1,588 research outputs found
Interaction patterns of brain activity across space, time and frequency. Part I: methods
We consider exploratory methods for the discovery of cortical functional
connectivity. Typically, data for the i-th subject (i=1...NS) is represented as
an NVxNT matrix Xi, corresponding to brain activity sampled at NT moments in
time from NV cortical voxels. A widely used method of analysis first
concatenates all subjects along the temporal dimension, and then performs an
independent component analysis (ICA) for estimating the common cortical
patterns of functional connectivity. There exist many other interesting
variations of this technique, as reviewed in [Calhoun et al. 2009 Neuroimage
45: S163-172]. We present methods for the more general problem of discovering
functional connectivity occurring at all possible time lags. For this purpose,
brain activity is viewed as a function of space and time, which allows the use
of the relatively new techniques of functional data analysis [Ramsay &
Silverman 2005: Functional data analysis. New York: Springer]. In essence, our
method first vectorizes the data from each subject, which constitutes the
natural discrete representation of a function of several variables, followed by
concatenation of all subjects. The singular value decomposition (SVD), as well
as the ICA of this new matrix of dimension [rows=(NT*NV); columns=NS] will
reveal spatio-temporal patterns of connectivity. As a further example, in the
case of EEG neuroimaging, Xi of size NVxNW may represent spectral density for
electric neuronal activity at NW discrete frequencies from NV cortical voxels,
from the i-th EEG epoch. In this case our functional data analysis approach
would reveal coupling of brain regions at possibly different frequencies.Comment: Technical report 2011-March-15, The KEY Institute for Brain-Mind
Research Zurich, KMU Osak
The neural correlates of subjectively perceived and passively matched loudness perception in auditory phantom perception
International audienceIntroduction: A fundamental question in phantom perception is determining whether the brain creates a network that represents the sound intensity of the auditory phantom as measured by tinnitus matching (in dB), or whether the phantom perception is actually only a representation of the subjectively perceived loudness. Methods: In tinnitus patients, tinnitus loudness was tested in two ways, by a numeric rating scale for subjectively perceived loudness and a more objective tinnitus-matching test, albeit it is still a subjective measure. Results: Passively matched tinnitus does not correlate with subjective numeric rating scale, and has no electrophysiological correlates. Subjective loudness, in a whole-brain analysis, is correlated with activity in the left anterior insula (alpha), the rostral/dorsal anterior cingulate cortex (beta), and the left parahip-pocampus (gamma). A ROI analysis finds correlations with the auditory cortex (high beta and gamma) as well. The theta band links gamma band activity in the auditory cortex and parahippocampus via theta–gamma nesting. Conclusions: Apparently the brain generates a network that represents subjectively perceived tinnitus loudness only, which is context dependent. The subjective loudness network consists of the anterior cingulate/insula, the parahippocam-pus, and the auditory cortex. The gamma band activity in the parahippocampus and the auditory cortex is functionally linked via theta–gamma nested lagged phase synchronization
Predictive learning, prediction errors, and attention: evidence from event-related potentials and eye tracking
Prediction error (‘‘surprise’’) affects the rate of learning: We learn more rapidly about cues for which we initially make incorrect predictions than cues for which our initial predictions are correct. The current studies employ electrophysiological measures to reveal early attentional differentiation of events that differ in their previous involvement in errors of predictive judgment.
Error-related events attract more attention, as evidenced by features of event-related scalp potentials previously implicated in selective visual attention (selection negativity, augmented anterior N1). The earliest differences detected occurred around 120 msec after stimulus onset, and distributed source localization (LORETA)
indicated that the inferior temporal regions were one source of the earliest differences. In addition, stimuli associated with the production of prediction errors show higher dwell times in an eyetracking procedure. Our data support the view that early attentional processes play a role in human associative learning
Automated long-term EEG analysis to localize the epileptogenic zone
OBJECTIVE: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. METHODS: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. RESULTS: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. SIGNIFICANCE: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus
Biclustering of gene expression data by non-smooth non-negative matrix factorization
BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms
Coherent intracerebral brain oscillations during learned continuous tracking movements
The aim of the present study was to assess changes in electroencephalogram (EEG) phase locking between fronto-parietal areas, including the frontal and parietal motor areas, during audiomotor learning of continuous tracking movements. Subjects learned to turn a steering wheel according to a given trajectory in order to minimise the discrepancy between a changing foreground stimulus (controllable by the subjects) and a constant background stimulus. The results of the present study show that increasing practice of continuous tracking movements that are continuously performed in the presence of auditory feedback is not accompanied by decrease in phase locking between areas involved. Moreover, the study confirms that internally produced movements show enhanced coherent activities compared to externally guided movements and therefore suggests that the motor-parietal network is more engaged during internally produced than externally produced movement
Isolated effective coherence (iCoh): causal information flow excluding indirect paths
A problem of great interest in real world systems, where multiple time series
measurements are available, is the estimation of the intra-system causal
relations. For instance, electric cortical signals are used for studying
functional connectivity between brain areas, their directionality, the direct
or indirect nature of the connections, and the spectral characteristics (e.g.
which oscillations are preferentially transmitted). The earliest spectral
measure of causality was Akaike's (1968) seminal work on the noise contribution
ratio, reflecting direct and indirect connections. Later, a major breakthrough
was the partial directed coherence of Baccala and Sameshima (2001) for direct
connections. The simple aim of this study consists of two parts: (1) To expose
a major problem with the partial directed coherence, where it is shown that it
is affected by irrelevant connections to such an extent that it can
misrepresent the frequency response, thus defeating the main purpose for which
the measure was developed, and (2) To provide a solution to this problem,
namely the "isolated effective coherence", which consists of estimating the
partial coherence under a multivariate auto-regressive model, followed by
setting all irrelevant associations to zero, other than the particular
directional association of interest. Simple, realistic, toy examples illustrate
the severity of the problem with the partial directed coherence, and the
solution achieved by the isolated effective coherence. For the sake of
reproducible research, the software code implementing the methods discussed
here (using lazarus free-pascal "www.lazarus.freepascal.org"), including the
test data as text files, are freely available at:
https://sites.google.com/site/pascualmarqui/home/icoh-isolated-effective-coherenceComment: 2014-02-21 pre-print, technical report, KEY Institute for Brain-Mind
Research, University of Zurich, et a
The resting microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow
A brain microstate is characterized by a unique, fixed spatial distribution
of electrically active neurons with time varying amplitude. It is hypothesized
that a microstate implements a functional/physiological state of the brain
during which specific neural computations are performed. Based on this
hypothesis, brain electrical activity is modeled as a time sequence of
non-overlapping microstates with variable, finite durations (Lehmann and
Skrandies 1980, 1984; Lehmann et al 1987). In this study, EEG recordings from
109 participants during eyes closed resting condition are modeled with four
microstates. In a first part, a new confirmatory statistics method is
introduced for the determination of the cortical distributions of electric
neuronal activity that generate each microstate. All microstates have common
posterior cingulate generators, while three microstates additionally include
activity in the left occipital/parietal, right occipital/parietal, and anterior
cingulate cortices. This appears to be a fragmented version of the
metabolically (PET/fMRI) computed default mode network (DMN), supporting the
notion that these four regions activate sequentially at high time resolution,
and that slow metabolic imaging corresponds to a low-pass filtered version. In
the second part of this study, the microstate amplitude time series are used as
the basis for estimating the strength, directionality, and spectral
characteristics (i.e., which oscillations are preferentially transmitted) of
the connections that are mediated by the microstate transitions. The results
show that the posterior cingulate is an important hub, sending alpha and beta
oscillatory information to all other microstate generator regions.
Interestingly, beyond alpha, beta oscillations are essential in the maintenance
of the brain during resting state.Comment: pre-print, technical report, The KEY Institute for Brain-Mind
Research (Zurich), Kansai Medical University (Osaka
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