Event Abstract

Combined Modality of the Brain Code Approach for Early Detection and Long-term Monitoring of Neurodegenerative Processes

  • 1 Massachusetts Institute of Technology, United States
  • 2 Brain Sciences Foundation, United States
  • 3 University of Oxford, United Kingdom


Encoding from behavioral information towards cognitive states can be done computationally by proposing a “brain code” model. The “brain code” will provide a more basic scheme that applies abstraction to connect the different systems and in the process provides a linkage between different dimensions. The model can also integrate electroencephalography (EEG) to provide a meaningful link to brain activity. The overall approach is depicted in Figure 2. The suggested approach will have a physiological reference to both brain activity (EEG) and imaging data that can be associated with the brain code. This allows for encoding of the effect of functional and structural changes within the brain. In addition, it will assist in better understand the interaction between how cognition and our everyday behaviour is linked by measuring these modalities directly. The “brain code” is the abstracted technique that shows how information can be encoded, used and reproduced across different systems. It relies on the concept that brain function is shaped in our environment (Hari and Kujala, 2009) and as such that is where we should measure in order to gain knowledge. The aim of this research is to explore a potential new method that can be used to detect changes in the brain, due to neurodegenerative disease. The research will provide an overview of the current work in process, with a broader goal of adapting this technology on the long-term for detection and monitoring of neurodegenerative process.
The procedures required to extract relevant information from large quantities of data have become known as data mining. A set of specific methods and approaches aimed at extracting patterns from data (Fayyad et al., 1996) will be the foundation of the data analyctics. The overall process of finding useful knowledge in raw data requires several steps of discovering knowledge (He, 2009). Data mining approaches are illustrated in figure 1.


There is a great interest to decode brain activity to help researchers understand complex ailments (ranging from stroke to Parkinson's disease) that affect cognition. The majority of the research focuses on intrusive techniques that within an experimental context can be applied to explore cause and effects. This research is extremely important and has already brought many insights into brain functioning. However, relevant knowledge can also be gained by integration of brain and behavioral models of cognitive function (Park et al., 2001). This understanding arises from the knowledge that humans and their brains are shaped, and normally function, in continuous interaction with other people (Hari and Kujala, 2009). This requires a focus on human-environment interaction. It has been proposed by Neuroscientist Daniel Wolpert that the brain itself evolved to control movement. On this premise our understanding of the brain should reflect measurements of human movement in natural environments.
The interaction between cognitive and sensorimotor functions has been well established (Bonnard et al., 2004). There is now increasing evidence showing the positive effect of physical activity on maintaining cognitive function (Ratey and Loehr, 2011; Angevaren et al., 2008). There is also support for the idea that both cognitive training and physical activity will outperform isolated cognitive or physical exercise, but more research is still needed to establish this (Kraft, 2012). Opposite to movement, balance was long thought to be unaffected by cognitive loading (Resch et al., 2011). This effect was called the posture first strategy, which relied on the understanding that individuals would always prioritize balance. However, a recent study that aimed to mimic more real-life scenarios showed that balance was affected with cognitive loading (Liston et al., 2013). This indicates that even balance performance reflects cognitive functioning.


Based on the recent findings the idea that cognition could be monitored by unobtrusive movement measurement started to emerge. Thus far the focus has been on facial recognition, but it could be that the facial expressions might also be able to show small alterations as a consequence of cognitive decline. Moreover, it is known that not only movement and balance can be affected by cognition. Another parameter that is affected by cognition, and of particular interest, is speech. Speech analysis has emerged as a robust alternative to clinical tools in assessing cognitive function (Rapcan et al., 2009; Rochford et al., 2012). Assessing these multiple modalities in an unobtrusive way during real-life should help understand brain function. The data obtained from these measurements can be referenced against a cognitive function measurement to identify the associations between them in real-life. An experimental method of defining how these behavioral streams relate to cognition is by comparing between cognitive loaded or unloaded conditions. This experimental approach can investigate how increased cognitive loading will affect the modalities of interest. Results from these experiments will show what happens if processing can’t just be dedicated to our everyday task(s) or if cognitive processing starts to change due to disease. It reflects to some extent the changes in processing and attention that is seen in neurodegenerative disease. The overall approach is depicted in Figure 2.

Figure 1
Figure 2


Angevaren, M., Aufdemkampe, G., Verhaar, H. J., Aleman, A. & Vanhees, L. 2008. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev, Cd005381.
Bonnard, M., De Graaf, J. & Pailhous, J. 2004. Interactions between cognitive and sensorimotor functions in the motor cortex: evidence from the preparatory motor sets anticipating a perturbation. Rev Neurosci, 15, 371-82.
Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. 1996. From data mining to knowledge discovery in databases. AI magazine, 17, 37.
Hari, R. & Kujala, M. V. 2009. Brain basis of human social interaction: from concepts to brain imaging. Physiol Rev, 89, 453-79.
He, J. Advances in data mining: history and future. IITA 2009. Third International Symposium on Intelligent Information Technology Application, 2009. IEEE., 2009. IEEE, 634-636.
Irimia, A., Chambers, M. C., Torgerson, C. M., Filippou, M., Hovda, D. A., Alger, J. R., Gerig, G., Toga, A. W., Vespa, P. M. & Kikinis, R. 2012. Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Frontiers in neurology, 3.
Kraft, E. 2012. Cognitive function, physical activity, and aging: Possible biological links and implications for multimodal interventions. Aging, Neuropsychology, and Cognition, 19, 248-263.
Liston, M. B., Bergmann, J. H., Keating, N., Green, D. A. & Pavlou, M. 2013. Postural prioritization is differentially altered in healthy older compared to younger adults during visual and auditory coded spatial multitasking. Gait & posture.
Park, D. C., Polk, T. A., Mikels, J. A., Taylor, S. F. & Marshuetz, C. 2001. Cerebral aging: integration of brain and behavioral models of cognitive function. Dialogues in clinical neuroscience, 3, 151.
Rapcan, V., D'arcy, S., Penard, N., Robertson, I. H. & Reilly, R. B. The use of telephone speech recordings for assessment and monitoring of cognitive function in elderly people. Interspeech, 2009. 943-946.
Ratey, J. J. & Loehr, J. E. 2011. The positive impact of physical activity on cognition during adulthood: a review of underlying mechanisms, evidence and recommendations. Rev Neurosci, 22, 171-85.
Resch, J. E., May, B., Tomporowski, P. D. & Ferrara, M. S. 2011. Balance performance with a cognitive task: a continuation of the dual-task testing paradigm. Journal of athletic training, 46, 170.
Rochford, I., Rapcan, V., D'arcy, S. & Reilly, R. B. Dynamic minimum pause threshold estimation for speech analysis in studies of cognitive function in ageing. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012. IEEE, 3700-3703.

Keywords: computational neuroscience, body-sensor networks, behavioral cognition, Electroencephalography, big data, digital atlasing, brain code

Conference: Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013.

Presentation Type: Poster presentation

Topic: Poster session

Citation: Howard N, Bergmann J and Stein J (2013). Combined Modality of the Brain Code Approach for Early Detection and Long-term Monitoring of Neurodegenerative Processes. Front. Neuroinform. Conference Abstract: Imaging the brain at different scales: How to integrate multi-scale structural information?. doi: 10.3389/conf.fninf.2013.10.00043

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 23 Aug 2013; Published Online: 22 Oct 2013.

* Correspondence: Dr. Newton Howard, Massachusetts Institute of Technology, Cambridge, United States, Nhmit@me.com