Deep brain stimulation (DBS) is being studied as a potential treatment for Alzheimer’s disease (AD). Currently, relatively little is known about prospective users’ attitudes toward the surgically invasive treatment. In 2021, our team conducted a qualitative interview study to explore the views of individuals considered at risk for dementia. Respondents were considered at risk for dementia due to factors including family history, genetic biomarkers, or mild cognitive impairment. They were asked for their perspectives on the hypothetical use of DBS devices to assist individuals living with dementia. Transcripts from 34 interviews were coded and analyzed using ATLAS.ti, with attention to users’ reported interest in the device as well as several main themes that emerged related to participant concerns. Of the 34 participants, one expressed low interest in the DBS device, four expressed a high level of interest, and the vast majority (29) expressed ambivalent interest (a combination of excitement about the treatment and nuanced concerns about various potential impacts). Five thematic areas of concern emerged: timing of implantation, skepticism, invasiveness of the surgery, impact of memory loss, and the value of forgetting. The responses revealed that prospective users have nuanced considerations that inform their interest in neural devices to treat memory loss. Though the majority felt positively about potential surgical treatments for memory loss, they raised concerns about complex issues that may arise related to consent, surgical complications, and losing the ability to forget. User-centered design recommends early input from potential users of devices to ensure that their needs and values are recognized in the design process. As clinical trials for DBS in AD continue, understanding the values and concerns of prospective users will be vital for both the design process and successful clinical trials.

The mechanisms guiding the sensory detection of pain and the subsequent sensitization of damaged tissue to mechanical and thermal stimuli are relatively well understood. However, mechanisms guiding the transformation of nociception into the negative feelings associated with pain remain largely unknown. This affective component, notably in chronic pain, translates into an intense emotional impact on patients and can contribute to the development of comorbid psychiatric disorders. The elderly population have a propensity to be socially isolated and face exacerbated effects of chronic pain. In 2021, an estimated 20.9% of U.S adults suffer from chronic pain with persons over 65 years of age having the greatest propensity of acquiring the disease. Due to this, clinical intervention models call for a more holistic approach to pain intervention that incorporates lifestyle and nutritional factors, extending beyond pharmacological treatments. One of these promising non-pharmacological interventions is positive social interaction, which has been shown to alleviate pain and suffering.  Several studies show that humans who maintain strong social bonds recover from injuries faster than people without them. However, it has not yet been evaluated the extent to which this phenomenon occurs in geriatric animals and its relative efficacy as a social intervention to alleviate chronic pain in injured mice. My project seeks to gauge whether social intervention can alleviate chronic pain symptoms in aged mice and to unveil the underlying mechanisms guiding these successful non-pharmacological treatments. I will achieve this through two aims: evaluation of social self-administration as an intervention for chronic pain, and transcriptomic analysis to identify gene expression changes as a result of social interaction. Future research will include miniscope endomicroscopy recordings to visualize cell activity within major brain regions, and comparison of cell ensemble activity between groups of mice will lead to the identification of structures encoding behavioral shifts caused by pain.

Chronic pain affects about 20% of the adult population in the US, with more than 25% of these being pain that severely limit a person’s daily activities. In recent years, scientists in the field have been classifying pain as both a sensory response and emotional experience influenced by physiological and social factors. Newer research on pain behaviors and social behaviors have indicated that there is a positive association between the presence of cage mate in pain and the sensitivity to pain for a mouse. Although the behavioral responses are observed, the neural circuits mechanisms have yet to be examined. I will inject wild type mice with GCaMP in the medial prefrontal cortex (mPFC) and RCaMP in the basolateral amygdala (BLA). GCaMP and RCaMP are both genetically encoded Calcium indicators and are sensitive proxies for measuring excitatory transmission between brain regions. I will then implant fibers in both brain regions of all mice for fiber photometry recordings. After sensor expression time, I will check Calcium signals using a stressful stimulus known to stimulate excitatory pathways in mice then surgically induce pain in half of the mice. Mice will be split into chronic pain and pain-free groups, with their cage mate being either in pain or pain-free. I will perform a triad of behavioral pain testing simultaneously with fiber photometry recording, including tests for mechanical and thermal pain. I predict that for pain-free mice housed with a cage mate in pain, their pain threshold will decrease, as measured by all behavioral experiments. This should be accompanied by a stronger increase in BLA to mPFC Calcium signal when the mice are receiving painful stimuli. 

The ongoing opioid epidemic has made the need for alternative pain management strategies more urgent than ever. Nearly 1 in 5 Americans suffer from chronic pain, which has traditionally been treated with opioids and non-steroidal anti-inflammatory drugs (NSAIDs). However, both classes of drugs come with significant drawbacks. NSAIDs are often ineffective for managing chronic pain and can cause kidney and liver damage with prolonged use. Meanwhile, opioids lose their effectiveness over time, contributing to misuse, substance use disorders, and an increased risk of overdose. With few alternatives available that don't carry these risks, researchers are exploring new pain management options. One promising avenue is the use of cannabinoids, which are known for their anti-inflammatory and analgesic properties. In this study, I employ machine learning to create an unbiased kinematic and behavioral profile of mice experiencing chronic neuropathic pain using a custom-built linear track. Chronic pain and limb impairment are induced through partial sciatic nerve ligation, and a deep learning system analyzes videos of the mice to assess their movement patterns before and after treatment. I then compare these profiles to those of mice treated with NSAIDs, opioids, and cannabinoids, evaluating the effects of each treatment on behavioral measures like body position, which serves as a proxy for pain state and stress. We expect the mice treated with analgesics to show increased rearing and grooming behaviors. This research not only compares the analgesic effectiveness of cannabinoids to traditional pain-relief drugs but also helps reduce the stigma surrounding cannabinoid-based treatments.

The prefrontal cortex (PFC) is essential for cognitive functions such as decision-making, emotional regulation, and attention. Dysfunction in PFC circuitry is implicated in neuropsychiatric disorders, including Alzheimer’s disease, depression, and anxiety. Within the PFC, excitatory glutamatergic neurons and inhibitory GABAergic neurons coordinate activity to maintain proper network function. The excitatory-inhibitory balance is critical for cognitive processing, yet the role of the most abundant GPCR in the brain, the cannabinoid 1 receptor (CB1), in regulating these neuronal populations remains unclear. CB1 receptors are highly expressed across other cortical regions but have the most dense expression in the PFC where they are hypothesized to modulate synaptic transmission and plasticity. To investigate their cell-specific function, we utilized a CRISPR-Cas9 to locally knockout the CB1 receptor specific neuronal populations using a viral cre-dependent driver. This virus was administered in either vesicular GABA transporter (VGAT)-Cre or vesicular glutamate transporter (VGLUT)-Cre animals to select for inhibitory or excitatory neurons, respectively. We assessed CB1 receptor expression using RNAscope in situ hybridization to quantify CB1 mRNA in VGAT-expressing inhibitory neurons and VGLUT-expressing excitatory neurons. Fluorescence microscopy was used to visualize CB1 receptor distribution and determine whether its expression differs between these neuronal populations compared to controls. By mapping CB1 receptor expression and assessing its functional role in these neurons through previous behavioral experiments, this study provided insight into how the endocannabinoid system regulates PFC circuitry. Understanding CB1-mediated modulation of excitatory and inhibitory balance could have broad implications for neuropsychiatric disorders characterized by PFC dysfunction.

Fentanyl is a synthetic opioid that has become the leading driver of the U.S. opioid epidemic, contributing to over 70,000 overdose deaths annually. Opioid use disorder (OUD) is characterized by cycles of dependence, withdrawal, and relapse, with most fatal overdoses occurring during relapse, yet existing treatments for OUD do not effectively prevent relapse. Understanding how fentanyl affects brain activity and behavior is critical for developing more effective therapies. I investigated how fentanyl exposure modulates locomotion and the neural activity in the nucleus accumbens (NAc) across abstinence, dependence, withdrawal, and relapse. I hypothesized that each stage would show distinct neural activation patterns and that fentanyl exposure would reduce exploration and locomotion, reflecting compulsive drug-seeking behavior. To test this, I implanted silicon probes in the NAc of mice to monitor neural activity while tracking movement and behavior with high-resolution video. Mice received increasing fentanyl doses over five days, followed by a withdrawal period and, finally, a relapse challenge dose. I analyzed their behavior using deep learning-based pose estimation for correlations with neural activity across different stages of fentanyl exposure. I expect neural recordings to show that fentanyl significantly alters NAc activity, with each phase displaying unique neural patterns. I also expect fentanyl-exposed mice to show reduced exploratory movement, consistent with behavioral inflexibility and compulsive drug-seeking tendencies characteristic of OUD. These findings could provide critical insights into how fentanyl disrupts brain function and behavior, helping to identify new targets for addiction treatment. This research lays the groundwork for future studies on relapse prevention, with the goal of improving OUD therapies and reducing overdose deaths.