Target visibility and visual awareness modulate amygdala responses to fearful faces

The goals of the present study were twofold. First, we wished to investigate the neural correlates of aware and unaware emotional face perception after characterizing each subject's behavioral performance via signal detection theory methods. Second, we wished to investigate the extent to which amygdala responses to fearful faces depend on the physical characteristics of the stimulus independently of the percept. We show that amygdala responses depend on visual awareness. Under conditions in which subjects were not aware of fearful faces flashed for 33 ms, no differential activation was observed in the amygdala. On the other hand, differential activation was observed for 67 ms fearful targets that the subjects could reliably detect. When trials were divided into hits, misses, correct rejects, and false alarms, we show that target visibility is an important factor in determining amygdala responses to fearful faces. Taken together, our results further challenge the view that amygdala responses occur automatically.     

Time course of recovery showing initial prefrontal cortex changes at 16 weeks,extending to subcortical changes by 3 years in pediatric bipolar disorder

Objective

Activation changes at the interface of affective and cognitive systems are examined over a 3 year period in pediatric bipolar disorder (PBD).

Methods

Thirteen participants with PBD and 10 healthy controls (HC) matched on demographics and IQ were scanned at baseline, at 16 weeks, and after 3 years. All patients received pharmacotherapy based on a medication algorithm. A pediatric affective color matching paradigm was used to probe cognitive processing under emotional challenge.

Results

At baseline, in response to emotional vs. neutral words, patients with PBD showed greater activation than HC in the right dorsal lateral prefrontal cortex (DLPFC) and amygdala, ventral lateral prefrontal cortex (VLPFC), bilateral anterior cingulate cortex (ACC), and ventral striatum. Increased activation in DLPFC in the PBD group normalized by 16 weeks. By 3 years, normalization was observed in VLPFC, ACC, amygdala, and striatum.

Limitations

Small sample size renders the present findings preliminary.

Conclusions

Greater activation in fronto-striatal and fronto-limbic circuits were observed in unmedicated patients with PBD. Present findings suggest the possibility that DLPFC is most malleable to pharmacological intervention with systematic pharmacotherapy leading to immediate response, which extended to amygdalostriatal and ventral cortical regions at 3 years. The seminal observation from this study is the prolonged length of recovery time in the normalization of subcortical activity along with their interfacing cortical regions. Findings from this proof of concept study need to be replicated in a larger sample.     

Hyperthymic temperament and brightness preference in healthy subjects: Further evidence for involvement of left inferior orbitofrontal cortex in hyperthymic temperament

Background

Hyperthymic temperament has been generally accepted as one of premorbid temperament of bipolar disorders. Although several studies indicate that subjects with hyperthymic temperament receive more illuminance, our recent study suggests that the threshold of brightness and darkness judgment is not different between more and less hyperthymic subjects, and that hyperthymic temperament may be associated with left inferior orbitofrontal cortex, which has been reported to be associated with bipolar disorder. Therefore, at the next stage, it can be hypothesized that hyperthymic subjects may prefer brightness (i.e., heliotropism) and thereby seek illuminance, and that percent signal changes of left inferior orbitofrontal cortex during the preference task may be associated with hyperthymic temperament scores.

Methods

We compared brightness preference and un-preference between more and less hyperthymic subjects, and investigated percent signal changes of left inferior orbitofrontal cortex during brightness preference judgment, brightness un-preference judgment, and control task by using functional Magnetic Resonance Imaging (fMRI).

Results

There were significant differences in brightness preference judgment and un-preference judgment, showing that more hyperthymic subjects preferred brighter illuminace levels and un-preferred darker illuminance levels than less hyperthymic subjects. Moreover, fMRI signal changes of left inferior orbitofrontal cortex was significantly and negatively associated with hyperthymic temperament scores.

Limitations

It is unknown why left but not right inferior orbitofrontal cortex was associated with hyperthymic temperament scores.

Conclusions

The present findings suggest that more hyperthymic subjects may prefer brightness and un-prefer darkness than less hyperthymic subjects (i.e., heliotropism), and reconfirm that hyperthymic temperament may be associated with left inferior orbitofrontal cortex, which have been reported to be associated with bipolar disorders.     

Meta-analytic evidence for neuroimaging models of depression: State or trait?

Background

Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leading neurobiological models of MDD: limbic–cortical, cortico–striatal and the default mode network.

Methods

Searches of PubMed and Web of Knowledge, and manual searches, were undertaken in early 2011. Data from 34 case-control comparisons (n=1165) and 6 treatment studies (n=105) were analysed separately with two meta-analytic methods for imaging data: Activation Likelihood Estimation and Gaussian-Process Regression.

Results

There was broad support for limbic–cortical and cortico–striatal models in the case-control data. Evidence for the role of the default mode network was weaker. Treatment-sensitive regions were primarily in lateral frontal areas.

Limitations

In any meta-analysis, the increase in the statistical power of the inference comes with the risk of aggregating heterogeneous study pools. While we believe that this wide range of paradigms allows identification of key regions of dysfunction in MDD (regardless of task), we attempted to minimise such risks by employing GPR, which models such heterogeneity.

Conclusions

The focus of treatment effects in frontal areas indicates that dysregulation here may represent a biomarker of treatment response. Since the dysregulation in many subcortical regions in the case-control comparisons appeared insensitive to treatment, we propose that these act as trait vulnerability markers, or perhaps treatment insensitivity. Our findings allow these models of MDD to be applied to fMRI literature with some confidence.     

Improving the sensitivity of cluster‐based statistics for functional magnetic resonance imaging data

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false‐alarm rate, (b) that the sensitivity profiles of cluster‐based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.