Independent Component Maps Research Articles

P035 An examination of resting state functional connectivity in patients with active Crohn’s disease

Abstract Background Crohn’s disease (CD) and its comorbidities may be associated with a specific intrinsic neural phenotype that can be measured using resting state functional connectivity (RSFC). RSFC alterations have been reported in CD in remission but have not been investigated in the active phase of the disease. Here, the primary aim was to study the effects of active CD on resting state networks (RSNs), hence identifying RSFC alterations specific to active CD status. A second aim was to investigate associations between disease duration and abdominal pain with RSFC in active CD. Methods 25 active CD patients and 25 age-, BMI- and gender-matched healthy controls (HC) were recruited. Active disease was defined as CRP > 5 mg/dl, or faecal calprotectin (FCP) >250 μg/g or through ileocolonoscopy or MRE. RsfMRI datasets were acquired on a 3T Philips Achieva scanner, with data corrected for physiological noise and motion. ICA analysis was carried out using MELODIC (FSL software). A multi-session temporal concatenation was used to generate 30 independent component (IC) maps of RSNs. A dual regression analysis with variance normalization, using group IC maps from HC group as the network template, was performed. Differences in RSN between HCs and CD patients and associations with covariates of interest were assessed. Results RSNs comprising the visual, cerebellar, frontal parietal and frontal RSN, default mode network (DMN), salience, temporal and dorsal attention RSN were identified. Enhancement of activity and increase connectivity in the medial visual RSN (orbital frontal cortex), in the frontal parietal network (occipital fusiform gyrus, thalamus, caudate, posterior cingulate cortex, postcentral gyrus) were observed in CD relative to HC. Further, decreased activity was seen in the salience network (cerebellum, postcentral gyrus), cerebellar network (occipital fusiform gyrus, cerebellum) and the DMN (parahippocampal gyrus, cerebellum) in CD relative to HC. Greater abdominal pain scores were associated with lower connectivity in the precuneus in the medial visual RSN, in the parietal operculum in the salience RSN, as well as reduced connectivity in the cerebellum in the frontal RSN. Finally, greater disease duration was associated with greater connectivity in the visual RSN in the middle temporal gyrus and planum temporale. Conclusion Alterations in RSFC between active CD and HC in RSNs implicated in cognition, attention, emotion and pain may reflect neuroplasticity in response to chronic systemic inflammation, abdominal pain, disease duration and severity. Identification of a neural phenotype specific to active CD status, may help guide intervention constituting a neurobehavioural precision medicine approach to treatment.

Dynamic brain connectivity attuned to the complexity of relative clause sentences revealed by a single-trial analysis

To explore the issue of how the human brain processes sentences with different levels of complexity, we sought to compare the neural substrates underlying the processing of Chinese subject-extracted relative clause (SRC) and object-extracted relative clause (ORC) sentences in a trial-by-trial fashion. Previous neuroimaging studies have demonstrated that the involvement of the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) is critical for the processing of relative clause (RC) sentences. In this study, we employed independent component analysis (ICA) to decompose brain activity into a set of independent components. Then, the independent component maps were spatially normalized using a surface-based approach in order to further spatially correlate and match the equivalent components from individual participants. The selected equivalent components indicated that the LIFG and the LSTG were consistently engaged in sentence processing among the participants. Subsequently, we observed alterations in the functional coupling between the LIFG and the LSTG in response to SRCs and ORCs using a Granger causality analysis. Specifically, comprehending Chinese ORCs with a canonical word order only involved a unidirectional connection from the LIFG to the LSTG for the integration of lexical-syntactic information. On the other hand, comprehending Chinese SRCs required bi-directional connectivity between the LIFG and the LSTG to fulfill increased integration demands in reconstructing the argument hierarchy due to a non-canonical word order. Furthermore, through a single-trial analysis, the strength of the connectivity from the LIFG to the LSTG was found to be significantly correlated with the complexity of the SRC sentences as quantified by eye-tracking measures. These findings indicated that the effective connectivity from the LIFG to the LSTG played an important role in the comprehension of complex sentences and that enhanced strength of this connectivity might reflect increased integration demands and restructuring attempts during sentence processing. Taken together, the results of the present study reveal that interregional interaction in the brain network for sentence processing can be dynamically engaged in response to different levels of complexity and also shed some light on the interpretation of neuroimaging and behavioral evidence when accounting for the nature of sentence complexity during reading.

An Ultra-Dense Haploid Genetic Map for Evaluating the Highly Fragmented Genome Assembly of Norway Spruce (Picea abies).

Norway spruce (Picea abies (L.) Karst.) is a conifer species of substanital economic and ecological importance. In common with most conifers, the P. abies genome is very large (∼20 Gbp) and contains a high fraction of repetitive DNA. The current P. abies genome assembly (v1.0) covers approximately 60% of the total genome size but is highly fragmented, consisting of >10 million scaffolds. The genome annotation contains 66,632 gene models that are at least partially validated (www.congenie.org), however, the fragmented nature of the assembly means that there is currently little information available on how these genes are physically distributed over the 12 P. abies chromosomes. By creating an ultra-dense genetic linkage map, we anchored and ordered scaffolds into linkage groups, which complements the fine-scale information available in assembly contigs. Our ultra-dense haploid consensus genetic map consists of 21,056 markers derived from 14,336 scaffolds that contain 17,079 gene models (25.6% of the validated gene models) that we have anchored to the 12 linkage groups. We used data from three independent component maps, as well as comparisons with previously published Picea maps to evaluate the accuracy and marker ordering of the linkage groups. We demonstrate that approximately 3.8% of the anchored scaffolds and 1.6% of the gene models covered by the consensus map have likely assembly errors as they contain genetic markers that map to different regions within or between linkage groups. We further evaluate the utility of the genetic map for the conifer research community by using an independent data set of unrelated individuals to assess genome-wide variation in genetic diversity using the genomic regions anchored to linkage groups. The results show that our map is sufficiently dense to enable detailed evolutionary analyses across the P. abies genome.

Au36(SePh)24 nanomolecules: synthesis, optical spectroscopy and theoretical analysis.

Here, we report the synthesis of selenophenol (HSePh) protected Au36(SePh)24 nanomolecules via a ligand-exchange reaction of 4-tert-butylbenzenethiol (HSPh-tBu) protected Au36(SPh-tBu)24 with selenophenol, and its spectroscopic and theoretical analysis. Matrix assisted laser desorption ionization (MALDI) mass spectrometry, electrospray ionization (ESI) mass spectrometry and optical characterization confirm that the composition of the as synthesized product is predominantly Au36(SePh)24 nanomolecules. Size exclusion chromatography (SEC) was employed to isolate the Au36(SePh)24 and temperature dependent optical absorption studies and theoretical analysis were performed. Theoretically, an Independent Component Maps of Oscillator Strength (ICM-OS) analysis of simulated spectra shows that the enhancement in absorption intensity in Au36(SePh)24 with respect to Au36(SPh)24 can be ascribed to the absence of interference and/or increased long-range coupling between interband metal core and ligand excitations. This work demonstrates and helps to understand the effect of Au-Se bridging on the properties of gold nanomolecules.

Brain activation inhomogeneity highlighted by the Isotropic Anomalous Diffusion filter.

The visual appealing nature of the now popular BOLD fMRI may give the false impression of extreme simplicity, as if the the functional maps could be generated with the press of a single button. However, one can only get plausible maps after long and cautious processing, considering that time and noise come into play during acquisition. One of the most popular ways to account for noise and individual variability in fMRI is the use of a Gaussian spatial filter. Although very robust, this filter may introduce excessive blurring, given the strong dependence of results on the central voxel value. Here, we propose the use of the Isotropic Anomalous Diffusion (IAD) approach, aiming to reduce excessive homogeneity while retaining the natural variability of signal across brain space. We found differences between Gaussian and IAD filters in two parameters gathered from Independent Component maps (ICA), identified on brain areas responsible for auditory processing during rest. Analysis of data gathered from 7 control subjects shows that the IAD filter rendered more localized active areas and higher contrast-to-noise ratios, when compared to equivalent Gaussian filtered data (Student t-test, p<0.05). The results seem promising, since the anomalous filter performs satisfactorily in filtering noise with less distortion of individual localized brain responses.

Automatic identification of gray and white matter components in polarized light imaging

Polarized light imaging (PLI) enables the visualization of fiber tracts with high spatial resolution in microtome sections of postmortem brains. Vectors of the fiber orientation defined by inclination and direction angles can directly be derived from the optical signals employed by PLI analysis. The polarization state of light propagating through a rotating polarimeter is varied in such a way that the detected signal of each spatial unit describes a sinusoidal signal. Noise, light scatter and filter inhomogeneities, however, interfere with the original sinusoidal PLI signals, which in turn have direct impact on the accuracy of subsequent fiber tracking. Recently we showed that the primary sinusoidal signals can effectively be restored after noise and artifact rejection utilizing independent component analysis (ICA). In particular, regions with weak intensities are greatly enhanced after ICA based artifact rejection and signal restoration. Here, we propose a user independent way of identifying the components of interest after decomposition; i.e., components that are related to gray and white matter. Depending on the size of the postmortem brain and the section thickness, the number of independent component maps can easily be in the range of a few ten thousand components for one brain. Therefore, we developed an automatic and, more importantly, user independent way of extracting the signal of interest. The automatic identification of gray and white matter components is based on the evaluation of the statistical properties of the so-called feature vectors of each individual component map, which, in the ideal case, shows a sinusoidal waveform. Our method enables large-scale analysis (i.e., the analysis of thousands of whole brain sections) of nerve fiber orientations in the human brain using polarized light imaging.

Unbiased group-level statistical assessment of independent component maps by means of automated retrospective matching

This report presents and validates a method for the group-level statistical assessment of independent component analysis (ICA) outcomes. The method is based on a matching of individual component maps to corresponding aggregate maps that are obtained from concatenated data. Group-level statistics are derived that include an explicit correction for selection bias. Outcomes were validated by means of calculations with artificial null data. Although statistical inferences were found to be incorrect if bias was neglected, the use of the proposed bias correction sufficed to obtain valid results. This was further confirmed by extensive calculations with artificial data that contained known effects of interest. While uncorrected statistical assessments systematically violated the imposed confidence level thresholds, the corrected method was never observed to exceed the allowed false positive rate. Yet, bias correction was found to result in a reduced sensitivity and a moderate decrease in discriminatory power. The method was also applied to analyze actual fMRI data. Various effects of interest that were detectable in the aggregate data were similarly revealed by the retrospective matching method. In particular, stimulus-related responses were extensive. Nevertheless, differences were observed regarding their spatial distribution. The presented findings indicate that the proposed method is suitable for neuroimaging analyses. Finally, a number of generalizations are discussed. It is concluded that the proposed method provides a framework that may supplement many of the currently available group ICA methods with validated unbiased group inferences.

Signal enhancement in polarized light imaging by means of independent component analysis

Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.

Independent components of the haemodynamic response in intrinsic optical imaging

Functional brain imaging methods are prone to contamination from global vascular artefacts. A variety of methods have been proposed to help segment functional from non-specific changes. Here we quantify the improvement in the signal to noise ratio (SNR) of functional maps, derived from intrinsic optical imaging studies of macaque visual cortex, through the application of Extended Spatial Decorrelation (ESD). The resulting independent component maps and their corresponding time courses reveal for the first time a fast vascular component in the haemodynamic response. ESD is a blind source separation algorithm that utilises spatial statistical features in brain images to separate the recorded mixed sources into independent components. We have investigated differential and single condition experiments using a variety of visual stimuli. To calculate the improvement of the SNR in decibel (dB) we back project separated components onto the original single trial data and analyse the corresponding Fourier spectrum. The application of ESD improved SNR in the functional brain maps from 0.52 to 16.88 dB on differential imaging data and from 1.69 to 12.83 dB in the case of single condition experiments. Analysing the independent components further we found that they can separate different functional compartments of the cortical vasculature. Some of the components, classified as arterial through slit spectroscopy, revealed a strong fast response to the stimulus onset/offset starting ∼ 0.2 s after the change of the stimulus and reaching a peak after ∼ 0.4 s. This fast haemodynamic response raises new questions concerning the spatial specificity of the so-called “initial dip”.

Application of independent component analysis to dynamic contrast-enhanced imaging for assessment of cerebral blood perfusion

Dynamic contrast-enhanced (DCE) imaging is widely used for in vivo assessment of the cerebral blood perfusion. In this work, we investigate the use of independent component analysis (ICA) on DCE imaging data for assessment of cerebral blood perfusion, without any prior knowledge of the underlying tissue vasculature and arterial input function. The minimum description length (MDL) criterion and principle component analysis (PCA) were employed to reduce the dimension of the data. An oscillating index method was used to select the components of interest. Numerical simulation and patient case studies were carried out to investigate the performance of ICA. The results show that ICA is able to extract physiologically meaningful components from the DCE imaging data. The advantages of ICA include its efficiency of computation, clarity of obtained component maps, and no need of the manually selected input function. The obtained independent component maps can provide reliable reference to identify the arterial and venous structure, and allow better demarcation of the tumor territories. The potential of ICA to be a useful clinical tool for diagnosis of cerebral vascular disease and for the assessment of treatment response has been demonstrated.

Groupwise independent component decomposition of EEG data and partial least square analysis

This paper focuses on two methodological developments for analysis of neuroimaging data. The first is the derivation of robust spatiotemporal activity patterns across a group of subjects using a combination of principal component analysis (PCA) and independent component analysis (ICA). In applications to ERP data, the space dimension is typically represented in terms of scalp electrodes. The signal recorded by high density electrode caps is known to be highly correlated due in part to volume conduction. Consequently, this redundancy is also reflected in spatiotemporal patterns characterizing signal differences across experimental conditions. We present an alternative spatial representation and signal compression based on PCA for dimensionality reduction and ICA conducted across all subjects and conditions simultaneously. The second advancement is the use of partial least squares (PLS) analysis to assess task-dependent changes in the expression of the independent components. In an application to empirical ERP data, we derive an efficient number of independent component maps. Comparative PLS analysis on the independent components versus original electrode data shows that task effects are not only preserved under compression, but also enhanced statistically.