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Among younger adults, the ability to willfully regulate negative affect, enabling effective responses to stressful experiences, engages regions of prefrontal cortex (PFC) and the amygdala. Because regions of PFC and the amygdala are known to influence the hypothalamic-pituitary-adrenal axis, here we test whether PFC and amygdala responses during emotion regulation predict the diurnal pattern of salivary cortisol secretion. We also test whether PFC and amygdala regions are engaged during emotion regulation in older (62- to 64-year-old) rather than younger individuals. We measured brain activity using functional magnetic resonance imaging as participants regulated (increased or decreased) their affective responses or attended to negative picture stimuli. We also collected saliva samples for 1 week at home for cortisol assay. Consistent with previous work in younger samples, increasing negative affect resulted in ventral lateral, dorsolateral, and dorsomedial regions of PFC and amygdala activation. In contrast to previous work, decreasing negative affect did not produce the predicted robust pattern of higher PFC and lower amygdala activation. Individuals demonstrating the predicted effect (decrease < attend in the amygdala), however, exhibited higher signal in ventromedial prefrontal cortex (VMPFC) for the same contrast. Furthermore, participants displaying higher VMPFC and lower amygdala signal when decreasing compared with the attention control condition evidenced steeper, more normative declines in cortisol over the course of the day. Individual differences yielded the predicted link between brain function while reducing negative affect in the laboratory and diurnal regulation of endocrine activity in the home environment.
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BACKGROUND: Autism is a syndrome of unknown cause, marked by abnormal development of social behavior. Attempts to link pathological features of the amygdala, which plays a key role in emotional processing, to autism have shown little consensus. OBJECTIVE: To evaluate amygdala volume in individuals with autism spectrum disorders and its relationship to laboratory measures of social behavior to examine whether variations in amygdala structure relate to symptom severity. DESIGN: We conducted 2 cross-sectional studies of amygdala volume, measured blind to diagnosis on high-resolution, anatomical magnetic resonance images. Participants were 54 males aged 8 to 25 years, including 23 with autism and 5 with Asperger syndrome or pervasive developmental disorder not otherwise specified, recruited and evaluated at an academic center for developmental disabilities and 26 age- and sex-matched community volunteers. The Autism Diagnostic Interview-Revised was used to confirm diagnoses and to validate relationships with laboratory measures of social function. MAIN OUTCOME MEASURES: Amygdala volume, judgment of facial expressions, and eye tracking. RESULTS: In study 1, individuals with autism who had small amygdalae were slowest to distinguish emotional from neutral expressions (P=.02) and showed least fixation of eye regions (P=.04). These same individuals were most socially impaired in early childhood, as reported on the Autism Diagnostic Interview-Revised (P<.04). Study 2 showed smaller amygdalae in individuals with autism than in control subjects (P=.03) and group differences in the relation between amygdala volume and age. Study 2 also replicated findings of more gaze avoidance and childhood impairment in participants with autism with the smallest amygdalae. Across the combined sample, severity of social deficits interacted with age to predict different patterns of amygdala development in autism (P=.047). CONCLUSIONS: These findings best support a model of amygdala hyperactivity that could explain most volumetric findings in autism. Further psychophysiological and histopathological studies are indicated to confirm these findings.
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Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.
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The anterior medial prefrontal (AMPFC) and retrosplenial (RSC) cortices are active during self-referential decision-making tasks such as when participants appraise traits and abilities, or current affect. Other appraisal tasks requiring an evaluative decision or mental representation, such as theory of mind and perspective-taking tasks, also involve these regions. In many instances, these types of decisions involve a subjective opinion or preference, but also a degree of ambiguity in the decision, rather than a strictly veridical response. However, this ambiguity is generally not controlled for in studies that examine self-referential decision-making. In this functional magnetic resonance imaging experiment with 17 healthy adults, we examined neural processes associated with subjective decision-making with and without an overt self-referential component. The task required subjective decisions about colors-regarding self-preference (internal subjective decision) or color similarity (external subjective decision) under conditions where there was no objectively correct response. Results indicated greater activation in the AMPFC, RSC, and caudate nucleus during internal subjective decision-making. The findings suggest that self-referential processing, rather than subjective judgments among ambiguous response alternatives, accounted for the AMPFC and RSC response.
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We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their 3D Euclidean distance, has been widely used in 3D brain images to increase the signal-to-noise ratio. When the observations lie on a convoluted brain surface, however, it is more natural to assign the weights based on the geodesic distance along the surface. We therefore develop a framework for geodesic distance-based kernel smoothing and statistical analysis on the cortical manifolds. As an illustration, we apply our methods in detecting the regions of abnormal cortical thickness in 16 high functioning autistic children via random field based multiple comparison correction that utilizes the new smoothing technique.
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The human brain and skull are three dimensional (3D) anatomical structures with complex surfaces. However, medical images are often two dimensional (2D) and provide incomplete visualization of structural morphology. To overcome this loss in dimension, we developed and validated a freely available, semi-automated pathway to build 3D virtual reality (VR) and hand-held, stereolithograph models. To evaluate whether surface visualization in 3D was more informative than in 2D, undergraduate students (n = 50) used the Gillespie scale to rate 3D VR and physical models of both a living patient-volunteer's brain and the skull of Phineas Gage, a historically famous railroad worker whose misfortune with a projectile tamping iron provided the first evidence of a structure-function relationship in brain. Using our processing pathway, we successfully fabricated human brain and skull replicas and validated that the stereolithograph model preserved the scale of the VR model. Based on the Gillespie ratings, students indicated that the biological utility and quality of visual information at the surface of VR and stereolithograph models were greater than the 2D images from which they were derived. The method we developed is useful to create VR and stereolithograph 3D models from medical images and can be used to model hard or soft tissue in living or preserved specimens. Compared to 2D images, VR and stereolithograph models provide an extra dimension that enhances both the quality of visual information and utility of surface visualization in neuroscience and medicine.
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Cognitive deficits have been reported in children who experienced early neglect, especially children raised in institutionalized settings. Previous research suggests that early neglect may differentially affect the directional organization of white matter in the prefrontal cortex (PFC). This may be one mechanism to explain cognitive deficits associated with neglect. To test this idea, properties of white matter and neurocognitive performance were assessed in children who suffered early neglect and those raised in typical environments (n = 63, Mage  = 11.75 years). As predicted, prefrontal white matter microstructure was affected, consistent with more diffuse organization, in children that suffered early neglect and this was related to neurocognitive deficits. Such findings underscore how early adversity may affect the PFC and explain cognitive deficits associated with neglect.
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BACKGROUND: Functional magnetic resonance imaging (fMRI) holds promise as a noninvasive means of identifying neural responses that can be used to predict treatment response before beginning a drug trial. Imaging paradigms employing facial expressions as presented stimuli have been shown to activate the amygdala and anterior cingulate cortex (ACC). Here, we sought to determine whether pretreatment amygdala and rostral ACC (rACC) reactivity to facial expressions could predict treatment outcomes in patients with generalized anxiety disorder (GAD). METHODS: Fifteen subjects (12 female subjects) with GAD participated in an open-label venlafaxine treatment trial. Functional magnetic resonance imaging responses to facial expressions of emotion collected before subjects began treatment were compared with changes in anxiety following 8 weeks of venlafaxine administration. In addition, the magnitude of fMRI responses of subjects with GAD were compared with that of 15 control subjects (12 female subjects) who did not have GAD and did not receive venlafaxine treatment. RESULTS: The magnitude of treatment response was predicted by greater pretreatment reactivity to fearful faces in rACC and lesser reactivity in the amygdala. These individual differences in pretreatment rACC and amygdala reactivity within the GAD group were observed despite the fact that 1) the overall magnitude of pretreatment rACC and amygdala reactivity did not differ between subjects with GAD and control subjects and 2) there was no main effect of treatment on rACC-amygdala reactivity in the GAD group. CONCLUSIONS: These findings show that this pattern of rACC-amygdala responsivity could prove useful as a predictor of venlafaxine treatment response in patients with GAD.
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Diminished gaze fixation is one of the core features of autism and has been proposed to be associated with abnormalities in the neural circuitry of affect. We tested this hypothesis in two separate studies using eye tracking while measuring functional brain activity during facial discrimination tasks in individuals with autism and in typically developing individuals. Activation in the fusiform gyrus and amygdala was strongly and positively correlated with the time spent fixating the eyes in the autistic group in both studies, suggesting that diminished gaze fixation may account for the fusiform hypoactivation to faces commonly reported in autism. In addition, variation in eye fixation within autistic individuals was strongly and positively associated with amygdala activation across both studies, suggesting a heightened emotional response associated with gaze fixation in autism.
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BACKGROUND: The broad autism phenotype includes subclinical autistic characteristics found to have a higher prevalence in unaffected family members of individuals with autism. These characteristics primarily affect the social aspects of language, communication, and human interaction. The current research focuses on possible neurobehavioral characteristics associated with the broad autism phenotype. METHODS: We used a face-processing task associated with atypical patterns of gaze fixation and brain function in autism while collecting brain functional magnetic resonance imaging (fMRI) and eye tracking in unaffected siblings of individuals with autism. RESULTS: We found robust differences in gaze fixation and brain function in response to images of human faces in unaffected siblings compared with typically developing control individuals. The siblings' gaze fixations and brain activation patterns during the face processing task were similar to that of the autism group and showed decreased gaze fixation along with diminished fusiform activation compared with the control group. Furthermore, amygdala volume in the siblings was similar to the autism group and was significantly reduced compared with the control group. CONCLUSIONS: Together, these findings provide compelling evidence for differences in social/emotional processing and underlying neural circuitry in siblings of individuals with autism, supporting the notion of unique endophenotypes associated with the broad autism phenotype.
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Recent studies have identified a distributed network of brain regions thought to support cognitive reappraisal processes underlying emotion regulation in response to affective images, including parieto-temporal regions and lateral/medial regions of prefrontal cortex (PFC). A number of these commonly activated regions are also known to underlie visuospatial attention and oculomotor control, which raises the possibility that people use attentional redeployment rather than, or in addition to, reappraisal as a strategy to regulate emotion. We predicted that a significant portion of the observed variance in brain activation during emotion regulation tasks would be associated with differences in how participants visually scan the images while regulating their emotions. We recorded brain activation using fMRI and quantified patterns of gaze fixation while participants increased or decreased their affective response to a set of affective images. fMRI results replicated previous findings on emotion regulation with regulation differences reflected in regions of PFC and the amygdala. In addition, our gaze fixation data revealed that when regulating, individuals changed their gaze patterns relative to a control condition. Furthermore, this variation in gaze fixation accounted for substantial amounts of variance in brain activation. These data point to the importance of controlling for gaze fixation in studies of emotion regulation that use visual stimuli.
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Using functional magnetic resonance imaging, we examined whether individual differences in amygdala activation in response to negative relative to neutral information are related to differences in the speed with which such information is evaluated, the extent to which such differences are associated with medial prefrontal cortex function, and their relationship with measures of trait anxiety and psychological well-being (PWB). Results indicated that faster judgments of negative relative to neutral information were associated with increased left and right amygdala activation. In the prefrontal cortex, faster judgment time was associated with relative decreased activation in a cluster in the ventral anterior cingulate cortex (ACC, BA 24). Furthermore, people who were slower to evaluate negative versus neutral information reported higher PWB. Importantly, higher PWB was strongly associated with increased activation in the ventral ACC for negative relative to neutral information. Individual differences in trait anxiety did not predict variation in judgment time or in amygdala or ventral ACC activity. These findings suggest that people high in PWB effectively recruit the ventral ACC when confronted with potentially aversive stimuli, manifest reduced activity in subcortical regions such as the amygdala, and appraise such information as less salient as reflected in slower evaluative speed.
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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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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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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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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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Given the central role of the amygdala in fear perception and expression and its likely abnormality in affective disorders and autism, there is great demand for a technique to measure differences in neurochemistry of the human amygdala. Unfortunately, it is also a technically complex target for magnetic resonance spectroscopy (MRS) due to a small volume, high field inhomogeneity and a shared boundary with hippocampus, which can undergo opposite changes in response to stress. We attempted to achieve reliable PRESS-localized single-voxel MRS at 3T of the isolated human amygdala by using anatomy to guide voxel size and location. We present data from 106 amygdala-MRS sessions from 58 volunteers aged 10 to 52 years, including two tests of one-week stability and a feasibility study in an adolescent sample. Our main outcomes were indices of spectral quality, repeated measurement variability (within- and between-subject standard deviations), and sensitivity to stable individual differences measured by intra-class correlation (ICC). We present metrics of amygdala-MRS reliability for n-acetyl-aspartate, creatine, choline, myo-Inositol, and glutamate+glutamine (Glx). We found that scan quality suffers an age-related difference in field homogeneity and modified our protocol to compensate. We further identified an effect of anatomical inclusion near the endorhinal sulcus, a region of high synaptic density, that contributes up to 29% of within-subject variability across 4 sessions (n=14). Remaining variability in line width but not signal-to-noise also detracts from reliability. Statistical correction for partial inclusion of these strong neurochemical gradients decreases n-acetyl-aspartate reliability from an intraclass correlation of 0.84 to 0.56 for 7-minute acquisitions. This suggests that systematic differences in anatomical inclusion can contribute greatly to apparent neurochemical concentrations and could produce false group differences in experimental studies. Precise, anatomically-based prescriptions that avoid age-related sources of inhomogeneity and use longer scan times may permit study of individual differences in neurochemistry throughout development in this late-maturing structure.
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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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