Spatial Independent Component Research Articles

Visual perceptual learning modulates decision network in the human brain: The evidence from psychophysics, modeling, and functional magnetic resonance imaging.

Perceptual learning refers to improved perceptual performance after intensive training and was initially suggested to reflect long-term plasticity in early visual cortex. Recent behavioral and neurophysiological evidence further suggested that the plasticity in brain regions related to decision making could also contribute to the observed training effects. However, how perceptual learning modulates the responses of decision-related regions in the human brain remains largely unknown. In the present study, we combined psychophysics and functional magnetic resonance imaging (fMRI), and adopted a model-based approach to investigate this issue. We trained participants on a motion direction discrimination task and fitted their behavioral data using the linear ballistic accumulator model. The results from model fitting showed that behavioral improvement could be well explained by a specific improvement in sensory information accumulation. A critical model parameter, the drift rate of the information accumulation, was correlated with the fMRI responses derived from three spatial independent components: ventral premotor cortex (PMv), supplementary eye field (SEF), and the fronto-parietal network, including intraparietal sulcus (IPS) and frontal eye field (FEF). In this decision network, we found that the behavioral training effects were accompanied by signal enhancement specific to trained direction in PMv and FEF. Further, we also found direction-specific signal reduction in sensory areas (V3A and MT+), as well as the strengthened effective connectivity from V3A to PMv and from IPS to FEF. These findings provide evidence for the learning-induced decision refinement after perceptual learning and the brain regions that are involved in this process.

THE STUDY OF RELATIONSHIPS BETWEEN TERRAIN AND THE SPECTRAL CHARACTERISTICS OF ECOSYSTEMS IN UKRAINIAN CARPATHIANS USING REMOTE SENSING SATELLITE DATA

Ukrainian Carpathians like many other mountainous areas possess significant ecological and societal values, thus requiring careful spatial management. This in its turn requires the careful consideration of relations between abiotic conditions and various ecosystem properties. While detailed spatial databases of these conditions and properties are still lacking for many regions, currently available remote sensing satellite data are the indispensable source of the spatially distributed detailed ecological information. Digital elevation models and multispectral images are especially useful for the acquisition of detailed, up-to-date and cheap spatial data on ecological factors and conditions. In this study the modern statistical methods of principal component analysis and the canonical correlation analysis have been applied to reveal the structure in the relationships between land surface reflectance values in different wavelength ranges and to investigate the relationships between the revealed image principal components and the set of topographic characteristic and indexes for the study area in Ukrainian Carpathians. Principal component analysis of LANDSAT 7 ETM+ image bands allowed to isolate three independent spatial components that together reveal the structure in the relationships between the land surface reflectance values in different spectral channels. Canonical correlation analysis allowed to discern two main canonical roots, proving the existence of rather strong relationships between the land surface morphometry and its spectral characteristics. The most important terrain factor that influences the land and vegetation cover structure appeared to be the elevation, while the other significant factors included a set of topographic characteristics and indices. Key words: remotely sensed imagery, digital elevation models, topographic variables, principal component analysis, canonical analysis, Ukrainian Carpathians.

Peculiarities in Interaction of Independent Components of Resting-State fMRI Signal in Patients with Mild Depressions.

Some aspects of resting-state fMRI signal can be the key markers of depression. fMRI was recoded over 4 min in evidently healthy persons (N=21) and in patients with mild depression (N=21). The data were separated into the independent spatial components, and the strength of their association with established brain networks was analyzed. The patients with mild depression were characterized with greater correlations between the components representing the ventral and dorsal subdivisions of default mode network (DMN), whereas correlations between the components relating to cerebellum and to the left hemisphere language system were less pronounced. The data revealed a significant role of DMN in the development of affective abnormalities and importance of its functional state as a probable marker of mild depression.

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Independent component analysis of cycle resolved combustion images from a spark ignition optical engine

This paper reports on the application of independent component analysis (ICA) to 2D cycle-resolved images of the luminous combustion, collected in a port fuel injection spark ignition optically accessible engine. The method of ICA is employed for the identification of the independent spatial components, which along with the corresponding time dependent coefficients, represent the spatiotemporal evolution of the luminous combustion during a single cycle and over a number of cycles. ICA is applied on the data preprocessed by proper orthogonal decomposition (POD). POD-filtered ICA permits to determine only few dominant and relatively easy interpretable independent components. Successively, and for comparison, ICA is applied to the non-truncated data. It is demonstrated that ICA applied to single cycle permits to extract independent structures, clearly separated in time. The time dependent coefficients correlate well with the integral flame luminosity, and characterize time evolution of the combustion pattern in the chamber. The analysis over several cycles shows that independent components carry information about the dominant morphology of the cyclic variations. The low correlation of the corresponding (in terms of time succession) components for successive cycles is in agreement with the high spatial variability of the combustion process in spark ignition engines, mainly due to the combustion of fuel pockets created in the combustion chamber by the injection process. A different approach is then proposed, enabling separation of sources corresponding to objects moving in the field of view. Each whole sequence of 2D images (video) is considered as a single observed mixture of independent signals. Both linear and nonlinear mixing models are considered, and artificial examples are used to illustrate features and limitations of linear ICA when applied to nonlinear mixing. The procedure is successfully applied to a video reporting the motion of a luminous flame over a portion of the cylinder head. ICA separates the moving source from the background, with a mechanism explained by a nonlinear mixing model.

Functional connectivity associated with gait velocity during walking and walking-while-talking in aging: a resting-state fMRI study.

Gait decline is common among older adults and is a risk factor for adverse outcomes. Poor gait performance in dual-task conditions, such as walking while performing a secondary cognitive interference task, is associated with increased risk of frailty, disability, and death. Yet, the functional neural substrates that support locomotion are not well established. We examined the functional connectivity associated with gait velocity in single- (normal pace walking) and dual-task (walking while talking) conditions using resting-state functional Magnetic Resonance Imaging (fMRI). We acquired 6 minutes of resting-state fMRI data in 30 cognitively healthy older adults. Independent components analyses were performed to separate resting-state fMRI data into group-level statistically independent spatial components that correlated with gait velocity in single- and dual-task conditions. Gait velocity in both task conditions was associated with similar functional connectivity in sensorimotor, visual, vestibular, and left fronto-parietal cortical areas. Compared to gait velocity in the single-task condition, the networks associated with gait velocity in the dual-task condition were associated with greater functional connectivity in supplementary motor and prefrontal regions. Our findings show that there are partially overlapping functional networks associated with single- and dual-task walking conditions. These initial findings encourage the future use of resting-state fMRI as tool in developing a comprehensive understanding of age-related mobility impairments.

Temporal Changes of Diffusion Patterns in Mild Traumatic Brain Injury via Group-Based Semi-blind Source Separation

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

Flexible connectivity in the aging brain revealed by task modulations.

Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n-back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time-courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age-related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2-back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling.

Task-Dependent Modulation of Effective Connectivity within the Default Mode Network.

The default mode network (DMN) has recently attracted widespread interest. Previous studies have found that task-related processing can induce deactivation and changes in the functional connectivity of this network. However, it remains unclear how tasks modulate the underlying effective connectivity within the DMN. Using recent advances in dynamic causal modeling (DCM), we investigated the modulatory effect of (gender judgment) task performance on directed connectivity within the DMN. Sixteen healthy subjects were scanned twice: at rest and while performing a gender judgment task. Group independent component analysis was used to identify independent spatial components. Four subject-specific regions of interest (ROIs) were defined according to the ensuing default mode component: the posterior cingulate cortex, the left lateral parietal cortex, the right lateral parietal cortex, and the medial prefrontal cortex. Effective connectivity among these regions was then characterized with stochastic DCM, revealing enhanced (extrinsic) between region connectivity within the DMN during task sessions – and a universal decrease in (intrinsic) self-inhibition – relative to resting sessions. These results suggest a distributed but systematic modulatory effect of cognitive and attentional set on the effective connectivity subtending the DMN: an effect that increases its sensitivity to inputs and may optimize distributed processing during task performance.

Independent spatial patterns: an integration of common spatial patterns with independent component analysis for multi- class discrimination in EEG based brain-computer interfaces

Independent spatial patterns (ISP) aims to integrate common spatial patterns (CSP) and Independent component analysis (ICA) by utilizing the best features of the two methods and at the same time going round some of the caveats associated with them. In the case of ICA importance of components cannot be pre-determined. Principal component analysis (PCA) dimensional reduction does not provide reasonable estimate [1]. A visual selection of ICA components [2] still performed sub-optimally. On the other hand, multi-class implementation of CSP is also plagued with number of issues. It works on heuristics by employing one-vs-rest scheme [3]. The resultant 4 projection matrices (in the case of 4-class problem) creates identification problem: there is no way to determine whether the observed differences in conditions are due to multiple projection matrices or distinct brain activity. No wonder, low detection rate is usually observed for unseen data. Based on the physiological considerations left and right hand movement can be envisioned as (-vely) correlated tasks. That is, a trial associated with left imagination would typically depict de-synchronization over contra-lateral region (around C4) followed by synchronization over ipsi-lateral region (around C3). Almost diametrically reverse phenomenon is observed in the case of right movement imagination. Same line of argument is valid for foot and tongue movement imaginations [4]. Therefore, one pair of correlated signals was separated from the other pair into distinct clusters- each containing only 4 components - by employing CSP. On account of CSP criterion of maximization of kurtosis by Eigen value decomposition [5], these clusters are nothing but orthogonal independent components. However, orthogonality with respect to independence criterion is an unnecessary constraint. Therefore, the basis was further rotated by employing infomax principle [6] to achieve maximal independence. Experimental paradigm and details of feature extraction and classification scheme can be found in [2]. Succinctly, analysis was performed on 4 class motor imagery data sets consisted of 8 subjects and two sessions. Spatial filters and classifiers were built on the first session and employed on second session. For estimating ICA and ISP data sets were pre-filtered with same settings. Evaluation and estimation of CSP is detailed in [2]. It should be noted that ISP has only one projection matrix and utilized 8 components which was significantly less than (full) 22 components of ICA and 24 CSP projections. The performance of ISP was found to be marginally better than ICA and slightly worse in comparison with CSP (see Figure ​Figure1).1). However, ISP has the potential of improving further its overall performance by the optimization of infomax. This can be achieved by incorporating prior information available in the form of distinct clusters as constraint in infomax cost function. Therefore, ICA decomposition can be performed by minimizing the information between clusters while at the same time maximizing information of individual components of each cluster. The work in this direction is in progress. Figure 1

P02-159 - Magnetic resonance imaging of baseline brain perfusion in major depressive disorder

IntroductionContinuous arterial spin labelling (cASL) is a non-invasive magnetic resonance imaging method increasingly used to study human brain function and perfusion physiology.ObjectivesIn this study, we investigated distinct patterns of cerebral blood flow (CBF) in MDD patients by means of resting state cASL.Methods11 healthy controls and 11 patients with MDD according to DSM-IV criteria were included. The individual cASL images underwent data preprocessing steps including the generation of perfusion-weighted images, followed by conversion to quantified CBF. An analysis of temporally coherent CBF patterns was performed using a spatial Independent Component Analysis.ResultsBoth groups showed distinct blood flow patterns in a medial frontoparietal and lateral temporal component (“default mode” network), a medial temporal and anterior cingulate component (“limbic” network) and bilateral prefrontal and parietal component (“cognitive” network). Within the “limbic” network, MDD patients showed a pattern of increased CBF in the bilateral ventrolateral prefrontal, cingulate, striatal and hippocampal regions. Within the “cognitive” network, MDD patients showed a pattern of increased CBF in left dorsolateral prefrontal and the inferior parietal areas. Decreased perfusion in MDD patients was found in the right precuneus, the right inferior parietal cortex, the left posterior cingulate and the left lateral temporal cortex.ConclusionsThese preliminary data demonstrate the sensitivity of cASL techniques for detecting resting state CBF changes in MDD patients. Thus, cASL studies in MDD could provide valuable information with regard to neurobiologically characterized endophenotypes, while resting state CBF measures could serve as objective predictors of treatment response in future studies.

A variant of logistic transfer function in Infomax and a postprocessing procedure for independent component analysis applied to fMRI data

Independent component analysis with Infomax algorithm can separate functional magnetic resonance imaging (fMRI) data into independent spatial components (brain activation maps) and their associated time courses. In the current study, we propose a variant of the logistic transfer function in Infomax, referred to as a-logistic Infomax, and a postprocessing procedure to combine a consistently task-related (CTR) component with transiently task-related (TTR) components for a better definition of brain functional localization. This a-logistic Infomax introduced parameter a into the standard logistic transfer function of conventional Infomax algorithm. For postprocessing method, we suggest the use of a stepwise linear regression of CTR and TTR components to fit reference function and then to sum up with different weights only those with significant contributions to the reference function in order to obtain a task component activation map. The effectiveness of both approaches on separating components and functional localization was evaluated with simulated and real fMRI data.

Filtering SPOT imagery by kriging analysis

Digital images are rich in data, but in many instances they are so complex as to require spatial filtering to distinguish the structures in them and facilitate interpretion. The filtering can be done geostatistically by kriging analysis. It proceeds in two stages. The first involves modelling the correlation structure in the imagery by decomposing the variogram into independent spatial components. The second takes each component in turn and kriges it, thereby filtering it from the others. The paper describes the theory and illustrates it with an example of an analysis of a SPOT image in a forested landscape of the south-eastern United States. Variograms of the three wavebands, originally recorded as digital numbers and for the red and infrared transformed to the logarithms, revealed spatial variation on two distinct scales with effective ranges of 300m and 3km. These variograms and that of the Normalized Difference Vegetation Index (NDVI) were fitted by nested (double) exponential models. The two spatial components in the scene were then estimated separately by kriging analysis and mapped. The maps of NDVI are displayed and compared with data from ground survey. The shortrange component represents an intricate pattern of dissection and its associated vegetation. The long-range component is that of the major landform units and associated ground cover classes.

Analysis of fMRI data by blind separation into independent spatial components

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129–1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component “map”), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11–20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks. Hum. Brain Mapping 6:160–188, 1998. © 1998 Wiley-Liss, Inc.

Analysis of fMRI data by blind separation into independent spatial components

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

LOCAL SUPERSYMMETRY IN ONE-LOOP QUANTUM COSMOLOGY

The contribution of physical degrees of freedom to the one-loop amplitudes of Euclidean supergravity is here evaluated in the case of flat Euclidean backgrounds bounded by a three-sphere, recently considered in perturbative quantum cosmology. In Euclidean supergravity, the spin-[Formula: see text] potential has the pair of independent spatial components [Formula: see text]. Massless gravitinos are here subject to the following local boundary conditions on [Formula: see text], where [Formula: see text] is the Euclidean normal to the three-sphere boundary. The physical degrees of freedom (denoted by PDF) are picked out imposing the supersymmetry constraints and choosing the gauge condition [Formula: see text], [Formula: see text]. These local boundary conditions are then found to imply the eigenvalue condition [Jn + 2 (E)]2 - [Jn + 3(E)]2 = 0, ∀ n ≥ 0, with degeneracy (n+4)(n+1). One can thus apply again a zeta-function technique previously used for massless spin-½ fields. The PDF contribution to the full ζ(0) value is found to be [Formula: see text]. Remarkably, for the massless gravitino field the PDF method and local boundary conditions lead to a result for ζ(0) which is equal to the PDF value one obtains using spectral boundary conditions on S3.

Structure and spin‐purity conditions for reduced density matrices of arbitrary order

AbstractFor arbitrary k, the separation of spin variables is performed in the reduced density matrix of the kth order (RDM‐k) on the basis of the Fock coordinate function method. The independent spatial components of RDM‐k are analyzed. For RDM‐k of the total spin eigenstate, their number is proved never to exceed its spin multiplicity 2s + 1. Integral and other nontrivial interrelations between spatial components are established which turn out to be the necessary and sufficient conditions of spin purity of a wavefunction corresponding to a given RDM‐k. It is shown that the r‐rank k‐particle spin distribution matrix F, defined as a spatial coefficient at the spin‐tensorial operator of rank r in the RDM‐k expansion, can be obtained by reduction of the (k + r)‐particle charge density matrix F. In particular, all spatial components of RDM‐2 are explicitly expressed in terms of the four‐electron charge density matrix only. This allows us to purpose some approximative formulas for the McWeeny‐Mizuno spin–orbit and spin–spin coupling functions in the case of the weak spin contamination.