Fast and precise independent component analysis for high field fMRI time series tailored using prior information on spatiotemporal structure

Suzuki, Kiyotaka; Kiryu, Tohru; Nakada, Tsutomu

doi:10.1002/hbm.1061

Cited by 49 publications

(59 citation statements)

References 18 publications

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“…Compared with other data-driven methods, spatial ICA has been shown to perform best in isolating voxels whose activity time courses (ATCs) correlate with distinct task conditions or with artifacts (McKeown et al, 1998c). Several applications and optimizations of ICA-fMRI have since been proposed (Calhoun et al, 2001a,b,c;Gu et al, 2001;McKeown, 2000b;Nakada et al, 2000;Nybakken et al, 2002;Stone et al, 2002;Suzuki et al, 2002). In our hands, ICA applied to conventional fMRI data sets of attention, emotion, and color-processing (Bartels and Zeki, 2000a,b;Zeki and Bartels, 1999) did equally well in identifying functionally activated areas as SPM, with the latter offering the advantage of sound statistical inference and a choice of a variety of statistical contrasts.…”

Section: Ica Applied To Fmri: Background and Motivationmentioning

confidence: 99%

The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain

2004

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A dominant tendency in cerebral studies has been the attempt to locate architecturally distinct parts of the cortex and assign special functions to each, through histological, clinical or hypothesis-based imaging experiments. Here we show that the cerebral cortex can also be subdivided into different components temporally, without any a priori hypotheses, based on the principle of functional independence. This states that distinct functional subdivisions have activity time courses (ATCs) that are, if not independent, at least characteristic to each when the brain is exposed to natural conditions. To approach a time-based anatomy experimentally, we recorded whole-brain activity using functional magnetic resonance imaging (fMRI) and analyzed the data with independent component analysis (ICA). Our results show that a multitude of cortical areas can be identified based purely on their characteristic ATCs during natural conditions. We demonstrate that a more 'rich' stimulation (free viewing of a movie) leads to more areas being activated in a specific way than conventional stimuli, allowing for a more detailed dissection of the cortex into its subdivisions. We show that stimulus-driven functionally specialized areas can be identified by intersubject correlation even if their function is unknown. Chronoarchitectonic mapping thus opens the prospect of identifying previously unknown cortical subdivisions based on natural viewing conditions by exploiting the characteristic temporal 'fingerprint' that is unique to each. D

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Section: Ica Applied To Fmri: Background and Motivationmentioning

confidence: 99%

The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain

2004

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“…As in the case of fMRI where application of higher-order cumulant measures has actually resulted in better performance than the original method, development of appropriate transfer functions for extracting vascular maps suitable to a given application will likely be identiˆed in the near future. 7,9 The robustness of f-MRA applications to clinical as well as basic scientiˆc investigations cannot be overemphasized.…”

Section: Discussionmentioning

confidence: 99%

“…In order to extract physiologically meaningful components, one has to rely on a priori knowledge of the expected temporal variations of given signals. 5,7 Strictly speaking, an accurate assumption of all the temporal variations is virtually impossible. Consequently, analytical errors are inevitable in these so called classical hypothesis driven methods which include all the currently available perfusion imaging algorithms.…”

Section: Introductionmentioning

confidence: 99%

All-phase MR Angiography Using Independent Component Analysis of Dynamic Contrast Enhanced MRI Time Series: .PHI.-MRA

et al. 2003

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Dynamic contrast enhanced magnetic resonance imaging (dynamic MRI) represents a MRI version of non-diŠusible tracer methods, the main clinical use of which is the physiological construction of what is conventionally referred to as perfusion images. The raw data utilized for constructing MRI perfusion images are time series of pixel signal alterations associated with the passage of a gadolinium containing contrast agent. Such time series are highly compatible with independent component analysis (ICA), a novel statistical signal processing technique capable of eŠectively separating a single mixture of multiple signals into their original independent source signals (blind separation). Accordingly, we applied ICA to dynamic MRI time series. The technique was found to be powerful, allowing for hitherto unobtainable assessment of regional cerebral hemodynamics in vivo.

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“…One of them exploits prior information on the independent source components (11) . Since the motivation in this approach is to explicitly assume some information about the underlying source signals or mixing matrix, it can be referred to as "semi-blind ICA."…”

Section: Introductionmentioning

confidence: 99%

Extraction of Neural Activity from In Vivo Optical Recordings Using Multiple Independent Component Analysis

et al. 2007

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In vivo optical recording using voltage-sensitive dyes enables neural activities in the cortex to be visualized as two-dimensional images. However, the signals provided are often contaminated with strong physiological noise, which mainly comes from pulsation and respiration. An effective approach is independent component analysis (ICA) to extract the neural activity component from these data without averaging. It has been used to denoise data obtained optical recordings.ICA extraction has problems in three points: 1) the appropriateness of ICA solutions is not guaranteed a priori; 2) in most ICA models, there is linear mixing of mutually independent random variables although the mixtures are not random variables; 3) considerable computational effort is often required because of repetitive large-matrix calculations.We have developed a multiple ICA method that resolves these problems and promises more efficient extraction of neural activity components. Multiple ICA overcomes the above problems by performing segmented and repeated ICAs and by using an algorithm based on the time structure of the variables. Multiple ICA repetitively performs a small ICA for a segmented region consisting of several adjacent channels. By sliding a segmental template channel by channel over the whole measurement region ( Fig. 1), we can obtain multiple solutions for each channel. The most appropriate solution is uniquely selected from these multiple solutions based on a predetermined criterion.We confirmed the effectiveness of multiple ICA by comparing its performance with that of conventional (batch) ICA, particularly in terms of the strength of the extracted neural activities in guinea pig auditory cortex evoked by click sound stimuli. The pulsatile component was reduced, and the neural activity was revealed by both batch and multiple ICA (Fig. 2). Multiple ICA extraction bears comparison with batch ICA extraction, although the computational cost of batch ICA is higher than that of multiple ICA.We found that this multiple ICA method would be a powerful way to extract neural activity from large-scale optical recordings. Non-memberA multiple independent component analysis (ICA) method based on the noisy time-delayed decorrelation algorithm is described that overcomes the problems and improves the usefulness of conventional ICA, which is commonly used for extracting the actual neural activity from data measured using optical recording with a voltage-sensitive dye to visualize neural activities in cortical areas as two-dimensional images. The problems with conventional ICA extraction include the lack of an a priori guarantee that the solution will be appropriate, the linear mixing of mutually independent random variables although the mixtures are not random variables but time signals in many applications, and the general requirement for repetitive calculation of large matrices. Application of multiple ICA to the extraction of neural activities in the guinea pig auditory cortex evoked by both click sounds and pure tones from optical rec...

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Fast and precise independent component analysis for high field fMRI time series tailored using prior information on spatiotemporal structure

Cited by 49 publications

References 18 publications

The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain

The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain

All-phase MR Angiography Using Independent Component Analysis of Dynamic Contrast Enhanced MRI Time Series: .PHI.-MRA

Extraction of Neural Activity from In Vivo Optical Recordings Using Multiple Independent Component Analysis

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