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ICA
Terms modified by ICA Selected AbstractsAnalysis of co-articulation regions for performance-driven facial animationCOMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 1 2004Douglas Fidaleo Abstract A facial gesture analysis procedure is presented for the control of animated faces. Facial images are partitioned into a set of local, independently actuated regions of appearance change termed co-articulation regions (CRs). Each CR is parameterized by the activation level of a set of face gestures that affect the region. The activation of a CR is analyzed using independent component analysis (ICA) on a set of training images acquired from an actor. Gesture intensity classification is performed in ICA space by correlation to training samples. Correlation in ICA space proves to be an efficient and stable method for gesture intensity classification with limited training data. A discrete sample-based synthesis method is also presented. An artist creates an actor-independent reconstruction sample database that is indexed with CR state information analyzed in real time from video. Copyright © 2004 John Wiley & Sons, Ltd. [source] Hormone receptor status in breast cancer , a comparison between surgical specimens and fine needle aspiration biopsiesCYTOPATHOLOGY, Issue 3 2003L. Löfgren The present study was performed to evaluate the immunocytochemical analysis (ICA) of oestrogen (ER) and progesterone receptor (PR) in fine needle aspiration (FNA) biopsies from primary breast cancers as compared with the established enzyme immunoassays (ER-EIA and PR-EIA) based on cytosol homogenates from the corresponding resected tumour specimens. A total of 967 primary breast cancers were assessed for ER and PR content by both methods. Correlations between EIA and ICA expressed as percentage of tumour cells with a positive staining were highly significant (P < 0.001) for ER and PR. Staining intensity yielded only limited additional information. The concordance between the two techniques was about 80%. Evaluation of biological parameters by FNA may be useful to decide the optimal treatment for breast cancer patients. [source] Diabetes classification: grey zones, sound and smoke: Action LADA 1DIABETES/METABOLISM: RESEARCH AND REVIEWS, Issue 7 2008R. D. G. Leslie Abstract Diseases gain identity from clinical phenotype as well as genetic and environmental aetiology. The definition of type 1 diabetes is clinically exclusive, comprising patients who are considered insulin dependent at diagnosis, whilst the definition of type 2 diabetes is inclusive, only excluding those who are initially insulin dependent. Ketosis-prone diabetes (KPD) and latent autoimmune diabetes in adults (LADA) are each exclusive forms of diabetes which are, at least initially, clinically distinct from type 2 diabetes and type 1 diabetes, and each have a different natural history from these major types of diabetes. KPD can be diagnosed unequivocally as diabetes presenting with the categorical clinical feature, ketoacidosis. In contrast, LADA can be diagnosed by the co-occurrence of three traits, not one of which is categorical or exclusive to the condition: adult-onset non-insulin-requiring diabetes, an islet autoantibody such as glutamic acid decarboxylase autoantibodies (GADA) or cytoplasmic islet cell autoantibodies (ICA), and no need for insulin treatment for several months post-diagnosis. But while some would split diabetes into distinct subtypes, there is a strong case that these subtypes form a continuum of varying severity of immune and metabolic dysfunction modified by genetic and non-genetic factors. This article discusses the nature of disease classification in general, and KPD and LADA in particular, emphasizing the potential value and pitfalls in classifying diabetes and suggesting a need for more research in this area. Copyright © 2008 John Wiley & Sons, Ltd. [source] Autoantibodies to the islet cell antigen SOX-13 are associated with duration but not type of diabetesDIABETIC MEDICINE, Issue 3 2003T. M. E. Davis Abstract Aims The autoantigen SOX-13 of the SRY-related high mobility group box is a low-frequency reactant in sera from patients with Type 1 diabetes. We further investigated the potential diagnostic role of anti-SOX-13, and in particular its ability to distinguish Type 1 from Type 2 diabetes, in two large, well-characterized cohorts. Methods SOX-13 autoantibody status was ascertained using a radioimmunoprecipitation assay in (i) a random sample of 546 participants in an Australian community-based study (the Fremantle Diabetes Study; FDS) of whom 119 had Type 1 and 427 Type 2 diabetes, and (ii) a sample of 333 subjects with Type 2 diabetes from the United Kingdom Prospective Diabetes Study (UKPDS) stratified by age, anti-glutamic acid decarboxylase (GAD) and islet cell antibody (ICA) status, and requirement for insulin therapy within 6 years of diagnosis. Results The frequencies of anti-SOX-13 in the FDS subjects were 16.0% and 14.8% for Type 1 and Type 2 patients, respectively, and levels were similar. In the UKPDS subjects, the frequency was 4.5%. In a logistic regression model involving demographic, anthropometric and metabolic variables, only diabetes duration was significantly associated with anti-SOX-13 positivity, especially for duration > 5 years (P < 0.002). When the coexistence of autoantibodies was assessed in the two study samples, there were no significant associations between anti-SOX-13 and ICA, anti-GAD or ICA512/IA-2. Conclusions Whilst the frequency of anti-SOX-13 may be increased in some populations of diabetic patients, this reactivity does not usefully distinguish Type 1 from Type 2 diabetes. However, the association with diabetes duration suggests that anti-SOX-13 may be a non-specific marker of tissue damage associated with chronic hyperglycaemia. Diabet. Med. 20, 198,204 (2003) [source] Gastric parietal cell antibodies are associated with glutamic acid decarboxylase-65 antibodies and the HLA DQA1*0501-DQB1*0301 haplotype in Type 1 diabetes mellitusDIABETIC MEDICINE, Issue 8 2000C. E. M. De Block SUMMARY Aims To assess the prevalence of thyrogastric autoimmunity in relation to age, sex, ,-cell antibody status and HLA DQ haplotypes in Type 1 diabetes mellitus. Methods One hundred and seventy-one patients with Type 1 diabetes mellitus were studied (male/female 86/85; mean age 19 ± 11 years; duration of diabetes 5 ± 4 years). Islet cell antibodies (ICA) and parietal cell antibodies (PCA) were measured using indirect immunofluorescence; glutamic acid decarboxylase-65 antibodies (GADA) by radiobinding assay and thyroid peroxidase antibodies (TPO) with an immunoradiometric assay (IRMA). Results The majority of subjects (81.3%) showed one or more autoantibodies. The prevalence rates were: GADA 64.9%, ICA 46.2%, PCA 19.9% and TPO 19.3%. Patients with ICA+ , 3 years after diagnosis had a higher prevalence of GADA (P = 0.03, odds ratio (OR) 2.66) and thyrogastric antibodies (P = 0.05, OR 2.23) than subjects ICA, after 3 years. PCA+ patients were older (P = 0.04), had a higher prevalence of GADA (P = 0.005, OR 3.89) and TPO (P = 0.05, OR 2.50) than PCA, subjects. Logistic regression analysis showed that PCA status was determined by the HLA DQA1*0501-DQB1*0301 haplotype (, = 2.94, P = 0.04) and GADA status (, = 2.44, P = 0.041). Conclusions Thyrogastric antibodies are highly prevalent in Type 1 diabetes mellitus, especially in patients with persisting ICA. Screening for gastric autoimmunity is particularly advised in patients who are positive for GADA and for the HLA DQA1*0501-DQB1*0301 haplotype. [source] Removing Eye-movement Artifacts from the EEG during the Intracarotid Amobarbital ProcedureEPILEPSIA, Issue 3 2005Weidong Zhou Summary:,Purpose: The EEG is often recorded during the intracarotid amobarbital procedure (IAP) to help in the assessment of the spatial extent and the duration of the effect of the drug. In scalp recordings, the EEG is always heavily contaminated with eye movement artifacts as the patient actively performs visual tasks. Methods: Independent component analysis (ICA) is a new technique for blind source separation. In this study, we separated the EEG data recorded during the IAP into independent components using ICA. The EEG signal was reconstructed by excluding the components related to eye movement and eye blinks. Results: EEGs from 10 IAP tests were analyzed. The experimental results indicate that ICA is very efficient at subtracting eye-movement artifacts, while retaining the EEG slow waves and making their interpretation easier. Conclusions: ICA appears to be a generally applicable and effective method for removing ocular artifacts from EEG recordings during IAP, although slow waves and ocular artifacts share similar frequency distributions. [source] Independent Component Analysis Removing Artifacts in Ictal RecordingsEPILEPSIA, Issue 9 2004Elena Urrestarazu Summary:,Purpose: Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset. Methods: We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample. Results: All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant. Conclusions: ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts. [source] Ruptured symptomatic internal carotid artery dorsal wall aneurysm with rapid configurational change.EUROPEAN JOURNAL OF NEUROLOGY, Issue 10 2010Clinical experience, management outcome: an original article Background:, Aneurysms located at non-branching sites, protruding from the dorsal wall of the supraclinoid internal carotid artery (ICA) with rapid configurational changes, were retrospectively reviewed in effort to identify and characterize these high-risk aneurysms. Methods:, A total of 447 patients with 491 intracranial aneurysms were treated from March 2005 to August 2008, and of these, eight patients had ICA dorsal wall aneurysms. Four of them suffered subarachnoid hemorrhage (SAH), and all had aneurysms undergoing rapid configuration changes during the treatment course. Digital subtraction cerebral angiography (DSA) performed soon after the SAH events. Data analyzed were patient age, sex, Hunt and Kosnik grade, time interval from first DSA to second DSA, aneurysm treatment, and modified Rankin scale score after treatment for 3 months. Success or failure of therapeutic management was examined among the patients. Results:, Digital subtraction cerebral angiography showed only lesions with small bulges in the dorsal walls of the ICAs. However, the patients underwent DSA again for re-bleeding or for post-treatment follow-up, confirming the SAH source. ICA dorsal wall aneurysms with rapid growth and configurational changes were found on subsequent DSA studies. Conclusions:, Among the four patients, ICA dorsal wall aneurysms underwent rapid growth with configurational change from a blister type to a saccular type despite different management. ICA trapping including the lesion segment can be considered as the first treatment option if the balloon occlusion test (BOT) is successful. If a BOT is not tolerated by the patient, extracranial,intracranial bypass revascularization surgery with endovascular ICA occlusion is another treatment option. [source] Long-term follow-up of patients with asymptomatic occlusion of the internal carotid artery with good and impaired cerebral vasomotor reactivityEUROPEAN JOURNAL OF NEUROLOGY, Issue 10 2010I. Kimiagar Background:, Cerebral hemodynamic status might be prognostic for either the symptomatic or asymptomatic course of carotid occlusive disease. It is determined by evaluating cerebral vasomotor reactivity (VMR). We assessed VMR in asymptomatic patients with total occlusion of the internal carotid artery (ICA) and followed them to evaluate the role of impaired VMR in predicting ischaemic stroke (IS). Methods:, Thirty-five patients (21 men, mean age ± SD 68 ± 7.5 years) with unilateral asymptomatic ICA occlusion were studied by transcranial Doppler and the Diamox test (intravenous 1.0 g acetazolamide) and followed for 48 months or until reaching the end-points of IS, transient ischaemic attack, or vascular death. VMR% was evaluated by recording the percent differences in peak systolic blood flow velocities in each middle cerebral artery at baseline and after Diamox administration. Results:, Based on VMR% calculations, 14 (40%) patients had good VMRs and 21 (60%) had impaired VMRs. The global annual risk of ipsilateral ischaemic events was 5.7%. The annual ipsilateral ischaemic event risk was 1.8% in patients with good VMRs, whilst it was 7.1% in patients with impaired VMRs. An impaired VMR was significantly correlated with ipsilateral IS (Kaplan,Meier log rank statistic, P = 0.04). Conclusions:, Our results support the value of VMR assessment for identifying asymptomatic patients with carotid occlusion who belong to a high-risk subgroup for IS. New trials using extracranial-to-intracranial bypass surgery in patients with asymptomatic ICA occlusion and impaired VMRs are warranted. [source] Heart and carotid artery disease in stroke patients with intermittent claudicationEUROPEAN JOURNAL OF NEUROLOGY, Issue 5 2000X. F. Liu Much has been published on the natural history of intermittent claudication (IC), but little is known about the clinical features of stroke patients with IC. The purpose of this study was to examine clinical features and risk factors in stroke patients with or without IC, including heart disease and carotid artery disease. A hospital-based study was conducted of 3901 stroke patients, who were prospectively coded and entered into a computerized databank. Of these patients, 219 had symptoms of IC. Patients were subdivided by age into 10-year categories. There were at least 12 times more non-IC than IC patients in each category. An age-matched random sample was obtained containing 12 non-IC cases for each IC case, resulting in 219 cases of IC and 2628 non-IC cases. The prevalence of IC in the total stroke population was 5.6%. IC prevalence increased sharply with age until about 70 years. Cardiac ischaemia and internal carotid artery (ICA) disease were significantly more frequent in stroke with IC than without IC. IC patients also exhibited a higher prevalence of atherosclerotic disease as well as other risk factors such as smoking, hypercholesterolaemia, elevated haematocrit, and family history of stroke. Ischaemic heart disease and ICA disease are especially common in stroke with IC. IC, large artery disease and stroke share similar risk factors. IC symptoms in stroke patients may indicate extensive generalized atherosclerosis. [source] Load-bearing capacity of all-ceramic three-unit fixed partial dentures with different computer-aided design (CAD)/computer-aided manufacturing (CAM) fabricated framework materialsEUROPEAN JOURNAL OF ORAL SCIENCES, Issue 4 2008Florian Beuer The purpose of this in vitro study was to compare the load-bearing capacity of posterior three-unit fixed dental prostheses (FDP) produced with three different all-ceramic framework materials: glass-infiltrated alumina (ICA), glass-infiltrated alumina strengthened with zirconia (ICZ), and yttria-stabilized polycrystalline zirconia (YZ). Additionally, the influence on aging of mechanical cyclic fatigue loading and thermal cycling in water were evaluated. A total of 20 frameworks each were fabricated from ICA, ICZ, and YZ by a computer-aided design (CAD)/computer-aided manufacturing (CAM) system. The framework designs were identical for all specimens. All frameworks were veneered with porcelain and cemented with glass,ionomer. Prior to fracture testing, 10 FDP of each experimental group were subjected to thermal and mechanical cycling. Additionally, fractographic analysis was performed. Statistical analysis showed that FDP made from YZ had significantly higher load to failure, whereas no difference was found between the other two materials. Aging did not have a significant effect on the fracture load. [source] Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signalsEXPERT SYSTEMS, Issue 4 2008Elif Derya Übeyli Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms. [source] Rupture of radiation-induced internal carotid artery pseudoaneurysm in a patient with nasopharyngeal carcinoma,Spontaneous occlusion of carotid artery due to long-term embolizing performance,HEAD & NECK: JOURNAL FOR THE SCIENCES & SPECIALTIES OF THE HEAD AND NECK, Issue 8 2008Kai-Yuan Cheng MD Abstract Background Rupture of internal carotid artery (ICA) pseudoaneurysm is a lethal complication in patients with nasopharyngeal carcinoma (NPC). Angiography is the best diagnostic and treatment method. The aim of embolization is to block the pseudoaneurysm; but sometimes, total occlusion of great vessels is ineludible. We describe a case of NPC post-radiation therapy and with ruptured pseudoaneurysm treated by angio-embolization. Methods The patient had received embolization with numerous tools such as stent grafts, balloons, and bare stents with or without filter protection. Results After failing to pass through the narrow lumen by embolizing tools, the right ICA finally occluded spontaneously by self-thrombosis. Conclusion Although the angio-embolization is a good method to resolve the problems of ruptured pseudoaneurysm, there is still high mortality and morbidity. Being aware of the clinical presentations and the changes of images may alert us to predict the happening earlier. © 2008 Wiley Periodicals, Inc. Head Neck, 2008 [source] Dip-Pen Nanolithography Using the Amide-Coupling Reaction with Interchain Carboxylic Anhydride- Terminated Self-Assembled Monolayers,ADVANCED FUNCTIONAL MATERIALS, Issue 8 2006S. Chi Abstract Herein we report on a new type of dip-pen nanolithography (DPN), which utilizes an interfacial organic reaction,the amide-coupling reaction,between chemically activated surfaces and amine ink molecules transferred from an atomic force microscopy tip. As a representative of the chemically activated surfaces that could react with amine compounds, we formed a self-assembled monolayer terminating in interchain carboxylic anhydride (ICA) groups on gold, and generated chemically derived nanopatterns using alkylamines as ink molecules. Amine inks showed diffusive behavior similar to thiol inks on gold in conventional DPN, and the pattern sizes were controlled by changing the tip dwell times. In addition, nanopatterns of hydrolyzed ICAs were generated by taking advantage of the participation of the water meniscus in the DPN process and the chemical nature of the ICAs. [source] Independent component analysis of erroneous and correct responses suggests online response controlHUMAN BRAIN MAPPING, Issue 9 2010Sven Hoffmann Abstract After errors in reaction tasks, a sharp negative wave emerges in the event-related potential (ERP), the error (related) negativity (Ne or ERN). However, also after correct trials, an Ne-like wave is seen, called CRN or Nc, which is much smaller than the Ne. This study tested the hypothesis whether Ne and Nc reflect the same functional process, and whether this process is linked to online response control. For this purpose, independent component analysis (ICA) was utilized with the EEG data of two types of reaction tasks: a flanker task and a mental rotation task. To control for speed-accuracy effects, speed and accuracy instructions were balanced in a between subjects design. For both tasks ICA and dipole analysis revealed one component (Ne-IC) explaining most of the variance for the difference between correct and erroneous trials. The Ne-IC showed virtually the same features as the raw postresponse ERP, being larger for erroneous compared to correct trials and for the flanker than for the rotation task. In addition, it peaked earlier for corrected than for uncorrected errors. The results favor the hypothesis that Ne and Nc reflect the same process, which is modulated by response correctness and type of task. On the basis of the literature and the present results, we assume that this process induces online response control, which is much stronger in error than correct trials and with direct rather than indirect stimulus response mapping. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc. [source] Functional segmentation of the brain cortex using high model order group PICAHUMAN BRAIN MAPPING, Issue 12 2009Vesa Kiviniemi Abstract Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source] The effects of the glutamate antagonist memantine on brain activation to an auditory perception taskHUMAN BRAIN MAPPING, Issue 11 2009Heidi van Wageningen Abstract Glutamate is critically involved in the regulation of cognitive functions in humans. There is, however, sparse evidence regarding how blocking glutamate action at the receptor site during a cognitive task affects brain activation. In the current study, the effects of the glutamate antagonist memantine were examined with functional magnetic resonance imaging (fMRI). Thirty-one healthy adults were scanned twice in a counter-balanced design, either in a no-drug session or after administration of memantine for 21 days. The subjects performed a simple auditory perception task with consonant-vowel stimuli. Group-level spatial independent component analysis (ICA) was used to decompose the data and to extract task-related activations. The focus was on four task-related ICA components with frontotemporal localization. The results showed that glutamate-blockage resulted in a significant enhancement in one component, with no significant effect in the other three components. The enhanced effect of memantine was in the middle temporal gyrus, superior frontal gyrus, and middle frontal gyrus. It is suggested that the results reflect effects of glutamatergic processes primarily through non- N -methyl- D -aspartate (NMDA) receptor pathways. Moreover, the results demonstrate that memantine can be used as a probe which allows for studying the effect of excitatory neurotransmission on neuronal activation. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source] Changes in the interaction of resting-state neural networks from adolescence to adulthoodHUMAN BRAIN MAPPING, Issue 8 2009Michael C. Stevens Abstract This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12,30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. Hum Brain Mapp 2009. © 2009 Wiley-Liss, Inc. [source] Brain network dynamics during error commissionHUMAN BRAIN MAPPING, Issue 1 2009Michael C. Stevens Abstract Previous studies suggest that the anterior cingulate and other prefrontal brain regions might form a functionally-integrated error detection network in the human brain. This study examined whole brain functional connectivity to both correct and incorrect button presses using independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data collected from 25 adolescent and 25 adult healthy participants (ages 11,37) performing a visual Go/No-Go task. Correct responses engaged a network comprising left lateral prefrontal cortex, left postcentral gyrus/inferior parietal lobule, striatum, and left cerebellum. In contrast, a similar network was uniquely engaged during errors, but this network was not integrated with activity in regions believed to be engaged for higher-order cognitive control over behavior. A medial/dorsolateral prefrontal-parietal neural network responded to all No-Go stimuli, but with significantly greater activity to errors. ICA analyses also identified a third error-related circuit comprised of anterior temporal lobe, limbic, and pregenual cingulate cortices, possibly representing an affective response to errors. There were developmental differences in error-processing activity within many of these neural circuits, typically reflecting greater hemodynamic activation in adults. These findings characterize the spatial structure of neural networks underlying error commission and identify neurobiological differences between adolescents and adults. Hum Brain Mapp 2009. © 2007 Wiley-Liss, Inc. [source] Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA,HUMAN BRAIN MAPPING, Issue 1 2009Jingyu Liu Abstract There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball task,detecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic ,-7cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-concept that genomic SNP factors can be investigated by using endophenotypic imaging findings in a multivariate format. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [source] The effect of respiration variations on independent component analysis results of resting state functional connectivityHUMAN BRAIN MAPPING, Issue 7 2008Rasmus M. Birn Abstract The analysis of functional connectivity in fMRI can be severely affected by cardiac and respiratory fluctuations. While some of these artifactual signal changes can be reduced by physiological noise correction routines, signal fluctuations induced by slower breath-to-breath changes in the depth and rate of breathing are typically not removed. These slower respiration-induced signal changes occur at low frequencies and spatial locations similar to the fluctuations used to infer functional connectivity, and have been shown to significantly affect seed-ROI or seed-voxel based functional connectivity analysis, particularly in the default mode network. In this study, we investigate the effect of respiration variations on functional connectivity maps derived from independent component analysis (ICA) of resting-state data. Regions of the default mode network were identified by deactivations during a lexical decision task. Variations in respiration were measured independently and correlated with the MRI time series data. ICA appears to separate the default mode network and the respiration-related changes in most cases. In some cases, however, the component automatically identified as the default mode network was the same as the component identified as respiration-related. Furthermore, in most cases the time series associated with the default mode network component was still significantly correlated with changes in respiration volume per time, suggesting that current methods of ICA may not completely separate respiration from the default mode network. An independent measure of the respiration provides valuable information to help distinguish the default mode network from respiration-related signal changes, and to assess the degree of residual respiration related effects. Hum Brain Mapp 2008. © 2008 Wiley-Liss, Inc. [source] Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasksHUMAN BRAIN MAPPING, Issue 7 2008Vince D. Calhoun Abstract Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. Hum Brain Mapp, 2008. © 2008 Wiley-Liss, Inc. [source] Functional source separation applied to induced visual gamma activityHUMAN BRAIN MAPPING, Issue 2 2008Giulia Barbati Abstract Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20,70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data. Hum Brain Mapp 29:131,141, 2008. © 2007 Wiley-Liss, Inc. [source] Estimating the number of independent components for functional magnetic resonance imaging dataHUMAN BRAIN MAPPING, Issue 11 2007Yi-Ou Li Abstract Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain function. Because of the high dimensionality and high noise level of the fMRI data, order selection, i.e., estimation of the number of informative components, is critical to reduce over/underfitting in such methods. Dependence among fMRI data samples in the spatial and temporal domain limits the usefulness of the practical formulations of information-theoretic criteria (ITC) for order selection, since they are based on likelihood of independent and identically distributed (i.i.d.) data samples. To address this issue, we propose a subsampling scheme to obtain a set of effectively i.i.d. samples from the dependent data samples and apply the ITC formulas to the effectively i.i.d. sample set for order selection. We apply the proposed method on the simulated data and show that it significantly improves the accuracy of order selection from dependent data. We also perform order selection on fMRI data from a visuomotor task and show that the proposed method alleviates the over-estimation on the number of brain sources due to the intrinsic smoothness and the smooth preprocessing of fMRI data. We use the software package ICASSO (Himberg et al. [ 2004]: Neuroimage 22:1214,1222) to analyze the independent component (IC) estimates at different orders and show that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation. Hum Brain Mapp, 2007. © 2007 Wiley-Liss, Inc. [source] Improving functional magnetic resonance imaging motor studies through simultaneous electromyography recordingsHUMAN BRAIN MAPPING, Issue 9 2007Bradley J. MacIntosh Abstract Specially designed optoelectronic and data postprocessing methods are described that permit electromyography (EMG) of muscle activity simultaneous with functional MRI (fMRI). Hardware characterization and validation included simultaneous EMG and event-related fMRI in 17 healthy participants during either ankle (n = 12), index finger (n = 3), or wrist (n = 2) contractions cued by visual stimuli. Principal component analysis (PCA) and independent component analysis (ICA) were evaluated for their ability to remove residual fMRI gradient-induced signal contamination in EMG data. Contractions of ankle tibialis anterior and index finger abductor were clearly distinguishable, although observing contractions from the wrist flexors proved more challenging. To demonstrate the potential utility of simultaneous EMG and fMRI, data from the ankle experiments were analyzed using two approaches: 1) assuming contractions coincided precisely with visual cues, and 2) using EMG to time the onset and offset of muscle contraction precisely for each participant. Both methods produced complementary activation maps, although the EMG-guided approach recovered more active brain voxels and revealed activity better in the basal ganglia and cerebellum. Furthermore, numerical simulations confirmed that precise knowledge of behavioral responses, such as those provided by EMG, are much more important for event-related experimental designs compared to block designs. This simultaneous EMG and fMRI methodology has important applications where the amplitude or timing of motor output is impaired, such as after stroke. Hum Brain Mapp 2006. © 2006 Wiley-Liss, Inc. [source] Source density-driven independent component analysis approach for fMRI dataHUMAN BRAIN MAPPING, Issue 3 2005Baoming Hong Abstract Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals. Hum Brain Mapping, 2005. © 2005 Wiley-Liss, Inc. [source] Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?HUMAN BRAIN MAPPING, Issue 3 2002Fabrizio Esposito Abstract Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004,1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273,279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160,188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques. Hum. Brain Mapping 16:146,157, 2002. © 2002 Wiley-Liss, Inc. [source] Blind MIMO equalization with optimum delay using independent component analysisINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2004Vicente Zarzoso Abstract Blind space,time equalization of multiuser time-dispersive digital communication channels consists of recovering the users' simultaneously transmitted data free from the interference caused by each other and the propagation effects, without using training sequences. In scenarios composed of mutually independent non-Gaussian i.i.d. users' signals, independent component analysis (ICA) techniques based on higher-order statistics can be employed to refine the performance of conventional linear detectors, as recently shown in a code division multiple access environment (Signal Process 2002; 82:417,431). This paper extends these results to the more general multi-input multi-output (MIMO) channel model, with the minimum mean square error (MMSE) as conventional equalization criterion. The time diversity introduced by the wideband multipath channel enables a reduction of the computational complexity of the ICA post-processing stage while further improving performance. In addition, the ICA-based detector can be tuned to extract each user's signal at the delay which provides the best MMSE. Experiments in a variety of simulation conditions demonstrate the benefits of ICA-assisted MIMO equalization. Copyright © 2004 John Wiley & Sons, Ltd. [source] Interpreting variability in global SST data using independent component analysis and principal component analysisINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 3 2010Seth Westra Abstract Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ,source signals' which drive the multivariate ,mixed' dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally, our analysis suggests that the interpretability of independent components (ICs) appears to be low. This does not diminish the statistical advantages of deriving components that are mutually independent, with potential applications ranging from synthetically generating multivariate datasets, developing statistical forecasts, and reconstructing spatial datasets from patchy observations at multiple point locations. Copyright © 2009 Royal Meteorological Society [source] Distribution learning for radio network planning tool simulationINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 12 2008Z. Nouir Abstract We propose a novel method that combines the simulation results of a model-based prediction tool with the knowledge contained in measurement data. This mixture of the a priori information and the posteriori knowledge aims at enhancing the prediction results by increasing their precision and quality. A multilayer perceptron (MLP) is trained to learn the mapping between the distributions of the measurement data and the simulation data. To make the complexity of the MLP tractable, we propose the utilization of independent component analysis (ICA). The ICA transformation makes the variables at the input of the MLP statistically independent so that it can perform its learning and generalization on individual one-dimensional distributions. Other contributions consist of the application of the k -means clustering algorithm on the incoming data and the use of the training data world model to enhance the generalization capability of the MLP. The world model consists of the aggregation of all the available data in the learning space. The proposed method is applied to a third generation mobile network to enhance the predictions of uplink and downlink base station loads. After a training performed on a given network configuration, mechanical antenna tilts are modified and we show that the results obtained by the supervised predictions are much closer to measurements than simulation results for cases that have not been encountered before. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||||||||||||||||||||||||||||||||||||||