Where is the state of play on technologies involved in diagnosing concussion?

Alan Pearce (La Trobe University and Australian Sports Brain Bank; Victoria, Australia) discusses various examples of technologies that are being used to measure and quantify the effects of concussion.

Go to the profile of Alan Pearce
Sep 25, 2019

Contact and combat sports carry inherent risks by virtue of their physicality. Impacts to the head are common, but not all hits to the head result in concussion. However, the growing evidence of the short- and long-term effects of these impacts is generating increased concern at all levels of sport. 

Current consensus is that technologies have a part to play in concussion research but the consensus is that diagnosis of concussion remains a clinical decision [1]. This reflects the complexities of concussion as a neurological injury: the disparity in symptoms, the evolving nature of the symptoms and individual recovery time following injury. Further, while we now recognise the risks associated with concussions, we are also learning about the long-term effects of repetitive head trauma in contact sports [2].

This article will present examples where technologies are placed in concussion research and discuss their strengths and limitations in clinical practice. The aim is to provide context on where currently technology is able to contribute towards our understanding of concussion and recovery following concussion, but also the limitations of technology in providing a diagnostic role which remains the responsibility of the medical practitioner [1].

Difficulties in concussion diagnosis

Concussion is a complex neurological injury [3]. Concussions represent a transient neurological disturbance involving a complicated neurometabolic timeline affecting the underlying neurophysiology and neurochemical processes [4,5]. While symptoms can resolve within minutes, concussion is usually defined as an ‘evolving injury’, where symptoms can continue to change and full recovery can take days or even weeks to occur. There is no longer a grading classification for severity of concussion due to inter-individual disparity in symptom presentation and recovery [6]. Similarly, there is agreement that there is no minimum threshold of impact that may or may not, result in concussion [1,3]. As a result, diagnosis following a thorough clinical evaluation from a medical practitioner remains ‘gold-standard’ [1]. Given the constant questioning from the wider community about the ‘subjectivity’ of diagnosis, particularly when made on the sidelines under intense pressure [3], questions remain if technologies can assist or replace a medical diagnosis.

Imaging technologies

It is acknowledged that standard clinical neuroimaging such MRI and CT scanning, has little efficacy in the diagnosis of sports concussion [1,7,8]. More advanced imaging such as functional MRI (fMRI) have demonstrated changes in resting activity within an interconnected group of brain structures including in the frontal and parietal regions, and deeper structures in the brain (cingulate and hippocampus)[9]. Research in this area is still ongoing to validate clinical utility in the evaluation of concussion [10], but fMRI has potential for concussion diagnosis in the acute setting and assist in return-to-play decision making.

Other advanced neuroimagining techniques, particularly in research investigating long-term outcomes, include positron emission tomography (PET) and diffusion tensor imaging (DTI). PET scan imaging of amyloid-β and tauopathies has been able to display progression of neurodegenerative diseases such as Alzheimer’s disease (AD) [11]. Research is now currently exploring if this technique can image tau deposition in chronic traumatic encephalopathy (CTE).  Currently, the work is still novel and limited, but recent studies by Bob Stern (Boston University; MA, USA) and colleagues who reported elevated tau in brain regions expected by CTE, shows promise [12]. 

DTI has been demonstrated to detect chronic white matter brain tissue changes following a history of repeated head trauma playing contact sports [13]. These studies have discovered that not only do athletes who sustain a concussion, but players who experience repetitive sub-concussive impacts (where there are no obvious signs or symptoms) also reveal changes in white matter tracts [14,15]. DTI is currently restricted to the research setting, thus limiting its accessibility and clinical use for athletes. 

Neurophysiological technologies

Brain physiology research is re-emerging as an area of interest with the development of new techniques. Conventional methods of measuring electrical activity of the brain such as the electroencephalogram (EEG) has been useful in evaluation in individuals with post-traumatic epilepsy but not useful as a clinical screening tool for those with non-seizure post-concussive symptoms [16]. However developments in EEG techniques, such as computerised quantitative EEG, allows for the identification of subtle variations in the patterns in the EEG data that may be clinically useful in diagnosing and monitoring recovery from concussion [16,17].

Transcranial magnetic stimulation (TMS) is another neurophysiological technique that demonstrates promise in the management of concussion. First developed in 1985 [18], TMS is an established, non-invasive technique that uses magnetic fields to stimulate nerve cells – called evoked potentials – that are quantified either by EEG or electromyography (measuring neuromuscular electrical activity) [19].

Whilst established across a number of areas within neurology and cognitive neuroscience, emerging research is demonstrating the efficacy of TMS to measure the neurophysiology of acute concussions, persistence post-concussion symptoms [20] and chronic effects in retired contact sport players with a history of concussions [21]. For example, studies in my laboratory have discovered that in Australian football athletes who had sustained a concussion, persistent changes in neurophysiology were still apparent and not yet returned to baseline by 10 days [22], even when clinical symptoms of concussion resolved and cognitive measures had returned to the individual’s baseline within 3–5 days. Conversely, TMS has demonstrated abnormal neurophysiology in retired athletes with a history of multiple concussions [23–25].

There is still debate on whether these persistent changes detected in highly sensitive instruments provide an objective measure of clinical significance. Particularly in the acute setting, it is still undetermined if neurophysiological measures reflect recovery that gives a clinician confidence in determining if an athlete is able to return to play. A recent systematic review concluded that while physiological dysfunction may outlast clinical measures of recovery, the disparate technologies and methodologies reflected the varied timelines of recovery [26]. These authors recommended based on current data, medical practitioners continue to use clinical data to manage diagnosis and prognosis for return-to-play decisions while keeping neurophysiological measures for research until more conclusive evidence is published [26].

Biomarker technologies 

With the recent US FDA approval of the first biomarker to screen for mild traumatic brain injury and concussion [27], there is increased interest in biomarkers as a potential diagnostic for concussion injury [28,29]. 

A number of protein biomarkers of injury to different cell types and structures within the brain can be detected in peripheral blood, but utility and detection have been limited. The recent FDA protein biomarkers for mild traumatic brain injury and concussion, the Banyon Brain Trauma Indicator [23], is useful to screen those with a concussion injury if an imaging scan is required, but is not a concussion marker per se. The research to date suggests that there are still no reliable biomarkers to monitor recovery following a concussion injury [26], but this is an emerging area.

An alternative to protein biomarkers in detecting concussion, are miRNAs that control gene expression. Studies on miRNAs in various neurological conditions including Alzheimer’s and Parkinson’s diseases [30], epilepsy [31] and stroke [30] suggests that it may potentially be useful in assessing recovery post-concussions. Whilst the use of a patient’s saliva to detect miRNA levels is possible, it suffers from reliability measures when compared to the test from blood samples. Similar to protein biomarkers, this is an emerging area of research, but a promising area nevertheless.

Surveillance technologies

Increased interest and concern in the past decade has seen the growth of concussion ‘surveillance’ systems. Various technologies have developed to capture biomechanical data with the aim to quantify head impacts experienced by players during training and play. Mostly confined to the research area, these systems hold promise to provide objective data that, along with video replay, can assist the team doctor to make informed decisions regarding a concussion diagnosis.

There are a number of these sensors now available and include helmet sensors where the accelerometers (which measure acceleration forces) are built into the helmet itself; head sensors (incorporating accelerometers but also a gyroscope, which measures rotation forces) are positioned behind the ear using adhesive; or more recently instrumented mouth guards. Collectively, these sensors measure the amount of straight-line (linear) and rotational (angular) force and the three-dimensional direction of the force on the head.

Whilst these devices have been used across sports such as American football, ice hockey, Australian football and rugby union, concerns have been raised about the reliability and validity of the impacts measured. For example, for helmet sensor systems, factors such as fit and padding type may affect sensor coupling to the human head and as a result create measurement error [32].

Similarly, sensors stuck to the skin are subject to measurement errors if:

  • The adhesion is not optimal
  • It is mounted under a helmet (such as in ice hockey [33])
  • It is not mounted suitably (i.e. position and orientation) over the correct anatomical location just behind the ear

In these cases, sensor movement on the skin can create artifacts or amplify measurement error. Recently, custom-fitted mouth guard sensors providing three-dimensional direction measures for linear and rotational acceleration forces have been developed to measure head impacts. Laboratory validation and field studies have so far indicated promise in having less measurement error when compared to skin-mounted or helmet sensor systems [34]. 


This article has highlighted various examples where technologies are being used to measure and quantify the effects of concussion, both in the acute and chronic settings. Currently, despite advancements, further validation is required before utility in the clinical setting. This requires more investment in research to demonstrate efficacy in diagnosis of concussion and prognosis of recovery. Given the heterogeneity in signs, symptoms and disparity in recovery times in concussed athletes, this will be the major challenge for the various technologies being developed. However, because clinical diagnosis and return-to-play decisions are dependent on the medical practitioner, this does not mean that we should dismiss technologies to the research laboratory. Technologies will improve confidence in diagnostics and that can only be a good outcome for all.


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Go to the profile of Alan Pearce

Alan Pearce

Associate Professsor, La Trobe University/Australian Sports Brain Bank (Victoria)

Alan Pearce is an Associate Professor in the College of Science, Health and Engineering at La Trobe University, and also the Research Manager at the Australian Sports Brain Bank (Victoria). Alan's research focuses on the neurophysiology of sports-related concussion, using non-invasive brain stimulation (NIBS) techniques.

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