Behaviours such as eating, copulating, defending oneself or taking addictive drugs begin with a motivation to initiate the behaviour. Both this motivational drive and the behaviours that follow are influenced by past and present experience with the reinforcing stimuli (such as drugs or energy-rich foods) that increase the likelihood and/or strength of the behavioural response (such as drug taking or overeating). At a cellular and circuit level, motivational drive is dependent on the concentration of extrasynaptic dopamine present in specific brain areas such as the striatum. Cues that predict a reinforcing stimulus also modulate extrasynaptic dopamine concentrations, energizing motivation. Repeated administration of the reinforcer (drugs, energy-rich foods) generates conditioned associations between the reinforcer and the predicting cues, which is accompanied by downregulated dopaminergic response to other incentives and downregulated capacity for top-down self-regulation, facilitating the emergence of impulsive and compulsive responses to food or drug cues. Thus, dopamine contributes to addiction and obesity through its differentiated roles in reinforcement, motivation and self-regulation, referred to here as the 'dopamine motive system', which, if compromised, can result in increased, habitual and inflexible responding. Thus, interventions to rebalance the dopamine motive system might have therapeutic potential for obesity and addiction.

The dopamine motive system: implications for drug and food addiction.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

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Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

https://archive-ouverte.unige.ch/unige:109889/ATTACHMENT01

Supra-Additive Effects of Combining Fat and Carbohydrate on Food Reward

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical, and environmental data, is performed to gain new insights into the phenotypic, genetic, and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

/pdf/brain-imaging-genomics-integrated-analysis-and-machine-ks9cwqisyt.pdf

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Obesity is a major public health concern that involves an interaction between genetic susceptibility and exposure to environmental cues (e.g., food marketing); however, the mechanisms that link these factors and contribute to unhealthy eating are unclear. Using a well-known obesity risk polymorphism (FTO rs9939609) in a sample of 78 children (ages 9-12 y), we observed that children at risk for obesity exhibited stronger responses to food commercials in the nucleus accumbens (NAcc) than children not at risk. Similarly, children at a higher genetic risk for obesity demonstrated larger NAcc volumes. Although a recessive model of this polymorphism best predicted body mass and adiposity, a dominant model was most predictive of NAcc size and responsivity to food cues. These findings suggest that children genetically at risk for obesity are predisposed to represent reward signals more strongly, which, in turn, may contribute to unhealthy eating behaviors later in life.

https://www.pnas.org/content/pnas/114/1/160.full.pdf

Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues

Biological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining We provide here a brief review of 3-D printing along with a case study to illustrate how it might be used in a research setting We present as a case study a genetic interaction network associated with grey matter density, an endophenotype for late onset Alzheimer’s disease, as a physical model constructed with a 3-D printer The synergy or interaction effects of multiple genetic variants were represented through a color gradient of the physical connections between nodes The digital gene-gene interaction network was then 3-D printed to generate a physical network model The physical 3-D gene-gene interaction network provided an easily manipulated, intuitive and creative way to visualize the synergistic relationships between the genetic variants and grey matter density in patients with late onset Alzheimer’s disease We discuss the advantages and disadvantages of this novel method of biological data mining visualization

/pdf/the-role-of-visualization-and-3-d-printing-in-biological-3kogbi3kx1.pdf

The role of visualization and 3-D printing in biological data mining

Background
Alzheimer’s disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer’s disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships.

/pdf/computational-genetics-analysis-of-grey-matter-density-in-1yysxsijs0.pdf

Amanda L. Zieselman

Papers

Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues

The role of visualization and 3-D printing in biological data mining

Computational genetics analysis of grey matter density in Alzheimer’s disease

The role of visualization and 3-D printing in biological data mining