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This past year has seen significant advances in our understanding of the physiology of emotion. Attention continues to focus on the amygdala and its interconnections with prefrontal cortical regions. New evidence underscores the importance of lateralization for emotion. There are also new findings on the physiological predictors of individual differences in emotional behavior and experience, and on the role of autonomic arousal in emotional memory.
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This article presents an overview of the author's recent electrophysiological studies of anterior cerebral asymmetries related to emotion and affective style. A theoretical account is provided of the role of the two hemispheres in emotional processing. This account assigns a major role in approach- and withdrawal-related behavior to the left and right frontal and anterior temporal regions of two hemispheres, respectively. Individual differences in approach- and withdrawal-related emotional reactivity and temperament are associated with stable differences in baseline measures of activation asymmetry in these anterior regions. Phasic state changes in emotion result in shifts in anterior activation asymmetry which are superimposed upon these stable baseline differences. Future directions for research in this area are discussed.
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Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.
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Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan-rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.
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Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.
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Neurosurgical treatment of psychiatric disorders has been influenced by evolving neurobiological models of symptom generation. The advent of functional neuroimaging and advances in the neurosciences have revolutionized understanding of the functional neuroanatomy of psychiatric disorders. This article reviews neuroimaging studies of depression from the last 3 decades and describes an emerging neurocircuitry model of mood disorders, focusing on critical circuits of cognition and emotion, particularly those networks involved in the regulation of evaluative, expressive and experiential aspects of emotion. The relevance of this model for neurotherapeutics is discussed, as well as the role of functional neuroimaging of psychiatric disorders.
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To better understand the neurobiological mechanisms by which mindfulness-based practices function in a psychotherapeutic context, this article details the definition, techniques, and purposes ascribed to mindfulness training as described by its Buddhist tradition of origin and by contemporary neurocognitive models. Included is theory of how maladaptive mental processes become habitual and automatic, both from the Buddhist and Western psychological perspective. Specific noting and labeling techniques in open monitoring meditation, described in the Theravada and Western contemporary traditions, are highlighted as providing unique access to multiple modalities of awareness. Potential explicit and implicit mechanisms are discussed by which such techniques can contribute to transforming maladaptive habits of mind and perceptual and cognitive biases, improving efficiency, facilitating integration, and providing the flexibility to switch between systems of self-processing. Finally, a model is provided to describe the timing by which noting and labeling practices have the potential to influence different stages of low- and high-level neural processing. Hypotheses are proposed concerning both levels of processing in relation to the extent of practice. Implications for the nature of subjective experience and self-processing as it relates to one's habits of mind, behavior, and relation to the external world, are also described.
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Studies have suggested that the default mode network is active during mind wandering, which is often experienced intermittently during sustained attention tasks. Conversely, an anticorrelated task-positive network is thought to subserve various forms of attentional processing. Understanding how these two systems work together is central for understanding many forms of optimal and sub-optimal task performance. Here we present a basic model of naturalistic cognitive fluctuations between mind wandering and attentional states derived from the practice of focused attention meditation. This model proposes four intervals in a cognitive cycle: mind wandering, awareness of mind wandering, shifting of attention, and sustained attention. People who train in this style of meditation cultivate their abilities to monitor cognitive processes related to attention and distraction, making them well suited to report on these mental events. Fourteen meditation practitioners performed breath-focused meditation while undergoing fMRI scanning. When participants realized their mind had wandered, they pressed a button and returned their focus to the breath. The four intervals above were then constructed around these button presses. We hypothesized that periods of mind wandering would be associated with default mode activity, whereas cognitive processes engaged during awareness of mind wandering, shifting of attention and sustained attention would engage attentional subnetworks. Analyses revealed activity in brain regions associated with the default mode during mind wandering, and in salience network regions during awareness of mind wandering. Elements of the executive network were active during shifting and sustained attention. Furthermore, activations during these cognitive phases were modulated by lifetime meditation experience. These findings support and extend theories about cognitive correlates of distributed brain networks.
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A review of behavioral and neurobiological data on mood and mood regulation as they pertain to an understanding of mood disorders is presented. Four approaches are considered: 1) behavioral and cognitive; 2) neurobiological; 3) computational; and 4) developmental. Within each of these four sections, we summarize the current status of the field and present our vision for the future, including particular challenges and opportunities. We conclude with a series of specific recommendations for National Institute of Mental Health priorities. Recommendations are presented for the behavioral domain, the neural domain, the domain of behavioral-neural interaction, for training, and for dissemination. It is in the domain of behavioral-neural interaction, in particular, that new research is required that brings together traditions that have developed relatively independently. Training interdisciplinary clinical scientists who meaningfully draw upon both behavioral and neuroscientific literatures and methods is critically required for the realization of these goals.
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Children with an anxious temperament (AT) are at risk for developing psychiatric disorders along the internalizing spectrum, including anxiety and depression. Like these disorders, AT is a multidimensional phenotype and children with extreme anxiety show varying mixtures of physiological, behavioral, and other symptoms. Using a well-validated juvenile monkey model of AT, we addressed the degree to which this phenotypic heterogeneity reflects fundamental differences or similarities in the underlying neurobiology. The rhesus macaque is optimal for studying AT because children and young monkeys express the anxious phenotype in similar ways and have similar neurobiology. Fluorodeoxyglucose (FDG)-positron emission tomography (FDG-PET) in 238 freely behaving monkeys identified brain regions where metabolism predicted variation in three dimensions of the AT phenotype: hypothalamic-pituitary-adrenal (HPA) activity, freezing behavior, and expressive vocalizations. We distinguished brain regions that predicted all three dimensions of the phenotype from those that selectively predicted a single dimension. Elevated activity in the central nucleus of the amygdala and the anterior hippocampus was consistently found across individuals with different presentations of AT. In contrast, elevated activity in the lateral anterior hippocampus was selective to individuals with high levels of HPA activity, and decreased activity in the motor cortex (M1) was selective to those with high levels of freezing behavior. Furthermore, activity in these phenotype-selective regions mediated relations between amygdala metabolism and different expressions of anxiety. These findings provide a framework for understanding the mechanisms that lead to heterogeneity in the clinical presentation of internalizing disorders and set the stage for developing improved interventions.
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Selfhood and self-awareness, at least in humans, can be dissected into many levels. At one level, self-awareness describes a metacognitive aspect of consciousness wherein higher-order thought is directed through attentional focus on the self-object and self-related matters. This chapter explores the insights gained from neuroimaging studies into the brain substrates and mechanisms underlying such “high-level” self-referential processing. At another level, selfhood is reflected in self-recognition processes which discriminate self-related stimuli from other similar stimuli. Here, we examine the relevant neuroimaging evidence, focusing on self-face recognition as an exemplar. At a more fundamental level, we review what is known about the mental representation of the body, focusing on studies suggesting that a primary sense of self is ultimately derived from the neural representation of the body via interoception. These studies emphasize the continuous mapping of dynamic changes in internal state, whereby physiological demands and homeostatic imperatives dictate motivations and shape the contents of cognition. Here, converging neuroimaging evidence suggests that brain regions involved in representing internal physiological processes and making them available to conscious appraisal contribute to self-referential cognitions. This link is further apparent in the neural correlates of cognitive control and detachment techniques, such as mindfulness, that increasingly find clinical utility. Ultimately, inferences from neuroimaging regarding selfhood and self-awareness must cohere with evidence from lesion studies and with an increasingly sophisticated understanding of the brain as a connected network generating self-representations via a range of overlapping mechanisms.

Neuroimage phenotyping for psychiatric and neurological disorders is performed using voxelwise analyses also known as voxel based analyses or morphometry (VBM). A typical voxelwise analysis treats measurements at each voxel (e.g., fractional anisotropy, gray matter probability) as outcome measures to study the effects of possible explanatory variables (e.g., age, group) in a linear regression setting. Furthermore, each voxel is treated independently until the stage of correction for multiple comparisons. Recently, multi-voxel pattern analyses (MVPA), such as classification, have arisen as an alternative to VBM. The main advantage of MVPA over VBM is that the former employ multivariate methods which can account for interactions among voxels in identifying significant patterns. They also provide ways for computer-aided diagnosis and prognosis at individual subject level. However, compared to VBM, the results of MVPA are often more difficult to interpret and prone to arbitrary conclusions. In this paper, first we use penalized likelihood modeling to provide a unified framework for understanding both VBM and MVPA. We then utilize statistical learning theory to provide practical methods for interpreting the results of MVPA beyond commonly used performance metrics, such as leave-one-out-cross validation accuracy and area under the receiver operating characteristic (ROC) curve. Additionally, we demonstrate that there are challenges in MVPA when trying to obtain image phenotyping information in the form of statistical parametric maps (SPMs), which are commonly obtained from VBM, and provide a bootstrap strategy as a potential solution for generating SPMs using MVPA. This technique also allows us to maximize the use of available training data. We illustrate the empirical performance of the proposed framework using two different neuroimaging studies that pose different levels of challenge for classification using MVPA.
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Functional neuroimaging investigations in the fields of social neuroscience and neuroeconomics indicate that the anterior insular cortex (AI) is consistently involved in empathy, compassion, and interpersonal phenomena such as fairness and cooperation. These findings suggest that AI plays an important role in social emotions, hereby defined as affective states that arise when we interact with other people and that depend on the social context. After we link the role of AI in social emotions to interoceptive awareness and the representation of current global emotional states, we will present a model suggesting that AI is not only involved in representing current states, but also in predicting emotional states relevant to the self and others. This model also proposes that AI enables us to learn about emotional states as well as about the uncertainty attached to events, and implies that AI plays a dominant role in decision making in complex and uncertain environments. Our review further highlights that dorsal and ventro-central, as well as anterior and posterior subdivisions of AI potentially subserve different functions and guide different aspects of behavioral regulation. We conclude with a section summarizing different routes to understanding other people’s actions, feelings and thoughts, emphasizing the notion that the predominant role of AI involves understanding others’ feeling and bodily states rather than their action intentions or abstract beliefs.
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We used fMRI to examine amygdala activation in response to fearful facial expressions, measured over multiple scanning sessions. 15 human subjects underwent three scanning sessions, at 0, 2 and 8 weeks. During each session, functional brain images centered about the amygdala were acquired continuously while participants were shown alternating blocks of fearful, neutral and happy facial expressions. Intraclass correlation coefficients calculated across the sessions indicated stability of response in left amygdala to fearful faces (as a change from baseline), but considerably less left amygdala stability in responses to neutral expressions and for fear versus neutral contrasts. The results demonstrate that the measurement of fMRI BOLD responses in amygdala to fearful facial expressions might be usefully employed as an index of amygdala reactivity over extended periods. While signal change to fearful facial expressions appears robust, the experimental design employed here has yielded variable responsivity within baseline or comparison conditions. Future studies might manipulate the experimental design to either amplify or attenuate this variability, according to the goals of the research.
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The scientific discovery of novel training paradigms has yielded better understanding of basic mechanisms underlying cortical plasticity, learning and development. This study is a first step in evaluating Tai Chi (TC), the Chinese slow-motion meditative exercise, as a training paradigm that, while not engaging in direct tactile stimulus training, elicits enhanced tactile acuity in long-term practitioners. The rationale for this study comes from the fact that, unlike previously studied direct-touch tactile training paradigms, TC practitioners focus specific mental attention on the body’s extremities including the fingertips and hands as they perform their slow routine. To determine whether TC is associated with enhanced tactile acuity, experienced adult TC practitioners were recruited and compared to age–gender matched controls. A blinded assessor used a validated method (Van Boven et al. in Neurology 54(12): 2230–2236, 2000) to compare TC practitioners’ and controls’ ability to discriminate between two different orientations (parallel and horizontal) across different grating widths at the fingertip. Study results showed that TC practitioners’ tactile spatial acuity was superior to that of the matched controls (P < 0.04). There was a trend showing TC may have an enhanced effect on older practitioners (P < 0.066), suggesting that TC may slow age related decline in this measure. To the best of our knowledge, this is the first study to evaluate a long-term attentional practice’s effects on a perceptual measure. Longitudinal studies are needed to examine whether TC initiates or is merely correlated with perceptual changes and whether it elicits long-term plasticity in primary sensory cortical maps. Further studies should also assess whether related somatosensory attentional practices (such as Yoga, mindfulness meditation and Qigong) achieve similar effects.
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