data collection

A Double-Blind Randomized Controlled Trial of Daridorexant for Alcohol Use Disorder

Project Summary/Abstract This R01 application proposes integrating a randomized, double-blinded, placebo-controlled clinical trial into a real-world treatment setting to test whether the dual orexin receptor antagonist (DORA) daridorexant reduces alcohol craving and use and improves total sleep time among patients with alcohol use disorder (AUD) and co-occurring sleep disturbance. DORAs have shown promise in modulating reward and reducing alcohol self- administration in preclinical models. Further, DORAs are FDA-approved for insomnia, are highly efficacious for treatment of sleep disturbance, have a favorable safety profile, and demonstrate low abuse liability. Thus, DORAs are a highly promising treatment for AUD, particularly among persons that have co-occurring sleep disturbance. To this end, the proposed study will recruit individuals from a residential treatment facility, following completion of medically managed withdrawal and stabilization. Eligible participants will be randomized to daridorexant to placebo, and will complete measures of alcohol craving, total sleep time (assessed through both wireless electroencephalography and biometric data collection), and adverse events. Following discharge from residential treatment, participants will continue taking the study medication for two weeks while submitting daily reports of alcohol use, alcohol craving, sleep diaries, and biometric sleep data. Participants will also be prompted to submit three-times weekly random breath alcohol level using a portable BACtrack S80 breathalyzer, and will attend weekly check-in visits to assess adverse events and to confirm daily alcohol reports. A one-month follow-up assessment will be conducted to collect long-term data on alcohol use, AUD symptoms, and sleep. Ultimately, this study has the potential to identify a novel treatment for co- occurring AUD and sleep disturbance, and will address the following specific aims: (1) Test whether daridorexant reduces alcohol craving and post-treatment alcohol use relative to placebo. (2) Test whether daridorexant improves objectively measured total sleep time relative to placebo. (3) Examine the frequency of adverse events in persons assigned to daridorexant relative to placebo. If these aims are supported, then we will also explore whether effects are moderated by insomnia severity. We will also examine if the effects replicate across residential environments (with structured sleep/wake times and close monitoring of medication adherence) and outpatient environments (with self-imposed sleep/wake times and self-dosing). Currently, there are no FDA approved medications indicated for both AUD and insomnia. This innovative strategy aims to address a critical gap by investigating the effectiveness of daridorexant in modulating alcohol craving and alcohol use. This study will contribute to a growing literature on the role of the orexin system in reward and alcohol use.

AI-enabled methods for de novo design of functional peptides

PROJECT SUMMARY Macrocyclic peptides offer unique therapeutic potential, particularly for targeting intracellular protein-protein interactions considered ‘undruggable’ with traditional therapeutic modalities. Additionally, peptides can combine the benefits and bridge the gap between conventional small molecule therapeutics and large biologics. However, developing new peptide-based therapeutics using traditional approaches, such as natural product discovery or high-throughput library screening, has remained slow and challenging. Moreover, these conventional approaches cover a small fraction of the chemical and structural space, are restricted to a few starting peptide scaffolds, and typically fail to optimize for multiple therapeutic properties simultaneously. Our central hypothesis is that structure-guided deep learning methods can rapidly explore the chemical and structural space beyond natural products and enable precise, rapid, and custom design of functional peptides simultaneously optimized for target binding, selectivity, and membrane permeability. In our recent work, we developed physics-based methods for designing constrained peptides and macrocycles and, more recently, introduced deep learning methods for structure prediction, sequence redesign, and de novo design of peptide monomers and targeted binders. Here, we propose to develop a new generation of structure-guided deep learning (DL) tools to address the current limitations of computational and experimental methods and enable accurate, accessible, and broadly applicable design of macrocycles. Specifically, we will pursue the projects focused on: (i) leveraging DL methods to systematically enumerate the chemical and structural space of constrained peptides and membrane-traversing peptides to develop scaffolds and core design principles for functional peptide design; (ii) high-throughput design and data collection to improve design selection, filtering metrics, and sequence design algorithms; (iii) developing generative DL methods that expand beyond current capabilities and allow sequence and structure design with vast chemical space of non-canonical amino acids; and (iv) use those new generative methods to design macrocyclic binders against different therapeutically-relevant targets, including the critical fusion and attachment proteins from viruses of pandemic concern. Our preliminary work in these proposed areas demonstrates the feasibility of this approach. The proposed computational tools, scaffold sets, and designed peptides will significantly advance therapeutic design beyond the state-of-the-art and enable rapid and custom design of drug- like peptides tailored for addressing complex therapeutic, diagnostic and research challenges.

HealthCore: A modular data collection ecosystem to connect the dots in Neurorehab

Biopsychosocial pathways in dementia inequalities

In the United States, racial/ethnic inequalities in Alzheimer's disease and related dementias persist even after controlling for socioeconomic factors and physical health. These persistent and unexplained disparities suggest: (1) there are unrecognized dementia risk factors that are socially patterned and/or (2) known dementia risk factors exhibit differential impact across social groups. Pursuing these research directions with data from multiple longitudinal studies of brain and cognitive aging has revealed several challenges to the study of late-life health inequalities, highlighted evidence for both risk and resilience within marginalized communities, and inspired new data collection efforts to advance the field.

A discussion on the necessity for Open Source Hardware in neuroscience research

Research tools are paramount for scientific development, they enable researchers to observe and manipulate natural phenomena, learn their principles, make predictions and develop new technologies, treatments and improve living standards. Due to their costs and the geographical distribution of manufacturing companies access to them is not widely available, hindering the pace of research, the ability of many communities to contribute to science and education and reap its benefits. One possible solution for this issue is to create research tools under the open source ethos, where all documentation about them (including their designs, building and operating instructions) are made freely available. Dubbed Open Science Hardware (OSH), this production method follows the established and successful principles of open source software and brings many advantages over traditional creation methods such as: economic savings (see Pearce 2020 for potential economic savings in developing open source research tools), distributed manufacturing, repairability, and higher customizability. This development method has been greatly facilitated by recent technological developments in fast prototyping tools, Internet infrastructure, documentation platforms and lower costs of electronic off-the-shelf components. Taken together these benefits have the potential to make research more inclusive, equitable, distributed and most importantly, more reliable and reproducible, as - 1) researchers can know their tools inner workings in minute detail - 2) they can calibrate their tools before every experiment and having them running in optimal condition everytime - 3) given their lower price point, a)students can be trained/taught with hands on classes, b) several copies of the same instrument can be built leading to a parallelization of data collection and the creation of more robust datasets. - 4) Labs across the world can share the exact same type of instruments and create collaborative projects with standardized data collection and sharing.

Panel discussion: Practical advice for reproducibility in neuroscience

This virtual, interactive panel on reproducibility in neuroscience will focus on practical advice that researchers at all career stages could implement to improve the reproducibility of their work, from power analyses and pre-registering reports to selecting statistical tests and data sharing. The event will comprise introductions of our speakers and how they came to be advocates for reproducibility in science, followed by a 25-minute discussion on reproducibility, including practical advice for researchers on how to improve their data collection, analysis, and reporting, and then 25 minutes of audience Q&A. In total, the event will last one hour and 15 minutes. Afterwards, some of the speakers will join us for an informal chat and Q&A reserved only for students/postdocs.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

MidsummerBrains - computational neuroscience from my point of view

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.