Posted on by Dr. Francis Collins
Flip the image above upside down, and the shape may remind you of something. If you think it resembles a pyramid, then you and a lot of great neuroscientists are thinking alike. What you are viewing is a colorized, 3D reconstruction of a pyramidal tract, which are bundles of nerve fibers that originate from the brain’s cerebral cortex and relay signals to the brainstem or the spinal cord. These signals control many important activities, including the voluntary movement of our arms, legs, head, and face.
For a while now, it’s been possible to combine a specialized form of magnetic resonance imaging (MRI) with computer modeling tools to produce 3D reconstructions of complicated networks of nerve fibers, such as the pyramidal tract. Still, for technical reasons, the quality of these reconstructions has remained poor in parts of the brain where nerve fibers cross at angles of 40 degrees or less.
The video above demonstrates how adding a sophisticated algorithm, called Orientation Distribution Function (ODF)-Fingerprinting, to such modeling can help overcome this problem when reconstructing a pyramidal tract. It has potential to enhance the reliability of these 3D reconstructions as neurosurgeons begin to use them to plan out their surgeries to help ensure they are carried out with the utmost safety and precision.
In the first second of the video, you see gray, fuzzy images from a diffusion MRI of the pyramidal tract. But, very quickly, a more colorful, detailed 3D reconstruction begins to appear, swiftly filling in from the top down. Colors are used to indicate the primary orientations of the nerve fibers: left to right (red), back to front (green), and top to bottom (blue). The orange, magenta, and other colors represent combinations of these primary directional orientations.
About three seconds into the video, a rough draft of the 3D reconstruction is complete. The top of the pyramidal tract looks pretty good. However, looking lower down, you can see distortions in color and relatively poor resolution of the nerve fibers in the middle of the tract—exactly where the fibers cross each other at angles of less than 40 degrees. So, researchers tapped into the power of their new ODF-Fingerprinting software to improve the image—and, starting about nine seconds into the video, you can see an impressive final result.
The researchers who produced this amazing video are Patryk Filipiak and colleagues in the NIH-supported lab of Steven Baete, Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York. The work paired diffusion MRI data from the NIH Human Connectome Project with the ODF-Fingerprinting algorithm, which was created by Baete to incorporate additional MRI imaging data on the shape of nerve fibers to infer their directionality .
This innovative approach to imaging recently earned Baete’s team second place in the 2021 “Show Us Your BRAINs” Photo and Video contest, sponsored by the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative. But researchers aren’t stopping there! They are continuing to refine ODF-Fingerprinting, with the aim of modeling the pyramidal tract in even higher resolution for use in devising new and better ways of helping people undergoing neurosurgery.
 Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles. Baete SH, Cloos MA, Lin YC, Placantonakis DG, Shepherd T, Boada FE. Neuroimage. 2019 Sep;198:231-241.
Human Connectome Project (University of Southern California, Los Angeles)
Steven Baete (Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York)
Show Us Your BRAINs! Photo and Video Contest (BRAIN Initiative/NIH)
NIH Support: National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Cancer Institute
Posted on by Dr. Francis Collins
There’s been quite a bit of discussion in the news lately about whether to pause or resume college athletics during the pandemic. One of the sticking points has been uncertainty about how to monitor the health of student athletes who test positive for SARS-CoV-2, the novel coronavirus that causes COVID-19. As a result, college medical staff don’t always know when to tell athletes that they’ve fully recovered and it’s safe to start training again.
The lack of evidence owes to two factors. Though it may not seem like it, this terrible coronavirus has been around for less than a year, and that’s provided little time to conduct the needed studies with young student athletes. But that’s starting to change. An interesting new study in the journal JAMA Cardiology provides valuable and rather worrisome early data from COVID-positive student athletes evaluated for an inflammation of the heart called myocarditis, a well-known complication .
Saurabh Rajpal and his colleagues at the Ohio State University, Columbus, used cardiac magnetic resonance imaging (MRI) to visualize the hearts of 26 male and female student athletes. They participated in a range of sports, including football, soccer, lacrosse, basketball, and track. All of the athletes were referred to the university’s sports medicine clinic this past summer after testing positive for SARS-CoV-2. All had mild or asymptomatic cases of COVID-19.
Even so, the MRI scans, taken 11-53 days after completion of quarantine, showed four of the student athletes (all males) had swelling and tissue damage to their hearts consistent with myocarditis. Although myocarditis often resolves on its own over time, severe cases can compromise the heart muscle’s ability to beat. That can lead to heart failure, abnormal heart rhythms, and even sudden death in competitive athletes with normal heart function .
The investigators also looked for more subtle findings of cardiac injury in these athletes, using a contrast agent called gadolinium and measuring its time to appear in the cardiac muscle during the study. Eight of the 26 athletes (31 percent) had late gadolinium enhancement, suggestive of prior myocardial injury.
Even though it’s a small study, these results certainly raise concerns. They add more evidence to a prior study, published by a German group, that suggested subtle cardiac consequences of SARS-CoV-2 infection may be common in adults .
Rajpal and his colleagues will continue to follow the athletes in their study for several more months. The researchers will keep an eye out for other lingering symptoms of COVID-19, generate more cardiac MRI data, and perform exercise testing.
As this study shows, we still have a lot to learn about the long-term consequences of COVID-19, which can take people on different paths to recovery. For athletes, that path is the challenge to return to top physical shape and feel ready to compete at a high level. But getting back in uniform must also be done safely to minimize any risks to an athlete’s long-term health and wellbeing. The more science-based evidence that’s available, the more prepared athletes at large and small colleges will be to compete safely in this challenging time.
 Cardiovascular magnetic resonance findings in competitive athletes recovering from COVID-19 infection. Rajpal S, Tong MS, Borchers J, et al. JAMA Cardiol. 2020 September 11. [Published online ahead of print.]
 Eligibility and disqualification recommendations for competitive athletes with cardiovascular abnormalities: Task Force 3: Hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and other cardiomyopathies, and myocarditis. Maron BJ, Udelson JE, Bonow RO, et al. Circulation. 2015;132(22):e273-e280.
 Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from Coronavirus Disease 2019 (COVID-19). Puntmann VO, Carej ML, Wieters I. JAMA Cardiol. 2020 Jul 27:e203557. [Published online ahead of print.]
Coronavirus (COVID-19) (NIH)
Heart Inflammation (National Heart, Lung, and Blood Institute/NIH)
Saurabh Rajpal (Ohio State College of Medicine, Columbus)
Posted on by Dr. Francis Collins
Last year, nearly 175,000 American men were diagnosed with prostate cancer . Most got the bad news after a blood test or physical exam raised concerns that warranted a biopsy of the prostate, a walnut-sized gland just below the bladder.
Traditional biopsies sample tissue from 12 systematically placed points within the prostate that are blind to tumor locations. Such procedures have helped to save many lives, but are prone to missing or misclassifying prostate cancers, which has led doctors both to over and under treat their patients.
Now, there may be a better approach. In a study of more than 2,000 men, NIH researchers and their colleagues recently found that combining the 12-point biopsy with magnetic resonance imaging (MRI)-targeted biopsy during the same session more accurately diagnoses prostate cancer than either technique alone .
The findings address a long-standing challenge in prostate cancer diagnostics: performing a thorough prostate biopsy to allow a pathologist to characterize as accurately as possible the behavior of a tumor. Some prostate tumors are small, slow growing, and can be monitored closely without treatment. Other tumors are aggressive and can grow rapidly, requiring immediate intervention with hormonal therapy, radiation, or surgery.
But performing a thorough prostate biopsy can run into technical difficulties. The 12-point biopsy blindly samples tissue from across the prostate gland, but it can miss a cancer by not probing in the right places.
Several years ago, researchers at the NIH Clinical Center, Bethesda, MD, envisioned a solution. They’d use specially designed MRI images of a man’s prostate to guide the biopsy needle to areas in the prostate that look suspicious and deserve a closer look under a microscope.
Through a cooperative research-and-development agreement, NIH and the now- Florida-based Philips Healthcare created an office-based, outpatient prostate biopsy device, called UroNav, that was later approved by the Food and Drug Administration. The UroNav system relies on software that overlays MRI images highlighting suspicious areas onto real-time ultrasound images of the prostate that are traditionally used to guide biopsy procedures.
The new technology led to a large clinical study led by Peter Pinto, a researcher with NIH’s National Cancer Institute. The study results, published in 2015, found that the MRI-targeted approach was indeed superior to the 12-point biopsy at detecting aggressive prostate cancers .
But some doctors had questions about how best to implement the UroNav system and whether it could replace the 12-point biopsy. The uncertainty led to a second clinical study to nail down more answers, and the results were just published in The New England Journal of Medicine.
The research team enrolled 2,103 men who had visible prostate abnormalities on an MRI. Once in the study, each man underwent both the 12-point blind biopsy and the MRI-targeted approach—all in a single office visit. Based on this two-step approach, 1,312 people were diagnosed with prostate cancer. Of that total, 404 men had evidence of aggressive cancer, and had their prostates surgically removed.
The researchers then compared the diagnoses from each approach alone versus the two combined. The data showed that the combined biopsy found 208 cancers that the standard 12-point biopsy alone would have missed. Adding the MRI-targeted biopsy also helped doctors find and sample the more aggressive cancers. This allowed them to upgrade the diagnosis of 458 cancers to aggressive and in need of more full treatment.
Combining the two approaches also led to more accurate diagnoses. By carefully analyzing the 404 removed prostates and comparing them to the biopsy results, the researchers found the 12-point biopsy missed the most aggressive cancers about 40 percent of the time. But the MRI-targeted approach alone missed it about 30 percent of the time. Combined, they did much better, underestimating the severity of less than 15 percent of the cancers.
Even better, the combined biopsy missed only 3.5 percent of the most aggressive tumors. That’s compared to misses of about 17 percent for the most-aggressive cancers for the 12-point biopsy alone and about 9 percent for MRI-targeted biopsy alone.
It may take time for doctors to change how they detect prostate cancer in their practices. But the findings show that combining both approaches will significantly improve the accuracy of diagnosing prostate cancer. This will, in turn, help to reduce risk of suboptimal treatment (too much or too little) by allowing doctors and patients to feel more confident in the biopsy results. That should come as good news now and in the future for the families and friends of men who will need an accurate prostate biopsy to make informed treatment decisions.
 Cancer State Facts: Prostate Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.
 MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. Ahdoot M, Wilbur AR, Reese SE, Lebastchi AH, Mehralivand S, Gomella PT, Bloom J, Gurram S, Siddiqui M, Pinsky P, Parnes H, Linehan WM, Merino M, Choyke PL, Shih JH, Turkbey B, Wood BJ, Pinto PA. N Engl J Med. 2020 Mar 5;382(10):917-928.
 Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. Siddiqui M, Rais-Bahrami, George AK, Rothwax J, Shakir N, Okoro C, Raskolnikov D, Parnes HL, Linehan WM, Merino MJ, Simon RM, Choyke PL, Wood BJ, and Pinto PA. JAMA. 2015 January 27;313(4):390-397.
Prostate Cancer (National Cancer Institute/NIH)
Video: MRI-Targeted Prostate Biopsy (YouTube)
Pinto Lab (National Cancer Institute/NIH)
NIH Support: National Cancer Institute; NIH Clinical Center
Posted on by Dr. Francis Collins
Hop aboard as we fly up, down, left, and right through the information highways of the human brain! This captivating and eye-catching video was one of the winners of the 2019 “Show us Your Brain!” contest sponsored by the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative.
The video travels through several portions of the brain’s white matter—bundles of fiber that carry nerve signals between the brain and the body, as well as within the brain itself. Fiber colors indicate directionality: left-right fibers (red), front-back fibers (green), and top-bottom fibers (blue).
Looking from the back, we start our journey deep within the brain in the limbic system, the area that helps control emotion, learning, and memory. About three seconds in, visual fibers pop into view extending from the eyes to various brain areas into the occipital lobe (one of four major brain lobes) in the back of the brain.
About two seconds later, flying over top as the brain starts rotating, we see various fiber bundles spray upward throughout the cerebral cortex, communicating information related to language processing, short-term memory, and other functions. About halfway through the video, several green bundles emerge arching across the brain’s midline. These bundles, called the corpus callosum, house the fibers enabling communication between left and right sides of the brain. Finally, the video closes as we see many different fiber bundles lighting up all over, enabling communication between different cortical and subcortical portions of the brain through association and projection pathways.
Dynamic maps like these are created using a 3D imaging technique called diffusion MRI tractography . The technique tracks subtle pathways of water movement in the brain, and allows researchers to model the physical properties (connectional anatomy) that underlie the brain’s electrical properties (neuronal signaling). Postdoctoral researcher Ryan Cabeen and Arthur Toga, director of the University of Southern California Mark and Mary Stevens Neuroimaging and Informatics Institute, Los Angeles, used the method to study how white matter changes in developing and aging brains, as well as in brains affected by neurodegenerative or neurological disorders.
Scientific animator Jim Stanis produced the video with Cabeen and Toga. The team first created a population-averaged brain using high-quality diffusion MRI datasets from the Human Connectome Project ,and then used sophisticated computational tools to delineate each bundle manually .
The tractography technique lets scientists visualize and quantitatively analyze the brain’s wiring patterns, complementing our understanding of how the brain functions. Such methods are especially useful to learn about the organization of deep-brain areas that remain out of reach for scientists using current tools and imaging techniques.
 Kernel regression estimation of fiber orientation mixtures in diffusion MRI. Cabeen RP, Bastin ME, Laidlaw DH. Neuroimage. 2016 Feb 15;127:158-172.
Arthur Toga (USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Los Angeles)
Ryan Cabeen (USC Mark and Mary Stevens Neuroimaging and Informatics Institute)
Human Connectome Project (USC)
Show Us Your Brain Contest! (BRAIN Initiative/NIH)
NIH Support: National Institute of Neurological Disorders and Stroke; National Institute of Mental Health
Posted on by Dr. Francis Collins
Credit: Itamar Terem, Stanford University, Palo Alto, CA, and Samantha Holdsworth, University of Auckland, New Zealand
Though our thoughts can wander one moment and race rapidly forward the next, the brain itself is often considered to be motionless inside the skull. But that’s actually not correct. When the heart beats, the pumping force reverberates throughout the body and gently pulsates the brain. What’s been tricky is capturing these pulsations with existing brain imaging technologies.
Recently, NIH-funded researchers developed a video-based approach to magnetic resonance imaging (MRI) that can record these subtle movements . Their method, called phase-based amplified MRI (aMRI), magnifies those tiny movements, making them more visible and quantifiable. The latest aMRI method, developed by a team including Itamar Terem at Stanford University, Palo Alto, CA, and Mehmet Kurt at Stevens Institute of Technology, Hoboken, NJ. It builds upon an earlier method developed by Samantha Holdsworth at New Zealand’s University of Auckland and Stanford’s Mahdi Salmani Rahimi .