Independent Component Analysis Analysis Research Articles

Alterations in functional connectivity of resting state networks during experimental endotoxemia – An exploratory study in healthy men

Systemic inflammation impairs mood and cognitive functions, and seems to be involved in the pathophysiology of psychiatric disorders. Functional magnetic resonance imaging (fMRI) studies revealed altered task-related blood-oxygen-level-dependent (BOLD) responses during experimental endotoxemia, but little is known about effects of systemic inflammation on resting-state activity of the brain. Thus, we conducted a randomized, placebo-controlled study in healthy men receiving an intravenous injection of either low-dose (0.4ng/kg) lipopolysaccharide (LPS) (N=20) or placebo (N=25). Resting state activity was measured at baseline and 3.5h post-injection. Based on a two (condition)×two (group) design, we used multi-subject independent component analysis (ICA) to decompose and estimate functional connectivity within resting-state networks (RSNs). Seed-based analyses were applied to investigate the effect of LPS on the functional coupling for a priori-defined regions-of-interest (ROIs). ICA analyses identified 13 out of 35 components displaying common RSNs. Seed based analysis revealed greater functional connectivity between the left thalamus and the cerebellum after LPS compared to placebo administration, while the functional coupling between seeds within the amygdala, insula, and cingulate cortex and various brain regions including parieto-frontal networks was significantly reduced. Within the LPS group, endotoxin-induced increases in Interleukin (IL)-6 were significantly associated with resting-state connectivity between the left thalamus and left precuneus as well as the right posterior cingulate cortex. In summary, this exploratory study provides first evidence that systemic inflammation affects the coupling and regulation of multiple networks within the human brain at rest.

Testing different ICA algorithms and connectivity analyses on MS patients.

Multiple sclerosis (MS) is a progressive neurological disorder that affects the central nervous system. Functional magnetic resonance imaging (fMRI) has been employed to track the course and disease progression in patients with MS. The two main aims of this study were to apply in a data-driven approach the independent component analysis (ICA) in the spatial domain to depict the active sources and to look at the effective connectivity between the identified spatial sources. Several ICA algorithms have been proposed for fMRI data analysis. In this study, we aimed to test two well characterized algorithms, namely, the fast ICA and the complex infomax algorithms, followed by two effective connectivity algorithms, namely, Granger causality (GC) and generalized partial directed coherence (GPDC), to illustrate the connections between the spatial sources in patients with MS. The results obtained from the ICA analyses showed the involvement of the default mode network sources. The connectivity analyses depicted significant changes between the two applied algorithms. The significance of this study was to demonstrate the robustness of the analyzed algorithms in patients with MS and to validate them before applying them on larger datasets of patients with MS.

Neurovascular uncoupling in resting state fMRI demonstrated in patients with primary brain gliomas.

To demonstrate that the problem of brain tumor-related neurovascular uncoupling (NVU) is a significant issue with respect to resting state blood oxygen level dependent (BOLD) functional MRI (rsfMRI) similar to task-based BOLD fMRI, in which signal detectability can be compromised by breakdown of normal neurovascular coupling. We evaluated seven de novo brain tumor patients who underwent resting state fMRI as part of comprehensive clinical fMRI exams at 3 Tesla. For each of the seven patients who demonstrated evidence of NVU on task-based motor fMRI, we performed both an independent component analysis (ICA) and an atlas-based parcellation-based seed correlation analysis (SCA) of the resting state fMRI data. For each patient, ipsilesional (IL) and contralesional (CL) regions of interest (ROIs) comprising primary motor and somatosensory cortices were used to evaluate BOLD signal changes on Z score maps derived from both ICA and SCA analysis for evidence of NVU. A subsequent two-tailed t-test was performed to determine whether statistically significant differences between the two sides were present that were consistent with NVU. In seven patients, overall decreased BOLD signal (based on suprathreshold voxels in ICA and SCA-derived Z-score maps) was noted in IL compared with CL ROIs (P < 0.01), consistent with NVU. We have demonstrated that NVU can result in false negative BOLD signal changes on rsfMRI comparable to previously published findings on standard motor task-based fMRI.

THE SPITZER SURVEY OF STELLAR STRUCTURE IN GALAXIES (S 4 G): PRECISE STELLAR MASS DISTRIBUTIONS FROM AUTOMATED DUST CORRECTION AT 3.6 μ m

The mid-infrared is an optimal window to trace stellar mass in nearby galaxies and the 3.6$\mu m$ IRAC band has been exploited to this effect, but such mass estimates can be biased by dust emission. We present our pipeline to reveal the old stellar flux at 3.6$\mu m$ and obtain stellar mass maps for more than 1600 galaxies available from the Spitzer Survey of Stellar Structure in Galaxies (S$^{4}$G). This survey consists of images in two infrared bands (3.6 and 4.5$\mu m$), and we use the Independent Component Analysis (ICA) method presented in Meidt et al. (2012) to separate the dominant light from old stars and the dust emission that can significantly contribute to the observed 3.6$\mu m$ flux. We exclude from our ICA analysis galaxies with low signal-to-noise ratio (S/N < 10) and those with original [3.6]-[4.5] colors compatible with an old stellar population, indicative of little dust emission (mostly early Hubble types, which can directly provide good mass maps). For the remaining 1251 galaxies to which ICA was successfully applied, we find that as much as 10-30% of the total light at 3.6$\mu m$ typically originates from dust, and locally it can reach even higher values. This contamination fraction shows a correlation with specific star formation rates, confirming that the dust emission that we detect is related to star formation. Additionally, we have used our large sample of mass estimates to calibrate a relationship of effective mass-to-light ratio ($M/L$) as a function of observed [3.6]-[4.5] color: $\log(M/L)=-0.339 (\pm 0.057) \times ([3.6]-[4.5]) -0.336 (\pm 0.002)$. Our final pipeline products have been made public through IRSA, providing the astronomical community with an unprecedentedly large set of stellar mass maps ready to use for scientific applications.

High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer’s disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN.

Evidence of stimulus correlated empathy modes – Group ICA of fMRI data

The analysis of the cognitive processes in response to a narrative as presented in a movie provides an insight into momentary reaction to a single depicted action like a facial expression and an aggregate processing of the entire sequence of events. In this study we report results from fMRI data analyzed by group independent component analysis (ICA) method from a free viewing experiment using a diverse set of movie clips – an animation, a Hollywood and an Indian Hindi movie. The fMRI data were collected from 15 college students as they viewed 5–8min clips from three movies. The movie clips were rated for depiction of emotional expressions, the emotion as per the narrative and the viewer’s own empathy response. The neural correlates attributed to cognitive, motor and emotional empathy were the focus of the study. The methodology of using long duration stimuli in free viewing mode combined with ICA analysis has the potential to tease out spatially distributed but temporally coherent brain activity as demonstrated in this study. The independent components obtained from group ICA method isolated spatial maps with activations which can be safely ascribed to stimulus processing. We found that the activity in the areas attributable to cognitive and motor empathy was comparable for all the three stimuli while certain critical areas for emotional empathy were not noticed for the animation movie. These findings lead to interesting questions on possible differential emotion response in viewer(s) for computer generated actors compared to actors in live-action movies and the role of narrative and exposure to different genre of movies on racial, ethnic and cultural differences in empathy response.

Uncovering beam position monitor noise at the Relativistic Heavy Ion Collider

We apply the independent component analysis (ICA) algorithm to uncover intrinsic noise in the beam position monitor (BPM) system. Numerical simulations found that ICA is efficient in the BPM noise estimation. The ICA algorithm is applied to the turn-by-turn data at the Relativistic Heavy Ion Collider. We found the distribution of the BPM noise level, which is consistent with the Johnson-Nyquist thermal noise model. The ICA analysis of turn-by-turn data can be used in neuronetwork feasibility of monitoring a storage ring parasitically.

A comparative study of independent component analysis with principal component analysis in geological objects identification. Part II: A case study of Pinghe District, Fujian, China

In a sister paper (Paper I), emphasis was on method comparison between Principal Component Analysis (PCA) and independent component analysis (ICA), suggesting that the main components generated by PCA usually represent dominant populations of samples such as large geological bodies with extensive coverage, whereas the main components of ICA may represent the directions along which there is more divergence of populations of samples. This Paper II contains further demonstration of the differences and similarities between ICA and PCA in applications to a regional stream sediment geochemical data set for geological object identification in the Pinghe district, Fujian Province, China. Considering the input elements have log-normal-like distributions that may significantly influence the results of ICA, the raw data were log-transformed and standardized prior to ICA and PCA analysis. The results show that the second and fourth principal components (PC) obtained by PCA may indicate five geo-objects. Similarly, the second, fifth, sixth, eighth and tenth independent components (IC) obtained by ICA may indicate eleven geo-objects. The first several PCs are most likely to represent geo-objects, and the ICs with low order in kurtosis rank are more likely to reflect some geo-objects. An unsupervised classification method (ISODATA clustering algorithm) applied to scores of the PCs and ICs shows that classification of geo-objects based on the results obtained by ICs is relatively more accurate than that obtained by PCs. The 3D scatterplots for three components show that the samples belonging to the same rock unit have more clear structure in the space of ICs than in the space of PCs. Moreover, IC results show geochemical element patterns depicting different associations between hydrothermal system of Zhongteng plutonic complex and hydrothermal mineralization in the northwestern and southeastern parts of the study area. The loadings of PC indicate hydrothermal systems at different temperatures occurred in the Nanyuan Group and the intrusions. In general, ICA is more effective than PCA for characterizing geo-objects in the study area.

Generation of the probabilistic template of default mode network derived from resting-state fMRI.

Default-mode network (DMN) has become a prominent network among all large-scale brain networks which can be derived from the resting-state fMRI (rs-fMRI) data. Statistical template labeling the common location of hubs in DMN is favorable in the identification of DMN from tens of components resulted from the independent component analysis (ICA). This paper proposed a novel iterative framework to generate a probabilistic DMN template from a coherent group of 40 healthy subjects. An initial template was visually selected from the independent components derived from group ICA analysis of the concatenated rs-fMRI data of all subjects. An effective similarity measure was designed to choose the best-fit component from all independent components of each subject computed given different component numbers. The selected DMN components for all subjects were averaged to generate an updated DMN template and then used to select the DMN for each subject in the next iteration. This process iterated until the convergence was reached, i.e., the overlapping region between the DMN areas of the current template and the one generated from the previous stage is more than 95%. By validating the constructed DMN template on the rs-fMRI data from another 40 subjects, the generated probabilistic DMN template and the proposed similarity matching mechanism were demonstrated to be effective in automatic selection of independent components from the ICA analysis results.

Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.

Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift).

Robust data driven model order estimation for independent component analysis of FMRI data with low contrast to noise.

Independent component analysis (ICA) has been successfully utilized for analysis of functional MRI (fMRI) data for task related as well as resting state studies. Although it holds the promise of becoming an unbiased data-driven analysis technique, a few choices have to be made prior to performing ICA, selection of a method for determining the number of independent components (nIC) being one of them. Choice of nIC has been shown to influence the ICA maps, and various approaches (mostly relying on information theoretic criteria) have been proposed and implemented in commonly used ICA analysis packages, such as MELODIC and GIFT. However, there has been no consensus on the optimal method for nIC selection, and many studies utilize arbitrarily chosen values for nIC. Accurate and reliable determination of true nIC is especially important in the setting where the signals of interest contribute only a small fraction of the total variance, i.e. very low contrast-to-noise ratio (CNR), and/or very focal response. In this study, we evaluate the performance of different model order selection criteria and demonstrate that the model order selected based upon bootstrap stability of principal components yields more reliable and accurate estimates of model order. We then demonstrate the utility of this fully data-driven approach to detect weak and focal stimulus-driven responses in real data. Finally, we compare the performance of different multi-run ICA approaches using pseudo-real data.

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.

The effect of echo time and post-processing procedure on blood oxygenation level-dependent (BOLD) functional connectivity analysis

While spontaneous BOLD fMRI signal is a common tool to map functional connectivity, unexplained inter- and intra-subject variability frequently complicates interpretation. Similar to evoked BOLD fMRI responses, spontaneous BOLD signal is expected to vary with echo time (TE) and corresponding intra/extravascular sensitivity. This may contribute to discrepant conclusions even following identical post-processing pipelines. Here we applied commonly-utilized independent component analysis (ICA) as well as seed-based correlation analysis and investigated default mode network (DMN) and visual network (VN) detection from BOLD data acquired at three TEs (3T; TR=2500ms; TE=15ms, 35ms, and 55ms) and from quantitative R2* maps. Explained variance in ICA analysis was significantly higher (P<0.05) when R2*-derived maps were considered relative to single-TE data with no post-processing. While explained variance in the BOLD data increased with motion correction, R2* derived DMN and VN were minimally affected by motion correction. Explained variance increased in all data when physiological noise confounds were removed using CompCor. Notably, the R2*-derived connectivity patterns were least affected by motion and physiological noise confounds in a seed-based correlation analysis. Intermediate (35ms) and long (55ms) TE data provided similar spatial and temporal characteristics only after reducing motion and physiological noise contamination. Results provide an exemplar for how 3T spontaneous BOLD network detection varies with TE and post-processing procedure over the range of commonly acquired TE values.

Discrimination of three dimensional fluorescence spectra based on wavelet analysis and independent component analysis

Fluorescence spectroscopy is a rapid and non-destructive method for monitoring water quality. In this work, wavelet analysis, together with independent component analysis (ICA), was applied for component recognition of seriously overlapped, multi-component, three dimensional fluorescence spectra. Wavelet analysis extracts the features of the spectra and amplifies differences among phenolic homologs. ICA analysis in blind signal separation was used to separate single component before multiple linear regression (MLR). The proposed method increases the correct classification rate and enriches the spectra library. As such, it is a useful alternative to traditional techniques in component recognition.

Comparison of Two Methods for Composite Score Generation in Dry Eye Syndrome

To compare two methods of composite score generation in dry eye syndrome (DES). Male patients seen in the Miami Veterans Affairs eye clinic with normal eyelid, corneal, and conjunctival anatomy were recruited to participate in the study. Patients filled out the Dry Eye Questionnaire 5 (DEQ5) and underwent measurement of tear film parameters. DES severity scores were generated by independent component analysis (ICA) and latent class analysis (LCA). A total of 247 men were included in the study. Mean age was 69 years (SD 9). Using ICA analysis, osmolarity was found to carry the largest weight, followed by eyelid vascularity and meibomian orifice plugging. Conjunctival injection and tear breakup time (TBUT) carried the lowest weights. Using LCA analysis, TBUT was found to be best at discriminating healthy from diseased eyes, followed closely by Schirmer's test. DEQ5, eyelid vascularity, and conjunctival injection were the poorest at discrimination. The adjusted correlation coefficient between the two generated composite scores was 0.63, indicating that the shared variance was less than 40%. Both ICA and LCA produced composite scores for dry eye severity, with weak to moderate agreement; however, agreement for the relative importance of single diagnostic tests was poor between the two methods.

P 142. Functional connectivity of the left somatosensory cortex and the putative mirror neuron system

Several brain areas have been identified as active during action perception and execution (see Rizzolatti and Sinigaglia, 2010). This parieto-frontal system of regions has been referred to as the putative Mirror Neuron System (pMNS). Primary somatosensory cortex (SI), has also been shown to be active during action perception and execution (see Keysers et al., 2010) but whether it is part of the pMNS or a separate somatosensory network, is still not clear. To answer this question we chose to deliver inhibitory Transcranial Magnetic Stimulation (TMS) (the protocol used was continuous theta burst (cTBS, see Huang et al., 2005) over the left SI and then collect resting state functional magnetic resonance (RSfMRI) data from our subjects. 18 healthy volunteers participated in a three days study. On Day 1, subjects were scanned while observing and performing simple single-handed actions. The target area for the stimulation was selected as the part of SI active in both conditions (see Fig. 1A). On the other two days in a randomized order subjects received cTBS or sham stimulation over the left SI. After stimulation participants were transported to the scanner and RSfMRI data was collected. Three different types of functional connectivity analysis were applied to the data from Day 2 and Day 3: (1) a seed-based correlational approach, (2) partial correlations between the nodes of the pMNS and (3) independent component analysis (ICA). The seed based correlational approach indicated a small decrease in the connectivity between the left SI and the dorsal premotor areas (dPM) after cTBS as compared to sham (cluster level corrected p (FWE) = 0.09; T = 4.47). Partial correlation analysis between the nodes of the pMNS calculated separately for the data from the sham and cTBS sessions suggested a decrease in the connectivity between the stimulated region and the left dorsal premotor area (dPM), part of the pMNS. ICA, which enables differentiation of independent functional networks, suggested a decrease in the synchronization of one network as a result of cTBS stimulation to SI (cluster level corrected p (FWE) = 0.03; T = 6.59) after cTBS. The change was localized in the same area of the left dPM as identified in the other two analyses (see Fig. 1B). Taken together our results suggest that the effect of inhibitory stimulation over the left SI is not focal but visible in the functionally connected distal brain areas. ICA analysis indicates that SI is part of the same functional network which includes the regions of the pMNS, but the seed based and partial correlation analyses show that this connection is disrupted by our stimulation, i.e. stimulation over SI does not have an effect on the synchronization of the network as a whole but causes a decrease of the connectivity between SI and dPM. Considering their co-activation during mental simulations of actions, we propose that SI also belongs to the pMNS.

Abnormal Resting-State Activities and Functional Connectivities of the Anterior and the Posterior Cortexes in Medication-Naïve Patients with Obsessive-Compulsive Disorder

BackgroundObsessive-compulsive disorder (OCD) is a mental illness characterized by the loss of control. Because the cingulate cortex is believed to be important in executive functions, such as inhibition, we used functional magnetic resonance imaging (fMRI) techniques to examine whether and how activity and functional connectivity (FC) of the cingulate cortex were altered in drug-naïve OCD patients.MethodsTwenty-three medication-naïve OCD patients and 23 well-matched healthy controls received fMRI scans in a resting state. Functional connectivities of the anterior cingulate (ACC) and the posterior cingulate (PCC) to the whole brain were analyzed using correlation analyses based on regions of interest (ROI) identified by the fractional amplitude of low-frequency fluctuation (fALFF). Independent Component Analysis (ICA) was used to identify the resting-state sub-networks.ResultsfALFF analysis found that regional activity was increased in the ACC and decreased in the PCC in OCD patients when compared to controls. FC of the ACC and the PCC also showed different patterns. The ACC and the PCC were found to belong to different resting-state sub-networks in ICA analysis and showed abnormal FC, as well as contrasting correlations with the severity of OCD symptoms.ConclusionsActivity of the ACC and the PCC were increased and decreased, respectively, in the medication-naïve OCD patients compared to controls. Different patterns in FC were also found between the ACC and the PCC with respect to these two groups. These findings implied that the cardinal feature of OCD, the loss of control, may be attributed to abnormal activities and FC of the ACC and the PCC.

Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant's fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain.

Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

Dysfunctional neural networks associated with impaired social interactions in early psychosis: an ICA analysis

The "default mode", or baseline of brain function is a topic of great interest in schizophrenia research. Recent neuroimaging studies report that the symptoms of chronic schizophrenia subjects are associated with temporal frequency alterations as well as with the disruption of local spatial patterns in the default mode network (DMN). Previous studies both on chronic and medicated subjects with psychosis suffered from limitations; on this basis, it was hypothesized that the default mode network showed abnormal activation and connectivity in young and neuroleptic-naïve patients with first-episode psychosis. This study investigated emotional responses to pleasant and unpleasant/disgusting visual stimuli by a resting-state analysis of fMRI-data from 12 untreated first-episode psychosis patients with prevalently negative symptomatology versus 12 healthy subjects. We chose this experimental task to explore the functional link between default mode network and hedonic processing which has been proposed as a marker of cerebral dysfunction in psychotic disorder and implicated in its pathophysiology. Independent Component Analysis (ICA) was used to identify the default mode component. Both healthy and first-episode subjects showed significant spatial differences in the default mode network. In first-episode subjects, medial frontal hypoactivity and cerebellar hyperactivity were correlated with the severity of negative symptoms.