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Diagnosing and Assessing Processed Food Addiction
Published in Joan Ifland, Marianne T. Marcus, Harry G. Preuss, Processed Food Addiction: Foundations, Assessment, and Recovery, 2017
Dennis M. Donovan, Joan Ifland
Cognitive impairment is a significant problem because of the need to acquire meal preparation and cue avoidance skills. Online brain training such as Lumosity and Brain Age may be effective for cognitive restoration (Hardy et al., 2015; Kesler et al., 2013; Mayas, Parmentier, Andres, & Ballesteros, 2014; Nouchi et al., 2012; Nouchi et al., 2013). At the same time, the burden of engaging in brain training games is light and flexible, so clients are generally willing to participate.
Measurement of Brain Age: Conceptual Issues and Neurobiological Indices
Published in Richard C. Adelman, George S. Roth, Endocrine and Neuroendocrine Mechanisms of Aging, 2017
The major concern in selecting measures of brain aging, of course, is whether or not a particular variable correlates with age. That is, a brain variable must obviously be measurably different in aged subjects vs. young-mature subjects if it is to be used to assess brain age. Moreover, the more discrimination between age groups that it provides (i.e., the less overlap) the more useful it is presumed to be. (See Reference 11 for a discussion of the effect of variance and population size on the measurement of aging.) It is also often assumed that the earlier in life at which an aging correlate can be detected, the more valuable it is. However, this latter assumption may not be completely valid since, as noted above, all correlates of brain aging may not be directly linked, and some may proceed independently of others. Conceivably, therefore, some later appearing correlates may be of greater relevance to, for example, functional decline. Nevertheless, since brain aging appears to begin by mid-life, age-correlated variables that can be detected early in mature life seem of potential interest in terms of assessing "primary" mechanisms.
Precision neuroimaging biomarkers for bipolar disorder
Published in International Review of Psychiatry, 2022
Delfina Janiri, Sophia Frangou
The investigation of accelerated ageing in BD benefits from machine learning methods applied to neuroimaging data that can harness the multidimensional nature of age-related brain changes (Franke & Gaser, 2019). The application of machine learning methods enables the prediction of the biological age of the brain from neuroimaging phenotypes based on the population norms for these phenotypes. The ‘brain-age-gap-estimation (brainAGE) is an individualized measure of brain ageing based on the discrepancy between neuroimaging-predicted age and chronological age (Franke & Gaser, 2019). A positive brainAGE indicates that the biological age of an individual's brain appears ‘older’ than their actual age, and a negative brainAGE reflects the inverse. Individual studies examining brainAGE in BD using sMRI or DTI-derived brain features have been generally conflicting (Ballester et al., 2022) (Table 2 and Supplementary Table 2). The largest study to date used morphometric data from 459 patients with BD whose brainAGE was approximately 2 years older than that of their healthy counterparts (Kaufmann et al., 2019). The underlying mechanisms remain unclear but are likely to involve cardiometabolic dysregulation (Ryan et al., 2022) while lithium may confer a protective effect (Van Gestel et al., 2019) via a yet unspecified mechanism.
Considering patient age when treating multiple sclerosis across the adult lifespan
Published in Expert Review of Neurotherapeutics, 2021
Dejan Jakimovski, Svetlana P Eckert, Robert Zivadinov, Bianca Weinstock-Guttman
Another way of determining the biological brain age includes machine learning analysis of neuroimaging data. A pattern recognition procedure is used to determine brain-predicted age from a large set of healthy individuals and later applied to pwMS. In one study, when compared to healthy controls, the MS brains were on average 6 years older than their chronologically age-matched controls with significant MS phenotype effect (SPMS brains were up to 9 years older) [36]. Furthermore, the predicted brain age could predict future disability worsening and resulted in additional marginal gap widening [36]. In a similar analysis, the cross-sectional MS brain age was 4.4 years older when compared to chronological age-matched healthy controls [37]. pwMS also experienced significantly faster brain aging with an increase of 0.4 additional biological years for every chronological year [37]. Both studies suggest that MS accelerates neurological and immunological aging when compared to chronologically-matched controls.