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

Newton Howard1*, Jeroen Bergmann2 and John Stein3
  • 1 Massachusetts Institute of Technology, United States
  • 2 Brain Sciences Foundation, United States
  • 3 University of Oxford, United Kingdom

Abstract

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.


Introduction

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.

Approach

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
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Figure 2
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References

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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

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

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