Ingo R. Keck

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities.

Functional and structural brain modifications induced by oculomotor training in patients with age-related macular degeneration

Patients with age-related macular degeneration (AMD) are reliant on their peripheral visual field. Oculomotor training can help them to find the best area on intact peripheral retina and to efficiently stabilize eccentric fixation. In this study, nine patients with AMD were trained over a period of 6 months using oculomotor training protocols to improve fixation stability. They were followed over an additional period of 6 months, where they completed an auditory memory training as a sham training. In this cross-over design five patients started with the sham training and four with the oculomotor training. Seven healthy age-matched subjects, who did not take part in any training procedure, served as controls. During the 6 months of training the AMD subjects and the control group took part in three functional and structural magnetic resonance imaging (MRI) sessions to assess training-related changes in the brain function and structure. The sham-training phase was accompanied by two more fMRI measurements, resulting in five MRI sessions at intervals of 3 months for all participants. Despite substantial variability in the training effects, on average, AMD patients benefited from the training measurements as indexed by significant improvements in their fixation stability, visual acuity, and reading speed. The patients showed a significant positive correlation between brain activation changes and improvements in fixation stability in the visual cortex during training. These correlations were less pronounced on the long-term after training had ceased. We also found a significant increase in gray and white matter in the posterior cerebellum after training in the patient group. Our results show that functional and structural brain changes can be associated, at least on the short-term, with benefits of oculomotor and/or reading training in patients with central scotomata resulting from AMD.

Sliding Empirical Mode Decomposition

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called Sliding Empirical Mode Decomposition (SEMD), which is shown to perform well on large scale time series.

Empirical Mode Decomposition - an introduction

Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The method is fully adaptive and generates the basis to represent the data solely from these data and based on them. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications.

Functional MRI analysis by a novel spatiotemporal ICA algorithm

Data sets acquired from functional magnetic resonance imaging (fMRI) contain both spatial and temporal structures. In order to blindly extract underlying activities, the common approach however only uses either spatial or temporal independence. More convincing results can be achieved by requiring the transformed data to be as independent as possible in both domains. First introduced by Stone, spatiotemporal independent component analysis (ICA) is a promising algorithm for fMRI decomposition. We propose an algebraic spatiotemporal ICA algorithm with increased performance and robustness. The feasibility of the algorithm is demonstrated in an application to the analysis of an fMRI data sets of a human brain performing an auditory task.

3D Spatial Analysis of fMRI Data on a Word Perception Task

We discuss a 3D spatial analysis of fMRI data taken during a combined word perception and motor task. The event – based experiment was part of a study to investigate the network of neurons involved in the perception of speech and the decoding of auditory speech stimuli. We show that a classical general linear model analysis using SPM does not yield reasonable results. With blind source separation (BSS) techniques using the FastICA algorithm it is possible to identify different independent components (IC) in the auditory cortex corresponding to four different stimuli. Most interesting, we could detect an IC representing a network of simultaneously active areas in the inferior frontal gyrus responsible for word perception.

Spatiotemporal group ICA applied to fMRI datasets

Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on a priori hypotheses and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. We present fMRI data of two different subject-groups (young and old), which performed a modified Wisconsin Card Sorting Test (WCST). Spatiotemporal ICA and SPM-generated brain maps of the subject data are compared. For the group analysis a singular value decomposition approach was used. Spatiotemporal ICA reveals a frontoparietal network being activated while subjects performed different variants of the WCST. Contrary to the SPM analysis, ICA analysis revealed significant differences between young and old subjects as well as significant within-group differences.While young subjects showed with increasing task demands (A>B>C) increasing activation of the right lateral prefrontal cortex and of the medial orbito-frontal cortex, old subjects showed no such gradient in activation pattern and appeared to be more distributed.

Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed SEMD algorithm and presents some applications to biomedical time series recorded during neuromonitoring.

3D spatial analysis of fMRI data: a comparison of ICA and GLM analysis on a word perception task

We discuss a comparative 3D spatial analysis of fMRI data taken during a combined word perception and motor task. We show that a classical GLM analysis using SPM does not yield reasonable results. Only with BSS techniques using the fastICA algorithm can we get meaningful and interesting results. The event-based experiment was part of a study to investigate the network of neurons involved in the perception of speech and the decoding of auditory speech stimuli. Corresponding to 4 different stimuli different independent components (IC) could be identified in the auditory cortex and, most interesting, an IC representing a network of 3 simultaneously active areas in the inferior frontal gyrus could be detected.

Brain status data analyzed by Empirical Mode Decomposition

Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Intelligent signal processing is crucial to unravel the information content buried in biomedical time series. Empirical Mode Decomposition is ideally suited to extract all pure oscillatory modes which are contained in the signal. These modes, called Intrinsic Mode Functions (IMFs), represent a complete set of locally orthogonal basis functions with time-varying amplitude and frequency. The contribution discusses the application of an online variant, called SEMD, to non-stationary biomedical time series recorded during neuromonitoring.