Anna C.K. van Duijvenvoorde, Hilde M. Huizenga, Leah H. Somerville, Mauricio R. Delgado, Alisa Powers, Wouter D. Weeda, B.J. Casey, Elke U. Weber, and Bernd Figner | The Journal of Neuroscience | January 28, 2015

Abstract: 

Adolescence is often described as a period of increased risk taking relative to both childhood and adulthood. This inflection in risky choice behavior has been attributed to a neurobiological imbalance between earlier developing motivational systems and later developing top-down control regions. Yet few studies have decomposed risky choice to investigate the underlying mechanisms or tracked their differential developmental trajectory. The current study uses a risk–return decomposition to more precisely assess the development of processes underlying risky choice and to link them more directly to specific neural mechanisms. This decomposition specifies the influence of changing risks (outcome variability) and changing returns (expected value) on the choices of children, adolescents, and adults in a dynamic risky choice task, the Columbia Card Task. Behaviorally, risk aversion increased across age groups, with adults uniformly risk averse and adolescents showing substantial individual differences in risk sensitivity, ranging from risk seeking to risk averse. Neurally, we observed an adolescent peak in risk-related activation in the anterior insula and dorsal medial PFC. Return sensitivity, on the other hand, increased monotonically across age groups and was associated with increased activation in the ventral medial PFC and posterior cingulate cortex with age. Our results implicate adolescence as a developmental phase of increased neural risk sensitivity. Importantly, this work shows that using a behaviorally validated decision-making framework allows a precise operationalization of key constructs underlying risky choice that inform the interpretation of results.

Read the full paper here.

Larry Seidman et al | Schizophrenia Research | February 11, 2015

Abstract:

Background:
Although many endophenotypes for schizophrenia have been studied individually, few studies have examined the extent to which common neurocognitive and neurophysiological measures reflect shared versus unique endophenotypic factors. It may be possible to distill individual endophenotypes into composite measures that reflect dissociable, genetically informative elements.

Methods:
The first phase of the Consortium on the Genetics of Schizophrenia (COGS-1) is a multisite family study that collected neurocognitive and neurophysiological data between 2003 and 2008. For these analyses, participants included schizophrenia probands (n = 83), their nonpsychotic siblings (n = 151), and community comparison subjects (n = 209) with complete data on a battery of 12 neurocognitive tests (assessing domains of working memory, declarative memory, vigilance, spatial ability, abstract reasoning, facial emotion processing, and motor speed) and 3 neurophysiological tasks reflecting inhibitory processing (P50 gating, prepulse inhibition and antisaccade tasks). Factor analyses were conducted on the measures for each subject group and across the entire sample. Heritability analyses of factors were performed using SOLAR.

Results:
Analyses yielded 5 distinct factors: 1) Episodic Memory, 2) Working Memory, 3) Perceptual Vigilance, 4) Visual Abstraction, and 5) Inhibitory Processing. Neurophysiological measures had low associations with these factors. The factor structure of endophenotypes was largely comparable across probands, siblings and controls. Significant heritability estimates for the factors ranged from 22% (Episodic Memory) to 39% (Visual Abstraction).

Conclusions:
Neurocognitive measures reflect a meaningful amount of shared variance whereas the neurophysiological measures reflect largely unique contributions as endophenotypes for schizophrenia. Composite endophenotype measures may inform our neurobiological and genetic understanding of schizophrenia.

Read the full paper here.

Alexandra Touroutoglou, Kristen A. Lindquist, Bradford C. Dickerson and Lisa Feldman Barrett | Social Cognitive and Affective Neuroscience | February 12, 2015

Abstract:

We tested two competing models for the brain basis of emotion, the basic emotion theory and the conceptual act theory of emotion, using resting-state functional connectivity magnetic resonance imaging (rs-fcMRI). The basic emotion view hypothesizes that anger, sadness, fear, disgust and happiness each arise from a brain network that is innate, anatomically constrained, and homologous in other animals. The conceptual act theory of emotion hypothesizes that an instance of emotion is a brain state constructed from the interaction of domain-general, core systems within the brain such as the salience, default mode, and frontoparietal control networks. Using peak coordinates derived from a meta-analysis of task-related emotion fMRI studies, we generated a set of whole-brain rs-fcMRI “discovery” maps for each emotion category, and examined the spatial overlap in their conjunctions. Instead of discovering a specific network for each emotion category, variance in the discovery maps was accounted for by the known domain-general network. Furthermore, the salience network observed as part of every emotion category. These results indicate that specific networks for each emotion do not exist within the intrinsic architecture of the human brain, and instead support the conceptual act theory of emotion.

Read the full paper here.

Congratulations to CLBB Faculty, Harvard Law School Professor and former federal Judge Nancy Gertner, who was named the 2015 recipient of the New England First Amendment Coalition (NEFAC)’s Stephen Hamblett First Amendment Award. Judge Gertner advocated for courtroom transparency by expressing support for courtroom cameras and publicly speaking out about her own cases.

See the original press release on the NEFAC site:

By Drake Bennett | Bloomberg | February 12, 2015