Michelle Jin

Sundowning, a common yet poorly understood neuropsychiatric syndrome in Alzheimer’s disease (AD), manifests as evening-specific increases in agitation, confusion, and anxiety. Despite its prevalence and contribution to patient distress, its neural mechanisms remain elusive. Here, we establish a preclinical model of sundowning by characterizing sleep-wake, behavioral, and network-level alterations in an AD mouse model. Aged AD mice exhibit disrupted sleep-wake patterns and reduced slow-wave sleep. Behavioral and pose-tracking analyses revealed motor agitation and a distinct sundowning-like behavioral fingerprint selectively at Sundown. The suprachiasmatic nucleus (SCN) showed disruptions in time-of-day-dependent activation of vasopressin-expressing cells and brain-wide activity-dependent tagging identified hyperconnectivity amongst sensorimotor regions in AD mice at Sundown. Resting-state fMRI data from the Alzheimer’s Disease Neuroimaging Initiative revealed analogous Salience Network alterations in AD subjects. Overall, these cross-species findings define a systems-level framework for sundowning and highlight regions that may be targeted to alleviate a debilitating symptom of AD.

In the last decade, activity-dependent strategies for labeling multiple immediate early gene ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is simple multi-ensemble atlas registration and statistical testing in R (SMARTTR), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization, and support for import of external datasets. We demonstrate the versatility of SMARTTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training, results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region that shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

In the last decade, activity-dependent strategies for labelling multiple immediate early gene (IEG) ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally-sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is simple multi-ensemble atlas registration and statistical testing in R (SMARTTR), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization, and support for import of external datasets. We demonstrate the versatility of SMARTTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training (CT), results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region which shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

Background: As high as 50% of Alzheimer’s disease (AD) patients experience “sundowning”, which refers to an increased severity of neuropsychiatric symptoms (NPS), including agitation, confusion, and anxiety, selectively in the evening. Although sundowning significantly influences the decision to institutionalize patients, few preclinical models of this phenomenon exist and the underlying neural mechanisms are unknown. Here, we establish a model of sundowning by phenotyping the sleep‐wake cycle and anxiety and exploratory behavior at different times of day in an AD mouse model. We then investigate differences in brain‐wide activity patterns present at sundown and sunrise time in AD and control (Ctrl) mice.Method: We recorded sleep‐wake activity in control (Ctrl) and APP/PS1de9 (AD) mice across 2‐, 6‐, 12‐, and 24‐month‐old male and female mice. Next, we measured anxiety/locomotion in 2‐, 12‐, and 24‐month‐old Ctrl and AD mice during the murine evening (ZT22‐0, sundown) and murine morning (ZT12‐14, sunrise) using the elevated‐plus maze (EPM) and open field (OF) assays. To identify brain‐wide ensemble patterns mediating sundowning, we bred the ArcCreER^T2 x EYFP activity‐dependent tagging strategy with the AD line, which allows for temporally precise labelling of two active neural populations. We then applied a custom pipeline to label and map ensembles active during exposure to the EPM at sundown and sunrise.Result: We found a degradation in strength of sleep‐wake rhythms with aging selectively in AD mice. We further found that older AD mice (24‐month‐old males and 12‐ and 24‐month‐old females) preferentially explore the open arms of the EPM and display hyperlocomotion in the OF (24‐month‐old males and 24‐month‐old females) selectively during sundown. Network analysis of mapping results showed selective decreases in topological measures of connectivity, such as degree, clustering, and efficiency, in AD mice at sundown but not sunrise time when compared with Ctrl mice.Conclusion: We identified a striking behavioral abnormality in an AD mouse model resembling “nighttime wandering” and agitation seen in sundowning patients. Mapping of concurrent brain‐wide activity patterns showed global decreases in network connectivity at sundown time in contrast to sunrise time in AD mice, revealing novel insights about the neural patterns underlying this debilitating syndrome.

A new era of disease-modifying therapy for Alzheimer's disease (AD) arrived in 2021 following the Food and Drug Administration's (FDA) decision to grant accelerated approval for aducanumab, an anti-β-amyloid (Aβ) monoclonal antibody designed to target Aβ aggregates, a biological component of AD. More recently, trial outcomes for lecanemab and donanemab, two additional antibodies of this drug class, have shown favorable and significant slowing of metrics for cognitive and functional decline. Lecanemab and donanemab have since received similar FDA approval to aducanumab in January 2023 and July 2024, respectively. Given that these therapies are a clearly emerging tool in the repertoire of clinicians treating AD and related dementias, a critical dialogue has been ongoing regarding the potential impacts and place for these therapies. Here, we seek to contextualize this debate by first considering factors involved in theoretically extrapolating current randomized control trial outcomes to estimate meaningful clinical impacts. In the process of this exercise, we outline a generally useful concept termed Summative Treatment-Associated Benefit measuring Long-term Efficacy/Effectiveness Area as a metric of summative benefits of treatment over the life course of an individual. Second, we consider current real-world factors, such as conditions of FDA approval and other points involved in clinical decision-making that will influence and/or temper the actual impacts of this drug class.

In the last decade, activity-dependent strategies for labelling multiple immediate early gene (IEG) ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally-sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is s imple m ulti-ensemble a tlas r egistration and s t atistical t esting in R ( SMARTTR ), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization, and support for import of external datasets. We demonstrate the versatility of SMARTTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training (CT), results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region which shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

BACKGROUND: Serotonin (5-HT) receptors and N -methyl-D-aspartate receptors (NMDARs) have both been implicated in the pathophysiology of depression and anxiety disorders. Here, we evaluated whether targeting both receptors through combined dosing of ( R , S )-ketamine, an NMDAR antagonist, and prucalopride, a serotonin type IV receptor (5-HT ₄ R) agonist, would have additive effects, resulting in reductions in stress-induced fear, behavioral despair, and hyponeophagia. METHODS: A single injection of saline (Sal), ( R , S )-ketamine (K), prucalopride (P), or a combined dose of ( R , S )-ketamine and prucalopride (K+P) was administered before or after contextual fear conditioning (CFC) stress in both sexes. Drug efficacy was assayed using the forced swim test (FST), elevated plus maze (EPM), open field (OF), marble burying (MB), and novelty-suppressed feeding (NSF). Patch clamp electrophysiology was used to measure the effects of combined drug on neural activity in hippocampal CA3. c-fos and parvalbumin (PV) expression in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was examined using immunohistochemistry and network analysis. RESULTS: We found that a combination of K+P, given before or after stress, exerted additive effects, compared to either drug alone, in reducing a variety of stress-induced behaviors in both sexes. Combined K+P administration significantly altered c-fos and PV expression and network activity in the HPC and mPFC. CONCLUSIONS: Our results indicate that combined K+P has additive benefits for combating stress-induced pathophysiology, both at the behavioral and neural level. Our findings provide preliminary evidence that future clinical studies using this combined treatment strategy may prove advantageous in protecting against a broader range of stress-induced psychiatric disorders.

Mapping immediate early gene (IEG) expression across intact mouse brains allows for unbiased identification of brain-wide activity patterns underlying complex behaviors. Accurate registration of sample brains to a common anatomic reference is critical for precise assignment of IEG-positive (“active”) neurons to known brain regions of interest (ROIs). While existing automated voxel-based registration methods provide a high-throughput solution, they require substantial computing power, can be difficult to implement and fail when brains are damaged or only partially imaged. Additionally, it is challenging to cross-validate these approaches or compare them to any preexisting literature based on serial coronal sectioning. Here, we present the open-source R package SMART (Semi-Manual Alignment to Reference Templates) that extends the WholeBrain R package framework to automated segmentation and semi-automated registration of intact mouse brain light-sheet fluorescence microscopy (LSFM) datasets. The SMART package was created for novice programmers and introduces a streamlined pipeline for aligning, registering, and segmenting LSFM volumetric datasets across the anterior-posterior (AP) axis, using a simple “choice game” and interactive menus. SMART provides the flexibility to register whole brains, partial brains or discrete user-chosen images, and is fully compatible with traditional sectioned coronal slice-based analyses. We demonstrate SMART’s core functions using example datasets and provide step-by-step video tutorials for installation and implementation of the package. We also present a modified iDISCO+ tissue clearing procedure for uniform immunohistochemical labeling of the activity marker Fos across intact mouse brains. The SMART pipeline, in conjunction with the modified iDISCO+ Fos procedure, is ideally suited for examination and orthogonal cross-validation of brain-wide neuronal activation datasets.

Inappropriate and pathological aggression plays a leading role in the suffering and death of millions of people, and further places an untenable strain on the caregivers and families of those afflicted. In some cases, such as addictive drugs, aggression can be highly rewarding (appetitive) and continually pursued despite short- and long-term negative consequences. Similarly, recidivism (relapse) rates for repeat violent offenders are as high as relapse rates for drug addicts. Appetitive aggression and relapse to aggression seeking can be modeled in mice studies using conditioned place preference and self-administration procedures followed by a period of abstinence and subsequent tests for relapse to aggression preference and aggression seeking. These procedures allow for the study of the mechanisms that control the appetitive versus the consummatory (attack) phases of aggressive behavior. In this review, we first discuss the behavioral procedures developed to probe appetitive aggression in mouse models, spanning from Pavlovian to operant tasks, and we also describe the recently proposed phenomenon of “aggression addiction.” Next, we discuss the pharmacological and circuit mechanisms of aggression conditioned place preference and aggression self-administration, seeking, and relapse, highlighting mechanistic congruence and divergence between appetitive and consummatory phases of aggression. We conclude by discussing clinical implications of the studies reviewed.