Introducing Connectivity Analysis to NeuroIS Research

The integration of both neuroscience and psycho-physiological methods into Information Systems (IS) research in order to better understand how the brain operates in an IS-relevant context has gained importance. Articles highlighting the potential of NeuroIS have opened the discussion of methodological issues associated with the use of fMRI. NeuroIS research, however, must remain cognizant of the fact that the neural implementation of complex mental processes is based on activity in a network of varied brain areas. Against this background, the present article seeks to make a methodological contribution by introducing methods of connectivity analysis to IS research and by giving an overview of the basic principles. We describe different methods of connectivity analysis, discuss a concrete example, and show how connectivity analysis can inform IS research. The major objective of this paper is to contribute to a better understanding of advanced techniques for brain imaging data analysis.

[1]  René Riedl,et al.  Are There Neural Gender Differences in Online Trust? An fMRI Study on the Perceived Trustworthiness of eBay Offers , 2010, MIS Q..

[2]  Nathan S White,et al.  Impaired thalamocortical connectivity in humans during general-anesthetic-induced unconsciousness , 2003, NeuroImage.

[3]  S. Strother,et al.  An evaluation of methods for detecting brain activations from PET or fMRI images , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[4]  Karl J. Friston.  Functional integration and inference in the brain , 2002, Progress in Neurobiology.

[5]  Karl J. Friston,et al.  Investigating the Functional Role of Callosal Connections with Dynamic Causal Models , 2005, Annals of the New York Academy of Sciences.

[6]  Stephen C. Strother,et al.  An evaluation of methods for detecting brain activations from functional neuroimages , 2002, Artif. Intell. Medicine.

[7]  Karl J. Friston,et al.  Comparing Families of Dynamic Causal Models , 2010, PLoS Comput. Biol..

[8]  R. Riedl,et al.  The Biology of Trust: Integrating Evidence From Genetics, Endocrinology, and Functional Brain Imaging , 2012 .

[9]  Daniel L. Sherrell,et al.  Communications of the Association for Information Systems , 1999 .

[10]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[11]  Rudy Hirschheim,et al.  A paradigmatic and methodological examination of information systems research from 1991 to 2001 , 2004, Inf. Syst. J..

[12]  H Preißl,et al.  Dynamics of activity and connectivity in physiological neuronal networks , 1991 .

[13]  Fred D. Davis,et al.  NeuroIS: Neuroscientific Approaches in the Investigation and Development of Information Systems , 2010, Bus. Inf. Syst. Eng..

[14]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Fred D. Davis,et al.  Trusting Humans and Avatars: Behavioral and Neural Evidence , 2011, ICIS.

[16]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[17]  Christopher J Rennie,et al.  Mode of Functional Connectivity in Amygdala Pathways Dissociates Level of Awareness for Signals of Fear , 2006, The Journal of Neuroscience.

[18]  Fred D. Davis,et al.  NeuroIS: The Potential of Cognitive Neuroscience for Information Systems Research , 2008, ICIS.

[19]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[20]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[21]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[22]  René Riedl,et al.  Technostress from a Neurobiological Perspective , 2012, Business & Information Systems Engineering.

[23]  A. Auinger,et al.  Technostress from a Neurobiological Perspective : System Breakdown Increases the Stress Hormone Cortisol in Computer Users , 2012 .

[24]  Angelika Dimoka,et al.  On the Foundations of NeuroIS: Reflections on the Gmunden Retreat 2009 , 2010, Commun. Assoc. Inf. Syst..

[25]  E. Bullmore,et al.  How Good Is Good Enough in Path Analysis of fMRI Data? , 2000, NeuroImage.

[26]  C. Büchel,et al.  Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. , 1997, Cerebral cortex.

[27]  Kamryn T. Eddy,et al.  Amygdala-frontal connectivity during emotion regulation. , 2007, Social cognitive and affective neuroscience.

[28]  Gregory P. Lee,et al.  Different Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-Making , 1999, The Journal of Neuroscience.

[29]  Gereon R Fink,et al.  Cerebral localization, then and now , 2003, NeuroImage.

[30]  M. O. Locks,et al.  Note---The Logic of Policy as Argument , 1985 .

[31]  R Baumgartner,et al.  Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. , 2000, Magnetic resonance imaging.

[32]  Karl J. Friston.  Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[33]  H. Pashler,et al.  Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition 1 , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

[34]  Izak Benbasat,et al.  The Effects of Trust-Assuring Arguments on Consumer Trust in Internet Stores: Application of Toulmin's Model of Argumentation , 2006, Inf. Syst. Res..

[35]  Colin Camerer,et al.  Self-control in decision-making involves modulation of the vmPFC valuation system , 2009, NeuroImage.

[36]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[37]  Angelika Dimoka,et al.  What Does the Brain Tell Us About Trust and Distrust? Evidence from a Functional Neuroimaging Study , 2010, MIS Q..

[38]  J. Malmaud,et al.  Focusing Attention on the Health Aspects of Foods Changes Value Signals in vmPFC and Improves Dietary Choice , 2011, The Journal of Neuroscience.

[39]  Karl J. Friston,et al.  Modelling functional integration: a comparison of structural equation and dynamic causal models , 2004, NeuroImage.

[40]  Neurois: Challenges and solutions , 2010, ICIS.

[41]  Karl J. Friston,et al.  Ten simple rules for dynamic causal modeling , 2010, NeuroImage.

[42]  K. Amunts,et al.  Effective connectivity of the left BA 44, BA 45, and inferior temporal gyrus during lexical and phonological decisions identified with DCM , 2009, Human brain mapping.

[43]  Karl J. Friston,et al.  Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri , 2022 .

[44]  Angelika Dimoka,et al.  Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption , 2008, ICIS.

[45]  Karl J. Friston,et al.  Dynamic causal modelling for fMRI: A two-state model , 2008, NeuroImage.

[46]  L. Richard Ye,et al.  The Impact of Explanation Facilities in User Acceptance of Expert System Advice , 1995, MIS Q..

[47]  Karl J. Friston.  Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging , 2009, PLoS biology.

[48]  Angelika Dimoka,et al.  On the Use of Neuropyhsiological Tools in IS Research: Developing a Research Agenda for NeuroIS , 2012, MIS Q..

[49]  Roberto Viviani,et al.  Functional principal component analysis of fMRI data , 2005, Human brain mapping.

[50]  Robert L. Savoy,et al.  Experimental design in brain activation MRI: Cautionary tales , 2005, Brain Research Bulletin.

[51]  Karl J. Friston,et al.  Network discovery with DCM , 2011, NeuroImage.

[52]  A. Andersen,et al.  Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. , 1999, Magnetic resonance imaging.

[53]  Angelika Dimoka,et al.  Neuro IS: The Potential of Cognitive Neuroscience for Information Systems Research , 2007, ICIS.

[54]  Jonathan D. Cohen,et al.  The Neural Basis of Economic Decision-Making in the Ultimatum Game , 2003, Science.

[55]  A. Damasio,et al.  Deciding Advantageously Before Knowing the Advantageous Strategy , 1997, Science.

[56]  Angelika Dimoka,et al.  NeuroIS: Hype or Hope? , 2009, ICIS.

[57]  A. Damasio.  The somatic marker hypothesis and the possible functions of the prefrontal cortex. , 1996, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[58]  Karl J. Friston,et al.  Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution , 2003, NeuroImage.

[59]  Daniel T. Knoepfle,et al.  Value Computations in Ventral Medial Prefrontal Cortex during Charitable Decision Making Incorporate Input from Regions Involved in Social Cognition , 2010, The Journal of Neuroscience.

[60]  Leslie G. Ungerleider,et al.  Network analysis of cortical visual pathways mapped with PET , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[61]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[62]  Klaas Enno Stephan,et al.  On the role of general system theory for functional neuroimaging , 2004, Journal of anatomy.

[63]  Kerstin Preuschoff,et al.  Optimizing Experimental Design for Comparing Models of Brain Function , 2011, PLoS Comput. Biol..

[64]  Howard B. Lee,et al.  Foundations of Behavioral Research , 1973 .

[65]  Karl J. Friston,et al.  Incorporating Prior Knowledge into Image Registration , 1997, NeuroImage.

[66]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[67]  Eric J. Johnson,et al.  Mindful judgment and decision making. , 2009, Annual review of psychology.

[68]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[69]  N. Kriegeskorte,et al.  Neural correlates of trust , 2007, Proceedings of the National Academy of Sciences.

[70]  René Riedl,et al.  Historical Development of Research Methods in the Information Systems Discipline , 2011, AMCIS.

[71]  A. Damasio,et al.  Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. , 1996, Cerebral cortex.