The hidden dangers of repeated head trauma

Earlier this year, Aaron Hernandez, an American football player, killed himself in his cell after being tried for a double murder. He was a rising football star when his legal issues first became public and many lamented how this put an end to what could have been an extraordinary career. Hernandez’s family decided to donate his brain to the CTE research center in Boston, with the intention to discover whether the athlete was affected by this neurological condition.

Although I have heard about Chronic traumatic encephalopathy before, it wasn’t until I read the news on Aaron Hernandez that I decided to delve into it. CTE is barely mentioned outside of the United States and I think that European sports authorities should be aware of the problem of repeated head trauma in players.


The consequences of repeated brain trauma amongst boxers are well documented. Dementia pugilistica -a subtype of CTE- has been studied since the late 1920s. Repeated head injuries in boxers can cause a pattern of neurodegeneration that produces dementia-like symptoms; cognitive and motor impairment, as well as mood and personality changes, which range in severity.

For many years, it was thought to only affect boxers, leading some medical professionals to ask for a ban on the sport. It is a recent discovery that repeated mild traumatic brain injuries increase the risk of neurodegenerative disease in athletes who play contact sports.

Although most research has been carried out in the context of American football, it is important to note that there are cases of CTE in many other sports; hockey, rugby, baseball, martial arts, soccer and motocross (BMX), as well as in people with military service.

Chronic traumatic encephalopathy usually manifests itself after about a decade of repeated injuries. Considering that many athletes begin their sports practice early on (ages 12 to 18), first-stage symptoms can be observed in young players in their early twenties [1]. Nevertheless, problems usually begin later in life.


CTE shouldn’t be confused with a post-concussion syndrome where symptoms are present shortly after a concussion but eventually go away. CTE consists in a progressive degeneration of the cortex and neuronal loss that, once it has been triggered, won’t stop in the individual’s lifetime. It has many similarities with Alzheimer’s and Parkinson’s disease, but in the case of CTE, we do know it has a clear environmental cause.

The diagnosis can only be confirmed post-mortem, once brain material is available to be examined. The research center I mentioned at the beginning of the article has examined the brain tissue of 94 former NFL players that presented symptoms and has concluded that 90 of them suffered from CTE. [2] Some of them had committed suicide at an early age, just like Aaron Hernandez did.

So, what can be done about this?

Since CTE is caused by recurrent concussions, the only way to prevent it, it’s by reducing the amount of exposure to head trauma. Unfortunately, that’s not an easy task. Sports-specific helmets seem to reduce injury in some sports. It is important to pay attention to the design and materials of the helmet as well as to make sure that it is replaced once it has taken a big hit.

Another very important factor is the “Return-to-play” criteria after a head injury. Studies suggest that a head injury should require at least 4 to 6 weeks of recovery time to avoid re-injury. Sadly, in some cases, these guidelines are not strictly enforced.

Lastly, athletes would benefit from some sports rules changes as well as from severe penalization for intentional hits to the head.


[1] Chronic Traumatic Encephalopathy in Athletes: Progressive Tauopathy following Repetitive Head Injury

[2] CTE Research Center Case Studies


Psychology and Machine Learning

Machine learning is quite the buzzword of 2017, as a subfield of artificial intelligence (another overused term), it has been around for decades. I remember being introduced to it by an enthusiastic professor in 2008 that told us how it was revolutionizing image analysis and the study of visual perception.

The boom of machine learning, which started when we gained the ability to work with big volumes of data, has been fueled by its versatility. Countless fields of work; financial services, fraud detection, logistics, medical diagnosis, natural language processing, marketing, and sales, are already benefiting from analyzing data through machine learning techniques. We are now starting to see the first applications of Machine Learning to Psychology problems.

Suicide prevention and machine learning

Earlier this year, a group of researchers from Florida State and Vanderbilt universities presented a study wherein a prediction model was developed to accurately identify the risk of attempting suicide in general and psychiatric patient population. [1]

The model can predict the risk of a suicide attempt with an accuracy of 80 percent (two years prior) and 84 percent (one week prior). In the general patient population, the accuracy is slightly higher. The false negatives are also lower than usual (from 1.2 to 3.5 percent). It is important to note that little progress had been made in the study of suicide prediction after decades of research.

The model

Since the study has not yet been published, I will quote an article written by Paul Govern on the Vanderbilt website [2] detailing the development of the model:

“[Researchers] started with de-identified records of adult patients seen at Vanderbilt from 1998 to 2005. They found 5,167 patients with billing codes indicating self-injury. A pair of clinical experts undertook separate reviews of this set, finding 3,250 cases, that is, 3,250 patients with a history of attempted suicide, and 1,917 controls, or patients with a history of nonsuicidal self-injury.

The de-identified records were pared down to demographics, diagnoses (…), socioeconomic status (…), health care utilization and medication information. To find predictors within these data, a machine learning technique called “random decision forests” shuffled this set of records repeatedly, each time building a “decision tree” upon comparing the shuffled set to the expert-ordered set’s strict segregation of cases and controls.

After thousands of shuffles, the algorithm became expert at predicting whether a randomly selected record from the training set was a case or a control. Finally, with a method called bootstrapping, the team used their training set to synthesize new data sets with which to measure the performance of their predictive models.

The second round of testing was set in the general patient population, using an additional 13,000 de-identified records as controls.”

As always, these results need to be taken carefully. The model identifies a combination of factors in the electronic records that could most accurately predict a future suicide attempt. Researchers are now waiting to see how useful the model is, once it is put to the test. The idea is to use it similarly as physicians use a cardiovascular risk score.


[1] Walsh, C.G., Ribeiro, J.D., & Franklin, J.C. (in press). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science.

[2] Investigators use machine learning to predict suicide risk by Paul Govern



The benefits of being bilingual

If you are multilingual and make use of more than one language regularly, you are in luck! There is a growing body of evidence that confirms the benefits of being bilingual; chiefly the positive effect it seems to have on cognitive performance. In recent times, we are also seeing more evidence [1][2] about the protective effects bilingualism seems to have against dementia and cognitive aging. On average, bilinguals seem to develop dementia five years later than monolinguals.

Bilingual people are better at carrying out tasks that require focusing on one piece of information without being distracted by others. Years of managing interference between two languages have made the bilingual brain an expert at executive control.


A recent research [3] of brain activity directed by Dr. Ana Inés Ansaldo gives us more insight into how this happens. The study showed how monolinguals need to involve and connect five different brain areas to solve the same task that bilinguals can do with fewer and more clustered regions. The bilingual brain is more efficient whereas monolinguals seem to consume more neurofunctional resources. This could be making them more vulnerable to cognitive aging.

In a review of nine recent studies on this topic [4], Amy L Atkinson indicates that the protective effect against dementia doesn’t seem to occur when the second language was learned later in life or when it is not used frequently enough. However, different studies use different definitions of bilingualism, which makes it hard to draw a conclusion about what type of bilinguals benefit from these protective effects against cognitive decline.

I would also like to note, that most studies that I have talked about so far, make use of visual tasks to assess bilingual and monolingual cognitive performance. A different study measuring auditory attention [5] showed that the effects of bilingualism in the auditory domain are not confined to childhood bilinguals, extending to early and late bilinguals.

It seems that both early and late acquisition of a second language is linked to enhanced cognitive performance as well as cognitive flexibility during multitasking. If you are monolingual, maybe this is the final push you needed to start learning a second language!

1 Bilingualism delays age at onset of dementia, independent of education and immigration status

2 Bilingualism delays clinical manifestation of Alzheimer’s disease

3 Interference control at the response level: Functional networks reveal higher efficiency in the bilingual brain

4 Does Bilingualism Delay the Development of Dementia? Metastudy

5 Never too late? An advantage on tests of auditory attention extends to late bilinguals

Why are dementia rates going down?

A recently published, observational study [1] led by Dr. Kenneth Langa shows that the prevalence of dementia in the US has declined significantly from 2000 to 2012.

Making use of the Health and Retirement Study (HRS), a source of nationally representative, longitudinal surveyed data, they compared the dementia rates of the 2000 (n = 10 546) and the 2012 (n = 10 511) waves. Their findings show that dementia prevalence declined from 11.6% in 2000 to 8.8% in 2012.

Another study published in 2016 [2] of participants in the Framingham Heart Study showed that the incidence of dementia in the last three decades had been declining.

Although an increase in total years of education was associated with a lower risk of developing dementia in the first study, many other social and medical factors associated with the onset of the disease are still uncertain.

At this point, we can only speculate about the role that physical activity, leisure time and intellectual stimulation play in these results. What I find very interesting is that this decrease in dementia rates have occurred while the prevalence of hypertension and obesity increases. I would like to see more research done about whether these conditions are protective factors in themselves or the medication prescribed for these conditions -cholesterol-lowering drugs (statins) and antihypertensive drugs- are lowering the rates of dementia in the aging population as some studies have started to suggest. [3] [4]


[1] A Comparison of the Prevalence of Dementia in the United States in 2000 and 2012
Kenneth M. Langa, MD, PhD; Eric B. Larson, MD; Eileen M. Crimmins, PhD; et al

[2] Incidence of Dementia over Three Decades in the Framingham Heart Study
Claudia L. Satizabal, Ph.D., Alexa S. Beiser, Ph.D., Vincent Chouraki, M.D., Ph.D., Geneviève Chêne, M.D., Ph.D., Carole Dufouil, Ph.D., and Sudha Seshadri, M.D.

[3] The age-dependent relation of blood pressure to cognitive function and dementia.

[4] Do Statins Reduce Risk of Incident Dementia and Alzheimer Disease?

(This post was originally published on on January 7th, 2017)