Functional biomarkers for neurodegenerative disorders based on the network paradigm

This commentary provides a brief introduction to the various uses that functional neuroimaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders. It then goes on to explain why the emphasis of much recent work has shifted to network-based biomarkers, as opposed to those that examine individual brain regions. A number of examples are referenced that illustrate the points made.

[1]  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.

[2]  Karl J. Friston,et al.  Predictive coding under the free-energy principle , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[3]  Nick C. Fox,et al.  Biomarkers for Alzheimer's disease therapeutic trials , 2011, Progress in Neurobiology.

[4]  C. Grady,et al.  Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease , 1987, Brain Research.

[5]  V. Dhawan,et al.  Network modulation in the treatment of Parkinson's disease. , 2006, Brain : a journal of neurology.

[6]  A. McIntosh,et al.  Neural modeling, functional brain imaging, and cognition , 1999, Trends in Cognitive Sciences.

[7]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

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

[9]  Scott T. Grafton,et al.  Network analysis of motor system connectivity in Parkinson's disease: Modulation of thalamocortical interactions after pallidotomy , 1994 .

[10]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[12]  L. Senhadji,et al.  From EEG signals to brain connectivity: A model-based evaluation of interdependence measures , 2009, Journal of Neuroscience Methods.

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

[14]  Barry Horwitz,et al.  Investigating the neural basis for functional and effective connectivity. Application to fMRI , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

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

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

[18]  S. Rombouts,et al.  Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study , 2005, Human brain mapping.

[19]  Todd B. Parrish,et al.  Altered Effective Connectivity within the Language Network in Primary Progressive Aphasia , 2007, The Journal of Neuroscience.

[20]  J V Haxby,et al.  Network analysis of PET-mapped visual pathways in Alzheimer type dementia. , 1995, Neuroreport.

[21]  Wendy R. Sanhai,et al.  Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives , 2010, Nature Reviews Drug Discovery.

[22]  J. Rowe.  Frontiers in Systems Neuroscience Systems Neuroscience , 2010 .

[23]  J. Morris,et al.  Functional deactivations: Change with age and dementia of the Alzheimer type , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[24]  A. Bokde,et al.  Assessing neuronal networks: Understanding Alzheimer's disease , 2009, Progress in Neurobiology.

[25]  Bram Stieltjes,et al.  Longitudinal changes in fiber tract integrity in healthy aging and mild cognitive impairment: a DTI follow-up study. , 2010, Journal of Alzheimer's disease : JAD.

[26]  Kewei Chen,et al.  Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems , 2010, NeuroImage.

[27]  F. Gonzalez-Lima,et al.  Structural equation modeling and its application to network analysis in functional brain imaging , 1994 .

[28]  Mojtaba Zarei,et al.  White matter tract integrity in aging and Alzheimer's disease , 2009, Human brain mapping.

[29]  Efstathios D. Gennatas,et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. , 2010, Brain : a journal of neurology.

[30]  D. Prvulovic,et al.  Functional magnetic resonance imaging as a dynamic candidate biomarker for Alzheimer's disease , 2011, Progress in Neurobiology.

[31]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[32]  David E. Kuhl,et al.  Journal of Cerebral Blood Flow and Metabolism Cerebral Metabolic Relationships for Selected Brain Regions in Alzheimer's, Huntington's, and Parkinson's Diseases , 2022 .

[33]  Jieun Kim,et al.  How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study , 2009, NeuroImage.

[34]  Roger A. Barker,et al.  Dynamic causal modelling of effective connectivity from fMRI: Are results reproducible and sensitive to Parkinson's disease and its treatment? , 2010, NeuroImage.