Autism is a neurodevelopmental disorder affecting behavioral and social cognition, but there is little understanding about the link between the functional deficit and its underlying neuroanatomy. We applied a 2D version of voxel-based morphometry (VBM) in differentiating the white matter concentration of the corpus callosum for the group of 16 high functioning autistic and 12 normal subjects. Using the white matter density as an index for neural connectivity, autism is shown to exhibit less white matter concentration in the region of the genu, rostrum, and splenium removing the effect of age based on the general linear model (GLM) framework. Further, it is shown that the less white matter concentration in the corpus callosum in autism is due to hypoplasia rather than atrophy.
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The Lhasa Project aims at documenting the contemporary neighborhoods of Lhasa, including their histories, using mapping, visual documentation, textual research and oral histories. The initiative consists of many distinct projects with separate administration. Some of the major projects include the Sera project, the Lingkor project, the Meru Monastery project, and others.
Diffusion tensor imaging (DTI) plays a key role in analyzing the physical structures of biological tissues, particularly in reconstructing fiber tracts of the human brain in vivo. On the one hand, eigenvalues of diffusion tensors (DTs) estimated from diffusion weighted imaging (DWI) data usually contain systematic bias, which subsequently biases the diffusivity measurements popularly adopted in fiber tracking algorithms. On the other hand, correctly accounting for the spatial information is important in the construction of these diffusivity measurements since the fiber tracts are typically spatially structured. This paper aims to establish test-based approaches to identify anisotropic water diffusion areas in the human brain. These areas in turn indicate the areas passed by fiber tracts. Our proposed test statistic not only takes into account the bias components in eigenvalue estimates, but also incorporates the spatial information of neighboring voxels. Under mild regularity conditions, we demonstrate that the proposed test statistic asymptotically follows a $\chi^2$ distribution under the null hypothesis. Simulation and real DTI data examples are provided to illustrate the efficacy of our proposed methods.
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General linear modeling (GLM) is one of the most commonly used approaches to perform voxel based analyses (VBA) for hypotheses testing in neuroimaging. In this paper we tie support vector machine based regression (SVR) and classical significance testing to provide the benefits of max margin estimation in the GLM setting. Using Welch-Satterthwaite approximations, we compute degrees of freedom (df) of error (also known as residual df) for ε-SVR. We demonstrate that ε-SVR can result not only in robustness of estimation but also improved residual df compared to the very commonly used ordinary least squares (OLS) estimation. This can result in higher sensitivity to signal in neuroimaging studies and also allow for better control of confounding effects of nuisance covariates. We demonstrate the application of our approach in white matter analyses using diffusion tensor imaging (DTI) data from autism and emotion-regulation studies.
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Through the application of Material Engagement Theory (MET) to enactivist analyses of social cognition, this paper seeks to examine the role of material culture in shaping the development of intersubjectivity and long-term scalar transformations in social interaction. The deep history of human sociality reveals a capacity for communities to self-organise at radically emergent scales across a variety of temporal and spatial ranges. This ability to generate and participate in heterogenous, multiscalar relationships and identities demonstrates the developmental plasticity of human intersubjectivity. Perhaps human sociality's most unique feature is this intersubjective plasticity, that is, the ability for diverse collectivities of individuals and groups to adopt and transition between numerous social identities and behaviours with profound rapidity and flexibility. However, the most influential models in the study of social cognition, the Social Intelligence Hypothesis and Theory of Mind, promote a view of intersubjectivity that is rooted in methodological individualism and primarily understood as a capacity for observation and prediction. This approach leads to significant issues when confronted with the diversity and plasticity of hominin social organisation, particularly in regards to the computational burdens and information processing bottlenecks such scalar changes imply for cognitivist models. This paper examines the metaphysical assumptions in computational models of the mind that result in representational apriorism and an epiphenomenal treatment of material culture that hinder our understanding of the evolution and development of social cognition. Specifically, this article critiques the logics of computation, information processing, representationalism, and content within Neo-Darwinian frameworks that obscure and distort the interrelationships of evolutionary, developmental, ecological and cultural processes. Through a synthesis of material engagement and enactivist approaches to social cognition, this article argues that it is possible to explain the emergent and multiscalar dynamics of hominin sociality in terms of ecologically distributed and developmentally plastic interactions between brains, bodies and material culture.
Through the application of Material Engagement Theory (MET) to enactivist analyses of social cognition, this paper seeks to examine the role of material culture in shaping the development of intersubjectivity and long-term scalar transformations in social interaction. The deep history of human sociality reveals a capacity for communities to self-organise at radically emergent scales across a variety of temporal and spatial ranges. This ability to generate and participate in heterogenous, multiscalar relationships and identities demonstrates the developmental plasticity of human intersubjectivity. Perhaps human sociality’s most unique feature is this intersubjective plasticity, that is, the ability for diverse collectivities of individuals and groups to adopt and transition between numerous social identities and behaviours with profound rapidity and flexibility. However, the most influential models in the study of social cognition, the Social Intelligence Hypothesis and Theory of Mind, promote a view of intersubjectivity that is rooted in methodological individualism and primarily understood as a capacity for observation and prediction. This approach leads to significant issues when confronted with the diversity and plasticity of hominin social organisation, particularly in regards to the computational burdens and information processing bottlenecks such scalar changes imply for cognitivist models. This paper examines the metaphysical assumptions in computational models of the mind that result in representational apriorism and an epiphenomenal treatment of material culture that hinder our understanding of the evolution and development of social cognition. Specifically, this article critiques the logics of computation, information processing, representationalism, and content within Neo-Darwinian frameworks that obscure and distort the interrelationships of evolutionary, developmental, ecological and cultural processes. Through a synthesis of material engagement and enactivist approaches to social cognition, this article argues that it is possible to explain the emergent and multiscalar dynamics of hominin sociality in terms of ecologically distributed and developmentally plastic interactions between brains, bodies and material culture.
Greenhouse gases from human activities are causing climate change, creating risks for people around the globe. Behaviors involving transportation, diet, energy use, and purchasing drive greenhouse gas emissions, but are also related to health and well-being, providing opportunity for co-benefits. Replacing shorter automobile trips with walking or cycling, or eating plants rather than animals, for example, may increase personal health, while also reducing environmental impact. Mindfulness-based practices have been shown to enhance a variety of health outcomes, but have not been adapted towards environmental purposes. We designed the Mindful Climate Action (MCA) curriculum to help people improve their health while simultaneously lowering their carbon footprints. Combining mindfulness-based practices with the Stages of Change theory, the MCA program aims to: (1) improve personal health and well-being; (2) decrease energy use; (3) reduce automobile use; (4) increase active transport; (5) shift diet towards plant-based foods; and (6) reduce unnecessary purchasing. Mindfulness practices will foster attentional awareness, openness, and response flexibility, supporting positive behavior change. We plan to test MCA in a randomized controlled trial, with rigorous assessment of targeted outcomes. Our long-term goal is to refine and adapt the MCA program to a variety of audiences, in order to enhance public health and environmental sustainability.
Contextualizing the back-to-the-land experience with mindfulness, a variant of meditative phenomena, within deep ecology's contention that humankind requires a fundamental shift in consciousness in order to insure ecological sustainability, this study compares and contrasts those variables that explain variance in mindfulness, ope rationalized as a quasi-religious indicator, with those that explain variance in church attendance, a measure of formal religious behavior. Drawing on a national sample for a mailed questionnaire survey of back-to-the-landers, the study found that the predictor variables for mindfulness share little overlap with those that explain variance for church attendance. The exception is spiritual mindedness, itself a quasi-religious measure, which has a statistically significant relationship with both mindfulness and church attendance. The data suggest, then, that the religious and the quasi-religious are relatively independent spheres of human behavior and sentiment. It would appear, consequently, at least in terms of the back-to-the-land sample and the assumptions of deep ecology, that the adherents of organized religion are not as likely to be disposed towards ecologically sustainable frames of mind as those who center their spirituality on quasi-religious practices such as mindfulness.
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The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.
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Critical questions include: What is an emotion? How are emotions organized in the brain? How do emotion and cognition interact? How are emotions embodied in the social world? How and why are emotions communicated? How are emotions physically embodied? What develops in emotional development?
<p>A short study of a copper plate (tāmā patra) photographed at a bazaar in Bodhnath, Kathmandu in February, 1962. (Mark Turin 2004-04-01)</p>
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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BACKGROUND: Anxious temperament (AT) is identifiable early in life and predicts the later development of anxiety disorders and depression. Neuropeptide Y (NPY) is a putative endogenous anxiolytic neurotransmitter that adaptively regulates responses to stress and might confer resilience to stress-related psychopathology. With a well-validated nonhuman primate model of AT, we examined expression of the NPY system in the central nucleus (Ce) of the amygdala, a critical neural substrate for extreme anxiety.
METHODS: In 24 young rhesus monkeys, we measured Ce messenger RNA (mRNA) levels of all members of the NPY system that are detectable in the Ce with quantitative real time polymerase chain reaction. We then examined the relationship between these mRNA levels and both AT expression and brain metabolism.
RESULTS: Lower mRNA levels of neuropeptide Y receptor 1 (NPY1R) and NPY5R but not NPY or NPY2R in the Ce predicted elevated AT; mRNA levels for NPY1R and NPY5R in the motor cortex were not related to AT. In situ hybridization analysis provided for the first time a detailed description of NPY1R and NPY5R mRNA distribution in the rhesus amygdala and associated regions. Lastly, mRNA levels for these two receptors in the Ce predicted metabolic activity in several regions that have the capacity to regulate the Ce.
CONCLUSIONS: Decreased NPY signaling in the Ce might contribute to the altered metabolic activity that is a component of the neural substrate underlying AT. This suggests that enhancement of NPY signaling might reduce the risk to develop psychopathology.
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<p>An attempt to sort the confusing materials and redirect attention to Tamang traditions about their own past. (Mark Turin 2004-06-18)</p>
In children, behavioral inhibition (BI) in response to potential threat predicts the development of anxiety and affective disorders, and primate lesion studies suggest involvement of the orbitofrontal cortex (OFC) in mediating BI. Lesion studies are essential for establishing causality in brain-behavior relationships, but should be interpreted cautiously because the impact of a discrete lesion on a complex neural circuit extends beyond the lesion location. Complementary functional imaging methods assessing how lesions influence other parts of the circuit can aid in precisely understanding how lesions affect behavior. Using this combination of approaches in monkeys, we found that OFC lesions concomitantly alter BI and metabolism in the bed nucleus of stria terminalis (BNST) region and that individual differences in BNST activity predict BI. Thus it appears that an important function of the OFC in response to threat is to modulate the BNST, which may more directly influence the expression of BI.
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Pain can be modulated by several cognitive techniques, typically involving increased cognitive control and decreased sensory processing. Recently, it has been demonstrated that pain can also be attenuated by mindfulness. Here, we investigate the underlying brain mechanisms by which the state of mindfulness reduces pain. Mindfulness practitioners and controls received unpleasant electric stimuli in the functional magnetic resonance imaging scanner during a mindfulness and a control condition. Mindfulness practitioners, but not controls, were able to reduce pain unpleasantness by 22% and anticipatory anxiety by 29% during a mindful state. In the brain, this reduction was associated with decreased activation in the lateral prefrontal cortex and increased activation in the right posterior insula during stimulation and increased rostral anterior cingulate cortex activation during the anticipation of pain. These findings reveal a unique mechanism of pain modulation, comprising increased sensory processing and decreased cognitive control, and are in sharp contrast to established pain modulation mechanisms.
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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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Depression is the most common mental illness in the elderly, and cost-effective treatments are required. Therefore, this study is aimed at evaluating the effectiveness of a mindfulness-based cognitive therapy (MBCT) on depressive symptoms, mindfulness skills, acceptance, and quality of life across four domains in patients with late-onset depression. A single case design with pre- and post-assessment was adopted. Five patients meeting the specified inclusion and exclusion criteria were recruited for the study and assessed on the behavioral analysis pro forma, geriatric depression scale, Hamilton depression rating scale, Kentucky inventory of mindfulness skills, Acceptance and Action Questionnaire II, The World Health Organization quality of life Assessment Brief version (WHOQO-L-BREF). The therapeutic program consisted of education regarding the nature of depression, training in formal and informal mindfulness meditation, and cognitive restructuring. A total of 8 sessions over 8 weeks were conducted for each patient. The results of this study indicate clinically significant improvement in the severity of depression, mindfulness skills, acceptance, and overall quality of life in all 5 patients. Eight-week MBCT program has led to reduction in depression and increased mindfulness skills, acceptance, and overall quality of life in patients with late-life depression.
Depression is the most common mental illness in the elderly, and cost-effective treatments are required. Therefore, this study is aimed at evaluating the effectiveness of a mindfulness-based cognitive therapy (MBCT) on depressive symptoms, mindfulness skills, acceptance, and quality of life across four domains in patients with late-onset depression. A single case design with pre- and post-assessment was adopted. Five patients meeting the specified inclusion and exclusion criteria were recruited for the study and assessed on the behavioral analysis pro forma, geriatric depression scale, Hamilton depression rating scale, Kentucky inventory of mindfulness skills, Acceptance and Action Questionnaire II, The World Health Organization quality of life Assessment Brief version (WHOQO-L-BREF). The therapeutic program consisted of education regarding the nature of depression, training in formal and informal mindfulness meditation, and cognitive restructuring. A total of 8 sessions over 8 weeks were conducted for each patient. The results of this study indicate clinically significant improvement in the severity of depression, mindfulness skills, acceptance, and overall quality of life in all 5 patients. Eight-week MBCT program has led to reduction in depression and increased mindfulness skills, acceptance, and overall quality of life in patients with late-life depression.
Depression is the most common mental illness in the elderly, and cost-effective treatments are required. Therefore, this study is aimed at evaluating the effectiveness of a mindfulness-based cognitive therapy (MBCT) on depressive symptoms, mindfulness skills, acceptance, and quality of life across four domains in patients with late-onset depression. A single case design with pre- and post-assessment was adopted. Five patients meeting the specified inclusion and exclusion criteria were recruited for the study and assessed on the behavioral analysis pro forma, geriatric depression scale, Hamilton depression rating scale, Kentucky inventory of mindfulness skills, Acceptance and Action Questionnaire II, The World Health Organization quality of life Assessment Brief version (WHOQO-L-BREF). The therapeutic program consisted of education regarding the nature of depression, training in formal and informal mindfulness meditation, and cognitive restructuring. A total of 8 sessions over 8 weeks were conducted for each patient. The results of this study indicate clinically significant improvement in the severity of depression, mindfulness skills, acceptance, and overall quality of life in all 5 patients. Eight-week MBCT program has led to reduction in depression and increased mindfulness skills, acceptance, and overall quality of life in patients with late-life depression.
PURPOSE: To investigate the effectiveness of a mindfulness meditation intervention on working memory capacity (WMC) in adolescents via a randomized controlled trial comparing mindfulness meditation to hatha yoga and a waitlist control group. METHODS: Participants (N = 198 adolescents) were recruited from a large public middle school in southwest United States and randomly assigned to mindfulness meditation, hatha yoga, or a waitlist control condition. Participants completed a computerized measure of WMC (Automated Operational Span Task) and self-report measures of perceived stress (Perceived Stress Scale) and anxiety (Screen for Childhood Anxiety Related Emotional Disorders) at preintervention and postintervention/waitlist. A series of mixed-design analyses of variance were used to examine changes in WMC, stress, and anxiety at preintervention and postintervention. RESULTS: Participants in the mindfulness meditation condition showed significant improvements in WMC, whereas those in the hatha yoga and waitlist control groups did not. No statistically significant between-group differences were found for stress or anxiety. CONCLUSIONS: This is the first study to provide support for the benefits of short-term mindfulness practice, specifically mindfulness meditation, in improving WMC in adolescents. Results highlight the importance of investigating the components of mindfulness-based interventions among adolescents given that such interventions may improve cognitive function. More broadly, mindfulness interventions may be delivered in an abridged format, thus increasing their potential for integration into school settings and into existing treatment protocols.
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