It is well-known that education has many positive effects. People who spend more time in school are generally healthier, smarter, and have better jobs and higher incomes than those with less education. However, whether prolonged education actually causes changes in brain structure over the long term and protects against brain aging, was still unknown.

It is challenging to study this, because alongside education, many other factors influence brain structure, such as the conditions under which someone grows up, DNA traits, and environmental pollution. Nonetheless, researchers Rogier Kievit (PI of the Lifespan Cognitive Dynamics lab) and Nicholas Judd from Radboudumc and the Donders Institute found a unique opportunity to very precisely examine the effects of an extra year of education.

Aging

In 1972, a change in the law in the United Kingdom raised the number of mandatory school years from fifteen to sixteen, while all other circumstances remained constant. This created an interesting ‘natural experiment’, an event not under the control of researchers which divides people into an exposed and unexposed group. Data from approximately 30,000 people who attended school around that time, including MRI scans taken much later (46 years after), is available. This dataset is the world’s largest collection of brain imaging data.

The researchers examined the MRI scans for the structure of various brain regions, but they found no differences between those who attended school longer and those who did not. ‘This surprised us’, says Judd. ‘We know that education is beneficial, and we had expected education to provide protection against brain aging. Aging shows up in all of our MRI measures, for instance we see a decline in total volume, surface area, cortical thickness, and worse water diffusion in the brain. However, the extra year of education appears to have no effect here.’

Brain structure

It’s possible that the brain looked different immediately after the extra year of education, but that wasn’t measured. ‘Maybe education temporarily increases brain size, but it returns to normal later. After all, it has to fit in your head’, explains Kievit. ‘It could be like sports: if you train hard for a year at sixteen, you’ll see a positive effect on your muscles, but fifty years later, that effect is gone.’ It’s also possible that extra education only produces microscopic changes in the brain, which are not visible with MRI.

Both in this study and in other, smaller studies, links have been found between more education and brain benefits. For example, people who receive more education have stronger cognitive abilities, better health, and a higher likelihood of employment. However, this is not visible in brain structure via MRI. Kievit notes: ‘Our study shows that one should be cautious about assigning causation when only a correlation is observed. Although we also see correlations between education and the brain, we see no evidence of this in brain structure.’

The faulty predictions are linked to researchers’ use of AI to assist genome-wide association studies. Such studies scan through hundreds of thousands of genetic variations across many people to hunt for links between genes and physical traits. Of particular interest are possible connections between genetic variations and certain diseases.

Genetics’ link to disease not always straightforward

Genetics play a role in the development of many health conditions. While changes in some individual genes are directly connected to an increased risk for diseases like cystic fibrosis, the relationship between genetics and physical traits is often more complicated.

Genome-wide association studies have helped to untangle some of these complexities, often using large databases of individuals’ genetic profiles and health characteristics, such as the National Institutes of Health’s All of Us project and the UK Biobank. However, these databases are often missing data about health conditions that researchers are trying to study.

“Some characteristics are either very expensive or labor-intensive to measure, so you simply don’t have enough samples to make meaningful statistical conclusions about their association with genetics,” says Qiongshi Lu, an associate professor in the UW-Madison Department of Biostatistics and Medical Informatics and an expert on genome-wide association studies.

The risks of bridging data gaps with AI

Researchers are increasingly attempting to work around this problem by bridging data gaps with ever more sophisticated AI tools.

“It has become very popular in recent years to leverage advances in machine learning, so we now have these advanced machine-learning AI models that researchers use to predict complex traits and disease risks with even limited data,” Lu says.

Now, Lu and his colleagues have demonstrated the peril of relying on these models without also guarding against biases they may introduce. The team describe the problem in a paper recently published in the journal Nature Genetics. In it, Lu and his colleagues show that a common type of machine learning algorithm employed in genome-wide association studies can mistakenly link several genetic variations with an individual’s risk for developing Type 2 diabetes.

“The problem is if you trust the machine learning-predicted diabetes risk as the actual risk, you would think all those genetic variations are correlated with actual diabetes even though they aren’t,” says Lu.

These “false positives” are not limited to these specific variations and diabetes risk, Lu adds, but are a pervasive bias in AI-assisted studies.

New statistical method can reduce false positives

In addition to identifying the problem with overreliance on AI tools, Lu and his colleagues propose a statistical method that researchers can use to guarantee the reliability of their AI-assisted genome-wide association studies. The method helps removing bias that machine learning algorithms can introduce when they’re making inferences based on incomplete information.

“This new strategy is statistically optimal,” Lu says, noting that the team used it to better pinpoint genetic associations with individuals’ bone mineral density.

AI not the only problem with some genome-wide association studies

While the group’s proposed statistical method could help improve the accuracy of AI-assisted studies, Lu and his colleagues also recently identified problems with similar studies that fill data gaps with proxy information rather than algorithms.

In another recently published paper appearing in Nature Genetics, the researchers ring the alarm about studies that over-rely on proxy information in an attempt to establish connections between genetics and certain diseases.

For instance, large health databases like the UK Biobank have a ton of genetic information about large populations, but they don’t have very much data regarding the incidence of diseases that tend to crop up later in life, like most neurodegenerative diseases.

For Alzheimer’s disease specifically, some researchers have attempted to bridge that gap with proxy data gathered through family health history surveys, where individuals can report a parent’s Alzheimer’s diagnosis.

The UW-Madison team found that such proxy-information studies can produce “highly misleading genetic correlation” between Alzheimer’s risk and higher cognitive abilities.

“These days, genomic scientists routinely work with biobank datasets that have hundreds of thousands of individuals, however, as statistical power goes up, biases and the probability of errors are also amplified in these massive datasets,” says Lu. “Our group’s recent studies provide humbling examples and highlight the importance of statistical rigor in biobank-scale research studies.”

“Phosphorus removal structures have been developed to capture dissolved phosphorus from tile drainage systems, but current phosphorus sorption materials are either inefficient or they are industrial waste products that aren’t easy to dispose of. This motivated us to develop an eco-friendly and acceptable material to remove phosphorus from tile drainage systems,” said study author Hongxu Zhou, who completed the study as a doctoral student in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at U. of I.

Zhou and his co-authors used sawdust and lime sludge, byproducts from milling and drinking water treatment plants, respectively. They mixed the two ingredients, formed the mixture into pellets, and slow-burned them under low-oxygen conditions to create a “designer” biochar with significantly higher phosphorus-binding capacity compared to lime sludge or biochar alone. Importantly, once these pellets bind all the phosphorus they can hold, they can be spread onto fields where the captured nutrient is slowly released over time.

Leveraging designer biochar’s many sustainable properties, the team tested pellets in working field conditions for the first time, monitoring phosphorus removal in Fulton County, Illinois, fields for two years. Like the majority of Midwestern corn and soybean fields, the experimental fields were fitted with subsurface drainage pipes. This drainage water flowed through phosphorus removal structures filled with designer biochar pellets of two different sizes. The team tested 2-3 centimeter biochar pellets during the first year of the experiment, then replaced them with 1 cm pellets for the second year.

Both pellet sizes removed phosphorus, but the 1-centimeter pellets performed much better, reaching 38 to 41% phosphorus removal efficiency, compared with 1.3 to 12% efficiency for the larger pellets.

The result was not a surprise for study co-author Wei Zheng, who said smaller particle sizes allow more contact time for phosphorus to stick on designer biochar. Zheng, a principal research scientist at the Illinois Sustainable Technology Center (ISTC), part of the Prairie Research Institute at U. of I., has done previous laboratory studies showing a powdered form of designer biochar is highly efficient for phosphorus removal. But powdered materials wouldn’t work in the field.

“If we put powder-form biochar in the field, it would easily wash away,” Zhou said. “This is why we have to make pellets. We have to sacrifice some efficiency to ensure the system will work under field conditions.”

After showing the pellets are effective in real-world scenarios, the research team performed techno-economic and life-cycle analyses to evaluate the economic breakdown for farmers and the overall sustainability of the system.

The cost to produce designer biochar pellets was estimated at $413 per ton, less than half the market cost of alternatives such as granular activated carbon ($800-$2,500 per ton). The team also estimated the total cost of phosphorus removal using the system, arriving at an average cost of $359 per kilogram removed. This figure varied according to inflation and depending on the frequency of replacing pellets — two years appeared to be the most cost-effective scenario.

The life cycle analysis showed the system — including returning spent biochar pellets to crop fields and avoiding additional phosphorus and other inputs — could save 12 to 200 kilograms of carbon dioxide-equivalent per kilogram of phosphorus removed. Zhou says the benefits go beyond nutrient loss reduction and carbon sequestration to include energy production, reduction of eutrophication, and improving soils.

“At the moment, there’s no regulation that requires farmers to remove phosphorus from drainage water. But we know there are many conservation conscious farmers who want to reduce nitrate and phosphorus losses from their fields,” said co-author Rabin Bhattarai, associate professor in ABE. “If they’re already installing a woodchip bioreactor to remove nitrate, all they’d have to do is add the pellets to the control structure to remove the phosphorus at the same time. And there’s something very attractive about being able to reuse the pellets on the fields.”