Cognitive behavioural therapy for weight gain associated with antipsychotic drugs

BACKGROUND: Overweight and obesity are common concerns in individuals with severe mental disorders. In particular, antipsychotic drugs (AP) frequently induce weight gain. This phenomenon lacks current management and no previous controlled studies seem to use cognitive therapy to modify eating and weight-related cognitions. Moreover, none of these studies considered binge eating or eating and weight-related cognitions as possible outcomes. AIM: The main aim of this study is to assess the effectivity of cognitive and behavioural treatment (CBT) on eating and weight-related cognitions, binge eating symptomatology and weight loss in patients who reported weight gain during AP treatment. METHOD: A randomized controlled study (12-week CBT vs. Brief Nutritional Education) was carried out on 61 patients treated with an antipsychotic drug who reported weight gain following treatment. Binge eating symptomatology, eating and weight-related cognitions, as well as weight and body mass index were assessed before treatment, at 12 weeks and at 24 weeks. RESULTS: The CBT group showed some improvement with respect to binge eating symptomatology and weight-related cognitions, whereas the control group did not. Weight loss occurred more progressively and was greater in the CBT group at 24 weeks. CONCLUSION: The proposed CBT treatment is particularly interesting for patients suffering from weight gain associated with antipsychotic treatment

Summary of Early Triassic carbon isotope records

Much attention has been given to the negative d13C anomaly nearly coincident with the Permian-Triassic boundary. New data indicate a stepwise decline in d13C initiating before the Latest Permian extinction event followed by highly variable d13C values during the remaining Early Triassic. d13C values appear much less erratic as global metazoan diversity increased in the Middle Triassic. Given the previously unappreciated magnitude of isotopic change and the number of large d13C excursions that occurred during the Early Triassic, catastrophic mechanisms like methane release/bolide impact become less attractive to explain the Early Triassic carbon isotopic record as a whole.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.