Response from Martin McKeown, Makeig, Brown, Jung, Kindermann, Bell and Sejnowski

Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

EEG is known to contain considerable inter-trial and inter-subject variability, which poses a challenge in any group-level EEG analyses. A true experimental effect must be reproducible even with variabilities in trials, sessions, and subjects. Extracting components that are reproducible across trials and subjects benefits both understanding common mechanisms in neural processing of cognitive functions and building robust brain-computer interfaces. This study extends our previous method (task-related component analysis, TRCA) by maximizing not only trial-by-trial reproducibility within single subjects but also similarity across a group of subjects, hence referred to as group TRCA (gTRCA). The problem of maximizing reproducibility of time series across trials and subjects is formulated as a generalized eigenvalue problem. We applied gTRCA to EEG data recorded from 35 subjects during a steady-state visual-evoked potential (SSVEP) experiment. The results revealed: (1) The group-representative data computed by gTRCA showed higher and consistent spectral peaks than other conventional methods; (2) Scalp maps obtained by gTRCA showed estimated source locations consistently within the occipital lobe; And (3) the high-dimensional features extracted by gTRCA are consistently mapped to a low-dimensional space. We conclude that gTRCA offers a framework for group-level EEG data analysis and brain-computer interfaces alternative in complement to grand averaging.

BrainModes: a principled approach to modeling and measuring large-scale neuronal activity

Complex systems, such as the brain, exhibit multiple levels of organization due to processes which support the separation of scales across time and/or space. That is, cooperative phenomena--or "modes" of activity--occurring at one scale give rise to coherent spatiotemporal structures at a coarser scale. In turn, structures at the coarser scale constrain--and hence influence--emerging activity at a finer scale. BrainModes is an annual scientific summit which seeks to bring together experimental, computational and theoretical neuroscientists engaged at different levels of organization, with the goal of advancing a principled approach to understanding brain function based on the concept of cooperative phenomena in complex systems. Phenomena of particular interest include synchronization, stochastic influences, and spatiotemporal processes in both healthy and pathological states such as seizures. This Special Issue reports the 2008 BrainModes Workshop, held in Amsterdam (December 2008) which focused on the application of this framework to the analysis of brain oscillations and synchronization phenomena across time scales.

A new method for detecting causality in fMRI data of cognitive processing

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.

Imaging Brain Dynamics Using Independent Component Analysis

The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

Novelty indices: identifiers of potentially interesting time-courses in functional MRI data

In a systematic study of hybrid MR time-series with simulated "activation" (contrast-to-noise range 1-10) we investigated the power and compared the ability of the "novelty indices" (NI) kurtosis, negentropy, and 1-lag autocorrelation, to discriminate between potentially interesting ("structured") and uninteresting ("noisy") time-courses (TCs). NIs may be employed as preprocessing tools to focus only on the interesting TCs prior to any further exploratory or confirmatory approach.