Department of Statistics

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

BACKGROUND: Fetal exposure to maternal mood dysregulation influences child cognitive and emotional development, which may have long-lasting implications for mental health. However, the neurobiological alterations associated with this dimension of adversity have yet to be explored. Here, we tested the hypothesis that fetal exposure to entropy, a novel index of dysregulated maternal mood, would predict the integrity of the salience network, which is involved in emotional processing. METHODS: A sample of 138 child-mother pairs (70 females) participated in this prospective longitudinal study. Maternal negative mood level and entropy (an index of variable and unpredictable mood) were assessed 5 times during pregnancy. Adolescents engaged in a functional magnetic resonance imaging task that was acquired between 2 resting-state scans. Changes in network integrity were analyzed using mixed-effect and latent growth curve models. The amplitude of low frequency fluctuations was analyzed to corroborate findings. RESULTS: Prenatal maternal mood entropy, but not mood level, was associated with salience network integrity. Both prenatal negative mood level and entropy were associated with the amplitude of low frequency fluctuations of the salience network. Latent class analysis yielded 2 profiles based on changes in network integrity across all functional magnetic resonance imaging sequences. The profile that exhibited little variation in network connectivity (i.e., inflexibility) consisted of adolescents who were exposed to higher negative maternal mood levels and more entropy. CONCLUSIONS: These findings suggest that fetal exposure to maternal mood dysregulation is associated with a weakened and inflexible salience network. More broadly, they identify maternal mood entropy as a novel marker of early adversity that exhibits long-lasting associations with offspring brain development.

I provide a personal perspective on metastudies and emphasize lesser-known benefits. I stress the need for integrative theories to establish commensurability between experiments. I argue that mathematical social scientists should be engaged to develop integrative theories, and that likelihood functions provide a common mathematical framework across experiments. The development of quantitative theories promotes commensurability engineering on a larger scale.

Our previous studies find that subcutaneously administered (s.c.) subanesthetic ketamine promotes sustained cortical disinhibition and plasticity in adult mouse binocular visual cortex (bV1). We hypothesized that intranasal delivery (i.n.) of subanesthetic ketamine may have similar actions. To test this, we delivered ketamine (10 mg/kg, i.n.) to adult mice and then recorded excitatory pyramidal neurons or PV+ interneurons in L2/3 of bV1 slices. In pyramidal neurons the baseline IPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of neuregulin-1 (NRG1) to cortical slices increases these IPSC amplitudes in mice treated with ketamine but not in controls. In PV+ interneurons, the baseline EPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of NRG1 to cortical slices increases these EPSC amplitudes in mice treated with ketamine but not in controls. We also found that mice treated with ketamine exhibit increased pCREB staining in L2/3 of bV1. Together, our results show that a single intranasal delivery of ketamine reduces PV+ interneuron excitation and reduces pyramidal neuron inhibition and that these effects are acutely reversed by NRG1. These results are significant as they show that intranasal delivery of ketamine induces cortical disinhibition, which has implications for the treatment of psychiatric, neurologic, and ophthalmic disorders.

Research shows that brain circuits controlling vital physiological processes are closely linked with endogenous time-keeping systems. In this study, we aimed to examine oscillatory gene expression patterns of well-characterized neuronal circuits by reanalyzing publicly available transcriptomic data from a spatiotemporal gene expression atlas of a non-human primate. Unexpectedly, brain structures known for regulating circadian processes (e.g., hypothalamic nuclei) did not exhibit robust cycling expression. In contrast, basal ganglia nuclei, not typically associated with circadian physiology, displayed the most dynamic cycling behavior of its genes marked by sharp temporally defined expression peaks. Intriguingly, the mammillary bodies, considered hypothalamic nuclei, exhibited gene expression patterns resembling the basal ganglia, prompting reevaluation of their classification. Our results emphasize the potential for high throughput circadian gene expression analysis to deepen our understanding of the functional synchronization across brain structures that influence physiological processes and resulting complex behaviors.

Introduction

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BACKGROUND: In the demyelinating disease multiple sclerosis (MS), chronic-active brain inflammation, remyelination failure and neurodegeneration remain major issues despite immunotherapy. While B cell depletion and blockade/sequestration of T and B cells potently reduces episodic relapses, they act peripherally to allow persistence of chronic-active brain inflammation and progressive neurological dysfunction. N-acetyglucosamine (GlcNAc) is a triple modulator of inflammation, myelination and neurodegeneration. GlcNAc promotes biosynthesis of Asn (N)-linked-glycans, which interact with galectins to co-regulate the clustering/signaling/endocytosis of multiple glycoproteins simultaneously. In mice, GlcNAc crosses the blood brain barrier to raise N-glycan branching, suppress inflammatory demyelination by T and B cells and trigger stem/progenitor cell mediated myelin repair. MS clinical severity, demyelination lesion size and neurodegeneration inversely associate with a marker of endogenous GlcNAc, while in healthy humans, age-associated increases in endogenous GlcNAc promote T cell senescence. OBJECTIVES AND METHODS: An open label dose-escalation mechanistic trial of oral GlcNAc at 6 g (n = 18) and 12 g (n = 16) for 4 weeks was performed in MS patients on glatiramer acetate and not in relapse from March 2016 to December 2019 to assess changes in serum GlcNAc, lymphocyte N-glycosylation and inflammatory markers. Post-hoc analysis examined changes in serum neurofilament light chain (sNfL) as well as neurological disability via the Expanded Disability Status Scale (EDSS). RESULTS: Prior to GlcNAc therapy, high serum levels of the inflammatory cytokines IFNγ, IL-17 and IL-6 associated with reduced baseline levels of a marker of endogenous serum GlcNAc. Oral GlcNAc therapy was safe, raised serum levels and modulated N-glycan branching in lymphocytes. Glatiramer acetate reduces TH1, TH17 and B cell activity as well as sNfL, yet the addition of oral GlcNAc dose-dependently lowered serum IFNγ, IL-17, IL-6 and NfL. Oral GlcANc also dose-dependently reduced serum levels of the anti-inflammatory cytokine IL-10, which is increased in the brain of MS patients. 30% of treated patients displayed confirmed improvement in neurological disability, with an average EDSS score decrease of 0.52 points. CONCLUSIONS: Oral GlcNAc inhibits inflammation and neurodegeneration markers in MS patients despite concurrent immunomodulation by glatiramer acetate. Blinded studies are required to investigate GlcNAcs potential to control residual brain inflammation, myelin repair and neurodegeneration in MS.

In this paper we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy—a recently developed technique for whole-brain imaging. Most existing statistical methods solve this problem by partitioning the brain regions into two classes: significantly and nonsignificantly activated. However, for the brain imaging problem at the center of our study, such binary grouping may provide overly simplistic discoveries by filtering out weak but important signals that are typically adulterated by the noise present in the data. To overcome this limitation, we introduce a new Bayesian approach that allows classifying the brain regions into several tiers with varying degrees of relevance. Our approach is based on a combination of shrinkage priors, widely used in regression and multiple hypothesis testing problems, and mixture models, commonly used in model-based clustering. In contrast to the existing regularizing prior distributions, which use either the spike-and-slab prior or continuous scale mixtures, our class of priors is based on a discrete mixture of continuous scale mixtures and devises a cluster shrinkage version of the horseshoe prior. As a result, our approach provides a more general setting for Bayesian sparse estimation, drastically reduces the number of shrinkage parameters needed, and creates a framework for sharing information across units of interest. We show that this approach leads to more biologically meaningful and interpretable results in our brain imaging problem, since it allows the discrimination between active and inactive regions, while at the same time ranking the discoveries into clusters representing tiers of similar importance.

Hippocampal sclerosis of aging (HS-A) is a common age-related neuropathological lesion characterized by neuronal loss and astrogliosis in subiculum and CA1 subfield of hippocampus. HS-A is associated with cognitive decline that mimics Alzheimer's disease. Pathological diagnosis of HS-A is traditionally binary based on presence/absence of the lesion. We compared this traditional measure against our novel quantitative measure for studying the relationship between HS-A and other neuropathologies and cognitive impairment. We included 409 participants from The 90+ study with neuropathological examination and longitudinal neuropsychological assessments. In those with HS-A, we examined digitized H&E and LFB stained hippocampal slides. The length of HS-A in each subfield of hippocampus and subiculum, each further divided into three subregions, was measured using Aperio eSlide Manager. For each subregion, the proportion affected by HS-A was calculated. Using regression models, both traditional/binary and quantitative measures were used to study the relationship between HS-A and other neuropathological changes and cognitive outcomes. HS-A was present in 48 (12%) of participants and was always focal, primarily affecting CA1 (73%), followed by subiculum (9%); overlapping pathology (subiculum and CA1) affected 18% of individuals. HS-A was more common in the left (82%) than the right (25%) hemisphere and was bilateral in 7% of participants. HS-A traditional/binary assessment was associated with limbic-predominant age-related TDP-43 encephalopathy (LATE-NC; OR = 3.45, p < 0.001) and aging-related tau astrogliopathy (ARTAG; OR = 2.72, p = 0.008). In contrast, our quantitative approach showed associations between the proportion of HS-A (CA1/subiculum/combined) and LATE-NC (p = 0.001) and arteriolosclerosis (p = 0.005). While traditional binary assessment of HS-A was associated with impaired memory (OR = 2.60, p = 0.007), calculations (OR = 2.16, p = 0.027), and orientation (OR = 3.56, p < 0.001), our quantitative approach revealed additional associations with impairments in language (OR = 1.33, p = 0.018) and visuospatial domains (OR = 1.37, p = 0.006). Our novel quantitative method revealed associations between HS-A and vascular pathologies and impairment in cognitive domains that were not detected using traditional/binary measures.