Biology subjects

Doya, K.

Publications and source records attributed to Doya, K..

4 recordsLinked to original sources

bioRxiv2022

Neuronal representation of a working memory-based decision strategy in the motor and prefrontal cortico-basal ganglia loops

While animal and human decision strategies are typically explained by model-free and model-based reinforcement learning, their choice sequences often follow simple procedures based on working memory of past actions and rewards. Here we address how working memory-based choice strategies, such as win-stay-lose-switch (WSLS), are represented in the prefrontal and motor cortico-basal ganglia loops by simultaneous recording of neuronal activities in the dorsomedial striatum (DMS), the dorsolateral striatum (DLS), the medial prefrontal cortex (mPFC), and the primary motor cortex (M1). In order to compare neuronal representations when rats employ working memory-based strategies, we developed a new task paradigm, a continuous/intermittent choice task, consisting of choice and no-choice trials. While the continuous condition (CC) consisted of only choice trials, in the intermittent condition (IC), a no-choice trial was inserted after each choice trial to disrupt working memory of the previous choice and reward. Behaviors in CC showed high proportions of win-stay and lose-switch choices, which could be regarded as "a noisy WSLS strategy." Poisson regression of neural spikes revealed encoding specifically in CC of the previous action and reward before action choice and prospective coding of WSLS action during action execution. A striking finding was that the DLS and M1 in the motor cortico-basal ganglia loop carry substantial WM information about previous choices, rewards, and their interactions, in addition to current action coding. Significance StatementWorking memory-based decision strategies, such as win-stay-lose-switch (WSLS), are widely observed in humans and animals. To address neuronal bases of these strategies, we recorded neuronal activities of rat prefrontal and motor cortico-basal ganglia loops during continuous/intermittent choice tasks. The rat choice strategy was a noisy WSLS in the continuous choice condition, whereas non-WSLS was selected in the intermittent choice condition. In the continuous choice condition, the primary motor cortex and the dorsolateral striatum in the motor loop more strongly conveyed information about previous choices, rewards, and their interactions than the medial prefrontal cortex and the dorsomedial striatum in the prefrontal loop. These results demonstrate that the motor cortico-basal ganglia loop contributes to working memory-based decision strategies.

neuroscience↗

bioRxiv2022

Optogenetic activation of dorsal raphe serotonin neurons induces a brain-wide response in reward network

Serotonin is a neuromodulator that affects multiple behavioral and cognitive functions. Nonetheless, how serotonin causes such a variety of effects via brain-wide projections and various receptors remains unclear. Here we measured brain-wide responses to optogenetic stimulation of serotonin neurons in the dorsal raphe nucleus (DRN) of the male mouse brain using functional MRI with an 11.7T scanner and a cryoprobe. Transient activation of DRN serotonin neurons caused brain-wide activation, including the medial prefrontal cortex, the striatum, and the ventral tegmental area. The same stimulation under anesthesia with isoflurane decreased brain-wide activation, including the hippocampal complex. These brain-wide response patterns can be explained by DRN serotonergic projection topography and serotonin receptor expression profiles, with enhanced weights on 5-HT1 receptors. Together, these results provide insight into the DR serotonergic system, which is consistent with recent discoveries of its functions in adaptive behaviors.

neuroscience↗

bioRxiv2022

The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space

We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.

neuroscience↗

bioRxiv2021

Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

evolutionary biology↗