Breaking News
February 16, 2019 - Therapeutic endoscopy has an expanding role in the treatment of IBD
February 16, 2019 - Catalyst Biosciences Presents Updated Data from Its Phase 2/3 Trial of Subcutaneous Marzeptacog Alfa (Activated) in Individuals with Hemophilia A or B with Inhibitors at the 12th Annual EAHAD Congress
February 16, 2019 - Rerouting nerves during amputation reduces phantom limb pain before it starts
February 16, 2019 - A Hormone Produced When We Exercise Might Help Fight Alzheimer’s
February 16, 2019 - Millions of British people breathe toxic air travelling to GPs
February 16, 2019 - Conformance of genetic characteristics found to be crucial for longer preservation of kidney graft
February 16, 2019 - Researchers use optogenetic tool to control, visualize receptor signals in neural cells
February 16, 2019 - New reversible antiplatelet therapy could reduce risk of blood clots, prevent cancer metastasis
February 16, 2019 - Testosterone is not the only hormone needed for penis development
February 16, 2019 - FDA Advisory Committee Recommends Approval of Spravato (esketamine) Nasal Spray for Adults with Treatment-Resistant Depression
February 15, 2019 - Heart surgery technology developed at Baptist Health debuts after years of secrecy
February 15, 2019 - Prescription Opioids Double Risk of Triggering Fatal Car Crash
February 15, 2019 - New study helps doctors better understand high blood pressure in pregnant women
February 15, 2019 - Beta wave control in Parkinson’s diseased brain could be a potential therapy
February 15, 2019 - Media representations of love may justify gender-based violence in young people
February 15, 2019 - Yoga May Help With Rheumatoid Arthritis Symptoms, Severity
February 15, 2019 - Obstructive sleep apnea linked to inflammation, organ dysfunction
February 15, 2019 - Master your mind: A challenge from WELL for Life
February 15, 2019 - Why Some Brain Tumors Respond to Immunotherapy
February 15, 2019 - Must-Reads Of The Week From Brianna Labuskes
February 15, 2019 - Researchers uncover novel mechanism and potential new therapeutic target for Alzheimer’s
February 15, 2019 - Genetic variations in a fourth gene associated with higher ALL risk in Hispanic children
February 15, 2019 - Disruptive behavioral problems in kindergarten linked with lower employment earnings in adulthood
February 15, 2019 - New bioengineered device enhances the production of T-cells
February 15, 2019 - HDL proteome behaves like a tiny Velcro ball that is rolling on surfaces
February 15, 2019 - Puerto Rican children more likely to have poor or decreasing use of asthma inhalers
February 15, 2019 - Quality of patient care does not improve after physician-hospital integration
February 15, 2019 - Synopsys release new software for implant design and patient-specific planning
February 15, 2019 - 6 out of 10 hip replacements last 25 years or longer
February 15, 2019 - Health Tip: What You Should Know About Antibiotics
February 15, 2019 - New research challenges medical consensus that adenoids and tonsils significantly shrink during teenage years
February 15, 2019 - Discovery of weakness in a rare cancer could be exploited with drugs
February 15, 2019 - UVA scientists find potential explanation for mysterious cell death in Alzheimer’s, Parkinson’s
February 15, 2019 - New rules requiring female athletes to lower testosterone levels are based on flawed data
February 15, 2019 - Researchers comprehensively sequence the human immune system
February 15, 2019 - Researchers study animal venoms to identify new medicines for treating diseases
February 15, 2019 - Movement of wrist bones revealed by MRI and computer modeling
February 15, 2019 - Philips introduces new premium digital X-ray room to help shorten patient wait times
February 15, 2019 - Women fare worse than men following aortic heart surgery, study finds
February 15, 2019 - High-protein and low-calorie diet helps older adults lose weight safely, shows study
February 15, 2019 - Drug microdosing effects may not measure up to big expectations
February 15, 2019 - Discharged, Dismissed: ERs Often Miss Chance To Set Overdose Survivors On ‘Better Path’
February 15, 2019 - A digitized lab environment to be showcased at smartLAB 2019
February 15, 2019 - Scientists uncover main mechanisms of fluconazole drug resistance
February 15, 2019 - New study seeks to understand how colibactin causes cancer
February 15, 2019 - Photoacoustic imaging accurately measures the temperature of deep tissues
February 15, 2019 - Large study finds no association between phthalate exposure and breast cancer risk
February 15, 2019 - New research explains presence of ‘natural’ magnetism in human cells
February 15, 2019 - Bio-Rad launches new digital PCR system and kit for monitoring treatment response in CML patients
February 15, 2019 - Excessive daytime sleepiness in OSA patients linked to greater risk for cardiovascular diseases
February 15, 2019 - Scientists shed light on damaging cell effects linked to aging
February 15, 2019 - Celiac disease may be caused by stomach bug in childhood
February 15, 2019 - NHS performance figures highlight the true scale of Emergency Department crisis
February 15, 2019 - High intensity exercise may improve health by increasing gut microbiota diversity
February 15, 2019 - Apellis’ APL-2 Receives Orphan Drug Designation from the FDA for the Treatment of Autoimmune Hemolytic Anemia
February 15, 2019 - Couples creating art or playing board games release ‘love hormone’
February 15, 2019 - Glimpsing The Future At Gargantuan Health Tech Showcase
February 15, 2019 - Common herbicide found to increase the risk of lymphoma
February 15, 2019 - Over-abundance of energy to cells could increase cancer risk
February 15, 2019 - Oxford Genetics appoints Jocelyne Bath as new Chief Operating Officer
February 15, 2019 - Castration-resistant metastatic prostate cancer responds to combination of immune checkpoint inhibitors
February 15, 2019 - Large-scale clinical trial begins to study liver transplantation between people with HIV
February 15, 2019 - Cannabis use among adolescents linked with increased risk of depression in adulthood
February 15, 2019 - Fractures, head injuries common in electric scooter accidents, UCLA study finds
February 15, 2019 - Prenatal maternal depression has important consequences for infant temperament, study shows
February 15, 2019 - Stereotactic body radiotherapy effective in treating men with low- or intermediate-risk prostate cancer
February 15, 2019 - Zogenix Submits New Drug Application to U.S. Food & Drug Administration for Fintepla for the Treatment of Dravet Syndrome
February 15, 2019 - Certain birthmarks warrant quick treatment, pediatricians say
February 15, 2019 - New machine learning method predicts if atypical ductal hyperplasia will turn cancerous
February 15, 2019 - Whole-genome sequencing and sharing real-time data could limit spread of foodborne bacteria
February 15, 2019 - FDA warns doctor for illegally marketing unapproved implantable device
February 15, 2019 - New injury documentation tool may provide better evidence for elder abuse cases
February 15, 2019 - Physiological age is a better predictor of survival than chronological age, shows study
February 15, 2019 - New study reveals high success rate for hip and knee replacements
February 15, 2019 - Prenatal exposures to BPA may pose threat to human ovarian function
February 15, 2019 - Suspicious spots on the lungs of children with rhabdomyosarcoma do not behave like metastases
February 15, 2019 - Diet drinks daily could raise stroke risk says study
February 15, 2019 - Many Systematic Reviews Do Not Fully Report Adverse Events
February 15, 2019 - Seven tips to protect your child from burns
February 15, 2019 - Keynote speakers announced for CBD Expo MIDWEST
Machine learning IDs markers to help predict Alzheimer’s

Machine learning IDs markers to help predict Alzheimer’s

image_pdfDownload PDFimage_print
Neurologists use structural and diffusion magnetic resonance imaging (MRI) to identify changes in brain tissue (both gray and white matter) that are characteristic of Alzheimer’s disease and other forms of dementia. The MRI images are analyzed using morphometry and tractography techniques, which detect changes in the shape and dimensions of the brain and in the tissue microstructure, respectively. In this example, the images show the normal brain of an elderly patient. Credit: US Department of Energy

Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. These irreversible brain disorders slowly cause memory loss and destroy thinking skills, eventually to such an extent that self-care becomes very difficult or impossible.

While no cure currently exists, certain medications can delay the progression of symptoms for several years, extending the quality of life for patients. However, in order for these medications to be effective, the disease must be diagnosed at an early stage, before the symptoms of cognitive decline become apparent. Current research suggests that the brain damage associated with Alzheimer’s likely starts a decade or more prior to symptom onset. Reliable screening tools for predicting individuals at risk of developing Alzheimer’s disease are urgently needed.

Recently, a team from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Columbia University Medical Center, Stony Brook University, and Ilsan Hospital in South Korea has shown that a combination of two different modes of magnetic resonance imaging (MRI), computer-based image analysis, and image classification using machine learning models may be a promising approach to accurately predicting Alzheimer’s risk.

“Such multimodal imaging analysis can enhance predictive power by identifying key diagnostic markers of the disease,” said team member Shinjae Yoo, a computational scientist in Brookhaven Lab’s Computational Science Initiative.

Brain images reveal changes

Previous MRI studies in which scientists analyzed the shape and dimensions (morphometry) of the brain have revealed that Alzheimer’s disease involves characteristic changes in the brain’s anatomy. For example, thinning of the cortex and shrinking of the hippocampus—both brain structures that play an important role in memory—are diagnostic markers of Alzheimer’s. Although structural MRI is routinely used as part of the clinical assessment when someone is suspected of having the disease, it lacks the level of accuracy and generalizability across patient populations to stand by itself as a reliable predictive tool.

Within the past decade, scientists have started investigating whether a different mode of MRI—called diffusion MRI—could provide additional information for physicians to improve predictive power. Diffusion MRI captures how water molecules move around in biological tissues, and mapping this diffusion process can reveal subtle changes in tissue microstructure.

Using diffusion MRI, scientists have found abnormalities in the white matter—a type of brain tissue—in patients with Alzheimer’s disease. White matter, which lies beneath the cortex, consists of millions of bundled nerve fibers (axons) that connect neurons in different gray matter regions of the brain—a structured network that runs like tracts of communication cables across the brain. The white color comes from the fatty layer of electrical insulation (myelin) that coats the axons, allowing them to more quickly send nerve impulses across the brain. Gray matter has relatively few myelinated axons, so it takes on the color of the nerve cell bodies it is composed of.

A new research direction

The majority of Alzheimer’s research to date has focused on gray matter degeneration, but because of recent advances in computational modeling tools, scientists are turning their attention toward white matter. For example, improvements in algorithms for tractography—a computational method to reconstruct white matter tracts from biophysical models of nerve fiber orientations—are enabling more accurate estimations of the microstructure of white matter.

Preliminary research suggests that the integrity of white matter declines in those at risk for Alzheimer’s disease. On MRI scans, the degeneration appears as bright white spots called hyperintensities. However, scientists are unsure to what extent the white matter “structural connectome”—the brain’s wiring system, or the unique pattern of connections between the billions of neurons in the brain—carries additional information for Alzheimer’s risk beyond what is shown by morphometry analysis based on structural MRI.

Prediction powered by machine learning

Yoo and his team set out to address this question, using structural and diffusion MRI images that were collected from a group of more than 200 elderly patients who visited a dementia clinic at Ilsan Hospital. Neurologists had already diagnosed these patients with Alzheimer’s disease, mild cognitive impairment (the stage between dementia and the expected cognitive decline from normal aging), or subjective cognitive decline (thought to be the earliest sign of dementia, in which patients report a decline in memory or other cognitive functions but perform normally on standard screening tests).

To process and analyze the raw images, the team members designed a rigorous pipeline consisting of several existing algorithms. Next, they applied machine learning to train image-derived classification models on the brain “phenotypes” resulting from their analysis—estimations of brain shapes and volumes (morphometric data) and of white matter structural connectivity (tractography data) in patients from each diagnostic category. They then used the models to make predictions of diagnosis.

“In one study using data from a dementia clinic, we achieved up to 98 percent accuracy in detecting Alzheimer’s disease and 84 percent accuracy in predicting mild cognitive impairment, the precursor to Alzheimer’s,” said team member Jiook Cha, a research scientist and assistant professor of neurobiology in the Department of Psychiatry at Columbia University Medical Center. “The accuracy of our machine learning models trained on brain connectome estimates surpassed that of existing imaging-based markers used in clinical settings (e.g., white matter hyperintensities) by 10 and 29 percent, respectively. Using independent data from the Alzheimer’s Disease Neuroimaging Initiative, we replicated these results.”

By comparing the performance of their different machine learning models, the team members determined that the structural connectome may be a clinically useful imaging marker for Alzheimer’s disease.

“The model trained on both morphometric and connectome data more accurately classified Alzheimer’s disease and mild cognitive impairment than the model trained on morphometric data alone,” explained Yoo. “In addition, the connectome model classified mild cognitive impairment and subjective cognitive decline as accurately as the combined model—unlike the morphometry model, which did not classify accurately.”

These results suggest that diffusion MRI could be a valuable tool in the early detection of Alzheimer’s disease. Neuroscientists believe that mild cognitive impairment and subjective cognitive decline are precursors to Alzheimer’s, so abnormal changes in white matter that are detected in these preclinical stages could indicate patients who are at an increased risk of eventually developing Alzheimer’s. The ability to identify such microscopic changes years before more severe macroscopic changes set in could lead to better treatments and possibly even a cure.

“This study strongly indicates the feasibility of using multimodal MRI—particularly diffusion MRI–based analysis of the structural connectome—to accurately predict Alzheimer’s risk,” said Cha.

Follow-on studies based on retrospective patient data will further assess whether this approach could be implemented in clinical settings.

Explore further:
Imaging shows brain connection breakdown in early Alzheimer’s disease

More information:
Yun Wang et al. Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes, biorxiv (2018). DOI: 10.1101/255141 ,

Provided by:
US Department of Energy

Tagged with:

About author

Related Articles