Back to Table of contents

Primeur weekly 2019-08-12

Focus

The quest for European HPC research and innovation funding ...

Quantum computing

Unique electrical properties in quantum materials can be controlled using light ...

Light for the nanoworld ...

Middleware

Ohio Supercomputer Center to host seventh meeting of the MVAPICH Users Group ...

Hardware

Cray reports second quarter 2019 financial results ...

Mellanox Ethernet and InfiniBand solutions deliver breakthrough performance for AMD EPYC 7002 processor-based data centres ...

Phison at the forefront of PCIe Gen4 storage market with a portfolio of products ...

Researchers embrace imperfection to improve biomolecule transport ...

2nd Gen AMD EPYC processors set new standard for the modern data centre with record-breaking performance and significant TCO savings ...

HPE ProLiant shatters 37 world records ...

Cray awarded contracts with the U.S. Army Research Laboratory and the Army Engineering and Research Development Center ...

Cray Shasta supercomputer to power weather forecasting for the U.S. Air Force ...

GRC partners with Prasa to bring liquid immersion cooling to data centres in India ...

Excelero debuts Excelero NVEdge, software for creating NVMe All Flash Arrays (NAFA) from high-availability (HA) servers ...

Excelero honoured with Flash Memory Summit 2019 Best of Show Award for third year in a row ...

Simulation technique can predict microstructures of alloy materials used in jet engines - before they are made ...

Supermicro now offering AMD EPYC 7002 series processor-based systems to customers who want to transform their data centres ...

Boston now offers AMD EPYC 7002 series processor-based systems to customers ...

Lenovo and Intel announce multiyear global collaboration to extend HPC and AI leadership ...

Xilinx expands Alveo portfolio with industry's first adaptable compute, network and storage accelerator card built for any server and any Cloud ...

Penguin Computing expands Altus product family with AMD EPYC 7002 series processor-based systems, reaching new levels of data centre performance ...

Applications

Stanford researcher develops data standards for brain imaging and applies rigorous computational methods to work ...

HPE advances its intelligent data platform with acquisition of MapR business assets ...

Turbulence meets a shock ...

The Cloud

UC San Diego, UC Berkeley, and University of Washington announce 'CloudBank' Award ...

Stanford researcher develops data standards for brain imaging and applies rigorous computational methods to work


Prediction of target outcomes using survey factor scores, estimated from 2500 shuffles of the target outcome. Ontological fingerprints displayed as polar plots indicate the standardized beta value for each significant survey factor. The ontological fingerprint for the two best predicted outcomes are reproduced at the top.
6 Aug 2019 Austin - In recent years, efforts to understand the workings of the mind have taken on new-found urgency. Not only are psychological and neurological disorders - from Alzheimer's disease and strokes to autism and anxiety - becoming more widespread, new tools and methods have emerged that allow scientists to explore the structure of, and activity within, the brain with greater granularity.
Psychological graph of all dependent variables (DVs). Graphical lasso was used to estimate a sparse undirected graph representing the relationships amongst all DVs. Nodes represent DVs while edges represent partial correlation between two DVs (thickness reflects strength).

The White House launched the BRAIN Initiative on April 2, 2013, with the goal of supporting the development and application of innovative technologies that can create a dynamic understanding of brain function. The initiative has supported more than $1 billion in research and has led to new insights, new drugs, and new technologies to help individuals with brain disorders.

But this wealth of research comes with challenges, according to Russell Poldrack, a psychology professor with a computing bent at Stanford University. Psychology and neuroscience struggle to build on the knowledge of its disparate researchers.

"Science is meant to be cumulative, but both methodological and conceptual problems have impeded cumulative progress in psychological science", Russell Poldrack and collaborators from Stanford, Dartmouth College and Arizona State University wrote in aNature Communicationspaper out in May 2019.

Part of the problem is practical. With hundreds of research groups undertaking original research, a central repository is needed to host and share data, compare and combine studies, and encourage data reuse. To address this curatorial challenge, in 2010 Russell Poldrack launched a platform called OpenFMRI for sharing fMRI studies.

"I'd thought for a long time that data sharing was important for a number of reasons", explained Russell Poldrack, "for transparency and reproducibility and also to help us aggregate across lots of small studies to improve our power to answer questions."

OpenFMRI grew to nearly a hundred datasets, and in 2016 was subsumed into OpenNeuro, a more general platform for hosting brain imaging studies. That platform today has more than 220 datasets, including some like "The Stockholm Sleepy Brain Study" and "Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression", that have been downloaded hundreds of times.

Brain imaging datasets are relatively large and require a large repository to house them. When he was developing OpenFMRI, Poldrack turned to the Texas Advanced Computing Center (TACC) at the University of Texas at Austin to host and serve up the data.

A grant from the Arnold Foundation allowed him to host OpenNeuro on Amazon Web Services for a few years, but recently Russell Poldrack turned again to TACC and to other systems that are part of the National Science Foundation (NSF)-funded Extreme Science and Engineering Discovery Environment (XSEDE) to serve as the cyberinfrastructure for the database.

Part of the success of the project is due to the development of a common standard, BIDS - Brain Imaging Data Structure - that allows researchers to compare and combine studies in an apples-to-apples way. Introduced by Russell Poldrack and others in 2016, it earned near-immediate acceptance and has grown into the lingua franca for neuro-imaging data.

As part of the standard creation, Russell Poldrack and his collaborators built a web-based validator to make it easy to determine whether one's data meets the standard.

"Researchers convert their data into BIDS format, upload their data and it gets validated on upload", Russell Poldrack stated. "Once it passes the validator and gets uploaded, with a click of a button it can be shared."

Data sharing alone is not the end goal of these efforts. Ultimately, Russell Poldrack would like to develop pipelines for computation that can rapidly analyze brain imaging datasets in a variety of way. He is working with the CBrain project, based at McGill University in Montreal, Canada, to create containerized workflows that researchers can use to perform these analyses without requiring a lot of advanced computing expertise, and independent of what system they are using.

He is also working with another project called BrainLife.io based at Indiana University, which uses XSEDE resources, including those at TACC, to process data, including data from OpenNeuro.

Many of the datasets from OpenNeuro are now available on BrainLife, and there is a button on those datasets that takes one directly to the relevant page at BrainLife, where they can be processed and analyzed using a variety of scientist-developed apps.

"In addition to sharing data, one of the things that having this common data standard affords us is the ability to automatically analyze data and do the kind of pre-processing and quality control that we often do on imaging data", he explained. "You just point the container at the data set, and it just runs it."

Things would be simple if formatting, storage, and sharing were the only problems the field faced. But what if the common methods researchers used for analyzing studies introduced biases and errors, leading to a lack of reproducibility? Moreover, what if the underlying assumptions about the way the mind worked were fundamentally flawed?

A study published in 2018 in Nature Human Behaviour that sought to replicate 21 social and behavioural science papers fromNature and Sciencefound that only 13 could be successfully replicated. Another meta-study under the auspices of the Center for Open Science, re-ran 28 classic and contemporary studies in psychology and found that 14 failed to replicate. This has led to retroactive suspicions about decades worth of results.

Russell Poldrack and his collaborators addressed both the methodological and assumption problems in their recent Nature Communications paper by applying more rigorous statistical methods to try to uncover the underlying structures of the mind, a process they call 'data-driven ontology discovery'.

Applying the approach to studies of self-regulation, the researchers tested the ability of survey questionnaires and task-based studies to predict an individual's likelihood of being at risk for alcoholism, obesity, drug abuse, or other self-regulation-related issues.

In their study, 522 participants took 23 self-report studies and performed 37 behavioural tasks. From each of these 60 measures, the team derived multiple dependent variables thought to capture psychological constructs. Using the dependent variables, the team first tried to create "a psychological space" - a way of quantifying the distance between dependent variables to determine how various types of behaviour that are often seen as separate cluster or correlate to each other. They used these "ontological fingerprints" to determine the contribution of various psychological constructs to the final predictive model.

The statistical approach used in the study, and enabled by supercomputers at TACC, goes far beyond the standard methods used in typical psychological studies.

"We're bringing to bear serious machine learning methods to determine what's correlated with what, and what has generalizable predictive accuracy, using methods that are still fairly new to this area of research", Russell Poldrack stated.

They found that some predicted targets, like mental health and obesity, had simple ontological fingerprints, such as "emotional control" and "problematic eating", but that other fingerprints were more complicated. They also found that task-based studies - common in psychological research - had almost no predictive ability.

"I'm always leery of saying our research will be useful for diagnosis, but it almost certainly will be useful for a better understanding of how to do diagnosis and the underlying functions that relate to certain outcomes, like smoking or problem drinking or obesity", Russell Poldrack stated.

Motivating the effort is a re-examination of the way that we talk about mental illness.

"Breaking these disorders into diagnostic categories like schizophrenia, bipolar disorder, or depression, is just not biologically realistic", he stated. "Both genetics and neuroscience show that those disorders have way more overlap in terms of their genetics and their neurobiology, than differences. So, I think that there are new paradigms that might emerge that would be helped by a better understanding of the brain."

High performance computing allows researchers to apply much more sophisticated methods to determine knowledge distributions and figure out how significant results are.

"We can use sampling techniques to randomize the data 5,000 times and re-run big models many times", Russell Poldrack stated. "That's not realistically possible without supercomputers."

It used to be the case that the progress of science was dependent on the ability to create a molecule or synthesize a chemical. But increasingly progress in science depends on the ability to ask the right question about a Big Data set, and then to be able to actually feasibly get an answer to that question.

"And", stated Russell Poldrack, "there's a lot of questions that, without high performance computing, you can't feasibly get an answer to."

Despite the crises of faith that has struck the field in recent years, Russell Poldrack believes psychological science has a lot to say that is very reliable about why humans do what they do, and that neuroscience gives us ways to understand where that comes from.

"We're trying to understand really complex things", he stated. "It has to be realized that everything we say is probably wrong, but the hope is that it can get us a little bit closer to what's right."
Survey ontology. 66 survey dependent variables were projected onto 12 factors discovered using exploratory factor analysis, represented by the heatmap. Rows are factors and columns are separate dependent variables ordered based on the dendrogram above.
Source: University of Texas at Austin, Texas Advanced Computing Center - TACC

Back to Table of contents

Primeur weekly 2019-08-12

Focus

The quest for European HPC research and innovation funding ...

Quantum computing

Unique electrical properties in quantum materials can be controlled using light ...

Light for the nanoworld ...

Middleware

Ohio Supercomputer Center to host seventh meeting of the MVAPICH Users Group ...

Hardware

Cray reports second quarter 2019 financial results ...

Mellanox Ethernet and InfiniBand solutions deliver breakthrough performance for AMD EPYC 7002 processor-based data centres ...

Phison at the forefront of PCIe Gen4 storage market with a portfolio of products ...

Researchers embrace imperfection to improve biomolecule transport ...

2nd Gen AMD EPYC processors set new standard for the modern data centre with record-breaking performance and significant TCO savings ...

HPE ProLiant shatters 37 world records ...

Cray awarded contracts with the U.S. Army Research Laboratory and the Army Engineering and Research Development Center ...

Cray Shasta supercomputer to power weather forecasting for the U.S. Air Force ...

GRC partners with Prasa to bring liquid immersion cooling to data centres in India ...

Excelero debuts Excelero NVEdge, software for creating NVMe All Flash Arrays (NAFA) from high-availability (HA) servers ...

Excelero honoured with Flash Memory Summit 2019 Best of Show Award for third year in a row ...

Simulation technique can predict microstructures of alloy materials used in jet engines - before they are made ...

Supermicro now offering AMD EPYC 7002 series processor-based systems to customers who want to transform their data centres ...

Boston now offers AMD EPYC 7002 series processor-based systems to customers ...

Lenovo and Intel announce multiyear global collaboration to extend HPC and AI leadership ...

Xilinx expands Alveo portfolio with industry's first adaptable compute, network and storage accelerator card built for any server and any Cloud ...

Penguin Computing expands Altus product family with AMD EPYC 7002 series processor-based systems, reaching new levels of data centre performance ...

Applications

Stanford researcher develops data standards for brain imaging and applies rigorous computational methods to work ...

HPE advances its intelligent data platform with acquisition of MapR business assets ...

Turbulence meets a shock ...

The Cloud

UC San Diego, UC Berkeley, and University of Washington announce 'CloudBank' Award ...