Independent component analysis of fMRI group studies by self-organizing clustering

Esposito, Fabrizio; Scarabino, Tommaso; Hyvärinen, Aapo; Himberg, Johan; Formisano, Elia; Comani, Silvia; Tedeschi, Gioacchino; Goebel, Rainer; Seifritz, Erich; Salle, Francesco Di

doi:10.1016/j.neuroimage.2004.10.042

Cited by 328 publications

(334 citation statements)

References 45 publications

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“…Goebel et al (2006) compared self-organizing group ICA (Esposito et al, 2005) with GLM on a sentence repetition task using fMRI, and concluded that group ICA provided a functional connectivity model by separating different patterns from the data. In another study that used subject-wise concatenation group ICA (Calhoun et al, 2001b) on a series of complex natural environment (Malinen et al, 2007), it was found that the GLM method only revealed sub-areas of the ICArevealed activations.…”

Section: Comparison Of Ica and Glm Resultsmentioning

confidence: 99%

“…In cases with more than two decompositions, clustering techniques or similar tools are necessary (Esposito et al, 2005;van de Ven et al, 2007).…”

Section: Selection Of Components Of Interest In Icamentioning

confidence: 99%

“…The time courses and spatial maps of the components can be decomposed by estimating the mixing matrix using a variety of algorithms (Hyvärinen and Oja, 2000). Methods have also been proposed to extend ICA to analyze multi-subject fMRI data (Calhoun et al, 2001b;Svensen et al, 2002;Esposito et al, 2005;Beckmann and Smith, 2005b). Schmithorst and Holland (2004b) compared three methods of group ICA analysis (across-subject averaging, subject-wise concatenation, and row-wise concatenation) and concluded that the subject-wise concatenation approach proposed by Calhoun et al (2001b) provided the best overall performance among the three approaches.…”

Section: Introductionmentioning

confidence: 99%

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Group independent component analysis of language fMRI from word generation tasks

et al. 2008

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Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the languagespecific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.

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Section: Comparison Of Ica and Glm Resultsmentioning

confidence: 99%

“…In cases with more than two decompositions, clustering techniques or similar tools are necessary (Esposito et al, 2005;van de Ven et al, 2007).…”

Section: Selection Of Components Of Interest In Icamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Group independent component analysis of language fMRI from word generation tasks

et al. 2008

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“…assuming shared time courses/weights, but unique components) was proposed in Svensen et al [2002] and both were compared in Schmithorst and Holland [2004]. Alternative approaches assuming both shared and unique components [Lukic et al, 2002], clustering single-subject results [Esposito et al, 2005], or stacking into a cube via tensorial methods [Beckmann and Smith, 2005] have also been proposed. All of the approaches just mentioned utilize the entire multisubject spatiotemporal data set from a single task in various ways.…”

Section: Shared Participant Weightsmentioning

confidence: 99%

A method for multitask fMRI data fusion applied to schizophrenia

Calhoun

Adalı

Kiehl

et al. 2005

Human Brain Mapping

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Abstract:It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them. Hum Brain Mapp 27:598-610, 2006.

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“…2004; Esposito et al. 2005). These procedures can provide satisfying results for healthy subjects or in pathologies with a marginal effect on network spatial patterns.…”

Section: Introductionmentioning

confidence: 99%

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

et al. 2015

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IntroductionThe mildly invasive 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is a well‐established imaging technique to measure ‘resting state’ cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.ObjectiveWe assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG‐PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG‐PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.MethodsWe extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG‐PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked‐in patients.ResultsThe results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG‐PET and the fMRI based maps. FDG‐PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked‐in syndrome patients produced also consistent maps with healthy controls.ConclusionsThe constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

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Independent component analysis of fMRI group studies by self-organizing clustering

Cited by 328 publications

References 45 publications

Group independent component analysis of language fMRI from word generation tasks

Group independent component analysis of language fMRI from word generation tasks

A method for multitask fMRI data fusion applied to schizophrenia

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

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