to healthy athlete (n = 12) and non-athlete (n = 14) controls. For this purpose, we used degree centrality to investigate functional connectivity of the whole-brain network and then Granger causality to analyze effective connectivity (EC), to understand directional aspects of potential alterations.
We were able to show that the bilateral inferior frontal gyrus (IFG) is a region of special functional importance within the whole-brain network, in AN patients, revealing reduced functional connectivity compared to both healthy control groups. Furthermore, we found decreased EC from the right IFG to the midcingulum and increased EC from the bilateral orbitofrontal gyrus to the right IFG. For the left IFG, we only observed increased EC from the bilateral insula to the left IFG.
These results suggest that AN patients have reduced connectivity within the cognitive control system of the brain and increased connectivity within regions important for salience processing. Due to its fundamental role in inhibitory behavior, including motor response, altered integrity of the inferior frontal cortex could contribute to hyperactivity in AN.
An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.
Functional magnetic resonance imaging (fMRI) was used to determine whether visual responses to food in the human amygdala and related corticolimbic structures would be selectively altered by changes in states of hunger. Participants viewed images of motivationall y relevant (food) and motivationally irrelevant (tool) objects while undergoing fMRI in alternately hungry and satiated conditions. Food- related visual stimuli elicited greater responses in the amygdala, parahippocampal gyrus, and anterior fusiform gyrus when participants were in a hungry state relative to a satiated state. The state-dependent activation of these brain structures did not generalize to the motivationally irrelevant objects. These results support the hypothesis that the amygdala and associated inferotemporal regions are involved in the integration of subjective interoceptive states with relevant sensory cues processed along the ventral visual stream. The brain's limited capacity for handling information necessi- tates the selective allocation of processing resources to stimuli that are relevant to current drives and motivational needs. How the brain assigns salience to environmental cues related to relevant events has yet to be fully understood. The amygdala may play an important role in this process because of its neural connections, which link interoceptive information with information regarding sensory events in the external world (Amaral, Price, Pitkanen, & Carmichael, 1992; Herzog & Van Hoesen, 1976). Although there is support for this function of the amygdala in nonhuman animals, the evidence is more tentative in the human brain. Animal studies have documented that the amygdala and asso- ciated limbic forebrain regions play a crucial role in the coordi-
Previous PET imaging studies have demonstrated mixed findings regarding dopamine D2/D3 receptor availability in obese relative to non-obese humans. Nonspecific D2/D3 radioligands do not allow for separate estimation of D2 receptor (D2R) and D3 receptor (D3R) subtypes of the D2 receptor family, which may play different roles in behavior and are distributed differently throughout the brain. These radioligands are also displaceable by endogenous dopamine, confounding interpretation of differences in receptor availability with differing levels of dopamine release. The present study used PET imaging with the D2R-selective radioligand (N-[(11) C] methyl)benperidol ([(11) C]NMB), which is non-displaceable by endogenous dopamine, to estimate D2R specific binding (BPND ) and its relationship to body mass index (BMI) and age in 15 normal-weight (mean BMI = 22.6 kg/m(2) ) and 15 obese (mean BMI = 40.3 kg/m(2) ) men and women. Subjects with illnesses or taking medications that interfere with dopamine signaling were excluded. Striatal D2R BPND was calculated using the Logan graphical method with cerebellum as a reference region. D2R BPND estimates were higher in putamen and caudate relative to nucleus accumbens, but did not differ between normal-weight and obese groups. BMI values did not correlate with D2R BPND . Age was negatively correlated with putamen D2R BPND in both groups. These results suggest that altered D2R specific binding is not involved in the pathogenesis of obesity per se and underscore the need for additional studies evaluating the relationship between D3R, dopamine reuptake, or endogenous dopamine release and human obesity. Synapse, 2013. © 2013 Wiley Periodicals, Inc.
Obesity is emerging as the most significant health concern of the twenty-first century. A wealth of neuroimaging data suggest that weight gain might be related to aberrant brain function, particularly in prefrontal cortical regions modulating mesolimbic addictive responses to food. Nevertheless, food addiction is currently a model hotly debated. Here, we conduct a meta-analysis of neuroimaging data, examining the most common functional differences between normal-weight and obese participants in response to food stimuli.
We conducted a search using several journal databases and adhered to the 'Preferred Reporting Items for Systematic Reviews and Meta-analyses' (PRISMA) method. To this aim, 10 studies were found with a total of 126 obese participants, 129 healthy controls, equaling 184 foci (146 increased, 38 decreased activation) using the Activation Likelihood Estimation (ALE) technique. Out of the 10 studies, 7 investigated neural responses to food versus non-food images.
In response to food images, obese in comparison to healthy weight subjects had increased activation in the left dorsomedial prefrontal cortex, right parahippocampal gyrus, right precentral gyrus and right anterior cingulate cortex, and reduced activation in the left dorsolateral prefrontal cortex and left insular cortex.
Prefrontal cortex areas linked to cognitive evaluation processes, such as evaluation of rewarding stimuli, as well as explicit memory regions, appear most consistently activated in response to images of food in those who are obese. Conversely, a reduced activation in brain regions associated with cognitive control and interoceptive awareness of sensations in the body might indicate a weakened control system, combined with hypo-sensitivity to satiety and discomfort signals after eating in those who are prone to overeat.
Two aspects play a key role in recently developed strategies for fMRI data analysis: first, it is now recognized that the human brain is a complex adaptive system and exhibits the hallmarks of complexity such as emergence of patterns arising out of a multitude of interactions between its many constituents. Second, the field of fMRI has evolved into a data-intensive, ``big data'' endeavour with large databases and masses of data being shared around the world. At the same time, ultra-high field MRI scanners are now available producing data at previously unobtainable quality and quantity. Both aspects have led to shifts in the way in which we view fMRI data. Here, we review recent developments in fMRI data analysis methodology that resulted from these shifts in paradigm.
To investigate whether the metabolically important visceral adipose tissue (VAT) relates differently to structural and functional brain changes in comparison with body weight measured as body mass index (BMI). Moreover, we aimed to investigate whether these effects change with age.
Cross-sectional, exploratory.
University Clinic, Integrative Research and Treatment Centre.
We included 100 (mean BMI=26.0 kg/m², 42 women) out of 202 volunteers randomly invited by the city's registration office, subdivided into two age groups: young-to-mid-age (n=51, 20-45 years of age, mean BMI=24.9, 24 women) versus old (n=49, 65-70 years of age, mean BMI=27.0, 18 women).
VAT, BMI, subcutaneous abdominal adipose tissue, brain structure (grey matter density), functional brain architecture (eigenvector centrality, EC).
We discovered a loss of cerebellar structure with increasing VAT in the younger participants, most significantly in regions involved in motor processing. This negative correlation disappeared in the elderly. Investigating functional brain architecture showed again inverse VAT-cerebellum correlations, whereas now regions involved in cognitive and emotional processing were significant. Although we detected similar results for EC using BMI, significant age interaction for both brain structure and functional architecture was only found using VAT.
Visceral adiposity is associated with cerebellar changes of both structure and function, whereas the regions involved contribute to motor, cognitive and emotional processes. Furthermore, these associations seem to be age dependent, with younger adults' brains being adversely affected.
Obesity is a multifactorial disease caused by the interaction between genotype and environment, and it is considered to be a type of addictive alteration. The A1 allele of the DRD2/ANKK1-TaqIA gene has been associated with addictive disorders, with obesity and with the performance in executive functions. The 7 repeat allele of the DRD4 gene has likewise been associated with the performance in executive functions, as well as with addictive behaviors and impulsivity. Participants were included in the obesity group (N = 42) if their body mass index (BMI) was equal to or above 30, and in the lean group (N = 42) if their BMI was below 25. The DRD2/ANKK1-TaqIA and DRD4 VNTR polymorphisms were obtained. All subjects underwent neuropsychological assessment. Eating behavior traits were evaluated. The 'DRD2/ANKK1-TaqIA A1-allele status' had a significant effect on almost all the executive variables, but no significant 'DRD4 7R-allele status' effects were observed for any of the executive variables analyzed. There was a significant 'group' x 'DRD2/ANKK1-TaqIA A1-allele status' interaction effect on LN and 'group' x 'DRD4 7R-allele status' interaction effect on TMT B-A score. Being obese and a carrier of the A1 allele of DRD2/ANKK1-TaqIA or the 7R allele of DRD4 VNTR polymorphisms could confer a weakness as regards the performance of executive functions.
Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks - such as small-world topology, highly connected hubs and modularity - both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Obesity is characterized by an imbalance in the brain circuits promoting reward seeking and those governing cognitive control. Here we show that the dorsal caudate nucleus and its connections with amygdala, insula and prefrontal cortex contribute to abnormal reward processing in obesity. We measured regional brain glucose uptake in morbidly obese (n = 19) and normal weighted (n = 16) subjects with 2-[¹⁸F]fluoro-2-deoxyglucose ([¹⁸F]FDG) positron emission tomography (PET) during euglycemic hyperinsulinemia and with functional magnetic resonance imaging (fMRI) while anticipatory food reward was induced by repeated presentations of appetizing and bland food pictures. First, we found that glucose uptake rate in the dorsal caudate nucleus was higher in obese than in normal-weight subjects. Second, obese subjects showed increased hemodynamic responses in the caudate nucleus while viewing appetizing versus bland foods in fMRI. The caudate also showed elevated task-related functional connectivity with amygdala and insula in the obese versus normal-weight subjects. Finally, obese subjects had smaller responses to appetizing versus bland foods in the dorsolateral and orbitofrontal cortices than did normal-weight subjects, and failure to activate the dorsolateral prefrontal cortex was correlated with high glucose metabolism in the dorsal caudate nucleus. These findings suggest that enhanced sensitivity to external food cues in obesity may involve abnormal stimulus-response learning and incentive motivation subserved by the dorsal caudate nucleus, which in turn may be due to abnormally high input from the amygdala and insula and dysfunctional inhibitory control by the frontal cortical regions. These functional changes in the responsiveness and interconnectivity of the reward circuit could be a critical mechanism to explain overeating in obesity.
An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.
Obesity is a result of a relative excess in energy intake over energy expenditure. These processes are controlled by genetic, environmental, psychological and biological factors. One of the factors involved in the regulation of food intake and satiety is dopaminergic signalling. A small number of studies have reported that striatal dopamine D2/D3 receptor [D2/3R] availability is lower in morbidly obese subjects.
To confirm the role of D2/3R in obesity, we measured striatal D2/3R availability, using [123I]IBZM SPECT, in 15 obese women and 15 non-obese controls.
Striatal D2/3R availability was 23% (p = 0.028) lower in obese compared with non-obese women.
This study is an independent replication of the finding that severely obese subjects have lower striatal D2/3R availability. Our findings invigorate the evidence for lower striatal D2/3R availability in obesity and confirm the role of the striatal dopaminergic reward system in obesity.
Causal reasoning is a ubiquitous feature of human cognition. We continuously seek to understand, at least implicitly and often explicitly, the causal scenarios in which we live, so that we may anticipate what will come next, plan a potential response and envision its outcome, decide among possible courses of action in light of their probable outcomes, make midstream adjustments in our goal-related activities as our situation changes, and so on. A considerable body of research shows that the lateral prefrontal cortex (PFC) is crucial for causal reasoning, but also that there are significant differences in the manner in which ventrolateral PFC, dorsolateral PFC, and anterolateral PFC support causal reasoning. We propose, on the basis of research on the evolution, architecture, and functional organization of the lateral PFC, a general framework for understanding its roles in the many and varied sorts of causal reasoning carried out by human beings. Specifically, the ventrolateral PFC supports the generation of basic causal explanations and inferences; dorsolateral PFC supports the evaluation of these scenarios in light of some given normative standard (e.g., of plausibility or correctness in light of real or imagined causal interventions); and anterolateral PFC supports explanation and inference at an even higher level of complexity, coordinating the processes of generation and evaluation with further cognitive processes, and especially with computations of hedonic value and emotional implications of possible behavioral scenarios - considerations that are often critical both for understanding situations causally and for deciding about our own courses of action.
Gender differences in the regulation of body-weight are well documented. Here, we assessed obesity-related influences of gender on brain structure as well as performance in the Iowa Gambling Task. This task requires evaluation of both immediate rewards and long-term outcomes and thus mirrors the trade-off between immediate reward from eating and the long-term effect of overeating on body-weight. In women, but not in men, we show that the preference for salient immediate rewards in the face of negative long-term consequences is higher in obese than in lean subjects. In addition, we report structural differences in the left dorsal striatum (i.e., putamen) and right dorsolateral prefrontal cortex for women only. Functionally, both regions are known to play complimentary roles in habitual and goal-directed control of behavior in motivational contexts. For women as well as men, gray matter volume correlates positively with measures of obesity in regions coding the value and saliency of food (i.e., nucleus accumbens, orbitofrontal cortex) as well as in the hypothalamus (i.e., the brain's central homeostatic center). These differences between lean and obese subjects in hedonic and homeostatic control systems may reflect a bias in eating behavior toward energy-intake exceeding the actual homeostatic demand. Although we cannot infer from our results the etiology of the observed structural differences, our results resemble neural and behavioral differences well known from other forms of addiction, however, with marked differences between women and men. These findings are important for designing gender-appropriate treatments of obesity and possibly its recognition as a form of addiction.
Objective:
In this study, we investigate the brain mechanisms of the conscious regulation of the desire for food using functional magnetic resonance imaging. Further, we examine associations between hemodynamic responses and participants' cognitive restraint of eating (CRE), as well as their susceptibility to uncontrolled eating.
Subjects:
Seventeen non-vegetarian, right-handed, female Caucasian participants (age: 20-30 years, mean 25.3 years±3.1 s.d.; BMI: 20.2-31.2 kg m(-2), mean 25.1±3.5 s.d.).
Measurements:
During scanning, our participants viewed pictures of food items they had pre-rated according to tastiness and healthiness. Participants were either allowed to admit to the desire for the food (ADMIT) or they were instructed to downregulate their desire using a cognitive reappraisal strategy, that is, thinking of negative long-term health-related and social consequences (REGULATE).
Results:
Comparing the hemodynamic responses of the REGULATE with the ADMIT condition, we observed robust activation in the dorsolateral prefrontal cortex (DLPFC), the pre-supplementary motor area, the inferior frontal gyrus (IFG), the dorsal striatum (DS), the bilateral orbitofrontal cortex (OFC), the anterior insula and the temporo-parietal junction (TPJ). Activation in the DLPFC and the DS strongly correlated with the degree of dietary restraint under both conditions.
Conclusion:
Cortical activation in the DLPFC, the pre-supplementary motor area and the inferior frontal gyrus (IFG) are known to underpin top-down control, inhibition of learned associations and pre-potent responses. The observed hemodynamic responses in the lateral OFC, the DS, the anterior insula and the TPJ support the notion of reward valuation and integration, interoceptive awareness, and self-reflection as key processes during active regulation of desire for food. In conclusion, an active reappraisal of unhealthy food recruits the brain's valuation system in combination with prefrontal cognitive control areas associated with response inhibition. The correlations between brain responses and CRE suggest that individuals with increased cognitive restraint show an automatic predisposition to regulate the hedonic aspects of food stimuli. This cognitive control might be necessary to counterbalance a lack of homeostatic mechanisms.
Obesity is a risk factor for stroke and neurodegenerative disease. Excess body fat has been linked to impaired glucose metabolism, insulin resistance, and impulsivity and may be a precursor to decline in attention and executive cognitive function. Here, we investigated the effects of high BMI on regional cerebral blood flow (rCBF) using single photon emission computed tomography (SPECT) imaging in healthy subjects. A total of 16 adult men and 20 adult women were recruited from the community between January 2003 and July 2009 as part of a healthy brain study (HBS) conducted at the Amen Clinics, a private medical facility. Participants in the study were screened to exclude medical, neurological, and psychiatric conditions, including substance abuse. Subjects were categorized as normal or overweight according to BMI. Using a two sample t-test, we determined the effects of BMI on rCBF in normal vs. overweight subjects. Subjects were matched for age and gender. Statistical parametric mapping (SPM) revealed a higher BMI in healthy individuals that is associated with decreased rCBF in Broadmann areas 8, 9, 10, 11, 32, and 44, brain regions involved in attention, reasoning, and executive function (P < 0.05, corrected for multiple comparisons). We found that an elevated BMI is associated with decreased rCBF in the prefrontal cortex of a healthy cohort. These results indicate that elevated BMI may be a risk factor for decreased prefrontal cortex function and potentially impaired executive function.
Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.