Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic pr...

Neurite degeneration is associated with early stages of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease (PD), and amyotrophic lateral sclerosis. One method that is commonly used to analyze neurite degeneration involves calculation of a Degeneration Index (DI) following utilization of the Analyze Particles tool of ImageJ to detect neurite fragments in micrographs of cultured cells. However, DI analyses are prone to several types of measurement error, can be time consuming to perform, and are limited in application. Here, we describe an improved method for performing DI analyses. Accuracy of measurements was enhanced through modification of selection criteria for detecting neurite fragments, removal of image artifacts and non-neurite materials from images, and optimization of image contrast. Such enhancements were implemented into an ImageJ macro that enables rapid and fully automated DI analysis of multiple images. The macro features operations for automated removal of cell bodi...

Incubation of craving refers to the intensification of drug-seeking behavior in response to reward-paired cues over the course of abstinence. In rodents, craving and drug-seeking behaviors have been measured by an increase in lever pressing in the absence of reinforcer availability in response to cue presentations. However, craving in rodents is difficult to define and little is known about the behavioral signatures that accompany increased drug-seeking behavior measured by lever pressing. The affective components of relapse are also important, but understudied in rodents. Hormonal fluctuations influence craving for psychostimulants, but little is known about the impact of the estrous cycle on opioid-seeking behavior. This study sought to delineate the behavioral and affective signatures associated with craving, and to examine the influence of the female estrous cycle on craving. Male and female rats underwent 10 d of intravenous opioid self-administration. Separate cohorts of control rats self-administere...

Here, we investigate stimulus generalization in a cerebellar learning paradigm, called eyeblink conditioning. Mice were conditioned to close their eyes in response to a 10-kHz tone by repeatedly pairing this tone with an air puff to the eye 250 ms after tone onset. After 10 consecutive days of training, when mice showed reliable conditioned eyelid responses to the 10-kHz tone, we started to expose them to tones with other frequencies, ranging from 2 to 20 kHz. We found that mice had a strong generalization gradient, whereby the probability and amplitude of conditioned eyelid responses gradually decreases depending on the dissimilarity with the 10-kHz tone. Tones with frequencies closest to 10 kHz evoked the most and largest conditioned eyelid responses and each step away from the 10-kHz tone resulted in fewer and smaller conditioned responses (CRs). In addition, we found that tones with lower frequencies resulted in CRs that peaked earlier after tone onset compared with those to tones with higher frequenci...

Gonadotropin-releasing hormone (GnRH)-secreting neurons control fertility. The release of GnRH peptide regulates the synthesis and release of both luteinizing hormone (LH) and Follicle stimulation hormone (FSH) from the anterior pituitary. While it is known that dopamine regulates GnRH neurons, the specific dopamine receptor subtype(s) involved remain unclear. Previous studies in adult rodents have reported juxtaposition of fibers containing tyrosine hydroxylase (TH), a marker of catecholaminergic cells, onto GnRH neurons and that exogenous dopamine inhibits GnRH neurons postsynaptically through dopamine D1-like and/or D2-like receptors. Our microarray data from GnRH neurons revealed a high level of Drd4 transcripts [i.e., dopamine D4 receptor (D4R)]. Single-cell RT-PCR and immunocytochemistry confirmed GnRH cells express the Drd4 transcript and protein, respectively. Calcium imaging identified changes in GnRH neuronal activity during application of subtype-specific dopamine receptor agonists and antagonis...

The neural basis of attention is thought to involve the allocation of limited neural resources. However, the quantitative validation of this hypothesis remains challenging. Here, we provide quantitative evidence that the nonuniform allocation of neural resources across the whole cerebral gray matter reflects the broad-task process of sustained attention. We propose a neural measure for the nonuniformity of whole-cerebral allocation using functional magnetic resonance imaging. We found that this measure was significantly correlated with conventional indicators of attention level, such as task difficulty and pupil dilation. We further found that the broad-task neural correlates of the measure belong to frontoparietal and dorsal attention networks. Finally, we found that patients with attention-deficit/hyperactivity disorder showed abnormal decreases in the level of the proposed measure, reflecting the executive dysfunction. This study proposes a neuromarker suggesting that the nonuniform allocation of neural...

We assessed similarities and differences in the electrographic signatures of local field potentials (LFPs) evoked by different pharmacological agents in zebrafish larvae. We then compared and contrasted these characteristics with what is known from electrophysiological studies of seizures and epilepsy in mammals, including humans. Ultimately, our aim was to phenotype neurophysiological features of drug-induced seizures in larval zebrafish for expanding knowledge on the translational potential of this valuable alternative to mammalian models. LFPs were recorded from the midbrain of 4-d-old zebrafish larvae exposed to a pharmacologically diverse panel of seizurogenic compounds, and the outputs of these recordings were assessed using frequency domain analysis. This included analysis of changes occurring within various spectral frequency bands of relevance to mammalian CNS circuit pathophysiology. From these analyses, there were clear differences in the frequency spectra of drug-exposed LFPs, relative to contr...

To better understand complex systems, such as the brain, studying the interactions between multiple brain regions is imperative. Such experiments often require delineation of multiple brain regions on microscopic images based on preexisting brain atlases. Experiments examining the relationships of multiple regions across the brain have traditionally relied on manual plotting of regions. This process is very intensive and becomes untenable with a large number of regions of interest (ROIs). To reduce the amount of time required to process multi-region datasets, several tools for atlas registration have been developed; however, these tools are often inflexible to tissue type, only supportive of a limited number of atlases and orientation, require considerable computational expertise, or are only compatible with certain types of microscopy. To address the need for a simple yet extensible atlas registration tool, we have developed FASTMAP, a Flexible Atlas Segmentation Tool for Multi-Area Processing. We demonst...

Many fundamental human behaviors contain multiple sequences performed to reach a desired outcome, such as cooking. Reward is inherently associated with sequence completion and has been shown to generally enhance cognitive control. However, the impact of reward on cognitive sequence processing remains unexplored. To address this key question, we focused on the rostrolateral prefrontal cortex (RLPFC). This area is necessary and exhibits increasing (“ramping”) activation during sequences, a dynamic that may be related to reward processing in other brain regions. To separate these dynamics, we designed a task where reward was only provided after multiple four-item sequences (“iterations”), rather than each individual sequence. Using fMRI in humans, we investigated three possible interactions of reward and sequential control signals in RLPFC: (1) with the visibility of sequential cues, i.e., memory; (2) equally across individual sequence iterations; and (3) differently across individual sequence iterations (e.g...

In a competitive game involving an animal and an opponent, the outcome is contingent on the choices of both players. To succeed, the animal must continually adapt to competitive pressure, or else risk being exploited and lose out on rewards. In this study, we demonstrate that head-fixed male mice can be trained to play the iterative competitive game “matching pennies” against a virtual computer opponent. We find that the animals’ performance is well described by a hybrid computational model that includes Q-learning and choice kernels. Comparing between matching pennies and a non-competitive two-armed bandit task, we show that the tasks encourage animals to operate at different regimes of reinforcement learning. To understand the involvement of neuromodulatory mechanisms, we measure fluctuations in pupil size and use multiple linear regression to relate the trial-by-trial transient pupil responses to decision-related variables. The analysis reveals that pupil responses are modulated by observable variables,...