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How Can State of the Art Econometrics Help Time Series Modeling in Neural Connectivity Mapping
Linköping University, Department of Management and Engineering, Economics. (Neuroeconomics)
School of Accounting, Economics and Finance, Deakin University Australia at Melbourne.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Understanding the human brain is one of the great scientific challenges of the 21st century. This project aims to contribute to a key aspect of this challenge: revealing the neural interconnection underlying decision making processes. In order to do so we will take advantage of the latest developments in econometric time series analysis

New studies have shown that, besides the formation of multiple networks, the human brain forms one integrated complex network, linking all brain regions and sub-networks together into one complex system. Exploiting new methods for examining the overall organization of this network can provide new valuable insights into how the human brain operates: How the functional connections between brain-regions are organized. What determines the brain’s capability of integrating information between different sub-systems? And how does it affect human decision-making? Decoding this amazingly complex wiring diagram of neural network, referred to as the Connectome, has the potential to uncover more about what makes us uniquely human and what makes every person different from all others.

In modern Neuroscience there is a major conceptual belief that the computational properties of the brain are a direct consequence of its circuitry (Freund 2002). This insight has received unparalleled attention in the past decades as technological advances transformed the acquisition of neural connectivity data from a slow paced, tentative groping, into a high throughput process of massive multimodal data acquisition including morphological, neurochemical and functional variables. There is no further advanced, nor better established method for the detection and delineation of regions of the brain that change their level of activation in response to experimental external incentives than functional magnetic resonance imaging fMRI. Based on changes in the blood oxygenation level dependent (BOLD) signal reflecting neuronal activation, although indirectly, fMRI produces activation maps which typically depict the average level of engagement during a specific task or in response to a specific stimulus, of different regions in the brain. Although fMRI has a temporal resolution less than the true speed of neural interactions, it provides whole-brain coverage at spatial resolutions of millimeters, and is an ideal tool for measuring intrinsic, steady-state hemodynamic fluctuations. Evidence continues to accumulate that connectivity measures by fMRI reflect meaningful aspects of cognitive processing in terms of task, load, behavior and decision making. As such, the aims within this proposal are focused on:


(i)             Designing innovative decision-making experiments able to capture how context- or condition-specific changes in connectivity modify underlying cognitive decision-making processes.


(ii)           Devising novel methodologies useful in identifying neural connectivity data elucidating decision making operations recorded in the fMRI scanner

National Category
Social Sciences Interdisciplinary
URN: urn:nbn:se:liu:diva-86969OAI: diva2:583902
Available from: 2013-01-08 Created: 2013-01-08

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Josephson, Camilla Maria Kyllikki
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