849 resultados para brain activity


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Despite the fact that physical health and cognitive abilities decline with aging, the ability to regulate emotion remains stable and in some aspects improves across the adult life span. Older adults also show a positivity effect in their attention and memory, with diminished processing of negative stimuli relative to positive stimuli compared with younger adults. The current paper reviews functional magnetic resonance imaging studies investigating age-related differences in emotional processing and discusses how this evidence relates to two opposing theoretical accounts of older adults’ positivity effect. The aging-brain model [Cacioppo et al. in: Social Neuroscience: Toward Understanding the Underpinnings of the Social Mind. New York, Oxford University Press, 2011] proposes that older adults’ positivity effect is a consequence of age-related decline in the amygdala, whereas the cognitive control hypothesis [Kryla-Lighthall and Mather in: Handbook of Theories of Aging, ed 2. New York, Springer, 2009; Mather and Carstensen: Trends Cogn Sci 2005;9:496–502; Mather and Knight: Psychol Aging 2005;20:554–570] argues that the positivity effect is a result of older adults’ greater focus on regulating emotion. Based on evidence for structural and functional preservation of the amygdala in older adults and findings that older adults show greater prefrontal cortex activity than younger adults while engaging in emotion-processing tasks, we argue that the cognitive control hypothesis is a more likely explanation for older adults’ positivity effect than the aging-brain model.

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The ability to change an established stimulus–behavior association based on feedback is critical for adaptive social behaviors. This ability has been examined in reversal learning tasks, where participants first learn a stimulus–response association (e.g., select a particular object to get a reward) and then need to alter their response when reinforcement contingencies change. Although substantial evidence demonstrates that the OFC is a critical region for reversal learning, previous studies have not distinguished reversal learning for emotional associations from neutral associations. The current study examined whether OFC plays similar roles in emotional versus neutral reversal learning. The OFC showed greater activity during reversals of stimulus–outcome associations for negative outcomes than for neutral outcomes. Similar OFC activity was also observed during reversals involving positive outcomes. Furthermore, OFC activity is more inversely correlated with amygdala activity during negative reversals than during neutral reversals. Overall, our results indicate that the OFC is more activated by emotional than neutral reversal learning and that OFC's interactions with the amygdala are greater for negative than neutral reversal learning.

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OBJECTIVE: This study modeled win and lose trials in a simple gambling task to examine the effect of entire win-lose situations (WIN, LOSS, or TIE) on single win/lose trials and related neural underpinnings. METHODS: The behavior responses and brain activities of 17 participants were recorded by an MRI scanner while they performed a gambling task. Different conditions were compared to determine the effect of the task on the behavior and brain activity of the participants. Correlations between brain activity and behavior were calculated to support the imaging results. RESULTS: In win trials, LOSS caused less intense posterior cingulate activity than TIE. In lose trials, LOSS caused more intense activity in the right superior temporal gyrus, bilateral superior frontal gyrus, bilateral anterior cingulate, bilateral insula cortex, and left orbitofrontal cortex than WIN and TIE. CONCLUSIONS: The experiences of the participants in win trials showed great similarity among different win-lose situations. However, the brain activity and behavior responses of the participants in lose trials indicated that they experienced stronger negative emotion in LOSS. The participants also showed an increased desire to win in LOSS than in WIN or TIE conditions.

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The neural mechanisms of music listening and appreciation are not yet completely understood. Based on the apparent relationship between the beats per minute (tempo) of music and the desire to move (for example feet tapping) induced while listening to that music it is hypothesised that musical tempo may evoke movement related activity in the brain. Participants are instructed to listen, without moving, to a large range of musical pieces spanning a range of styles and tempos during an electroencephalogram (EEG) experiment. Event-related desynchronisation (ERD) in the EEG is observed to correlate significantly with the variance of the tempo of the musical stimuli. This suggests that the dynamics of the beat of the music may induce movement related brain activity in the motor cortex. Furthermore, significant correlations are observed between EEG activity in the alpha band over the motor cortex and the bandpower of the music in the same frequency band over time. This relationship is observed to correlate with the strength of the ERD, suggesting entrainment of motor cortical activity relates to increased ERD strength

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Eating disorders are characterized by aberrant cognitions and behaviors around food. We used a novel functional magnetic resonance imaging task in a sample of recovered anorexia nervosa subjects to study the neural response to both pleasant and aversive food tastes and pictures compared with a group of matched female subjects who had never had the disorder. We report that individuals recovered from anorexia nervosa have an increased neural response to rewarding and aversive food stimuli, in the form of chocolate (e.g., in the ventral striatum) and moldy strawberries (e.g., in the caudate).

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Molecular neurobiology has provided an explanation of mechanisms supporting mental functions as learning, memory, emotion and consciousness. However, an explanatory gap remains between two levels of description: molecular mechanisms determining cellular and tissue functions, and cognitive functions. In this paper we review molecular and cellular mechanisms that determine brain activity, and then hypothetize about their relation with cognition and consciousness. The brain is conceived of as a dynamic system that exchanges information with the whole body and the environment. Three explanatory hypotheses are presented, stating that: a) brain tissue function is coordinated by macromolecules controlling ion movements, b) structured (amplitude, frequency and phase-modulated) local field potentials generated by organized ionic movement embody cognitive information patterns, and c) conscious episodes are constructed by a large-scale mechanism that uses oscillatory synchrony to integrate local field patterns. © by São Paulo State University.

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Brain activity contains three fundamental aspects: (a) The physiological aspect, covering all kinds of processes that involve matter and/or energy; (b) the mental unconscious aspect, consisting of dynamical patterns (i.e., frequency, amplitude and phase-modulated waves) embodied in neural activity. These patterns are variously operated (transmitted, stored, combined, matched, amplified, erased, etc), forming cognitive and emotional unconscious processes and (c) the mental conscious aspect, consisting of feelings experienced in the first-person perspective and cognitive functions grounded in feelings, as memory formation, selection of the focus of attention, voluntary behavior, aesthetical appraisal and ethical judgment. Triple-aspect monism (TAM) is a philosophical theory that provides a model of the relation of the three aspects. Spatially distributed neuronal dendritic potentials generate amplitude-modulated waveforms transmitted to the extracellular medium and adjacent astrocytes, prompting the formation of large waves in the astrocyte network, which are claimed to both integrate distributed information and instantiate feelings. According to the valence of the feeling, the large wave feeds back on neuronal synapses, modulating (reinforcing or depressing) cognitive and behavioral functions.

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Abstract Background Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis Results The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making. Conclusions PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

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This study examines the links between human perceptions, cognitive biases and neural processing of symmetrical stimuli. While preferences for symmetry have largely been examined in the context of disorders such as obsessive-compulsive disorder and autism spectrum disorders, we examine various these phenomena in non-clinical subjects and suggest that such preferences are distributed throughout the typical population as part of our cognitive and neural architecture. In Experiment 1, 82 young adults reported on the frequency of their obsessive-compulsive spectrum behaviors. Subjects also performed an emotional Stroop or variant of an Implicit Association Task (the OC-CIT) developed to assess cognitive biases for symmetry. Data not only reveal that subjects evidence a cognitive conflict when asked to match images of positive affect with asymmetrical stimuli, and disgust with symmetry, but also that their slowed reaction times when asked to do so were predicted by reports of OC behavior, particularly checking behavior. In Experiment 2, 26 participants were administered an oddball Event-Related Potential task specifically designed to assess sensitivity to symmetry as well as the OC-CIT. These data revealed that reaction times on the OC-CIT were strongly predicted by frontal electrode sites indicating faster processing of an asymmetrical stimulus (unparallel lines) relative to a symmetrical stimulus (parallel lines). The results point to an overall cognitive bias linking disgust with asymmetry and suggest that such cognitive biases are reflected in neural responses to symmetrical/asymmetrical stimuli.

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The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.