The identification of mental load has important practical significance for human life. In recent years, studies have used physiological signals of different modalities to measure mental load. However, there is no single indicator that can fully assess mental load, and the fusion of multimodal signals to measure this variable is still controversial. The emergence of simultaneous EEG and fMRI enables researchers to explore brain function, especially working memory, with high temporal and spatial resolution. In this study, we fused EEG and fMRI data on network features to estimate working memory load. By introducing a filter bank, the phase synchronization values covering the full frequency band were extracted as the EEG network features. Then, we proposed the Wasserstein distance to measure functional connectivity in single-trial fMRI data. Finally, Fisher vector was used to fuse these features from the above two modalities, and this method was compared with the direct splicing features. The results showed that the Fisher vector was more effective for recognition than direct splicing was, providing a new option for multimodality feature fusion. In addition, distance-based functional connectivity extraction on the individual level enriches the range of tools with which to study brain network.
Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Because the number of labeled samples is limited by the financial and safety consideration during fMRI data acquirement, it is not easy to train a robust classifier for fMRI data. Recently, semi-supervised learning has been proposed to train the classifier using both labeled training data and unlabeled data. Moreover, sparse representation based classification (SRC) has seldom been applied to fMRI data, although it exhibits a state-of-the-art classification performance in image processing. In this study, we proposed semi-supervised SRC with random sample subset ensemble strategy (semiSRC-RSSE) that used the average of class-specific coefficients as the SRC classification criterion and dynamically update the training dataset using the random sample subset ensemble method to measure the confidence of the prediction of each test sample. The results of the simulated and real fMRI experiments showed that semiSRC-RSSE method largely improved the classification accuracy of SRC and had better performance than support vector machine (SVM) and semi-supervised SVM with the random sample subset ensemble strategy (semiSVM-RSSE).
Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.
The functional magnetic resonance imaging (fMRI) researches on working memory have found that activation of cortical areas appeared dependent on memory load, and event-related potentials (ERP) studies have demonstrated that amplitudes of P300 decreased significantly when working memory load increased. However, the cortical activities related with P300 amplitudes under different memory loads remains unclear. Joint fMRI and EEG analysis which fusions the time and spatial information in simultaneous EEG-fMRI recording can reveal the regional activation at each ERP time point. In this paper, we first used wavelet transform to obtain the single-trial amplitudes of P300 caused by a digital N-back task in the simultaneous EEG-fMRI recording as the ERP feature sequences. Then the feature sequences in 1-back condition and 3-back condition were introduced into general linear model (GLM) separately as parametric modulations to compare the cortical activation under different memory loads. The results showed that the average amplitudes of P300 in 3-back significantly decreased than that in 1-back, and the activities induced by ERP feature sequences in 3-back also significantly decreased than that in the 1-back, including the insular, anterior cingulate cortex, right inferior frontal gyrus, and medial frontal gyrus, which were relevant to the storage, monitoring, and manipulation of information in working memory task. Moreover, the difference in the activation caused by ERP feature showed a positive correlation with the difference in behavioral performance. These findings demonstrated the locations of P300 amplitudes differences modulated by the memory load and its relationship with the behavioral performance.
Impervious surface is an important part of urban underlying surface, as well as an important monitoring index for city ecological system and environment changes. However, accurate impervious surface extraction is still a challenge. This paper uses the color, shape and overall heterogeneity features from the high spatial resolution remote sensing image to extract the impervious surface. An edge-based image segmentation algorithm is put forward to fuse heterogeneous objects which integrates edge features and multi-scale segmentation algorithm and uses the edge information to guide image objects generation. Results showed that this method can greatly improve the accuracy of image segmentation. Accuracy assessment indicated that the overall impervious surface classification accuracy and a Kappa coefficient yield 87% and 0.84, respectively.
Many studies have reported that discrete cortical areas in the ventral temporal cortex of humans were correlated with the perception of pictures visual stimuli. Moreover, event-related potentials caused by different kinds of picture stimuli
showed different amplitude levels of N170 which was maximal over occipito-temporal electrode sites. However, the
phenomenon which is mentioned above may be correlated with some local bold signal change, and where is the change happened is still unclear. Recently, research for EEG-fMRI has been widely performed through General Linear Model (GLM) to find the relationship between some feature of the ERP component and the activation of local brain area. In our study, we dealt with the simultaneously recorded EEG-fMRI data of picture stimuli to find the correlation between the change of the N170’s amplitude and the BOLD signal. The amplitudes of the N170 component from the average ERPs of 4 different kinds of picture stimuli were extracted from the EEG data and the activation map for the same stimuli was provided based on the fMRI data. GLM was performed including regressors that could represent the change of the N170’s amplitude. Our result showed that fusiform and occipital gyrus were activated by the parametric design and were overlapped by the activation map of the common fMRI design. Thus we might infer that these regions had relationship with the change of the amplitudes of N170. Our research may contribute to location of the source of N170 and bring a new approach for the parameter design of the fMRI signal in EEG-fMRI analysis.
The functional magnetic resonance imaging (fMRI) research on face processing have found that the significant
activation by face stimuli mainly locailized at the occipital temporal lobe especilly the fusiform gyrus. However, fMRI
cannot reflect the face processing as time changes. Event-related potential (ERP) can record electrophysiological
changes induced by neuronal activation in time, but spatial information is not well localized. Fusing fMRI and ERP data can perform that how the fMRI activation changes as time move at each ERP time point. Although most of fuse methods perform to analysis by constraint ERP or fMRI data, joint independent component analysis (jICA) method can equally use the ERP and fMRI data and simultaneously examine electrophysiologic and hemodynamic response. In this paper, we use jICA method to analysis two modalities in common data space in order to examine the dynamics of face stimuli response. The results showed that the ERP component N170 response associated with middle occipital gyrus, fusiform gyrus, inferior occipital gyrus, superior temporal gyrus and parahippocampa gyrus for face. Likewise, for non-face, the N170 component was mainly related to parahippocampa gyrus, middle occipital gyrus and inferior occipital gyrus. Further studying on the correlation of the localized ERP response and corresponding average ERP, it was also concluded that the spatial activations related to N170 response induced by face stimulus located in fusiform gyrus, and that induced by non-face stimulus located in parahippocampa gyrus. From the result, fusing fMRI and ERP data by jICA not only provides the time information on fMRI and the spatial source of ERP component, but also reflects spatiotemporal change during face processing.
Magnetic resonance diffusion tensor imaging (DTI) is a kind of effective measure to do non-invasive investigation on
brain fiber structure at present. Studies of fiber tracking based on DTI showed that there was structural connection of
white matter fiber among the nodes of resting-state functional network, denoting that the connection of white matter was
the basis of gray matter regions in functional network. Nevertheless, relationship between these structure connectivity
regions and functional network has not been clearly indicated. Moreover, research of fMRI found that activation of
default mode network (DMN) in Alzheimer's disease (AD) was significantly descended, especially in hippocampus and
posterior cingulated cortex (PCC). The relationship between this change of DMN activity and structural connection
among functional networks needs further research. In this study, fast marching tractography (FMT) algorithm was
adopted to quantitative calculate fiber connectivity value between regions, and hippocampus and PCC which were two
important regions in DMN related with AD were selected to compute white matter connection region between them in
elderly normal control (NC) and AD patient. The fiber connectivity value was extracted to do the correlation analysis
with activity intensity of DMN. Results showed that, between PCC and hippocampus of NC, there exited region with
significant high connectivity value of white matter fiber whose performance has relatively strong correlation with the
activity of DMN, while there was no significant white matter connection region between them for AD patient which
might be related with reduced network activation in these two regions of AD.
Real-time fMRI (rtfMRI) is a new technology which allows human subjects to observe and control their own BOLD
signal change from one or more localized brain regions during scanning. Current rtfMRI-neurofeedback studies mainly
focused on the target region itself without considering other related regions influenced by the real-time feedback.
However, there always exits important directional influence between many of cooperative regions. On the other hand,
rtfMRI based on motor imagery mainly aimed at somatomotor cortex or primary motor area, whereas supplement motor
area (SMA) was a relatively more integrated and pivotal region. In this study, we investigated whether the activities of
SMA can be controlled utilizing different motor imagery strategies, and whether there exists any possible impact on an
unregulated but related region, primary motor cortex (M1). SMA was first localized using overt finger tapping task, the
activities of SMA were feedback to subjects visually on line during each of two subsequent imagery motor movement
sessions. All thirteen healthy participants were found to be able to successfully control their SMA activities by self-fit
imagery strategies which involved no actual motor movements. The activation of right M1 was also found to be
significantly reduced in both intensity and extent with the neurofeedback process targeted at SMA, suggestive that not
only the part of motor cortex activities were influenced under the regulation of a key region SMA, but also the increased
difference between SMA and M1 might reflect the potential learning effect.
Neural mechanism of auditory-visual speech integration is always a hot study of multi-modal perception. The
articulation conveys speech information that helps detect and disambiguate the auditory speech. As important
characteristic of EEG, oscillations and its synchronization have been applied to cognition research more and more. This
study analyzed the EEG data acquired by unimodal and bimodal stimuli using time frequency and phase synchrony
approach, investigated the oscillatory activities and its synchrony modes behind evoked potential during auditory-visual
integration, in order to reveal the inherent neural integration mechanism under these modes. It was found that beta
activity and its synchronization differences had relationship with gesture N1-P2, which happened in the earlier stage of
speech coding to pronouncing action. Alpha oscillation and its synchronization related with auditory N1-P2 might be mainly responsible for auditory speech process caused by anticipation from gesture to sound feature. The visual gesture changing enhanced the interaction of auditory brain regions. These results provided explanations to the power and connectivity change of event-evoked oscillatory activities which matched ERPs during auditory-visual speech integration.
KEYWORDS: Independent component analysis, Brain, Functional magnetic resonance imaging, Head, Data analysis, Data acquisition, Shape memory alloys, Data processing, Neuroimaging, Signal processing
Real-time functional magnetic resonance imaging (rtfMRI) is a new technique which can present (feedback) brain
activity during scanning. Through fast acquisition and online analysis of BOLD signal, fMRI data are processed within
one TR. Current rtfMRI provides an activation map under specific task mainly through the GLM analysis to select region
of interest (ROI). This study was based on independent component analysis (ICA) and used the result of fast ICA
analysis to select the node of the functional network as the ROI. Real-time brain activity within the ROI was presented to
the subject who needed to find strategies to control his brain activity. The whole real-time processes involved three parts:
pre-processing (including head motion correction and smoothing), fast ICA analysis and feedback. In addition, the result
of fast head motion correction was also presented to the experimenter in a curve diagram. Based on the above analysis
processes, a real time feedback experiment with a motor imagery task was performed. An overt finger movement task as
localizer session was adopted for ICA analysis to get the motor network. Supplementary motor area (SMA) in such
network was selected as the ROI. During the feedback session, the average of BOLD signals within ROI was presented
to the subjects for self-regulation under a motor imagery task. In this experiment, TR was 1.5 seconds, and the whole
time of processing and presentation was within 1 second. Experimental results not only showed that the SMA was
controllable, but also proved that the analysis method was effective.
KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Data modeling, Brain, Signal to noise ratio, Interference (communication), Data analysis, Computer simulations, Superposition, Data processing
General linear model (GLM) and independent component analysis (ICA) are widely used methods in the community of
functional magnetic resonance imaging (fMRI) data analysis. GLM and ICA are all assuming that fMRI components are
location locked. Here we extend the Differentially variable component analysis (dVCA) and introduce it into fMRI data
to analyze the transient changes during fMRI experiments which are ignored in GLM and ICA. We apply the extended
dVCA to model fMRI images as the linear combination of ongoing activity and multiple fMRI components. We test our
extended dVCA method on simulated images that mimicked the fMRI slice images containing two components, and
employ the iterative maximum a posteriori (MAP) solution succeed to estimate each component's time-invariant spatial
patterns, and its time-variant amplitude scaling factors and location shifts. The extended dVCA algorithm also identify
two fMRI components that reflect the fact of hemispheric asymmetry for motor area in another test with fMRI data
acquired with the block design task of right/left hand finger tapping alternately. This work demonstrates that our
extended dVCA method is robustness to detect the variability of the fMRI components that maybe existent during the
fMRI experiments.
Recently, evidences from fMRI studies have shown that there was decreased activity among the default-mode network in
Alzheimer's disease (AD), and DTI researches also demonstrated that demyelinations exist in white matter of AD
patients. Therefore, combining these two MRI methods may help to reveal the relationship between white matter
damages and alterations of the resting state functional connectivity network. In the present study, we tried to address this
issue by means of correlation analysis between DTI and resting state fMRI images. The default-mode networks of AD
and normal control groups were compared to find the areas with significantly declined activity firstly. Then, the white
matter regions whose fractional anisotropy (FA) value correlated with this decline were located through multiple
regressions between the FA values and the BOLD response of the default networks. Among these correlating white
matter regions, those whose FA values also declined were found by a group comparison between AD patients and
healthy elderly control subjects. Our results showed that the areas with decreased activity among default-mode network
included left posterior cingulated cortex (PCC), left medial temporal gyrus et al. And the damaged white matter areas
correlated with the default-mode network alterations were located around left sub-gyral temporal lobe. These changes
may relate to the decreased connectivity between PCC and medial temporal lobe (MTL), and thus correlate with the
deficiency of default-mode network activity.
In vivo white matter tractography by diffusion tensor imaging (DTI) accurately represents the organizational architecture
of white matter in the vicinity of brain lesions and especially ischemic brain. In this study, we suggested an improved
fiber tracking algorithm based on TEND, called TENDAS, for tensor deflection with adaptive stepping, which had been
introduced a stepping framework for interpreting the algorithm behavior as a function of the tensor shape (linear-shaped
or not) and tract history. The propagation direction at each step was given by the deflection vector. TENDAS
tractography was used to examine a 17-year-old recovery patient with congenital right hemisphere artery stenosis
combining with fMRI. Meaningless picture location was used as spatial working memory task in this study. We detected
the shifted functional localization to the contralateral homotypic cortex and more prominent and extensive left-sided
parietal and medial frontal cortical activations which were used directly as seed mask for tractography for the
reconstruction of individual spatial parietal pathways. Comparing with the TEND algorithms, TENDAS shows smoother
and less sharp bending characterization of white matter architecture of the parietal cortex. The results of this preliminary
study were twofold. First, TENDAS may provide more adaptability and accuracy in reconstructing certain anatomical
features, whereas it is very difficult to verify tractography maps of white matter connectivity in the living human brain.
Second, our study indicates that combination of TENDAS and fMRI provide a unique image of functional cortical
reorganization and structural modifications of postischemic spatial working memory.
Magnetic resonance image (MRI) has provided an imageological support into the clinical diagnosis and prediction of
Alzheimer disease (AD) progress. Currently, the clinical use of MRI data on AD diagnosis is qualitative via visual
inspection and less accurate. To provide assistance to physicians in improving the accuracy and sensitivity of the AD
diagnose and the clinical outcome of the disease, we developed a computer-assisted analysis package that analyzed the
MRI data of an individual patient in comparison with a group of normal controls. The package is based on the principle
of the well established and widely used voxel-based morphometry (VBM) and SPM software. All analysis procedure is
automated and streamlined. With only one mouse-click, the whole procedure was finished within 15 minutes. With the
interactive display and anatomical automatic labeling toolbox, the final result and report supply the brain regional
structure difference, the quantitative assessment and visual inspections by physicians and scientific researcher. The brain
regions which affected by AD are consonant in the main with the clinical diagnosis, which are reviewed by physicians.
In result, the computer package provides physician with an automatic and assistant tool for prediction using MRI. This
package could be valuable tool assisting physicians in making their clinical diagnosis decisions.
Effective connectivity of brain regions based on brain data (e.g. EEG, fMRI, etc.) is a focused research at present. Many
researchers tried to investigate it using different methods. Granger causality model (GCM) is presently used to
investigate effective connectivity of brain regions more and more. It can explore causal relationship between time series,
meaning that if a time-series y causes x, then knowledge of y should help predict future values of x. In present work,
time invariant GCM was applied to fMRI data considering slow changing of blood oxygenation level dependent
(BOLD). The time invariant GCM often requires determining model order, estimating model parameters and significance
test. In particular, we extended significance test method to make results more reasonable. The fMRI data were acquired
from finger movement experiment of two right-handed subjects. We obtained the activation maps of two subjects using
SPM'2 software firstly. Then we chose left SMA and left SMC as regions of interest (ROIs) with different radiuses, and
calculated causality from left SMA to left SMC using the mean time courses of the two ROIs. The results from both
subjects showed that left SMA influenced on left SMC. Hence GCM was suggested to be an effective approach in
investigation of effective connectivity based on fMRI data.
In contrast to the FMRI methods widely used up to now, this method try to understand more profoundly how the brain
systems work under sentence processing task map accurately the spatiotemporal patterns of activity of the large
neuronal populations in the human brain from the analysis of ERP data recorded on the brain scalp. In this study, an
event-related brain potential (ERP) paradigm to record the on-line responses to the processing of sentences is chosen as
an example. In order to give attention to both utilizing the ERPs' temporal resolution of milliseconds and overcoming
the insensibility of cerebral location ERP sources, we separate these sources in space and time based on a combined
method of independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. ICA blindly
separate the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct
or overlapping brain or extra-brain sources. And then the spatial maps associated with each ICA component are
analyzed, with use of LORETA to uniquely locate its cerebral sources throughout the full brain according to the
assumption that neighboring neurons are simultaneously and synchronously activated. Our results show that the cerebral
computation mechanism underlies content words reading is mediated by the orchestrated activity of several spatially
distributed brain sources located in the temporal, frontal, and parietal areas, and activate at distinct time intervals and are
grouped into different statistically independent components. Thus ICA-LORETA analysis provides an encouraging and
effective method to study brain dynamics from ERP.
Kewei Chen, Xiaolin Ge, Li Yao, Dan Bandy, Gene Alexander, Anita Prouty, Christine Burns, Xiaojie Zhao, Xiaotong Wen, Ronald Korn, Michael Lawson, Eric Reiman
Having approved fluorodeoxyglucose positron emission tomography (FDG PET) for the diagnosis of Alzheimer's disease (AD) in some patients, the Centers for Medicare and Medicaid Services suggested the need to develop and test analysis techniques to optimize diagnostic accuracy. We developed an automated computer package comparing an individual's FDG PET image to those of a group of normal volunteers. The normal control group includes FDG-PET images from 82 cognitively normal subjects, 61.89±5.67 years of age, who were characterized demographically, clinically, neuropsychologically, and by their apolipoprotein E genotype (known to be associated with a differential risk for AD). In addition, AD-affected brain regions functionally defined as based on a previous study (Alexander, et al, Am J Psychiatr, 2002) were also incorporated. Our computer package permits the user to optionally select control subjects, matching the individual patient for gender, age, and educational level. It is fully streamlined to require minimal user intervention. With one mouse click, the program runs automatically, normalizing the individual patient image, setting up a design matrix for comparing the single subject to a group of normal controls, performing the statistics, calculating the glucose reduction overlap index of the patient with the AD-affected brain regions, and displaying the findings in reference to the AD regions. In conclusion, the package automatically contrasts a single patient to a normal subject database using sound statistical procedures. With further validation, this computer package could be a valuable tool to assist physicians in decision making and communicating findings with patients and patient families.
We report significant differences in UV resonance Raman (UVRR) spectra of DNA samples from normal and cancerous tissues. The four bases of DNA, adenosine, thymine, guanosine and cytidine, can be enhanced in UVRR spectra, and their intensities are very sensitive to base stacking and DNA H-bonding. 14 DNA samples from patients at different stages of ovarian cancer, 5 from normal, 2 from primary, 3 from metastasis primary and 4 from distant metastasis tumor tissues, were characterized by 257, 238, 229, 220 and 210 nm-excited UVRR spectra. Raman spectral difference between normal and tumor DNA could be readily detected.
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