Reversible optogenetic neural inactivation techniques are valuable for linking neural activity and behavior but they have serious limitations in macaques. To achieve powerful and temporally precise neural inactivation, we used an adeno-associated viral (AAV) vector carrying the channelrhodopsin-2 gene under the control of a Dlx5/6 enhancer, which restricts expression to GABAergic neurons. We tested this approach in the primary visual cortex, an area where neural inactivation leads to interpretable behavioral deficits. Optical stimulation modulated spiking activity and reduced visual sensitivity profoundly in the region of space represented by the stimulated neurons. Rebound firing, which can have unwanted effects on neural circuits following inactivation, was not observed, and the efficacy of the optogenetic manipulation on behavior was maintained across >1000 trials. We conclude that this inhibitory cell-type specific optogenetic approach is a powerful and spatiotemporally precise neural inactivation tool with broad utility for probing the functional contributions of different cortical areas in macaques.
All data have been uploaded to https://github.com/horwitzlab.
Data from: Fast and reversible neural inactivation in macaque cortex by optogenetic stimulation of GABAergic neuronshttps://github.com/horwitzlab.
- Gregory D Horwitz
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: Surgical procedures, experimental protocols and animal care conformed to the NIH Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the University of Washington (IACUC protocol #4167-01).
- Michael Schmid, Newcastle University, United Kingdom
© 2020, De et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The relationship between obesity and human brain structure is incompletely understood. Using diffusion-weighted MRI from ∼30,000 UK Biobank participants, we test the hypothesis that obesity (waist-to-hip ratio, WHR) is associated with regional differences in two micro-structural MRI metrics: isotropic volume fraction (ISOVF), an index of free water, and intra-cellular volume fraction (ICVF), an index of neurite density. We observed significant associations with obesity in two coupled but distinct brain systems: a prefrontal/temporal/striatal system associated with ISOVF and a medial temporal/occipital/striatal system associated with ICVF. The ISOVF~WHR system colocated with expression of genes enriched for innate immune functions, decreased glial density, and high mu opioid (MOR) and other neurotransmitter receptor density. Conversely, the ICVF~WHR system co-located with expression of genes enriched for G-protein coupled receptors and decreased density of MOR and other receptors. To test whether these distinct brain phenotypes might differ in terms of their underlying shared genetics or relationship to maps of the inflammatory marker C-reactive Protein (CRP), we estimated the genetic correlations between WHR and ISOVF (rg = 0.026, P = 0.36) and ICVF (rg = 0.112, P < 9×10−4) as well as comparing correlations between WHR maps and equivalent CRP maps for ISOVF and ICVF (P<0.05). These correlational results are consistent with a two-way mechanistic model whereby genetically determined differences in neurite density in the medial temporal system may contribute to obesity, whereas water content in the prefrontal system could reflect a consequence of obesity mediated by innate immune system activation.
The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.