Combining Graph Analysis and Recurrence Plot on fMRI data

In this work we investigate on the nonlinear properties of the brain networks using Graph Analysis and Cross Recurrence Plot. The nonlinear dynamics of the brain is analyzed using time series coming from fMRI data. Two groups of human subjects, one affected by schizophrenia and the other of healthy controls, are imaged during the completion of a working memory task. To examine the spatio-temporal properties of the BOLD signal, nonlinear recurrence properties are extracted from the time series of the most relevant voxels, using the cross recurrence plots and the corresponding measures. Then, a graph is built using such measures as weights between different brain regions (the nodes). The purpose of the paper is to give a description of the most relevant functional areas activated during the task completion and to capture the differences between the groups. Results are promising, since the methodology is still to be fully developed and explored.

[1]  S. Rombouts,et al.  Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity , 2010, PloS one.

[2]  Pietro Guccione,et al.  Analysis of fMRI data using the complex systems approach , 2014 .

[3]  Fabio Sambataro,et al.  Genetically Determined Measures of Striatal D2 Signaling Predict Prefrontal Activity during Working Memory Performance , 2010, PloS one.

[4]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[5]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[6]  E. Martin,et al.  Concise Medical Dictionary , 2010 .

[7]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[8]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

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

[10]  S. Strogatz Exploring complex networks , 2001, Nature.

[11]  Pietro Guccione,et al.  Data driven analysis of functional brain networks in fMRI for schizophrenia investigation , 2014, Int. J. Imaging Syst. Technol..

[12]  Karl J. Friston,et al.  Schizophrenia: a disconnection syndrome? , 1995, Clinical neuroscience.

[13]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[14]  Edward T. Bullmore,et al.  Schizophrenia, neuroimaging and connectomics , 2012, NeuroImage.

[15]  Habib Benali,et al.  Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements , 2014, NeuroImage.

[16]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[17]  Andreas Meyer-Lindenberg,et al.  From maps to mechanisms through neuroimaging of schizophrenia , 2010, Nature.

[18]  Giulio Tononi,et al.  Schizophrenia and the mechanisms of conscious integration , 2000, Brain Research Reviews.

[19]  N. Marwan,et al.  Nonlinear analysis of bivariate data with cross recurrence plots , 2002, physics/0201061.

[20]  A. G. Barnett,et al.  A time-domain test for some types of nonlinearity , 2005, IEEE Transactions on Signal Processing.

[21]  Luis Martí-Bonmatí,et al.  Increased amygdala and parahippocampal gyrus activation in schizophrenic patients with auditory hallucinations: An fMRI study using independent component analysis , 2010, Schizophrenia Research.