62 research outputs found

    Deflating the deep brain stimulation causes personality changes bubble: the authors reply

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    To conclude that there is enough or not enough evidence demonstrating that deep brain stimulation (DBS) causes unintended postoperative personality changes is an epistemic problem that should be answered on the basis of established, replicable, and valid data. If prospective DBS recipients delay or refuse to be implanted because they are afraid of suffering from personality changes following DBS, and their fears are based on unsubstantiated claims made in the neuroethics literature, then researchers making these claims bear great responsibility for prospective recipients' medical decisions and subsequent well-being. Our article “Deflating the ‘DBS causes personality’ bubble” reported an increase in theoretical neuroethics publications suggesting putative DBS-induced changes to personality, identity, agency, autonomy, authenticity and/or self (PIAAAS) and a critical lack of supporting primary empirical studies. This special issue of Neuroethics brings together responses to our initial publication, with our own counter-responses organized according to common themes. We provide a brief summary for each commentary and its main criticisms as well as a discussion of the way in which these responses can: 1) help clarify the meaning of PIAAAS, suggesting supplementary frameworks for understanding the impact of DBS on PIAAAS; 2) provide further empirical evidence of PIAAAS by presenting results from the researchers’ own work; and/or 3) offer a critique of our research approach and/or findings. Unintended postoperative putative changes to PIAAAS remain a critical ethical concern. It is beyond dispute that we need to develop reliable empirical and conceptual instruments able to measure complex cognitive, affective, and behavioural changes in order to investigate whether they are attributable to DBS alone

    Dopamine neurons modulate neural encoding and expression of depression-related behaviour

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    Major depression is characterized by diverse debilitating symptoms that include hopelessness and anhedonia1. Dopamine neurons involved in reward and motivation are among many neural populations that have been hypothesized to be relevant, and certain antidepressant treatments, including medications and brain stimulation therapies, can influence the complex dopamine system. Until now it has not been possible to test this hypothesis directly, even in animal models, as existing therapeutic interventions are unable to specifically target dopamine neurons. Here we investigated directly the causal contributions of defined dopamine neurons to multidimensional depression-like phenotypes induced by chronic mild stress, by integrating behavioural, pharmacological, optogenetic and electrophysiological methods in freely moving rodents. We found that bidirectional control (inhibition or excitation) of specified midbrain dopamine neurons immediately and bidirectionally modulates (induces or relieves) multiple independent depression symptoms caused by chronic stress. By probing the circuit implementation of these effects, we observed that optogenetic recruitment of these dopamine neurons potently alters the neural encoding of depression-related behaviours in the downstream nucleus accumbens of freely moving rodents, suggesting that processes affecting depression symptoms may involve alterations in the neural encoding of action in limbic circuitry

    Causal Network Accounts Of Ill-being: Depression & Digital Well-being

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    Depression is a common and devastating instance of ill-being which deserves an account. Moreover, the ill-being of depression is impacted by digital technology: some uses of digital technology increase such ill-being while other uses of digital technology increase well-being. So a good account of ill-being would explicate the antecedents of depressive symptoms and their relief, digitally and otherwise. This paper borrows a causal network account of well-being and applies it to ill-being, particularly depression. Causal networks are found to provide a principled, coherent, intuitively plausible, and empirically adequate account of cases of depression in every-day and digital contexts. Causal network accounts of ill-being also offer philosophical, scientific, and practical utility. Insofar as other accounts of ill-being cannot offer these advantages, we should prefer causal network accounts of ill-being

    Sex Differences in Social Interaction Behavior Following Social Defeat Stress in the Monogamous California Mouse (Peromyscus californicus)

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    Stressful life experiences are known to be a precipitating factor for many mental disorders. The social defeat model induces behavioral responses in rodents (e.g. reduced social interaction) that are similar to behavioral patterns associated with mood disorders. The model has contributed to the discovery of novel mechanisms regulating behavioral responses to stress, but its utility has been largely limited to males. This is disadvantageous because most mood disorders have a higher incidence in women versus men. Male and female California mice (Peromyscus californicus) aggressively defend territories, which allowed us to observe the effects of social defeat in both sexes. In two experiments, mice were exposed to three social defeat or control episodes. Mice were then behaviorally phenotyped, and indirect markers of brain activity and corticosterone responses to a novel social stimulus were assessed. Sex differences in behavioral responses to social stress were long lasting (4 wks). Social defeat reduced social interaction responses in females but not males. In females, social defeat induced an increase in the number of phosphorylated CREB positive cells in the nucleus accumbens shell after exposure to a novel social stimulus. This effect of defeat was not observed in males. The effects of defeat in females were limited to social contexts, as there were no differences in exploratory behavior in the open field or light-dark box test. These data suggest that California mice could be a useful model for studying sex differences in behavioral responses to stress, particularly in neurobiological mechanisms that are involved with the regulation of social behavior

    Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis

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    BACKGROUND: Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge. METHODS: Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate. RESULTS: MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate. CONCLUSION: Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior
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