Data Collection
data collection
Arun Antony MD
The Neuroscience Institute at Jersey Shore University Medical Center, New Jersey, USA is seeking a postdoctoral fellow to work on basic, clinical, and translational projects in the fields of seizures, epilepsy, human intracranial EEG, signal processing, cognition and consciousness. The fellow will join a multidisciplinary team of five epileptologists, neurosurgeons, epilepsy nurses, nurse practitioners, neuropsychologists and researchers providing holistic care to patients with epilepsy. The postdoctoral fellows will have access to the large clinical, imaging, and EEG databases, and outcome measures of cutting edge treatment modalities within the system for research purposes. The successful candidate will be well versed in data collection, processing, programming and will lead an independent research project working closely with collaborators and publish high-quality research.
HealthCore: A modular data collection ecosystem to connect the dots in Neurorehab
Towards open meta-research in neuroimaging
When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains. For this we need meta-research that is reproducible and updatable, or living meta-research. In this talk, we introduce the concept of living meta-research, examine prequels to this idea, and point towards standards and technologies that could assist researchers in doing living meta-research. We introduce technologies like natural language processing, which can help with automation of meta-research, which in turn will make the research easier to reproduce/update. Further, we showcase our open-source litmining ecosystem, which includes pubget (for downloading full-text journal articles), labelbuddy (for manually extracting information), and pubextract (for automatically extracting information). With these tools, you can simplify the tedious data collection and information extraction steps in meta-research, and then focus on analyzing the text. We will then describe some living meta-research projects to illustrate the use of these tools. For example, we’ll show how we used GPT along with our tools to extract information about study participants. Essentially, this talk will introduce you to the concept of meta-research, some tools for doing meta-research, and some examples. Particularly, we want you to take away the fact that there are many interesting open questions in meta-research, and you can easily learn the tools to answer them. Check out our tools at https://litmining.github.io/
Exploring the Potential of High-Density Data for Neuropsychological Testing with Coregraph
Coregraph is a tool under development that allows us to collect high-density data patterns during the administration of classic neuropsychological tests such as the Trail Making Test and Clock Drawing Test. These tests are widely used to evaluate cognitive function and screen for neurodegenerative disorders, but traditional methods of data collection only yield sparse information, such as test completion time or error types. By contrast, the high-density data collected with Coregraph may contribute to a better understanding of the cognitive processes involved in executing these tests. In addition, Coregraph may potentially revolutionize the field of cognitive evaluation by aiding in the prediction of cognitive deficits and in the identification of early signs of neurodegenerative disorders such as Alzheimer's dementia. By analyzing high-density graphomotor data through techniques like manual feature engineering and machine learning, we can uncover patterns and relationships that would be otherwise hidden with traditional methods of data analysis. We are currently in the process of determining the most effective methods of feature extraction and feature analysis to develop Coregraph to its full potential.
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.
CaImAn: large-scale batch and online analysis of calcium imaging data
Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.
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.