Allen Institute Releases Powerful New Data on the Aging Brain and Traumatic Brain Injury

Posted on April 26th, 2016

The Allen Institute for Brain Science has announced major updates to its online resources available at brain-map.org, including a new resource on Aging, Dementia and Traumatic Brain Injury (TBI) in collaboration with UW Medicine researchers at the University of Washington, and Group Health. The resource is the first of its kind to collect and share a wide variety of data modalities on a large sample of aged brains, complete with mental health histories and clinical diagnoses.

“The power of this resource is its ability to look across such a large number of brains, as well as a large number of data types,” says Ed Lein, Ph.D., Investigator at the Allen Institute for Brain Science. “The resource combines traditional neuropathology with modern ‘omics’ approaches to enable researchers to understand the process of aging, look for molecular signatures of disease and identify hallmarks of brain injury.”

The study samples come from the Adult Changes in Thought (ACT) study, a longitudinal research effort led by Dr. Eric B. Larson and Dr. Paul K. Crane of the Group Health Research Institute and the University of Washington to collect data on thousands of aging adults, including detailed information on their health histories and cognitive abilities. UW Medicine led efforts to collect post-mortem samples from 107 brains aged 79 to 102, with tissue collected from the parietal cortex, temporal cortex, hippocampus and cortical white matter.

“This collaborative research project aims to answer one of the most perplexing problems in clinical neuroscience,” says Dr. Richard G. Ellenbogen, UW Chair and Professor, Department of Neurological Surgery. “If a person suffers a traumatic brain injury during his or her lifetime, what is the risk of developing dementia?  We simply don’t know the answer at this time, but some of the answers might be found in this comprehensive dataset by people asking the right kind of questions. This issue is important because of the inherent risk for everyone who plays sports, exercises or in general, participates in the activities of daily life.”

“This study was made possible by the amazing generosity of the ACT participants and their families, incredible collaboration among our partners, and the generosity and vision of the Paul G. Allen Family Foundation,” says Dr. Dirk Keene, co-principal investigator and Director, UW Neuropathology. “For the first time, scientists and clinicians from around the world will have access to this unique dataset, which will advance the study of brain aging and hopefully contribute to development of novel diagnostic and therapeutic strategies for neurodegenerative disease.”

The final online resource includes quantitative image data to show the disease state of each sample, protein data related to those disease states, gene expression data and de-identified clinical data for each case. Because the data is so complex, the online resource also includes a series of animated “snapshots,” giving users a dynamic sampling of the ways they can interrogate the data.

“There are many fascinating conclusions to be drawn by diving into these data,” says Jane Roskams, Ph.D., Executive Director, Strategy and Alliances at the Allen Institute. “This is the first resource of its kind to combine a variety of data types and a large sample size, making it a remarkably holistic view of the aged brain in all its complexity.”

Researchers focused on examining the impact of mild to moderate TBI on the aged brain, comparing samples from patients with self-reported loss of consciousness incidents against meticulously matched controls.  “Interestingly, while we see many other trends in these data, we did not uncover a distinctive genetic signature or pathologic biomarker in patients with TBI and loss of consciousness in this population study,” says Lein.

“This new resource is an exciting addition to our suite of open science resources,” says Christof Koch, Ph.D., President and Chief Scientific Officer of the Allen Institute for Brain Science. “Researchers around the globe will be able to mine the data and explore many facets of the aged brain, which we hope will accelerate discoveries about health and disease in aging.”

Research to create this resource was funded with a $2.37 million grant from the Paul G. Allen Family Foundation to the University of Washington.

Two other resources have received significant updates in the latest data release. The Allen Cell Types Database now includes gene expression data on individual cells, in addition to shape, electrical activity and location in the brain. The number of cells in the database has also increased, and, in collaboration with the Blue Brain Project, a subset of cells are accompanied by a new robust biophysical model.

The Allen Mouse Brain Connectivity Atlas now includes its first public release of layer-specific connectivity in the visual cortex, including more specific targeting of cells using newly developed tracing methods.

 

Research on Largest Network of Cortical Neurons to Date Published in Nature

Posted on March 28th, 2016

Even the simplest networks of neurons in the brain are composed of millions of connections, and examining these vast networks is critical to understanding how the brain works. An international team of researchers, led by R. Clay Reid, Wei Chung Allen Lee and Vincent Bonin from the Allen Institute for Brain Science, Harvard Medical School and Neuro-Electronics Research Flanders (NERF), respectively, has published the largest network to date of connections between neurons in the cortex, where high-level processing occurs, and have revealed several crucial elements of how networks in the brain are organized. The results are published this week in the journal Nature.

A network of cortical neurons whose connections were traced from a multi-terabyte 3D data set. The data were created by an electron microscope designed and built at Harvard Medical School to collect millions of images in nanoscopic detail, so that every one of the “wires” could be seen, along with the connections between them. Some of the neurons are color-coded according to their activity patterns in the living brain. This is the newest example of functional connectomics, which combines high-throughput functional imaging, at single-cell resolution, with terascale anatomy of the very same neurons. (Image credit: Clay Reid, Allen Institute; Wei-Chung Lee, Harvard Medical School; Sam Ingersoll, graphic artist)

A network of cortical neurons whose connections were traced from a multi-terabyte 3D data set. The data were created by an electron microscope designed and built at Harvard Medical School to collect millions of images in nanoscopic detail, so that every one of the “wires” could be seen, along with the connections between them. Some of the neurons are color-coded according to their activity patterns in the living brain. This is the newest example of functional connectomics, which combines high-throughput functional imaging, at single-cell resolution, with terascale anatomy of the very same neurons. (Image credit: Clay Reid, Allen Institute; Wei-Chung Lee, Harvard Medical School; Sam Ingersoll, graphic artist)

“This is a culmination of a research program that began almost ten years ago. Brain networks are too large and complex to understand piecemeal, so we used high-throughput techniques to collect huge data sets of brain activity and brain wiring,” says R. Clay Reid, M.D., Ph.D., Senior Investigator at the Allen Institute for Brain Science. “But we are finding that the effort is absolutely worthwhile and that we are learning a tremendous amount about the structure of networks in the brain, and ultimately how the brain’s structure is linked to its function.”

“Although this study is a landmark moment in a substantial chapter of work, it is just the beginning,” says Wei-Chung Lee, Ph.D., Instructor in Neurobiology at Harvard Medicine School and lead author on the paper. “We now have the tools to embark on reverse engineering the brain by discovering relationships between circuit wiring and neuronal and network computations.”

“For decades, researchers have studied brain activity and wiring in isolation, unable to link the two,” says Vincent Bonin, Principal Investigator at Neuro-Electronics Research Flanders. “What we have achieved is to bridge these two realms with unprecedented detail, linking electrical activity in neurons with the nanoscale synaptic connections they make with one another.”

“We have found some of the first anatomical evidence for modular architecture in a cortical network as well as the structural basis for functionally specific connectivity between neurons,” Lee adds. “The approaches we used allowed us to define the organizational principles of neural circuits. We are now poised to discover cortical connectivity motifs, which may act as building blocks for cerebral network function.”

Lee and Bonin began by identifying neurons in the mouse visual cortex that responded to particular visual stimuli, such as vertical or horizontal bars on a screen. Lee then made ultra-thin slices of brain and captured millions of detailed images of those targeted cells and synapses, which were then reconstructed in three dimensions. Teams of annotators on both coasts of the United States simultaneously traced individual neurons through the 3D stacks of images and located connections between individual neurons.

Analyzing this wealth of data yielded several results, including the first direct structural evidence to support the idea that neurons that do similar tasks are more likely to be connected to each other than neurons that carry out different tasks. Furthermore, those connections are larger, despite the fact that they are tangled with many other neurons that perform entirely different functions.

“Part of what makes this study unique is the combination of functional imaging and detailed microscopy,” says Reid. “The microscopic data is of unprecedented scale and detail. We gain some very powerful knowledge by first learning what function a particular neuron performs, and then seeing how it connects with neurons that do similar or dissimilar things.

“It’s like a symphony orchestra with players sitting in random seats,” Reid adds. “If you listen to only a few nearby musicians, it won’t make sense. By listening to everyone, you will understand the music; it actually becomes simpler. If you then ask who eachmusician is listening to, you might even figure out how they make the music. There’s no conductor, so the orchestra needs to communicate.”

This combination of methods will also be employed in an IARPA contracted project with the Allen Institute for Brain Science, Baylor College of Medicine, and Princeton University, which seeks to scale these methods to a larger segment of brain tissue. The data of the present study is being made available online for other researchers to investigate.

Sorting the Brain’s Cells

Posted on January 8th, 2016

As part of our goal to understand the cellular building blocks of the brain, scientists at the Allen Institute for Brain Science are using gene expression information from single cells to help identify the different types of brain cells. Their work, published in Nature Neuroscience, identifies 49 distinct cell types in the mouse visual cortex in a detailed taxonomy.

The data and taxonomy are also available in an interactive, visual format as part of our Science Vignettes series on brain-map.org.

“Studying any system requires knowing what the system is made of,” says Bosiljka Tasic, Ph.D., Assistant Investigator at the Allen Institute for Brain Science. “There are many ways to define the brain’s cellular building blocks. Our approach was to look at all the genes that are expressed in individual cells in the mouse visual cortex and use that information to classify the cells.”

Since each cell expresses thousands of genes, classifying numerous cells by their gene expression is an enormous computational undertaking.

“Initially, the problem of classifying cells is like sorting Skittles in the dark,” says Vilas Menon, Scientist II at the Allen Institute for Brain Science. “With single-cell gene expression data, we get the equivalent of color, or type, information, but we still have to extract it from the large-scale data set. Ultimately, we wanted to figure out how many types there were in an unbiased, data-driven way.”

For more information, watch the animation above, view the full interactive vignette and read our press release.