Static and dynamic characteristics of cerebral blood flow during the resting state in schizophrenia

Background: The cerebral network that is active during rest and is deactivated during goal-oriented activity is called the default mode network (DMN). It appears to be involved in self-referential mental activity. Atypical functional connectivity in the DMN has been observed in schizophrenia. One hypothesis suggests that pathologically increased DMN connectivity in schizophrenia is linked with a main symptom of psychosis, namely, misattribution of thoughts. Methods: A resting-state pseudocontinuous arterial spin labeling (ASL) study was conducted to measure absolute cerebral blood flow (CBF) in 34 schizophrenia patients and 27 healthy controls. Using independent component analysis (ICA), the DMN was extracted from ASL data. Mean CBF and DMN connectivity were compared between groups using a 2-sample t test. Results: Schizophrenia patients showed decreased mean CBF in the frontal and temporal regions (P < .001). ICA demonstrated significantly increased DMN connectivity in the precuneus (x/y/z = -16/-64/38) in patients than in controls (P < .001). CBF was not elevated in the respective regions. DMN connectivity in the precuneus was significantly correlated with the Positive and Negative Syndrome Scale scores (P < .01). Conclusions: In schizophrenia patients, the posterior hub-which is considered the strongest part of the DMN-showed increased DMN connectivity. We hypothesize that this increase hinders the deactivation of the DMN and, thus, the translation of cognitive processes from an internal to an external focus. This might explain symptoms related to defective self-monitoring, such as auditory verbal hallucinations or ego disturbances.

(Table 1) GRACE-derived mass balance of the Greenland ice sheet 2002-2010

We re-evaluate the Greenland mass balance for the recent period using low-pass Independent Component Analysis (ICA) post-processing of the Level-2 GRACE data (2002-2010) from different official providers (UTCSR, JPL, GFZ) and confirm the present important ice mass loss in the range of -70 and -90 Gt/y of this ice sheet, due to negative contributions of the glaciers on the east coast. We highlight the high interannual variability of mass variations of the Greenland Ice Sheet (GrIS), especially the recent deceleration of ice loss in 2009-2010, once seasonal cycles are robustly removed by Seasonal Trend Loess (STL) decomposition. Interannual variability leads to varying trend estimates depending on the considered time span. Correction of post-glacial rebound effects on ice mass trend estimates represents no more than 8 Gt/y over the whole ice sheet. We also investigate possible climatic causes that can explain these ice mass interannual variations, as strong correlations between GRACE-based mass balance and atmosphere/ocean parallels are established: (1) changes in snow accumulation, and (2) the influence of inputs of warm ocean water that periodically accelerate the calving of glaciers in coastal regions and, feed-back effects of coastal water cooling by fresh currents from glaciers melting. These results suggest that the Greenland mass balance is driven by coastal sea surface temperature at time scales shorter than accumulation.

Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data

In this work, we present a novel method to compensate the movement in images acquired during free breathing using first-pass gadolinium enhanced, myocardial perfusion magnetic resonance imaging (MRI). First, we use independent component analysis (ICA) to identify the optimal number of independent components (ICs) that separate the breathing motion from the intensity change induced by the contrast agent. Then, synthetic images are created by recombining the ICs, but other then in previously published work (Milles et al. 2008), we omit the component related to motion, and therefore, the resulting reference image series is free of motion. Motion compensation is then achieved by using a multi-pass non-rigid image registration scheme. We tested our method on 15 distinct image series (5 patients) consisting of 58 images each and we validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration. The average correlation to the manually obtained curves before registration 0:89 0:11 was increased to 0:98 0:02

Signal-to-noise ratio of the MEG signal after preprocessing

Background Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5–10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

Comparison of linear and non-linear 2D+T registration methods for DE-MRI cardiac perfusion studies

A series of motion compensation algorithms is run on the challenge data including methods that optimize only a linear transformation, or a non-linear transformation, or both – first a linear and then a non-linear transformation. Methods that optimize a linear transformation run an initial segmentation of the area of interest around the left myocardium by means of an independent component analysis (ICA) (ICA-*). Methods that optimize non-linear transformations may run directly on the full images, or after linear registration. Non-linear motion compensation approaches applied include one method that only registers pairs of images in temporal succession (SERIAL), one method that registers all image to one common reference (AllToOne), one method that was designed to exploit quasi-periodicity in free breathing acquired image data and was adapted to also be usable to image data acquired with initial breath-hold (QUASI-P), a method that uses ICA to identify the motion and eliminate it (ICA-SP), and a method that relies on the estimation of a pseudo ground truth (PG) to guide the motion compensation.

Blind multiuser receiver for DS-CDMA wireless system

We consider blind signal detection in an asynchronous code-division multiple-access (CDMA) system employing short spreading sequences in the presence of unknown multipath fading. This approach is capable of countering the presence of multiple-access interference (MAI) in CDMA fading channels. The proposed blind multiuser detector is based on an independent component analysis (ICA) to mitigate both MAI and noise. This algorithm has been utilised in blind source separation (BSS) of unknown sources from their mixtures. It can also be used for estimating the basis vectors of BSS. The aim is to include an ICA algorithm within a wireless receiver in order to reduce the level of interference in wideband systems. This blind multiuser detector requires no training sequence compared with the conventional multiuser detection receiver. The proposed ICA blind multiuser detector is made robust with respect to knowledge of signature waveforms and the timing of the user of interest. Several experiments are performed in order to verify the validity of the proposed ICA algorithm.

Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex

Neuronal operations associated with the top-down control process of shifting attention from one locus to another involve a network of cortical regions, and their influence is deemed fundamental to visual perception. However, the extent and nature of these operations within primary visual areas are unknown. In this paper, we used magnetoencephalography (MEG) in combination with magnetic resonance imaging (MRI) to determine whether, prior to the onset of a visual stimulus, neuronal activity within early visual cortex is affected by covert attentional shifts. Time/frequency analyses were used to identify the nature of this activity. Our results show that shifting attention towards an expected visual target results in a late-onset (600 ms postcue onset) depression of alpha activity which persists until the appearance of the target. Independent component analysis (ICA) and dipolar source modeling confirmed that the neuronal changes we observed originated from within the calcarine cortex. Our results further show that the amplitude changes in alpha activity were induced not evoked (i.e., not phase-locked to the cued attentional task). We argue that the decrease in alpha prior to the onset of the target may serve to prime the early visual cortex for incoming sensory information. We conclude that attentional shifts affect activity within the human calcarine cortex by altering the amplitude of spontaneous alpha rhythms and that subsequent modulation of visual input with attentional engagement follows as a consequence of these localized changes in oscillatory activity. © 2005 Elsevier B.V. All rights reserved.

Attentional modulation of oscillatory activity in human visual cortex

The effects of attentional modulation on activity within the human visual cortex were investigated using magnetoencephalography. Chromatic sinusoidal stimuli were used to evoke activity from the occipital cortex, with attention directed either toward or away from the stimulus using a bar-orientation judgment task. For five observers, global magnetic field power was plotted as a function of time from stimulus onset. The major peak of each function occurred at about 120 ms latency and was well modeled by a current dipole near the calcarine sulcus. Independent component analysis (ICA) on the non-averaged data for each observer also revealed one component of calcarine origin, the location of which matched that of the dipolar source determined from the averaged data. For two observers, ICA revealed a second component near the parieto-occipital sulcus. Although no effects of attention were evident using standard averaging procedures, time-varying spectral analyses of single trials revealed that the main effect of attention was to alter the level of oscillatory activity. Most notably, a sustained increase in alpha-band (7-12 Hz) activity of both calcarine and parieto-occipital origin was evident. In addition, calcarine activity in the range of 13-21 Hz was enhanced, while calcarine activity in the range of 5-6 Hz was reduced. Our results are consistent with the hypothesis that attentional modulation affects neural processing within the calcarine and parieto-occipital cortex by altering the amplitude of alpha-band activity and other natural brain rhythms. © 2003 Elsevier Inc. All rights reserved.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Genetic parameters and principal component analysis for egg production from White Leghorn hens

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Quantitative component analysis of the coarse fraction of surface sediments from the Persian Gulf

In the Persian Gulf and the Gulf of Oman marl forms the primary sediment cover, particularly on the Iranian side. A detailed quantitative description of the sediment components > 63 µ has been attempted in order to establish the regional distribution of the most important constituents as well as the criteria governing marl sedimentation in general. During the course of the analysis, the sand fraction from about 160 bottom-surface samples was split into 5 phi° fractions and 500 to 800 grains were counted in each individual fraction. The grains were cataloged in up to 40 grain type catagories. The gravel fraction was counted separately and the values calculated as weight percent. Basic for understanding the mode of formation of the marl sediment is the "rule" of independent availability of component groups. It states that the sedimentation of different component groups takes place independently, and that variation in the quantity of one component is independent of the presence or absence of other components. This means, for example, that different grain size spectrums are not necessarily developed through transport sorting. In the Persian Gulf they are more likely the result of differences in the amount of clay-rich fine sediment brought in to the restricted mouth areas of the Iranian rivers. These local increases in clayey sediment dilute the autochthonous, for the most part carbonate, coarse fraction. This also explains the frequent facies changes from carbonate to clayey marl. The main constituent groups of the coarse fraction are faecal pellets and lumps, the non carbonate mineral components, the Pleistocene relict sediment, the benthonic biogene components and the plankton. Faecal pellets and lumps are formed through grain size transformation of fine sediment. Higher percentages of these components can be correlated to large amounts of fine sediment and organic C. No discernable change takes place in carbonate minerals as a result of digestion and faecal pellet formation. The non-carbonate sand components originate from several unrelated sources and can be distinguished by their different grain size spectrum; as well as by other characteristics. The Iranian rivers supply the greatest amounts (well sorted fine sand). Their quantitative variations can be used to trace fine sediment transport directions. Similar mineral maxima in the sediment of the Gulf of Oman mark the path of the Persian Gulf outflow water. Far out from the coast, the basin bottoms in places contain abundant relict minerals (poorly sorted medium sand) and localized areas of reworked salt dome material (medium sand to gravel). Wind transport produces only a minimal "background value" of mineral components (very fine sand). Biogenic and non-biogenic relict sediments can be placed in separate component groups with the help of several petrographic criteria. Part of the relict sediment (well sorted fine sand) is allochthonous and was derived from the terrigenous sediment of river mouths. The main part (coarse, poorly sorted sediment), however, was derived from the late Pleistocene and forms a quasi-autochthonous cover over wide areas which receive little recent sedimentation. Bioturbation results in a mixing of the relict sediment with the overlying younger sediment. Resulting vertical sediment displacement of more than 2.5 m has been observed. This vertical mixing of relict sediment is also partially responsible for the present day grain size anomalies (coarse sediment in deep water) found in the Persian Gulf. The mainly aragonitic components forming the relict sediment show a finely subdivided facies pattern reflecting the paleogeography of carbonate tidal flats dating from the post Pleistocene transgression. Standstill periods are reflected at 110 -125m (shelf break), 64-61 m and 53-41 m (e.g. coare grained quartz and oolite concentrations), and at 25-30m. Comparing these depths to similar occurrences on other shelf regions (e. g. Timor Sea) leads to the conclusion that at this time minimal tectonic activity was taking place in the Persian Gulf. The Pleistocene climate, as evidenced by the absence of Iranian river sediment, was probably drier than the present day Persian Gulf climate. Foremost among the benthonic biogene components are the foraminifera and mollusks. When a ratio is set up between the two, it can be seen that each group is very sensitive to bottom type, i.e., the production of benthonic mollusca increases when a stable (hard) bottom is present whereas the foraminifera favour a soft bottom. In this way, regardless of the grain size, areas with high and low rates of recent sedimentation can be sharply defined. The almost complete absence of mollusks in water deeper than 200 to 300 m gives a rough sedimentologic water depth indicator. The sum of the benthonic foraminifera and mollusca was used as a relative constant reference value for the investigation of many other sediment components. The ratio between arenaceous foraminifera and those with carbonate shells shows a direct relationship to the amount of coarse grained material in the sediment as the frequence of arenaceous foraminifera depends heavily on the availability of sand grains. The nearness of "open" coasts (Iranian river mouths) is directly reflected in the high percentage of plant remains, and indirectly by the increased numbers of ostracods and vertebrates. Plant fragments do not reach their ultimate point of deposition in a free swimming state, but are transported along with the remainder of the terrigenous fine sediment. The echinoderms (mainly echinoids in the West Basin and ophiuroids in the Central Basin) attain their maximum development at the greatest depth reached by the action of the largest waves. This depth varies, depending on the exposure of the slope to the waves, between 12 to 14 and 30 to 35 m. Corals and bryozoans have proved to be good indicators of stable unchanging bottom conditions. Although bryozoans and alcyonarian spiculae are independent of water depth, scleractinians thrive only above 25 to 30 m. The beginning of recent reef growth (restricted by low winter temperatures) was seen only in one single area - on a shoal under 16 m of water. The coarse plankton fraction was studied primarily through the use of a plankton-benthos ratio. The increase in planktonic foraminifera with increasing water depth is here heavily masked by the "Adjacent sea effect" of the Persian Gulf: for the most part the foraminifera have drifted in from the Gulf of Oman. In contrast, the planktonic mollusks are able to colonize the entire Persian Gulf water body. Their amount in the plankton-benthos ratio always increases with water depth and thereby gives a reliable picture of local water depth variations. This holds true to a depth of around 400 m (corresponding to 80-90 % plankton). This water depth effect can be removed by graphical analysis, allowing the percentage of planktonic mollusks per total sample to be used as a reference base for relative sedimentation rate (sedimentation index). These values vary between 1 and > 1000 and thereby agree well with all the other lines of evidence. The "pteropod ooze" facies is then markedly dependent on the sedimentation rate and can theoretically develop at any depth greater than 65 m (proven at 80 m). It should certainly no longer be thought of as "deep sea" sediment. Based on the component distribution diagrams, grain size and carbonate content, the sediments of the Persian Gulf and the Gulf of Oman can be grouped into 5 provisional facies divisions (Chapt.19). Particularly noteworthy among these are first, the fine grained clayey marl facies occupying the 9 narrow outflow areas of rivers, and second, the coarse grained, high-carbonate marl facies rich in relict sediment which covers wide sediment-poor areas of the basin bottoms. Sediment transport is for the most part restricted to grain sizes < 150 µ and in shallow water is largely coast-parallel due to wave action at times supplemented by tidal currents. Below the wave base gravity transport prevails. The only current capable of moving sediment is the Persian Gulf outflow water in the Gulf of Oman.

Combined analysis of categorical and numerical descriptors of Australian groundnut accessions using nonlinear principal component analysis

For users of germplasm collections, the purpose of measuring characterization and evaluation descriptors, and subsequently using statistical methodology to summarize the data, is not only to interpret the relationships between the descriptors, but also to characterize the differences and similarities between accessions in relation to their phenotypic variability for each of the measured descriptors. The set of descriptors for the accessions of most germplasm collections consists of both numerical and categorical descriptors. This poses problems for a combined analysis of all descriptors because few statistical techniques deal with mixtures of measurement types. In this article, nonlinear principal component analysis was used to analyze the descriptors of the accessions in the Australian groundnut collection. It was demonstrated that the nonlinear variant of ordinary principal component analysis is an appropriate analytical tool because subspecies and botanical varieties could be identified on the basis of the analysis and characterized in terms of all descriptors. Moreover, outlying accessions could be easily spotted and their characteristics established. The statistical results and their interpretations provide users with a more efficient way to identify accessions of potential relevance for their plant improvement programs and encourage and improve the usefulness and utilization of germplasm collections.