Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis

Turner, Greg H.; Twieg, Donald B.

doi:10.1109/tmi.2005.846852

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…This analysis captures the complex nature of fMRI data and produces consistent spatial components or networks (Turner and Twieg, 2005). Second, components for FNC analysis were selected by virtue of fulfilling one of two criteria: either component represented the DM or FT network or it displayed significantly differential modulation by the ToM task in the two groups.…”

Section: Methodsmentioning

confidence: 99%

Mentalizing in male schizophrenia patients is compromised by virtue of dysfunctional connectivity between task-positive and task-negative networks

Das

Calhoun

Malhi

2012

Schizophrenia Research

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Schizophrenia can be conceptualized as a disorder of functional connectivity within the fronto-temporal (FT) and/or default-mode (DM) networks. Recent evidence suggests that dysfunctional integration between these large neural networks may also contribute to the illness, and that the ability to mentalize or have a ‘theory of mind’ (ToM) is discernibly impaired in patients with schizophrenia. Hence in this study, we examined whether impaired functional network connectivity (FNC) contributes to a compromise in the ability to mentalize in patients with schizophrenia. Functional magnetic resonance imaging data were acquired from 20 male schizophrenia patients and 19 matched healthy controls whilst performing a well-known ToM task. The study revealed that relative to non-ToM the engagement of ToM produced reduced neural activity in the lateral FT and insula networks in patients, as compared to healthy subjects. The findings also indicated that in comparison to healthy subjects the DM and medial FT networks are less suppressed in patients irrespective of the task (ToM/non-ToM). Further, FNC analyses showed that the degree of functional connectivity between task-positive (lateral FT and insula) and task-negative (medial FT, posterior DM) networks was significantly reduced in patients as compared to controls. Of note, a significant correlation between the functional connectivity strength of the lateral FT network with the medial FT and the degree to which this is modulated by the ToM task, suggest that mentalizing deficits in male schizophrenia patients may stem from impaired communication between neural networks that comprehend the mental states of self (medial FT) and others (lateral FT).

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Section: Methodsmentioning

confidence: 99%

Mentalizing in male schizophrenia patients is compromised by virtue of dysfunctional connectivity between task-positive and task-negative networks

Das

Calhoun

Malhi

2012

Schizophrenia Research

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“…Each thresholded sICA map may consist of several remote brain regions forming a brain functional network. sICA generates consistent spatial maps (SMs) while modeling complex fMRI data collected during a task or in the resting-state (Turner and Twieg, 2005) although the task can result in a subtle modulation of the spatial patterns (Calhoun et al, 2008a). The dynamics of the BOLD signal within a single component is described by that component's TC.…”

Section: Introductionmentioning

confidence: 99%

Classification of schizophrenia patients based on resting-state functional network connectivity

Arbabshirani¹,

Kiehl²,

Pearlson³

et al. 2013

Front. Neurosci.

162

119

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There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors (KNNs) which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia.

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“…8 ICA has been found to be useful and able to capture the complex nature of fMRI time courses as well as to produce consistent spatial components. 9 Within a given component, the regions are strongly temporally coherent as the result of ICA assumption of linear mixing. In spatial ICA, the different components are spatially independent but can have temporal dependencies.…”

mentioning

confidence: 99%

Abnormal Functional Network Connectivity among Resting-State Networks in Children with Frontal Lobe Epilepsy

Widjaja

Zamyadi²,

Raybaud³

et al. 2013

AJNR Am J Neuroradiol

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Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis

Cited by 22 publications

References 28 publications

Mentalizing in male schizophrenia patients is compromised by virtue of dysfunctional connectivity between task-positive and task-negative networks

Mentalizing in male schizophrenia patients is compromised by virtue of dysfunctional connectivity between task-positive and task-negative networks

Classification of schizophrenia patients based on resting-state functional network connectivity

Abnormal Functional Network Connectivity among Resting-State Networks in Children with Frontal Lobe Epilepsy

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