Comparison of volumetric and shape changes of subcortical structures based on 3-dimensional image between obesity and normal-weighted subjects using 3.0 T MRI

Stress and the domestic cat: have humans accidentally created an animal mimic of neurodegeneration?

Many neurodegenerative diseases (NDD) appear to share commonality of origin, chronic ER stress. The endoplasmic reticulum (ER) is a dynamic organelle, functioning as a major site of protein synthesis and protein posttranslational modifications, required for proper folding. ER stress can occur because of external stimuli, such as oxidative stress or neuroinflammatory cytokines, creating the ER luminal environment permissive for the accumulation of aggregated and misfolded proteins. Unresolvable ER stress upregulates a highly conserved pathway, the unfolded protein response (UPR). Maladaptive chronic activation of UPR components leads to apoptotic neuronal death. In addition to other factors, physiological responses to stressors are emerging as a significant risk factor in the etiology and pathogenesis of NDD. Owned cats share a common environment with people, being exposed to many of the same stressors as people and additional pressures due to their "quasi" domesticated status. Feline Cognitive Dysfunction Syndrome (fCDS) presents many of the same disease hallmarks as human NDD. The prevalence of fCDS is rapidly increasing as more people welcome cats as companions. Barely recognized 20 years ago, veterinarians and scientists are in infancy stages in understanding what is a very complex disease. This review will describe how cats may represent an unexplored animal mimetic phenotype for human NDD with stressors as potential triggering mechanisms. We will consider how multiple variations of stressful events over the short-life span of a cat could affect neuronal loss or glial dysfunction and ultimately tip the balance towards dementia.

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity

Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.

Associations between maternal pre-pregnancy BMI and infant striatal mean diffusivity

BACKGROUND

It is well-established that parental obesity is a strong risk factor for offspring obesity. Further, a converging body of evidence now suggests that maternal weight profiles may affect the developing offspring's brain in a manner that confers future obesity risk. Here, we investigated how pre-pregnancy maternal weight status influences the reward-related striatal areas of the offspring's brain during in utero development.

METHODS

We used diffusion tensor imaging to quantify the microstructure of the striatal brain regions of interest in neonates (N = 116 [66 males, 50 females], mean gestational weeks at birth [39.88], SD = 1.14; at scan [43.56], SD = 1.05). Linear regression was used to test the associations between maternal pre-pregnancy body mass index (BMI) and infant striatal mean diffusivity.

RESULTS

High maternal pre-pregnancy BMI was associated with higher mean MD values in the infant's left caudate nucleus. Results remained unchanged after the adjustment for covariates.

CONCLUSIONS

In utero exposure to maternal adiposity might have a growth-impairing impact on the mean diffusivity of the infant's left caudate nucleus. Considering the involvement of the caudate nucleus in regulating eating behavior and food-related reward processing later in life, this finding calls for further investigations to define the prognostic relevance of early-life caudate nucleus development and weight trajectories of the offspring.

Obesity is associated with decreased gray matter volume in children: a longitudinal study

Childhood obesity has become a global health problem. Previous studies showed that childhood obesity is associated with brain structural differences relative to controls. However, few studies have been performed with longitudinal evaluations of brain structural developmental trajectories in childhood obesity. We employed voxel-based morphometry (VBM) analysis to assess gray matter (GM) volume at baseline and 2-year follow-up in 258 obese children (OB) and 265 normal weight children (NW), recruited as part of the National Institutes of Health Adolescent Brain and Cognitive Development study. Significant group × time effects on GM volume were observed in the prefrontal lobe, thalamus, right precentral gyrus, caudate, and parahippocampal gyrus/amygdala. OB compared with NW had greater reductions in GM volume in these regions over the 2-year period. Body mass index (BMI) was negatively correlated with GM volume in prefrontal lobe and with matrix reasoning ability at baseline and 2-year follow-up. In OB, Picture Test was positively correlated with GM volume in the left orbital region of the inferior frontal gyrus (OFCinf_L) at baseline and was negatively correlated with reductions in OFCinf_L volume (2-year follow-up vs. baseline). These findings indicate that childhood obesity is associated with GM volume reduction in regions involved with reward evaluation, executive function, and cognitive performance.

Possible Implications of Obesity-Primed Microglia that Could Contribute to Stroke-Associated Damage

Microglia, the resident macrophages of the central nervous system, are essential players during physiological and pathological processes. Although they participate in synaptic pruning and maintenance of neuronal circuits, microglia are mainly studied by their activity modulating inflammatory environment and adapting their phenotype and mechanisms to insults detected in the brain parenchyma. Changes in microglial phenotypes are reflected in their morphology, membrane markers, and secreted substances, stimulating neighbor glia and leading their responses to control stimuli. Understanding how microglia react in various microenvironments, such as chronic inflammation, made it possible to establish therapeutic windows and identify synergic interactions with acute damage events like stroke. Obesity is a low-grade chronic inflammatory state that gradually affects the central nervous system, promoting neuroinflammation development. Obese patients have the worst prognosis when they suffer a cerebral infarction due to basal neuroinflammation, then obesity-induced neuroinflammation could promote the priming of microglial cells and favor its neurotoxic response, potentially worsening patients' prognosis. This review discusses the main microglia findings in the obesity context during the course and resolution of cerebral infarction, involving the temporality of the phenotype changes and balance of pro- and anti-inflammatory responses, which is lost in the swollen brain of an obese subject. Obesity enhances proinflammatory responses during a stroke. Obesity-induced systemic inflammation promotes microglial M₁ polarization and priming, which enhances stroke-associated damage, increasing M₁ and decreasing M₂ responses.

Association between body mass index and subcortical volume in pre-adolescent children with autism spectrum disorder: An exploratory study

Conflicting associations exist between autism spectrum disorder (ASD) and subcortical brain volumes. This study assessed whether obesity might have a confounding influence on associations between ASD and brain subcortical volumes. A comprehensive investigation evaluating the relationship between ASD, obesity, and subcortical structure volumes was conducted. Data obtained included body mass index (BMI) and T1-weighted structural magnetic resonance images for children with and without ASD diagnoses from the Autism Brain Imaging Data Exchange database. Brain subcortical volumes were calculated using vol2Brain software. Hierarchical linear regression analyses were performed to explore the subcortical volumes similarly or differentially associated with BMI in children with or without ASD and examine association and interaction effects regarding ASD and subcortical volume impact on the Social Responsiveness Scale and Vineland Adaptive Behavior Scale (VABS) scores. Bilateral caudate nuclei were smaller in children with ASD than in control participants. Significant interactions were observed between ASD diagnosis and BMI regarding the left caudate, right and left putamen, and right and left ventral diencephalon (DC) volumes (β = -0.384, p = 0.010; β = -0.336, p = 0.030; β = -0.317, p = 0.040; β = 0.322, p = 0.010; β = 0.295, p = 0.021, respectively) and between ASD diagnosis and right and left ventral DC volumes regarding the VABS scores (β = 0.434, p = 0.014; β = 0.495, p = 0.007, respectively). However, each subcortical structure volume included in the ventral DC area could not be measured separately. The results identified subcortical volumes differentially associated with obesity in children with ASD compared with typically developing peers. BMI may need to be considered an important confounder in future research examining brain subcortical volumes within ASD.

Alteration of brain nuclei in obese children with and without Prader-Willi syndrome

Introduction: Prader-Willi syndrome (PWS) is a multisystem genetic imprinting disorder mainly characterized by hyperphagia and childhood obesity. Extensive structural alterations are expected in PWS patients, and their influence on brain nuclei should be early and profound. To date, few studies have investigated brain nuclei in children with PWS, although functional and structural alterations of the cortex have been reported widely. Methods: In the current study, we used T1-weighted magnetic resonance imaging to investigate alterations in brain nuclei by three automated analysis methods: shape analysis to evaluate the shape of 14 cerebral nuclei (bilateral thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, and nucleus accumbens), automated segmentation methods integrated in Freesurfer 7.2.0 to investigate the volume of hypothalamic subregions, and region of interest-based analysis to investigate the volume of deep cerebellar nuclei (DCN). Twelve age- and sex-matched children with PWS, 18 obese children without PWS (OB) and 18 healthy controls participated in this study. Results: Compared with control and OB individuals, the PWS group exhibited significant atrophy in the bilateral thalamus, pallidum, hippocampus, amygdala, nucleus accumbens, right caudate, bilateral hypothalamus (left anterior-inferior, bilateral posterior, and bilateral tubular inferior subunits) and bilateral DCN (dentate, interposed, and fastigial nuclei), whereas no significant difference was found between the OB and control groups. Discussion: Based on our evidence, we suggested that alterations in brain nuclei influenced by imprinted genes were associated with clinical manifestations of PWS, such as eating disorders, cognitive disability and endocrine abnormalities, which were distinct from the neural mechanisms of obese children.

Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Functional Magnetic Resonance Imaging and Obesity-Novel Ways to Seen the Unseen

Obesity remains a pandemic of the 21st century. While there are many causes of obesity and potential treatments that are currently known, source data indicate that the number of patients is constantly increasing. Neural mechanisms have become the subject of research and there has been an introduction of functional magnetic resonance imaging in obesity-associated altered neural signaling. Functional magnetic resonance imaging has been established as the gold standard in the assessment of neuronal functions related to nutrition. Thanks to this, it has become possible to delineate those regions of the brain that show altered activity in obese individuals. An integrative review of the literature was conducted using the keywords ““functional neuroimaging” OR “functional magnetic resonance “OR “fmri” and “obesity” and “reward circuit and obesity” in PubMed and Google Scholar databases from 2017 through May 2022. Results in English and using functional magnetic resonance imaging to evaluate brain response to diet and food images were identified. The results from functional magnetic resonance imaging may help to identify relationships between neuronal mechanisms and causes of obesity. Furthermore, they may provide a substrate for etiology-based treatment and provide new opportunities for the development of obesity pharmacotherapy.

The role of the nucleus accumbens and ventral pallidum in feeding and obesity

Obesity is a growing global epidemic that stems from the increasing availability of highly-palatable foods and the consequent enhanced calorie consumption. Extensive research has shown that brain regions that are central to reward seeking modulate feeding and evidence linking obesity to pathology in such regions have recently started to accumulate. In this review we focus on the contribution of two major interconnected structures central to reward processing, the nucleus accumbens and the ventral pallidum, to obesity. We first review the known literature linking these structures to feeding behavior, then discuss recent advances connecting pathology in the nucleus accumbens and ventral pallidum to obesity, and finally examine the similarities and differences between drug addiction and obesity in the context of these two structures. The understanding of how pathology in brain regions involved in reward seeking and consumption may drive obesity and how mechanistically similar obesity and addiction are, is only now starting to be revealed. We hope that future research will advance knowledge in the field and open new avenues to studying and treating obesity.

Relationship between obesity and structural brain abnormality: Accumulated evidence from observational studies

We aimed to evaluate the relationship between obesity and structural brain abnormalities assessed by magnetic resonance imaging using data from 45 observational epidemiological studies, where five articles reported prospective longitudinal results. In cross-sectional studies' analyses, the pooled weighted mean difference for total brain volume (TBV) and gray matter volume (GMV) in obese/overweight participants was -11.59 (95 % CI: -23.17 to -0.02) and -10.98 (95 % CI: -20.78 to -1.18), respectively. TBV was adversely associated with BMI and WC, GMV with BMI, and hippocampal volume with BMI, WC, and WHR. WC/WHR are associated with a risk of lacunar and white matter hyperintensity (WMH). In longitudinal studies' analyses, BMI was not statistically associated with the overall structural brain abnormalities (for continuous BMI: RR = 1.02, 95 % CI: 0.94-1.12; for categorial BMI: RR = 1.18, 95 % CI: 0.75-1.85). Small sample size of prospective longitudinal studies limited the power of its pooled estimates. A higher BMI is associated with lower brain volume while greater WC/WHR, but not BMI, is related to a risk of lacunar infarct and WMH. Future longitudinal research is needed to further elucidate the specific causal relationships and explore preventive measures.

Structural Brain Changes Associated with Overweight and Obesity

Obesity is a global health problem with a broad set of comorbidities, such as malnutrition, metabolic syndrome, diabetes, systemic hypertension, heart failure, and kidney failure. This review describes recent findings of neuroimaging and two studies of cell density regarding the roles of overnutrition-induced hypothalamic inflammation in neurodegeneration. These studies provided consistent evidence of smaller cortical thickness or reduction in the gray matter volume in people with overweight and obesity; however, the investigated brain regions varied across the studies. In general, bilateral frontal and temporal areas, basal nuclei, and cerebellum are more commonly involved. Mechanisms of volume reduction are unknown, and neuroinflammation caused by obesity is likely to induce neuronal loss. Adipocytes, macrophages of the adipose tissue, and gut dysbiosis in overweight and obese individuals result in the secretion of the cytokines and chemokines that cross the blood-brain barrier and may stimulate microglia, which in turn also release proinflammatory cytokines. This leads to chronic low-grade neuroinflammation and may be an important factor for apoptotic signaling and neuronal death. Additionally, significant microangiopathy observed in rat models may be another important mechanism of induction of apoptosis. Neuroinflammation in neurodegenerative diseases (such as Alzheimer's and Parkinson's diseases) may be similar to that in metabolic diseases induced by malnutrition. Poor cognitive performance, mainly in executive functions, in individuals with obesity is also discussed. This review highlights the neuroinflammatory and neurodegenerative mechanisms linked to obesity and emphasizes the importance of developing effective prevention and treatment intervention strategies for overweight and obese individuals.