Raman spectroscopy is a powerful technique for identifying chemical species by probing their vibrational energy levels, offering exceptional specificity with a relatively simple setup involving a laser source, spectrometer, and microscope/probe. However, the high cost of Raman systems lacking modularity often limits exploratory research hindering broader adoption. To address the need for an affordable, modular microscopy platform for multimodal imaging, we present a customizable confocal Raman spectroscopy setup alongside an open-source acquisition software, ORM (Open Raman Microscopy) Controller, developed in Python. This solution bridges the gap between expensive commercial systems and complex, custom-built setups used by specialist research groups. In this presentation, we will cover the components of the setup, the design rationale, assembly methods, limitations, and its modular potential for expanding functionality. Additionally, we will demonstrate ORM’s capabilities for instrument control, 2D and 3D Raman mapping, region-of-interest selection, and its adaptability to various instrument configurations. We will conclude by showcasing practical applications of this setup across different research fields.

Wearable sensors that detect and quantify biomarkers in retrievable biofluids (e.g., interstitial fluid, sweat, tears) provide information on human dynamic physiological and psychological states. This information can transform health and wellness by providing actionable feedback. Due to outdated and insufficiently sensitive technologies, current on-body sensing systems have capabilities limited to pH, and a few high-concentration electrolytes, metabolites, and nutrients. As such, wearable sensing systems cannot detect key low-concentration biomarkers indicative of stress, inflammation, metabolic, and reproductive status.  We are revolutionizing sensing. Our electronic biosensors detect virtually any signaling molecule or metabolite at ultra-low levels. We have monitored serotonin, dopamine, cortisol, phenylalanine, estradiol, progesterone, and glucose in blood, sweat, interstitial fluid, and tears. The sensors are based on modern nanoscale semiconductor transistors that are straightforwardly scalable for manufacturing. We are developing sensors for >40 biomarkers for personalized continuous monitoring (e.g., smartwatch, wearable patch) that will provide feedback for treating chronic health conditions (e.g., perimenopause, stress disorders, phenylketonuria). Moreover, our sensors will enable female fertility monitoring and the adoption of more healthy lifestyles to prevent disease and improve physical and cognitive performance.

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

Over the last few decades, the use of deep brain stimulation (DBS) to improve the treatment of those with neurological movement disorders represents a critical success story in the development of invasive neurotechnology and the promise of brain-computer interfaces (BCI) to improve the lives of those suffering from incurable neurological disorders. In the last decade, investigational devices capable of recording and streaming neural activity from chronically implanted therapeutic electrodes has supercharged research into clinical applications of BCI, enabling in-human studies investigating the use of adaptive stimulation algorithms to further enhance therapeutic outcomes and improve future device performance. In this talk, Dr. Herron will review ongoing clinical research efforts in the field of adaptive DBS systems and algorithms. This will include an overview of DBS in current clinical practice, the development of bidirectional clinical-use research platforms, ongoing algorithm evaluation efforts, a discussion of current adoption barriers to be addressed in future work.

International organizations, governments and companies are increasingly committed to developing measures that encourage adoption of sustainable consumption patterns among the population. However, their success requires a deep understanding of the everyday purchasing decision process and the elements that shape it. Price is an element that stands out. Prior research concluded that the influence of price on purchase decisions varies across consumer profiles. Yet no consumer behavior study to date has assessed the differences of price processing among consumers adopting sustainable habits (prosocial) as opposed to those who have not (non-prosocial). This is the first study to resort to neuroimaging tools to explore the underlying neural mechanisms that reveal the effect of price on prosocial and non-prosocial consumers. Self-reported findings indicate that prosocial consumers place greater value on collective costs and benefits while non-prosocial consumers place a greater weight on price. The neural data gleaned from this analysis offers certain explanations as to the origin of the differences. Non-prosocial (vs. prosocial) consumers, in fact, exhibit a greater activation in brain areas involved with reward, valuation and choice when evaluating price information. These findings could steer managers to improve market segmentation and assist institutions in their design of campaigns fostering environmentally sustainable behaviors

International organizations, governments and companies are increasingly committed to developing measures that encourage adoption of sustainable consumption patterns among the population. However, their success requires a deep understanding of the everyday purchasing decision process and the elements that shape it. Price is an element that stands out. Prior research concluded that the influence of price on purchase decisions varies across consumer profiles. Yet no consumer behavior study to date has assessed the differences of price processing among consumers adopting sustainable habits (prosocial) as opposed to those who have not (non-prosocial). This is the first study to resort to neuroimaging tools to explore the underlying neural mechanisms that reveal the effect of price on prosocial and non-prosocial consumers. Self-reported findings indicate that prosocial consumers place greater value on collective costs and benefits while non-prosocial consumers place a greater weight on price. The neural data gleaned from this analysis offers certain explanations as to the origin of the differences. Non-prosocial (vs. prosocial) consumers, in fact, exhibit a greater activation in brain areas involved with reward, valuation and choice when evaluating price information. These findings could steer managers to improve market segmentation and assist institutions in their design of campaigns fostering environmentally sustainable behaviors

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this presentation, I will provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. We present BrainGlobe, a new initiative aimed at developing common Python tools for computational neuroanatomy. These include cellfinder for fast, accurate cell detection in whole-brain microscopy images, brainreg for aligning images to a reference atlas, and brainrender for visualisation of anatomically registered data. These software packages are developed around the BrainGlobe Atlas API. This API provides a common Python interface to download and interact with reference brain atlases from multiple species (including human, mouse and larval zebrafish). This allows software to be developed agnostic to the atlas and species, increasing adoption and interoperability of software tools in neuroscience.