About

Dr. Anne M. Andrews is a Professor of Psychiatry at the University of California, Los Angeles. Her research focuses on understanding how the serotonin system, particularly the serotonin transporter, modulates neurotransmission to influence complex behaviors such as anxiety, mood, stress responsiveness, and learning and memory. She leads an interdisciplinary team of neuroscientists, biologists, chemists, and engineers that investigates the molecular basis of serotonin system function, its role in mood and anxiety disorders, and potential personalized treatments. Her work includes studying genetic and pharmacologic mouse models, human genetic variants, and key proteins like brain-derived neurotrophic factor, as well as neuronal architectures regulated by serotonin. Additionally, her research integrates nanoscience and neuroscience, designing nanomaterials for neurotransmitter recognition, in vivo nanobiosensors, and proteomics. Dr. Andrews has made significant contributions to the development of advanced neurochemical sensing techniques, including aptamer-based field-effect transistors and voltammetry methods, to study neurotransmitter signaling with high spatial, temporal, and chemical resolution.

Research topics

• Computer Science
• Engineering
• Genetics
• Risk analysis (engineering)
• Embedded system
• Engineering management
• Systems engineering
• Electrical engineering
• Biology
• Business
• Data science

Selected publications

• Safety in treatment: Classical pharmacotherapeutics and new avenues for addressing maternal depression and anxiety during pregnancy

  Pharmacological Reviews · 2025-02-10 · 5 citations

  reviewOpen accessSenior author

  We aimed to review clinical research on the safety profiles of antidepressant drugs and associations with maternal depression and neonatal outcomes. We focused on neuroendocrine changes during pregnancy and their effects on antidepressant pharmacokinetics. Pregnancy-induced alterations in drug disposition and metabolism impacting mothers and their fetuses are discussed. We considered evidence for the risks of antidepressant use during pregnancy. Teratogenicity associated with ongoing treatment, new prescriptions during pregnancy, or pausing medication while pregnant was examined. The Food and Drug Administration advises caution regarding prenatal exposure to most drugs, including antidepressants, largely owing to a dearth of safety studies caused by the common exclusion of pregnant individuals in clinical trials. We contrasted findings on antidepressant use with the lack of treatment where detrimental effects to mothers and children are well researched. Overall, drug classes such as selective serotonin reuptake inhibitors and serotonin norepinephrine reuptake inhibitors appear to have limited adverse effects on fetal health and child development. In the face of an increasing prevalence of major mood and anxiety disorders, we assert that individuals should be counseled before and during pregnancy about the risks and benefits of antidepressant treatment given that withholding treatment has possible negative outcomes. Moreover, newer therapeutics, such as ketamine and κ-opioid receptor antagonists, warrant further investigation for use during pregnancy. SIGNIFICANCE STATEMENT: The safety of antidepressant use during pregnancy remains controversial owing to an incomplete understanding of how drug exposure affects fetal development, brain maturation, and behavior in offspring. This leaves pregnant people especially vulnerable, as pregnancy can be a highly stressful experience for many individuals, with stress being the biggest known risk factor for developing a mood or anxiety disorder. This review focuses on perinatal pharmacotherapy for treating mood and anxiety disorders, highlighting the current knowledge and gaps in our understanding of consequences of treatment.

  PublisherDOI

• Interfacing with the Brain: How Nanotechnology Can Contribute

  ACS Nano · 2025-03-10 · 54 citations

  reviewOpen access

  Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.

  PublisherOA PDFDOI

• Chronic pain selectively reduces the motivation to work for remifentanil but not food reward

  Pain · 2025-05-13 · 3 citations

  articleOpen access

  ABSTRACT: Currently, preclinical research has reported conflicting evidence as to whether chronic pain imparts resilience or vulnerability to opioid drug seeking. Here, we investigated the impact of chronic pain on the intravenous self-administration (IVSA) profile of the short-acting opioid analgesic remifentanil in a mouse model. Using a chronic constriction injury model of chronic neuropathic pain, 7 days after injury, male and female C57Bl/6J mice began remifentanil IVSA. During the acquisition phase, there were no differences in the total number of reinforcers earned but an increase in the number of active nose pokes in pain mice. An increase in the rate of acquisition within sessions was observed in male but not female mice. When work effort increased (fixed ratio 3 and progressive ratio), pain mice unexpectedly showed a reduction in the number of reinforcers earned and their breakpoint. This change in motivational state was specific to the willingness to work for remifentanil, as these changes were not observed with higher effort for a food reward. We hypothesized that chronic pain altered the dopaminergic state of the striatum, which would impact the motivation to work for a reward. We found that pain mice had significantly decreased phasic dopamine release assessed via fast-scan cyclic voltammetry and reduced potassium-evoked extracellular dopamine measured by microdialysis. Future studies will investigate the causal relationship between this hypo-dopaminergic state and decreased behavioral motivation associated with a chronic pain state.

  PublisherDOI

• (<i>Invited</i>) Expanding the Target Toolkit for Wearable and Implantable Sensors

  ECS Meeting Abstracts · 2025-07-11

  article1st authorCorresponding

  The inability to monitor biomarkers of human health status continuously limits the current usefulness of sensing technologies. For example, most sensing systems cannot measure key low-concentration biochemicals indicative of stress, inflammation, metabolic, and reproductive status. This information can transform health and wellness by providing real-time actionable feedback to wearers and personalized data for patients and healthcare providers. We are revolutionizing sensing through electronic biosensors that can detect virtually any signaling molecule or metabolite at ultra-low levels. We have demonstrated sensing for serotonin, dopamine, norepinephrine, epinephrine, cortisol, phenylalanine, estradiol, progesterone, luteinizing hormone, and glucose in the brain, blood, sweat, interstitial fluid, and tears. Our sensors are based on nanoscale semiconductor transistors and DNA aptamers for target recognition and are straightforwardly scalable via MEMS fabrication methods for manufacturing. We are developing sensors for &gt;40 biochemical targets to advance neurotechnologies and personalized monitoring to provide high-resolution feedback for treating chronic health conditions, e.g. , mental health and stress disorders and infertility. Our sensors, combined with machine learning algorithms, enable predictions of preclinical states and help wearers adopt healthier lifestyles to prevent disease and improve physical and cognitive performance. Nakatsuka, N, et al., Aptamer-Field-Effect Transistors Overcome Debye Length Limitations for Small-Molecule Sensing. Science 2018, 362, 319-324. Wang, B, et al. , Wearable Aptamer Field-Effect Transistor Sensing System for Noninvasive Cortisol Monitoring. Science Advances 2022, 8, eabk0967. Zhao, C, et al. , Implantable Aptamer-Field-Effect Transistor Neuroprobes for in Vivo Neurotransmitter Monitoring. Science Advances 2021, 7 (48), eabj7422. Yang, K, et al. , A Functional Group-Guided Approach to Aptamers for Small Molecules. Science 2023, 380 (6648), 942-948.

  PublisherDOI

• Machine-learning-guided design of electroanalytical pulse waveforms

  Digital Discovery · 2025-01-01 · 7 citations

  articleOpen accessSenior author

  Bayesian optimization outperforms random- and human-designed waveforms for electrochemical detection of serotonin in physiologically-relevant environments.

  PublisherOA PDFDOI

• GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability

  Mathematical Biosciences · 2025-06-27 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects

  ACS Chemical Neuroscience · 2025-12-10 · 2 citations

  reviewOpen accessSenior authorCorresponding

  Chemical neuroscience wields tools to uncover the molecular mysteries of the brain. Sensors can be fabricated with properties tailored to the scales needed to decode neurochemical information. Current instrumentation is capable of measurement rates that exceed neurochemical release rates. Modern machine learning models are approaching parameterization near the number of brain synapses. Fast voltammetry has remained a neuroanalytical workhorse technique for nearly half a century and has undergone significant transformations in many aspects due to advances in hardware and computation. Here, we review current and future uses of machine learning coupled with fast voltammetry to quantify neurochemical dynamics in the brains of behaving animal and human subjects. We focus on the advances that machine learning offers to pervasive problems in fast voltammetry. We identify current challenges and limitations for in vivo studies and delineate several routes for future development.

  PublisherDOI

• SeroWare: An Open-Source Software Suite for Voltammetry Data Acquisition and Analysis

  ACS Chemical Neuroscience · 2025-02-24 · 4 citations

  articleOpen accessSenior authorCorresponding

  Voltammetry is widely used for fast, data-dense measurements of redox-active analytes in versatile environments, including the brain. Voltammetry requires minimal hardware beyond a potentiostat, a front-end amplifier, and a computer. Nonetheless, researchers must often develop or modify software packages for application-specific uses. Of the voltammetry software available, significant issues exist with source code inaccessible for updating or customization, nonconfigurable data processing procedures, and hardware incompatibilities. These limitations, coupled with the recent proliferation of waveform types and increased demands for high bandwidth data acquisition and efficient data processing, create the need for sophisticated, powerful, and flexible voltammetry software. We report developing "SeroWare", an open-source, end-to-end voltammetry acquisition and analysis software package designed to handle a wide variety of use cases encountered by voltammetry users. Although inspired by neurochemical analyses, this software is flexible, customizable, and compatible with open-source toolkits. The modular software architecture enables users to generate, acquire, and analyze voltammetry data of different types, ranging from pulse and sweep waveforms to fast and slow scans via easily accessible and exportable file formats. Template code is provided for communicating with a variety of standard external devices. We report several novel features for waveform applications and data flow. In-depth documentation in a User Guide and video tutorials are provided to enable new research directions, particularly regarding shareability and lowering the barriers to entry for new investigators.

  PublisherOA PDFDOI

• Boltzmann Graph Ensemble Embeddings for Aptamer Libraries

  2025-11-12

  articleSenior author

  Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate on a single graph, typically the minimal free energy (MFE) structure, for low-energy ensembles (conformations) representative of structures at thermodynamic equilibrium. We introduce a thermodynamically parameterized ex-ponential-family random graph (ERGM) embedding that models molecules as Boltzmann-weighted ensembles of interaction graphs. We evaluate this embedding on SELEX datasets, where experimental biases (e.g., PCR amplification or sequencing noise) can obscure true aptamer-ligand affinity, producing anomalous candidates whose observed abundance diverges from their actual binding strength. We show that the proposed embedding enables robust community detection and subgraph-level explanations for aptamer-ligand affinity, even in the presence of biased observations. This approach may be used to identify low-abundance aptamer candidates for further experimental evaluation.

  PublisherDOI

• Machine-learning-guided design of electroanalytical pulse waveforms

  ChemRxiv · 2024-12-23 · 2 citations

  preprintOpen accessSenior author

  Voltammetry is widely used to detect and quantify oxidizable or reducible species in complex environments. The neurotransmitter serotonin epitomizes an analyte that is challenging to detect in situ due to low concentrations and co-existing similarly structured analytes and interferents. We developed rapid-pulse voltammetry for brain neurotransmitter monitoring due to the high information content elicited from voltage pulses. Generally, the design of voltammetry waveforms remains challenging due to prohibitively large combinatorial search spaces and a lack of design principles. Here, we illustrate how Bayesian optimization can be used to hone searches for optimized rapid pulse waveforms. Our machine-learning-guided workflow (SeroOpt) outperformed random and human-guided waveform designs and is tunable a priori to enable selective analyte detection. We interpreted the black box optimizer and found that the logic of machine-learning-guided waveform design reflected domain knowledge. Our approach is straightforward and generalizable for all single and multi-analyte problems requiring optimized electrochemical waveform solutions. Overall, SeroOpt enables data-driven exploration of the waveform design space and a new paradigm in electroanalytical method development.

  PublisherDOI

Recent grants

• NIH Grant R03MH077978

  NIH · $158k · 2010

• NIH Grant R01MH064756

  NIH · $1.9M · 2013

• NIH Grant R03MH067713

  NIH · $136k · 2005

• Micro- to Nanoscale Neurochemical Sensors

  NIH · $5.8M · 2017–2023

• NIH Grant R03MH086108

  NIH · $149k · 2012

Frequent coauthors

• Seiya Miyamoto

  1023 shared

• Per E. Andrén

  Uppsala University

  866 shared

• Per Svenningsson

  855 shared

• Greg A. Gerhardt

  847 shared

• Lynette C. Daws

  847 shared

• Theodora Duka

  University of Sussex

  765 shared

• Martine Cador

  Institut de Neurosciences Cognitives et Intégratives d’Aquitaine

  758 shared

• Alfonso Abizaid

  Carleton University

  745 shared

Education

• Ph.D., National Institute of Mental Health, Laboratory of Clinical Science

  National Institutes of Health

  1998

• Ph.D., Chemistry

  American University

  1993

• B.S., Chemistry

  Pennsylvania State University

  1985

Awards & honors

• NIH Fellows Award for Research Excellence
• Eli Lilly Outstanding Young Analytical Chemist Award
• American Parkinson’s Disease Association Research Award
• Brain & Behavior Research Foundation (NARSAD) Independent In…
• Fulbright Specialist Program Award, 2024