Kaplan, C. M. | Search bioRxiv

bioRxiv2018

Acute alcohol administration dampens threat-related activation in the central extended amygdala

Alcohol abuse is common, imposes a staggering burden on public health, and is challenging to treat, underscoring the need to develop a deeper understanding of the underlying neurobiology. When administered acutely, ethyl alcohol reduces threat reactivity in humans and other animals, and there is growing evidence that threat-dampening and related negative reinforcement mechanisms support the etiology and recurrence of alcohol and other kinds of substance misuse. Converging lines of evidence motivate the hypothesis that these effects are mediated by the central extended amygdala (EAc)--including the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST)--but the relevance of this circuitry to acute alcohol effects in humans remains poorly understood. Using a single-blind, randomized-groups design, multiband imaging data were acquired from 49 social drinkers while they performed an fMRI-optimized emotional-faces/places paradigm after consuming alcohol or placebo. Relative to placebo, alcohol significantly dampened reactivity to threat-related emotional faces in the BST. To rigorously assess potential regional differences in activation, data were extracted from anatomically defined Ce and BST regions-of-interest. Analyses revealed a similar pattern of dampening across the two regions. In short, alcohol acutely dampens reactivity to threat-related faces in humans and it does so similarly across the two major divisions of the EAc. These observations provide a framework for understanding the translational relevance of addiction models derived from work in rodents, inform on-going debates about the functional organization of the EAc, and set the stage for bi-directional translational models aimed at developing improved treatment strategies for alcohol abuse and other addictions.

neuroscience↗

bioRxiv2018

The Impact of Spatial Normalization for Functional Magnetic Resonance Imaging Data Analyses Revisited

Spatial normalization--the process of aligning anatomical or functional data acquired from different individuals to a common stereotaxic atlas--is routinely used in the vast majority of functional neuroimaging studies, with important consequences for scientific inference and reproducibility. Although several approaches exist, multi-step techniques that leverage the superior contrast and spatial resolution afforded by T1-weighted anatomical images to normalize echo planar imaging (EPI) functional data acquired from the same individuals (T1EPI) is now standard. Yet, recent work suggests that direct alignment of functional data to a T2*-weighted template without recourse to an anatomical image--an EPI only (EPIO) approach--enhances normalization precision. This counterintuitive claim is intriguing, suggesting that a change in standard practices may be warranted. Here, we re-visit these conclusions, extending prior work to encompass newly developed measures of normalization precision, accuracy, and real-world statistical performance for the standard EPIO and T1EPI pipelines implemented in SPM12, a recently developed variant of the EPIO pipeline, and a novel T1EPI pipeline incorporating best practice tools from multiple software packages. The multi-tool T1EPI pipeline was consistently the most precise, most accurate, and resulted in the largest t values at the group level, in some cases dramatically so. The three SPM-based pipelines exhibited more modest and variable differences in performance relative to each another, with the widely used T1EPI pipeline showing the second best overall precision and accuracy, and the recently developed EPIO pipeline generally showing the poorest overall performance. The results demonstrate that standard pipelines can be easily improved and we encourage researchers to invest the resources necessary to do so. The multi-tool pipeline presented here provides a framework for doing so. In addition, the novel performance metrics described here should prove useful for reporting and validating future methods for pre-processing functional neuroimaging data.

neuroscience↗