137 research outputs found
Review of photoacoustic imaging plus X
Photoacoustic imaging (PAI) is a novel modality in biomedical imaging
technology that combines the rich optical contrast with the deep penetration of
ultrasound. To date, PAI technology has found applications in various
biomedical fields. In this review, we present an overview of the emerging
research frontiers on PAI plus other advanced technologies, named as PAI plus
X, which includes but not limited to PAI plus treatment, PAI plus new circuits
design, PAI plus accurate positioning system, PAI plus fast scanning systems,
PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus
deep learning, and PAI plus other imaging modalities. We will discuss each
technology's current state, technical advantages, and prospects for
application, reported mostly in recent three years. Lastly, we discuss and
summarize the challenges and potential future work in PAI plus X area
The challenges of purely mechanistic models in biology and the minimum need for a 'mechanism-plus-X' framework
Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
We applied several regression and deep learning methods to predict fluid
intelligence scores from T1-weighted MRI scans as part of the ABCD
Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel
intensities and probabilistic tissue-type labels derived from these as features
to train the models. The best predictive performance (lowest mean-squared
error) came from Kernel Ridge Regression (KRR; ), which produced a
mean-squared error of 69.7204 on the validation set and 92.1298 on the test
set. This placed our group in the fifth position on the validation leader board
and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at
MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl
Thinking Materially: Cognition as Extended and Enacted
Human cognition is extended and enacted. Drawing the boundaries of cognition to include the resources and attributes of the body and materiality allows an examination of how these components interact with the brain as a system, especially over cultural and evolutionary spans of time. Literacy and numeracy provide examples of multigenerational, incremental change in both psychological functioning and material forms. Though we think materiality, its central role in human cognition is often unappreciated, for reasons that include conceptual distribution over multiple material forms, the unconscious transparency of cognitive activity in general, and the different temporalities of metaplastic change in neurons and cultural forms
Contents, vehicles, and complex data analysis in neuroscience
The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call âalgorithmic homuncularism,â individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of âvehicle realism,â and I apply this view to debates about the explanatory role of representation
Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses
The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI â to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI â the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In
recent years, however, more and more researchers have recognized the necessity â and feasibility â of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence
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