Data Analysis

Ricardo Bruña Research line coordinated by Ricardo Bruña.

Research on and application of data analysis methods in our laboratory focuses on the following topics:

FUNCTIONAL CONNECTIVITY

Two important principles of brain organization are functional specialization and integration. Functional integration refers to the mechanisms by which several subfunctions supported by different brain areas are combined to carry out a basic sensory/cognitive function. A number of connectivity measures have been proposed that capture the statistical dependence among time series reflecting the activity of given brain areas. There is a trade-off between how general and how data demanding these measures are. A publicly available toolbox, Hermes, has been developed by the laboratory

HERMES (Connectivity Toolbox)

HERMES logoThe analysis of the interdependence between brain activity time-courses has become an important field of research in recent years. The CCNL has developed HERMES (http://hermes.ctb.upm.es/), a toolbox for the Matlab® environment, designed to study functional and effective brain connectivity from MEG and EEG multivariate recordings. A large number of connectivity measures are included such as the synchronization likelihood, phase synchronization, mutual information, Grainger causality, coherence and cross-correlation. HERMES also provides visualization tools and statistical methods to address the problem of multiple comparisons.

SOURCE RECONSTRUCTION

MEG offers the best combined spatiotemporal resolution to study brain function noninvasively in humans. A key analysis step to realize this potential is the estimation of sources of brain activity from the data recorded at the sensors. The sensor data does not uniquely determine a single solution. Additional assumptions are needed and different reconstruction methods exist such as beamforming, minimum-norm estimation or those based on Bayesian methods.

MULTIVARIATE ANALYSIS

Classification algorithms within the framework of machine learning can be applied to exploit the multivariate nature of MEG and EEG recordings. Considering the different sensors in a combined fashion can be advantageous both to understand neural coding and to separate experimental conditions and patient groups. Of particular interest for the laboratory is the early diagnosis in Alzheimer’s Disease.

PREPROCESSING METHODS

MEG and EEG recordings are contaminated by environmental noise and physiological artefacts. Researching and applying optimal preprocessing methods is essential to improve the signal to noise ratio of the data

BAYESIAN INFERENCE

The Bayesian framework is proving very valuable both to understand brain function and as analysis tool. According to Bayesian Inference the brain efficiently combines sensory information and prior knowledge according to their reliability to infer the causes of the sensory input, which is what is functionally relevant.

Selected publications

2014

  • Gonzalez-Moreno A, Aurtenetxe S, Lopez-Garcia ME, del Pozo F, Maestu F, Nevado A. Signal-to-noise ratio of the MEG signal after preprocessing. J. Neurosci. Methods. 2014 Jan; 222:56-61. PubMed ID: 24200506. PDF file PDF file.

2013

  • Niso G, Bruña R, Pereda E, Gutiérrez R, Bajo R, Maestú F, del-Pozo F. HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics. 2013 Oct; 11(4):405-34. PubMed ID: 23812847. PDF file PDF file.

2012

  • Zanin M, Sousa P, Papo D, Bajo R, García-Prieto J, del Pozo F, Menasalvas E, Boccaletti S. Optimizing functional network representation of multivariate time series. Sci Rep. 2012; 2:630. PubMed ID: 22953051.
  • Bruña R, Poza J, Gómez C, García M, Fernández A, Hornero R. Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures. J Neural Eng. 2012 Jun; 9(3):036007. PubMed ID: 22571870. PDF file PDF file.
  • Bajo R, Castellanos NP, Cuesta P, Aurtenetxe S, Garcia-Prieto J, Gil-Gregorio P, del-Pozo F, Maestu F. Differential patterns of connectivity in progressive mild cognitive impairment. Brain Connect. 2012; 2(1):21-4. PubMed ID: 22458376. PDF file PDF file.
  • Nevado A, Hadjipapas A, Kinsey K, Moratti S, Barnes GR, Holliday IE, Green GG. Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required. Neurosci. Lett.. 2012 Mar; 513(1):57-61. PubMed ID: 22329975.

2011

  • Castellanos NP, Bajo R, Cuesta P, Villacorta-Atienza JA, Paúl N, Garcia-Prieto J, Del-Pozo F, Maestú F. Alteration and reorganization of functional networks: a new perspective in brain injury study. Front Hum Neurosci. 2011; 5:90. PubMed ID: 21960965. PDF file PDF file.

2010

  • Bhattacharya J, Pereda E. An index of signal mode complexity based on orthogonal transformation. J Comput Neurosci. 2010 Aug; 29(1-2):13-22. PubMed ID: 19418211.

2009

  • Hadjipapas A, Casagrande E, Nevado A, Barnes GR, Green G, Holliday IE. Can we observe collective neuronal activity from macroscopic aggregate signals? Neuroimage. 2009 Feb; 44(4):1290-303. PubMed ID: 19041404.

2005

  • Pereda E, Quiroga RQ, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol.. 2005; 77(1-2):1-37. PubMed ID: 16289760.

2004

  • Guo K, Nevado A, Robertson RG, Pulgarin M, Thiele A, Young MP. Effects on orientation perception of manipulating the spatio-temporal prior probability of stimuli. Vision Res.. 2004; 44(20):2349-58. PubMed ID: 15246751.
  • Nevado A, Young MP, Panzeri S. Functional imaging and neural information coding. Neuroimage. 2004 Mar; 21(3):1083-95. PubMed ID: 15006676.

2003

  • Bhattacharya J, Pereda E, Petsche H. Effective detection of coupling in short and noisy bivariate data. IEEE Trans Syst Man Cybern B Cybern. 2003; 33(1):85-95. PubMed ID: 18238159.