87 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
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
Cortical electrical activity changes in healthy aging using EEG-eLORETA analysis
Brain aging causes loss of synaptic spines, neuronal apoptosis, and a reduction in neurotransmitter levels. These aging phenomena disturb cortical electrical activity and its synchronization with connected regions. Previous electroencephalography (EEG) studies reported an age-related decrease in electrical activity in the alpha frequency band at occipital, parietal, and temporal areas as well as a decrease in occipital delta activity. However, there is an ongoing debate about whether there is an increase or decrease of the activity in other frequency bands with aging due to inconsistent study findings. In this study, we aimed to detect age-related changes of cortical electrical activities in all five frequency bands (delta, theta, alpha, beta, and gamma) in a large sample of healthy subjects for the first time. Using eLORETA (exact low-resolution brain electromagnetic tomography) analysis, we applied an eLORETA source estimation method to resting-state EEG data in 147 healthy subjects (median age 55, IQR 26.5–67.0) to obtain cortical electrical activity and assessed age-related changes in this activity using correlation analysis with multiple comparison correction. The combination of the eLORETA source estimation method and correlation analysis implemented in eLORETA software detected age-related changes in specific cortical regions for each frequency band: (1) delta and theta cortical electrical activities decreased at the occipital area with age, (2) alpha cortical electrical activity decreased at the occipitoparietotemporal areas with age, (3) beta cortical electrical activity increased at the insula, sensorimotor area, supplementary motor area, premotor area, and right temporal areas with age (most significant correlation at the right insula), (4) gamma cortical electrical activity increased at the frontoparietal and left temporal areas with age. These findings extend previous EEG study findings and provide valuable information related to mechanisms of healthy aging. Overall, our findings revealed that even healthy aging greatly affects cortical electrical activities in a region-specific way
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
Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"
The problem of interest here is the study of brain functional and effective
connectivity based on non-invasive EEG-MEG inverse solution time series. These
signals generally have low spatial resolution, such that an estimated signal at
any one site is an instantaneous linear mixture of the true, actual, unobserved
signals across all cortical sites. False connectivity can result from analysis
of these low-resolution signals. Recent efforts toward "unmixing" have been
developed, under the name of "leakage correction". One recent noteworthy
approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which
forces the inverse solution signals to have zero cross-correlation at lag zero.
One goal is to show that Colclough's method produces false human connectomes
under very broad conditions. The second major goal is to develop a new
solution, that appropriately "unmixes" the inverse solution signals, based on
innovations orthogonalization. The new method first fits a multivariate
autoregression to the inverse solution signals, giving the mixed innovations.
Second, the mixed innovations are orthogonalized. Third, the mixed and
orthogonalized innovations allow the estimation of the "unmixing" matrix, which
is then finally used to "unmix" the inverse solution signals. It is shown that
under very broad conditions, the new method produces proper human connectomes,
even when the signals are not generated by an autoregressive model.Comment: preprint, technical report, under license
"Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND
4.0)", https://creativecommons.org/licenses/by-nc-nd/4.0
Functionally aberrant electrophysiological cortical connectivities in first episode medication-naive schizophrenics from three psychiatry centers
Functional dissociation between brain processes is widely hypothesized to
account for aberrations of thought and emotions in schizophrenic patients. The
typically small groups of analyzed schizophrenic patients yielded different
neurophysiological findings, probably because small patient groups are likely
to comprise different schizophrenia subtypes. We analyzed multichannel eyes-
closed resting EEG from three small groups of acutely ill, first episode
productive schizophrenic patients before start of medication (from three
centers: Bern N = 9; Osaka N = 9; Berlin N = 12) and their controls. Low
resolution brain electromagnetic tomography (LORETA) was used to compute
intracortical source model-based lagged functional connectivity not biased by
volume conduction effects between 19 cortical regions of interest (ROIs). The
connectivities were compared between controls and patients of each group.
Conjunction analysis determined six aberrant cortical functional
connectivities that were the same in the three patient groups. Four of these
six concerned the facilitating EEG alpha-1 frequency activity; they were
decreased in the patients. Another two of these six connectivities concerned
the inhibiting EEG delta frequency activity; they were increased in the
patients. The principal orientation of the six aberrant cortical functional
connectivities was sagittal; five of them involved both hemispheres. In sum,
activity in the posterior brain areas of preprocessing functions and the
anterior brain areas of evaluation and behavior control functions were
compromised by either decreased coupled activation or increased coupled
inhibition, common across schizophrenia subtypes in the three patient groups.
These results of the analyzed three independent groups of schizophrenics
support the concept of functional dissociation
Functional localization and effective connectivity of cortical theta and alpha oscillatory activity during an attention task
Objectives: The aim of this paper is to investigate cortical electric neuronal activity as an indicator of brain function, in a mental arithmetic task that requires sustained attention, as compared to the resting state condition. The two questions of interest are the cortical localization of different oscillatory activities, and the directional effective flow of oscillatory activity between regions of interest, in the task condition compared to resting state. In particular, theta and alpha activity are of interest here, due to their important role in attention processing.
Methods: We adapted mental arithmetic as an attention ask in this study. Eyes closed 61-channel EEG was recorded in 14 participants during resting and in a mental arithmetic task (“serial sevens subtraction”). Functional localization and connectivity analyses were based on cortical signals of electric neuronal activity estimated with sLORETA (standardized low resolution electromagnetic tomography). Functional localization was based on the comparison of the cortical distributions of the generators of oscillatory activity between task and resting conditions. Assessment of effective connectivity was based on the iCoh (isolated effective coherence) method, which provides an appropriate frequency decomposition of the directional flow of oscillatory activity between brain regions. Nine regions of interest comprising nodes from the dorsal and ventral attention networks were selected for the connectivity analysis.
Results: Cortical spectral density distribution comparing task minus rest showed significant activity increase in medial prefrontal areas and decreased activity in left parietal lobe for the theta band, and decreased activity in parietal-occipital regions for the alpha1 band. At a global level, connections among right hemispheric nodes were predominantly decreased during the task condition, while connections among left hemispheric nodes were predominantly increased. At more detailed level, decreased flow from right inferior frontal gyrus to anterior cingulate cortex for theta, and low and high alpha oscillations, and increased feedback (bidirectional flow) between left superior temporal gyrus and left inferior frontal gyrus, were observed during the arithmetic task.
Conclusions: Task related medial prefrontal increase in theta oscillations possibly corresponds to frontal midline theta, while parietal decreased alpha1 activity indicates the active role of this region in the numerical task. Task related decrease of intracortical right hemispheric connectivity support the notion that these nodes need to disengage from one another in order to not interfere with the ongoing numerical processing. The bidirectional feedback between left frontal-temporal-parietal regions in the arithmetic task is very likely to be related to attention network working memory function.
Significance: The methods of analysis and the results presented here will hopefully contribute to clarify the roles of the different EEG oscillations during sustained attention, both in terms of their functional localization and in terms of how they integrate brain function by supporting information flow between different cortical regions. The methodology presented here might be clinically relevant in evaluating abnormal attention function
bioNMF: a versatile tool for non-negative matrix factorization in biology
BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at
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