Targeted Neural Dynamical Modeling

Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of neural variability in their latent space. These approaches, however, are limited in their ability to capture the underlying neural dynamics (e.g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e.g. no time lag). To this end, we introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space model that jointly models the neural activity and external behavioural variables. TNDM decomposes neural dynamics into behaviourally relevant and behaviourally irrelevant dynamics; the relevant dynamics are used to reconstruct the behaviour through a flexible linear decoder and both sets of dynamics are used to reconstruct the neural activity through a linear decoder with no time lag. We implement TNDM as a sequential variational autoencoder and validate it on simulated recordings and recordings taken from the premotor and motor cortex of a monkey performing a center-out reaching task. We show that TNDM is able to learn low-dimensional latent dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

A Unified, Scalable Framework for Neural Population Decoding

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.Comment: Accepted at NeurIPS 202

Interhemispheric white matter integrity in young people with bipolar disorder and at high genetic risk

White matter (WM) impairments have been reported in patients with bipolar disorder (BD) and those at high familial risk of developing BD. However, the distribution of these impairments has not been well characterized. Few studies have examined WM integrity in young people early in the course of illness and in individuals at familial risk who have not yet passed the peak age of onset. WM integrity was examined in 63 BD subjects, 150 high-risk (HR) individuals and 111 participants with no family history of mental illness (CON). All subjects were aged 12 to 30 years. This young BD group had significantly lower fractional anisotropy within the genu of the corpus callosum (CC) compared with the CON and HR groups. Moreover, the abnormality in the genu of the CC was also present in HR participants with recurrent major depressive disorder (MDD) (n = 16) compared with CON participants. Our findings provide important validation of interhemispheric abnormalities in BD patients. The novel finding in HR subjects with recurrent MDD – a group at particular risk of future hypo/manic episodes – suggests that this may potentially represent a trait marker for BD, though this will need to be confirmed in longitudinal follow-up studies

Inferring brain-wide interactions using data-constrained recurrent neural network models

Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets

Comparison of the CDC Backpack aspirator and the Prokopack aspirator for sampling indoor- and outdoor-resting mosquitoes in southern Tanzania.

BACKGROUND\ud \ud Resting mosquitoes can easily be collected using an aspirating device. The most commonly used mechanical aspirator is the CDC Backpack aspirator. Recently, a simple, and low-cost aspirator called the Prokopack has been devised and proved to have comparable performance. The following study evaluates the Prokopack aspirator compared to the CDC backpack aspirator when sampling resting mosquitoes in rural Tanzania.\ud \ud METHODS\ud \ud Mosquitoes were sampled in- and outdoors of 48 typical rural African households using both aspirators. The aspirators were rotated between collectors and households in a randomized, Latin Square design. Outdoor collections were performed using artificial resting places (large barrel and car tyre), underneath the outdoor kitchen (kibanda) roof and from a drop-net. Data were analysed with generalized linear models.\ud \ud RESULTS\ud \ud The number of mosquitoes collected using the CDC Backpack and the Prokopack aspirator were not significantly different both in- and outdoors (indoors p = 0.735; large barrel p = 0.867; car tyre p = 0.418; kibanda p = 0.519). The Prokopack was superior for sampling of drop-nets due to its smaller size. The number mosquitoes collected per technician was more consistent when using the Prokopack aspirator. The Prokopack was more user-friendly: technicians preferred using the it over the CDC backpack aspirator as it weighs considerably less, retains its charge for longer and is easier to manoeuvre.\ud \ud CONCLUSIONS\ud \ud The Prokopack proved in the field to be more advantageous than the CDC Backpack aspirator. It can be self assembled using simple, low-cost and easily attainable materials. This device is a useful tool for researchers or vector-control surveillance programs operating in rural Africa, as it is far simpler and quicker than traditional means of sampling resting mosquitoes. Further longitudinal evaluations of the Prokopack aspirator versus the gold standard pyrethrum spray catch for indoor resting catches are recommended

Best Practices in Dengue Surveillance: A Report from the Asia-Pacific and Americas Dengue Prevention Boards

The Pediatric Dengue Vaccine Initiative organized Dengue Prevention Boards in the Asia-Pacific and the Americas regions consisting of dengue experts from endemic countries. Both Boards convened meetings to review issues in surveillance. Through presentations, facilitated discussions, and surveys, the Boards identified best practices in dengue surveillance including: (1) Dengue should be a notifiable disease in endemic countries; (2) World Health Organization regional case definitions should be consistently applied; (3) electronic reporting systems should be developed and used broadly to speed delivery of data to stakeholders; (4) minimum reporting should include incidence rates of dengue fever, dengue hemorrhagic fever, dengue shock syndrome, and dengue deaths, and hospitalization and mortality rates should be reported by age group; (5) periodic additional studies (e.g., capture/recapture) should be conducted to assess under-detection, under-reporting, and the quality of surveillance; (6) laboratory methods and protocols should be standardized; (7) national authorities should encourage laboratories to develop networks to share expertise and data; and (8) RT-PCR and virus isolation (and possibly detection of the NS1 protein) are the recommended methods for confirmation of an acute dengue infection, but are recommended only for the four days after onset of fever—after day 4, IgM-capture enzyme-linked immunosorbent assay is recommended