Following the people and events that make up the research community at Duke

Category: Neuroscience Page 1 of 12

Brain Structure May Not Influence Personality After All

New study casts doubt on links between personality and brain structure. MRI scan courtesy of Annchen Knodt, Duke University

We know personality comes from the brain, but does that mean the brain’s shape and composition affect personality as well?

Previous studies have attempted to find links between brain structure and personality types, but new data indicates otherwise. A new study, the largest of its kind, suggests these links may not be so strong after all. In fact, they may not even exist.

Recently Duke researchers, led by Reut Avinun Ph.D., a postdoctoral associate at Professor Ahmad Hariri’s lab, analyzed the MRI scans of over a thousand people to determine potential links between personality and brain shape.

Although there are many personality neuroscience studies, consistent and reliable findings have not been established. While most previous studies used less than 300 individuals, this study has a large sample of 1,107 individuals. Additionally, this research comprehensively measures personality with 240 items.

“When I got into the field, people were collecting data sets with only 10 people and doing analysis with only 20 participants,” said Avram Holmes, an asssociate professor of psychology at Yale who was not involved in the study.

Personality studies such as this typically use the “Big Five” personality traits: neuroticism, extraversion, agreeableness, conscientiousness, and openness-to-experience. Extraverted people tend to be outgoing and social and those with high openness-to-experience are imaginative, curious, and enjoy trying new things. High neuroticism and low conscientiousness have been associated with negative health behaviors such as smoking. These were even connected to negative life outcomes, such as depression, anxiety, and poor sleep. By understanding what underlies these behaviors, scientists may be able to better treat them.

For brain shape, Avinun and her colleagues examined brain morphometry, cortical thickness, cortical surface area, subcortical volume, and white matter microstructural integrity. She used a univariate approach, looking at the relationship between one phenotype and one behavior. Statistical analysis also accounted for the factors of race/ethnicity, sex, and age.

Last year, researchers published a paper finding 15 correlations between specific personality traits and neuroanatomical structures. However, Avinun’s new research found that none of these connections held true in the large Duke Neurogenetics Study sample.

When scientists analyze an MRI dataset, there is a lot of freedom in the phenotypes collected and the types of analyses. “With so many degrees of investigative freedom and the expectation that you should see something there, researchers may accidentally find false positives. It’s easy to fall into the trap of making a story about why the effect has this particular brain pattern and see an association that doesn’t exist,” Holmes explained.

Ultimately, Avinun found no links between the Big Five personality traits and multiple features of brain structure.

While this may seem anticlimactic, even null findings are incredibly useful and could lead to recommendations to future research in this area. By showing that links between brain morphometry and personality tend to be small, this research may push the field toward studies with larger samples and guidelines for higher replication rates.

“The brain is plastic and it is affected every day by our experiences, so expecting to find straightforward associations between brain morphometry and personality traits may be too naïve,” Avinun said. “We are beginning to realize that large samples and multivariate methods  are needed in neuroscience. Trying to understand what makes us who we are is exciting. Research is really challenging as the field is constantly changing, but it is constantly improving as well.”

Niba Nirmal is a multimedia science communicator based in San Francisco, CA. She graduated in the Duke class of 2020, with a Master’s degree in Genetics. Find samples of her work at

Dealing With Lead for Life

Though lead has been widely eliminated from use in products due to proven health risks, the lifelong consequences of childhood lead exposure for children born in the era of lead use in gasoline are still unknown.

Aaron Reuben, fifth-year Ph.D. candidate in clinical psychology at Duke, spoke about the long-term implications of childhood lead exposure Friday, September 18th through the Nicholas School’s Environmental Health and Toxicology Seminar series. He conducts research as a member of the Moffitt and Caspi Lab, studying genes, environment, health, and behavior.

Aaron Reuben

Reuben started with a brief history of lead exposure. After the United States’ initial use of lead in gasoline in 1923, the practice became widespread with the U.S. Public Health Services approval for expansion. Five decades later, in the mid-1970s, the Environmental Protection Agency issued the first restrictions on lead use in gasoline products. Simultaneously, surveillance of population-level blood-lead levels indicated cause for concern. Though lead was phased of out of gas completely by 1995, the peak led exposures in the 70s were on average three to four times higher than current levels that demand clinical attention. Despite lead regulations, the impacts of exposure did not miraculously cease as well.

Lead use in gasoline quickly increased after its initial introduction.

The research Reuben covered in his talk centers on the Dunedin Study. This study of 1,037 people born between April 1972 and March 1973 in Dunedin, New Zealand is an ongoing longitudinal research project comprised of over 30 years of data. The cohort of participants provide a unique chance for research in which social and economic factors do not have to be detangled from findings as they represent the full range of socioeconomic statuses in their city.

Reuben’s first question was about the impact of lead exposure on psychiatric and personality differences in adulthood. Study members were asked about symptoms such as substance dependence, depression, fears and phobias, or mania. These reports were transformed into a continuous measure of general psychopathology, which indicated that children with high lead levels experienced more psychiatric problems across adulthood. Though the developmental differences were modest, the associations between lead and psychopathological issues are of a similar magnitude to other known risk factors like childhood maltreatment and family history of mental illness. Yet, unlike the latter two risk factors, Reuben said, “Lead exposure is not preordained – it’s modifiable.”

The research team also measured participant personality using the Big Five Inventory and found that individuals with high-blood level levels as children exhibited more difficult personality styles as adults. The biggest difference between groups with high and low childhood blood-lead level was the trait of conscientiousness, which has impacts on goal obtainment within one’s education and occupation, as well as overall satisfaction with relationships.

Findings from the Big Five Inventory of Dunedin participants.

The next question of the presentation centered on differences in adulthood cognitive ability. At midlife, defined as age 38 for this question, children with higher blood-lead levels had lower cognitive ability, experiencing a deficit of two IQ points per five microgram per deciliter increase of blood-lead level. Once again, though these findings were relatively modest, the loss of IQ points was accompanied by downward social mobility compared to participants’ parents. Further, when evaluations that took place at age 45 were included in the data, researchers saw even larger declines in IQ points between exposure-level groups, which Reuben predicts may even represent a trend of acceleration. He believes that as the study continues with the participants, they will find rapid decline around age 65, with higher levels of dementia symptoms among participants compared to same-aged peers.

The last question evaluated the structural integrity of the brain at midlife. The team found that children with higher lead exposure had lower gray-matter integrity, lower white-matter integrity, and older estimated brain age at age 45. Estimated brain age was predicted by an algorithm based on MRI scans, as brains look physically different as they age and gray- and white-matter integrity refers to the conditions of physical structures in the brain. These findings suggest that childhood led exposure may result in an overall lowered brain integrity at midlife, as well as accelerated brain aging.

Reuben’s take-away findings from his presentation.

Reuben’s work is important for understanding how childhood exposure to this neurotoxin has the ability to influence continued development, behavior, emotion, and life outcomes decades later. It is crucial to evaluate long-term ramifications of childhood lead exposure – a phenomena experience by hundreds of millions of people across the globe during the era of lead in gasoline who are likely unknowingly dealing with impacts now.

Post by Cydney Livingston

Predictive maps in the brain

How do we represent space in the brain? Neuroscientists have been working to understand this question since the mid-20th century, when researchers like EC Tolman started experimenting with rats in mazes. When placed in a maze with a food reward that the rats had been trained to retrieve, the rats consistently chose the shortest path to the reward, even if they hadn’t practiced that path before.

Sam Gershman is interested in how we encode information about our environments.

Over 50 years later, researchers like Sam Gershman, PhD, of Harvard’s Gershman Lab are still working to understand how our brains encode information about space.

Gershman’s research questions center around the concept of a cognitive map, which allows the brain to represent landmarks in space and the distance between them. He spoke at a Center for Cognitive Neuroscience colloquium at Duke on Feb. 7.

Maps are formed via reinforcement learning, which involves predicting and maximizing future reward. When an individual is faced with problems that have multiple steps, they can do this by relying on previously learned predictions about the future, a method called successor representation (SR), which would suggest that the maps we hold in our brain are predictive rather than retroactive.

One specific region implicated in representations of physical space is the hippocampus, with hippocampal place cell activity corresponding to positions in physical space. In one study, Gershman found, as rats move through space, that place field activity corresponding to physical location in space skews opposite of the direction of travel; in other words, activity reflects both where the rodent currently is and where it just was. This pattern suggests encoding of information that will be useful for future travel through the same terrain: in Gershman’s words, “As you repeatedly traverse the linear track, the locations behind you now become predictive of where you are going to be in the future.”

Activation patterns in place cells correspond to both where the animal is and where the animal just was, pointing to the construction of a predictive map during learning. Graphic courtesy of Stachenfield et al., 2017.

This idea that cognitive activity during learning reflects construction of a predictive map is further supported by studies where the rodents encounter novel barriers. After being trained to retrieve a reward from a particular location, introducing a barrier along this known path leads to increased place cell activity as they get closer to the barrier; the animal is updating its predictive map to account for the novel obstacle.

This model also explains a concept called context preexposure facilitation effect, seen when animals are introduced to a new environment and subsequently exposed to a mild electrical shock. Animals who spend more time in the new environment before receiving the shock show a stronger fear response upon subsequent exposures to the box than those that receive a shock immediately in the new environment. Gershman attributes this observation to the time it takes the animal to construct its predictive map of the new environment; if the animal is shocked before it can construct its predictive map, it may be less able to generalize the fear response to the new environment.

With this understanding of cognitive maps, Gershman presents a compelling and far-reaching model to explain how we encode information about our environments to aid us in future tasks and decision making.

Brain networks change with age

Graph theory allows researchers to model the structural and functional connection between regions of the brain. Image courtesy of Shu-Hsien Chu et al.

As we age, our bodies change, and these changes extend into our brains and cognition. Although research has identified many changes to the brain with age, like decreases in gray matter volume or delayed recall from memory, researchers like Shivangi Jain, PhD, are interested in a deeper look at how the brain changes with age.

Shivangi Jain uses graph theory to study how the brain changes with age.

As a post-doctoral associate in the David Madden Lab at Duke, Jain is interested in how structural and functional connectivity in the brain change with age. Jain relies on the increasingly popular method of graph theory, which is a way of modeling the brain as a set of nodes or brain regions that are interconnected. Studying the brain in this way allows researchers to make connections between the physical layout of the brain and how these regions interact when they are active. Structural connectivity represents actual anatomical connections between regions in the brain, while functional connectivity refers to correlated activity between brain regions.

Jain’s studies use a series of tasks that test speed, executive function, and memory, each of which decline with age. Using fMRI data, Jain observed a decline in functional connectivity, where functional modules become less segregated with age.  In terms of structural connectivity, aging was associated with a decline in the strength of white matter connections and global efficiency, which represents the length between modules with shorter paths being more efficient. Thus, the aging brain shows changes at the anatomical, activational, and behavioral levels.

Jain then examined how these network-level changes played a role in the observed behavioral changes. Using statistical modeling, she found that the decline in performance in tasks for executive control could be explained by the observed changes in functional connectivity. Furthermore, Jain found that the changes in structural connectivity caused the change in functional connectivity. Taken together, these results indicate that the physical connections between areas in the brain deteriorate with age, which in turn causes a decrease in functional connectedness and a decline in cognitive ability.

Research like Jain’s can help explain the complicated relationships between brain structure and function, and how these relationships affect behavioral output.

Post by undergraduate blogger Sarah Haurin
Post by Sarah Haurin

Visual Perception in Congenitally Blind Adults

Vision provides a rich source of information that most people’s lives revolve around. Yet, for blind people, how do they conceive of visual intake and what happens to regions of the brain dedicated to vision if a person doesn’t have typical visual input? These are questions that drive Marina Bedny PhD, an Assistant Professor of Psychological and Brain Sciences and principal investigator of a neuroplasticity and development lab at John Hopkins University.

Bedny spoke at Duke’s Institute for Brain Sciences on Friday, January 17th, about her work with congenitally blind adults. Her lab explores similarities and distinctions of visual perceptions between blind and seeing people and seeks to understand how nuanced, natural variation in experience shapes the human mind and brain.

Many of the studies Bedny discussed have very important linguistic components. In one trial, she investigated the meaning of verbs pertaining to light events and visual perception as compared to touch, amodal, auditory, and motion verbs.

Both blind and sighted people displayed nearly identical results when comparing the different types of verbs used in the study. This showed that there were no differences in what blind people knew about the terms. Analysis of the verbs revealed that linguistic dimensions of intensity and instability were used to evaluate the words’ comparative meanings. Blind people agreed more on the comparison of sound emission and touch perception words. This shows that blind participants have more aligned comprehension of the meanings of other sensory terms compared to sighted people.

In other cases, Bedny’s lab assessed what blind individuals know about color. One study used three object types – natural kinds, functional artifacts, and non-functional artifacts. These categories were used to evaluate agreeance not only on color, but the relevancy of color to certain objects’ functions as well.

Another crucial question of Bedny’s work looks at how the innate structure of the brain constrains cortical function. The findings show that the visual system in blind participants has been repurposed for higher cognitive functions and that portions of the visual system connected to high cognitive abilities are invaded by the visual systems. Along with repurposing visual regions for linguistic use, Bedny’s lab found that visual regions of the brain are active during numerical processing tasks too.

Blind people display additional activity in the visual centers of their brain in numerous studies beyond having the same regional brain responsiveness as sighted people. Though further research is necessary, Bedny proposes that there is a sensitive period during development that is critical to the specialization of the brain. Study participants who have adult-onset blindness do not show the same sensitivity and patterned responses in visual cortices repurposed for different functions as congenitally blind subjects.

At birth, the human cortex is pluripotent – providing the best of both worlds, Bedny said. The brain is prepared but highly flexible. Her studies have repeatedly shown that the brain is built for and transformed by language, and they underscore the importance of nature and nurture in human development.

Post by Cydney Livingston

Inventing New Ways to Do Brain Surgery

This is the sixth and final 2019 post written by students at the North Carolina School of Science and Math as part of an elective about science communication with Dean Amy Sheck.

Dr. Patrick Codd is the Director of the Duke Brain Tool Laboratory and an Assistant Professor of Neurosurgery at Duke. Working as a neurosurgeon and helping with the research and development of various neurosurgical devices is “a delicate balance,” he said.

Patrick Codd

Codd currently runs a minimally invasive neurosurgery group. However, at Massachusetts General Hospital, he used to run the trauma section. When asked about which role was more stressful, he stated “they were both pretty stressful” but for different reasons. At Mass General, he was on call for most hours of the day and had to pull long shifts in the operating room. At Duke, he has to juggle surgery, teaching, and research and the development of new technology.

“I didn’t know I was going to be a neurosurgeon until I was in college,” Codd said. Despite all of the interesting specialties he learned about in medical school, he said “it was always neurosurgery that brought me back.”

Currently, he is exclusively conducting cranial surgery.

 Neurosurgeon U.S. Air Force Maj Jonathan Forbes,looks through loupes as he performs brain surgery at the Bagram Air Field in Afghanistan, Oct. 10, 2014. 

Though Dr. Codd has earned many leadership positions in his career, he said he was never focused on advancement. He simply enjoys working on topics which he loves, such as improving minimally invasive surgical techniques. But being in leadership lets him unite other people who are interested in working towards a common goal in research and development. He has been able to skillfully bring people together from various specialties and help guide them. However, it is difficult to meet everyone’s needs all of the time. What is important for him is to be a leader when he needs to be.

Dr. Codd said there are typically five to eight research papers necessary in to lay the groundwork for every device that is developed. However, some technologies are based on the development of a single paper. He has worked on devices that make surgery more efficient and less minimally invasive and those that help the surgical team work together better. When developing technologies, he tries to keep the original purpose of the devices the same. However, many revisions are made to the initial design plans as requirements from the FDA and other institutions must be met. Ironically, Dr. Codd can’t use the devices he develops in his own operating room because it would be a conflict of interest. Typically other neurosurgeons from across the country will use them instead.

Post by Andrew Bahhouth, NCSSM 2020

Does aging make our brains less efficient?

We are an aging population. Demographic projections predict the largest population growth will be in the oldest age group – one study predicted a doubling of people age 65 and over between 2012 and 2050. Understanding aging and prolonging healthy years is thus becoming increasingly important.

Michele Diaz and her team explore the effects of aging on cognition.

For Michele Diaz, PhD, of Pennsylvania State University, understanding aging is most important in the context of cognition. She’s a former Duke faculty member who visited campus recently to update us on her work.

Diaz said the relationship between aging and how we think is much more nuanced than the usual stereotype of a steady cognitive decline with age.

Research has found that change in cognition with age cannot be explained as a simple decline: while older people tend to decline with fluid intelligence, or information processing, they maintain crystallized intelligence, or knowledge.

Diaz’s work explores the relationship between aging and language. Aging in the context of language shows an interesting phenomenon: older people have more diverse vocabularies, but may take longer to produce these words. In other words, as people age, they continue to learn more words but have a more difficult time retrieving them, leading to a more frequent tip-of-the-tongue experience.

In order to understand the brain activation patterns associated with such changes, Diaz conducted a study where participants of varying ages were asked to name objects depicted in images while undergoing fMRI scanning. As expected, both groups showed less accuracy in naming of less common objects, and the older adult group showed a slightly lower naming accuracy than the younger.

Additionally, Diaz found that the approach older adults take to solving more difficult tasks may be different from younger adults: in younger adults, less common objects elicited an increase in activation, while older adults showed less activation for these more difficult tasks.

Additionally, an increase in activation was associated with a decrease in accuracy. Taken together, these results show that younger and older adults rely on different regions of the brain when presented with difficult tasks, and that the approach younger adults take is more efficient.

In another study, Diaz and her team explored picture recognition of objects of varying semantic and phonological neighborhood density. Rather than manipulation of how common the objects presented in the images are, this approach looks at networks of words based on whether they sound similar or have similar meanings. Words that have denser networks, or more similar sounding or meaning words, should be easier to recognize.

An example of a dense (left) and sparse (right) phonological neighborhood. Words with a greater number of similar sounding or meaning words should be more easily recognized. Image courtesy of Vitevitch, Ercal, and Adagarla, Frontiers in Psychology, 2011.

With this framework, Diaz found no age effect on recognition ability for differences in semantic or phonological neighborhood density. These results suggest that adults may experience stability in their ability to process phonological and semantic characteristics as they age.

Teasing out these patterns of decline and stability in cognitive function is just one part of understanding aging. Research like Diaz’s will only prove to be more important to improve care of such a growing demographic group as our population ages.

Post by undergraduate blogger Sarah Haurin

Post by undergraduate blogger Sarah Haurin

Predicting sleep quality with the brain

Modeling functional connectivity allows researchers to compare brain activation to behavioral outcomes. Image: Chu, Parhi, & Lenglet, Nature, 2018.

For undergraduates, sleep can be as elusive as it is important. For undergraduate researcher Katie Freedy, Trinity ’20, understanding sleep is even more important because she works in Ahmad Hariri’s Lab of Neurogenetics.

After taking a psychopharmacology class while studying abroad in Copenhagen, Freedy became interested in the default mode network, a brain network implicated in autobiographical thought, self-representation and depression. Upon returning to her lab at Duke, Freedy wanted to explore the interaction between brain regions like the default mode network with sleep and depression.

Freedy’s project uses data from the Duke Neurogenetics Study, a study that collected data on brain scans, anxiety, depression, and sleep in 1,300 Duke undergraduates. While previous research has found connections between brain connectivity, sleep, and depression, Freedy was interested in a novel approach.

Connectome predictive modeling (CPM) is a statistical technique that uses fMRI data to create models for connections within the brain. In the case of Freedy’s project, the model takes in data on resting state and task-based scans to model intrinsic functional connectivity. Functional connectivity is mapped as a relationship between the activation of two different parts of the brain during a specific task. By looking at both resting state and task-based scans, Freedy’s models can create a broader picture of connectivity.

To build the best model, a procedure is repeated for each subject where a single subject’s data is left out of the model. Once the model is constructed, its validity is tested by taking the brain scan data of the left-out subject and assessing how well the model predicts that subject’s other data. Repeating this for every subject trains the model to make the most generally applicable but accurate predictions of behavioral data based on brain connectivity.

Freedy presented the preliminary results from her model this past summer at the BioCORE Symposium as a Summer Neuroscience Program fellow. The preliminary results showed that patterns of brain connectivity were able to predict overall sleep quality. With additional analyses, Freedy is eager to explore which specific patterns of connectivity can predict sleep quality, and how this is mediated by depression.

Freedy presented the preliminary results of her project at Duke’s BioCORE Symposium.

Understanding the links between brain connectivity, sleep, and depression is of specific importance to the often sleep-deprived undergraduates.

“Using data from Duke students makes it directly related to our lives and important to those around me,” Freedy says. “With the field of neuroscience, there is so much we still don’t know, so any effort in neuroscience to directly tease out what is happening is important.”

Post by undergraduate blogger Sarah Haurin
Post by undergraduate blogger Sarah Haurin

Beyond Classroom Walls: Research as an Undergrad

“Science is slow,” says Duke undergraduate Jaan Nandwani. That’s one of the takeaways from her first experience with scientific research. For Nandwani, being part of a supportive lab makes it all worthwhile. But we’re getting ahead of ourselves. This statement needs context.

Nandwani, a prehealth sophomore, currently conducts research in the lab of neurologist Nicole Calakos, MD, PhD. The Calakos lab is focused on synaptic plasticity: changes that occur at the communication junctions between nerve cells in the brain. The lab researches how the brain responds to changes in experience. They also investigate the mechanistic mishaps that can occur with certain neurological conditions.

A neuron from a mouse brain. From Wikimedia Commons.

As a continuation of an 8-week summer research program she participated in earlier this year, Nandwani has been studying dystonia, a brain disorder that causes uncontrollable muscle contractions. She’s using western blot analysis to determine if the activity of a protein called eIF2α is dysregulated in the brain tissue of mice with dystonia-like symptoms, compared with their normal littermates. It is currently unclear if and when targeting the eIF2 signaling pathway can improve dystonia, as well as where in the brain “selective vulnerability” to the signaling occurs. If Nandwani is able to identify a specific region or time point “in which the pathway’s dysregulation is most predominant,” more effective drug therapy and pharmacological interventions can be used to treat the disorder. 

Outside of her particular project, Nandwani attends lab meetings, learning from and contributing to the greater Calakos lab community. Scientific work is highly collaborative and Nandwani’s experience is testament to that. Along with providing feedback to her own presentations in meetings and answering any questions she may have, Nandwani’s fellow labmates are always eager to discuss their projects with her, give her advice on her own work, and have helped her “develop a passion for what [she is] studying.” They’ve also helped her learn new and improved ways to conduct the western blot process that is so integral to her work. Though she admits it is tedious, Nandwani said that she enjoys being able to implement better techniques each time she conducts the procedure. She also says she is thankful to be surrounded by such a supportive lab environment.

It might seem hard to believe granted the scope and potential impacts of her work, but this is Nandwani’s first experience with research in a lab. She knew when coming to Duke that she wanted to get involved with research, but she says that her experience has surpassed any expectations she had – by far. Though she doesn’t necessarily foresee continuation of research in the form of a career and is more fascinated by clinical applications of scientific research, the experience cannot be replicated within a classroom setting. Beyond the technical skills that Nandwani has developed, she says that the important and valuable mentoring relationships she has gained simply couldn’t be obtained otherwise.

Duke undergraduate Jaan Nandwani doing research in the Calakos lab.

Nandwani hopes to continue in the Calakos lab for the remainder of her time at Duke – that’s two and a half more years. Though she will work on different projects, the quest to pose and answer scientific questions is endless – and as Nandwani said, science is slow. The scientific process of research takes dedication, curiosity, collaboration, failure, and a continued urge to grow. The scientific process of research takes time, and lots of it. Of course the results are “super exciting,” Nandwani says, but it is the experience of being part of such an amazing group of scholars and scientists that she values the most.

By Cydney Livingston

Researchers Urge a Broader Look at Alzheimer’s Causes

Just about every day, there’s a new headline about this or that factor possibly contributing to Alzheimer’s Disease. Is it genetics, lifestyle, diet, chemical exposures, something else?

The sophisticated answer is that it’s probably ALL of those things working together in a very complicated formula, says Alexander Kulminski, an associate research professor in the Social Science Research Institute. And it’s time to study it that way, he and his colleague, Caleb Finch at the Andrus Gerontology Center at the University of Southern California, argue in a recent paper that appears in the journal Alzheimer’s and Dementia, published by the Alzheimer’s Association.

Positron Emission Tomography scan of a brain affected by cognitive declines . (NIH)

“Life is not simple,” Kulminski says. “We need to combine different factors.”

“We propose the ‘AD Exposome’ to address major gaps in understanding environmental contributions to the genetic and non-genetic risk of AD and related dementias,” they write in their paper. “A systems approach is needed to understand the multiple brain-body interactions during neurodegenerative aging.”

The analysis would focus on three domains, Kulminski says: macro-level external factors like rural v. urban, pollutant exposures, socio-economcs; individual external factors like diet and infections; and internal factors like individual microbiomes, fat deposits, and hormones.

That’s a lot of data, often in disparate, broadly scattered studies. But Kulminski, who came to Duke as a physicist and mathematician, is confident modern statistics and computers could start to pull it together to make a more coherent picture. “Twenty years ago, we couldn’t share. Now the way forward is consortia,” Kulminski said.

The vision they outline in their paper would bring together longitudinal population data with genome-wide association studies, environment-wide association studies and anything else that would help the Alzheimer’s research community flesh out this picture. And then, ideally, the insights of such research would lead to ways to “prevent, rather than cure” the cognitive declines of the disease, Kulminsky says.  Which just happens to be the NIH’s goal for 2025.

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