Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes

This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3–5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.

[1]  D. O. Bos,et al.  EEG-based Emotion Recognition The Influence of Visual and Auditory Stimuli , 2007 .

[2]  G. Dawson,et al.  Resting State Cortical Connectivity Reflected in EEG Coherence in Individuals With Autism , 2007, Biological Psychiatry.

[3]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[4]  Trevor Hastie,et al.  The elements of statistical learning. 2001 , 2001 .

[5]  Nathan Intrator,et al.  Inference of Brain Mental States from Spatio-temporal Analysis of EEG Single Trials , 2011, BIOSIGNALS.

[6]  Yann LeCun,et al.  Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

[7]  M. Czisch,et al.  Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study. , 2006, Brain : a journal of neurology.

[8]  Nathan Intrator,et al.  Machine Learning Framework for Inferring Cognitive State from Magnetoencephalographic (MEG) Signals , 2008 .

[9]  M. Vazirgiannis,et al.  Clustering validity assessment using multi representatives , 2002 .

[10]  Giacomo Koch,et al.  Neural correlates of local contextual processing deficits in schizophrenic patients. , 2011, Psychophysiology.

[11]  Hujun Bao,et al.  A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Yanda Li,et al.  Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach , 2006, Physiological measurement.

[13]  N C Andreasen,et al.  Negative v positive schizophrenia. Definition and validation. , 1982, Archives of general psychiatry.

[14]  C. Stam,et al.  Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity , 2008, Human brain mapping.

[15]  Noa Fogelson,et al.  Contextual processing deficits in Parkinson’s disease: The role of the frontostriatal system , 2011, Clinical Neurophysiology.

[16]  Yue Han,et al.  A Variance Reduction Framework for Stable Feature Selection , 2010, 2010 IEEE International Conference on Data Mining.

[17]  P B Fenwick,et al.  Eeg Spectral Analysis in Schizophrenia , 1980, British Journal of Psychiatry.

[18]  T. Koenig,et al.  Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naı̈ve patients with schizophrenia: preliminary results , 2001, Schizophrenia Research.

[19]  Dewen Hu,et al.  Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity , 2012, Neuroreport.

[20]  A. Geva,et al.  Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering , 1998, IEEE Transactions on Biomedical Engineering.

[21]  L Carretié,et al.  N300, P300 and the emotional processing of visual stimuli. , 1997, Electroencephalography and clinical neurophysiology.

[22]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[23]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[24]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[25]  Michalis Vazirgiannis,et al.  A density-based cluster validity approach using multi-representatives , 2008, Pattern Recognit. Lett..

[26]  Nathan Intrator,et al.  Schizophrenia Detection and Classification by Advanced Analysis of EEG Recordings Using a Single Electrode Approach , 2015, PloS one.

[27]  Rui Zhang,et al.  Differentiating Between Psychogenic Nonepileptic Seizures and Epilepsy Based on Common Spatial Pattern of Weighted EEG Resting Networks , 2014, IEEE Transactions on Biomedical Engineering.

[28]  P. Benson,et al.  The P300 as a possible endophenotype for schizophrenia and bipolar disorder: Evidence from twin and patient studies , 2009, Psychiatry Research.

[29]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[30]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[31]  Leontios J. Hadjileontiadis,et al.  Emotion Recognition From EEG Using Higher Order Crossings , 2010, IEEE Transactions on Information Technology in Biomedicine.

[32]  J. Polich,et al.  Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. , 2003, Psychophysiology.

[33]  A. Geva,et al.  Functional Connectivity and Working Memory in Schizophrenia: An Eeg Study , 2001, The International journal of neuroscience.

[34]  Dewen Hu,et al.  Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI , 2010, NeuroImage.

[35]  Nathan Intrator,et al.  Inferring Functional Brain States Using Temporal Evolution of Regularized Classifiers , 2007, Comput. Intell. Neurosci..