POSTER PRESENTATION Open Access Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease Jeroen Bergmann 1* , Newton Howard 2 From Twenty First Annual Computational Neuroscience Meeting: CNS*2012 Decatur, GA, USA. 21-26 July 2012 Alzheimer’s disease is a syndrome of acquired cognitive defects that interferes with normal brain function. While its biochemical effects are well documented, the cause of Alzheimer’s disease is still not known and the best available interventions remain merely symptomatic. Different treatment modalities have been explored over the last few decades. Pharmaceutical interventions currently con- sist of medicines that focus on the neuropsychiatric symptoms and disease-modifying treatments. The impor- tance of early intervention to the efficacy of treatments has led to an emphasis on detection and diagnosis in clinical research, and techniques using imaging, biomar- kers and genetic information as tools for early detection have become prevalent. However, multifaceted non-invasive screening tools that incorporate computational algo- rithms and do not rely on imaging are not being widely developed. This paper argues that a computation method originally developed to explain mental processes can be adapted to assist in the early detection of Alzheimer’s disease. Temporal changes in behaviour and speech are occurring in the early stages of the disease. A Body Sen- sor Network (BSN) can be utilized to collect temporal information during everyday living which can be further processed with an algorithm that allows for natural ran- domness. This method shows that novel non-invasive screening tools for Alzheimer’ s disease can be devised based on measuring real-life behaviour. Conclusions It has been shown that BSNs can be utilized to measure a range of activities. A MSI classification algorithm can subsequently categorize the temporal changes during ADL activities, while allowing for natural occurring ran- domness [1]. The BSN can be unobtrusive to allow for monitoring over longer periods of time [2]. The techni- que outlined here can be used for diagnostic purposes, but it can also be implemented to screen for treatment effects of new and existing interventions. Author details 1 Medical Engineering Solutions in Osteoarthritis Centre of Excellence, Imperial College, London, W68RF, UK. 2 Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. Published: 16 July 2012 References 1. Howard N, Guidere M: Computational Methods for Clinical Applications: An Introduction. In Functional Neurology, Rehabilitation, and Ergonomics. Volume 1. NewYork: Springer-Verlag; 1998:237-250, 20112. Bower JM, Beeman D: The Book of Genesis, 2nd Edition. 2. Bergmann JHM, McGregor AH: Sensor design: what do patients want? The 2nd International Conference on Ambulatory Monitoring of Physical Activity and Movement Glasgow; 2011. doi:10.1186/1471-2202-13-S1-P178 Cite this article as: Bergmann and Howard: Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease. BMC Neuroscience 2012 13(Suppl 1):P178. * Correspondence: [email protected] 1 Medical Engineering Solutions in Osteoarthritis Centre of Excellence, Imperial College, London, W68RF, UK Full list of author information is available at the end of the article Bergmann and Howard BMC Neuroscience 2012, 13(Suppl 1):P178 http://www.biomedcentral.com/1471-2202/13/S1/P178 © 2012 Bergmann and Howard; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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POSTER PRESENTATION Open Access

Combining computational neuroscience andbody sensor networks to investigate Alzheimer’sdiseaseJeroen Bergmann1*, Newton Howard2

From Twenty First Annual Computational Neuroscience Meeting: CNS*2012Decatur, GA, USA. 21-26 July 2012

Alzheimer’s disease is a syndrome of acquired cognitivedefects that interferes with normal brain function. Whileits biochemical effects are well documented, the cause ofAlzheimer’s disease is still not known and the best avail-able interventions remain merely symptomatic. Differenttreatment modalities have been explored over the lastfew decades. Pharmaceutical interventions currently con-sist of medicines that focus on the neuropsychiatricsymptoms and disease-modifying treatments. The impor-tance of early intervention to the efficacy of treatmentshas led to an emphasis on detection and diagnosis inclinical research, and techniques using imaging, biomar-kers and genetic information as tools for early detectionhave become prevalent. However, multifaceted non-inva-sive screening tools that incorporate computational algo-rithms and do not rely on imaging are not being widelydeveloped. This paper argues that a computation methodoriginally developed to explain mental processes can beadapted to assist in the early detection of Alzheimer’s dis-ease. Temporal changes in behaviour and speech areoccurring in the early stages of the disease. A Body Sen-sor Network (BSN) can be utilized to collect temporalinformation during everyday living which can be furtherprocessed with an algorithm that allows for natural ran-domness. This method shows that novel non-invasivescreening tools for Alzheimer’s disease can be devisedbased on measuring real-life behaviour.

ConclusionsIt has been shown that BSNs can be utilized to measurea range of activities. A MSI classification algorithm can

subsequently categorize the temporal changes duringADL activities, while allowing for natural occurring ran-domness [1]. The BSN can be unobtrusive to allow formonitoring over longer periods of time [2]. The techni-que outlined here can be used for diagnostic purposes,but it can also be implemented to screen for treatmenteffects of new and existing interventions.

Author details1Medical Engineering Solutions in Osteoarthritis Centre of Excellence,Imperial College, London, W68RF, UK. 2Massachusetts Institute ofTechnology, Cambridge, MA, 02139, USA.

Published: 16 July 2012

References1. Howard N, Guidere M: Computational Methods for Clinical Applications:

An Introduction. In Functional Neurology, Rehabilitation, and Ergonomics.Volume 1. NewYork: Springer-Verlag; 1998:237-250, 20112. Bower JM,Beeman D: The Book of Genesis, 2nd Edition.

2. Bergmann JHM, McGregor AH: Sensor design: what do patients want? The2nd International Conference on Ambulatory Monitoring of Physical Activityand Movement Glasgow; 2011.

doi:10.1186/1471-2202-13-S1-P178Cite this article as: Bergmann and Howard: Combining computationalneuroscience and body sensor networks to investigate Alzheimer’sdisease. BMC Neuroscience 2012 13(Suppl 1):P178.

* Correspondence: [email protected] Engineering Solutions in Osteoarthritis Centre of Excellence,Imperial College, London, W68RF, UKFull list of author information is available at the end of the article

Bergmann and Howard BMC Neuroscience 2012, 13(Suppl 1):P178http://www.biomedcentral.com/1471-2202/13/S1/P178

© 2012 Bergmann and Howard; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

mailto:[email protected]

http://creativecommons.org/licenses/by/2.0