Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

Kelly, R. E.; Alexopoulos, George S.; Wang, Zhishun; Gunning, Faith M.; Murphy, Christina; Morimoto, Sarah Shizuko; Kanellopoulos, Dora; Jia, Zhiru; Lim, Kelvin O.; Hoptman, Matthew J.

doi:10.1016/j.jneumeth.2010.03.028

Cited by 324 publications

(277 citation statements)

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“…Guidelines for hand-classification of brain fMRI have been developed (Kelly et al, 2010;Griffanti et al, submitted), but may not be applicable to the spinal cord in their entirety. Nevertheless, a large number of features that indicate if components are noise will apply to the spinal cord as well (e.g.…”

Section: Resultsmentioning

confidence: 99%

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

et al. 2017

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractFunctional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies. In this review, we give an overview of the various methods that have been used to address the technical challenges in spinal fMRI, with a focus on reducing the impact of physiological noise. We start out by describing acquisition methods that have been tailored to the special needs of spinal cord fMRI and aim to increase the signal-to-noise ratio and reduce distortion in obtained images. Following this, we concentrate on image processing and analysis approaches that address the detrimental effects of noise. While these include variations of standard pre-processing methods such as motion correction and spatial filtering, the main focus lies on denoising techniques that can be applied to taskbased as well as resting-state data sets. We review both model-based approaches that rely on externally acquired respiratory and cardiac signals as well as data-driven approaches that estimate and correct for noise using the data themselves. We conclude with an outlook on techniques that have been successfully applied for noise reduction in brain imaging and whose use might be beneficial for fMRI of the human spinal cord.3

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Section: Resultsmentioning

confidence: 99%

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

et al. 2017

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“…All activations are reported at the FDR-corrected threshold, p \ 0.001. Based on the literature (Kelly et al 2010;Varoquaux et al 2010), 11 components were classified as neural networks ( Fig. 5; Table 6).…”

Section: Fmri Resultsmentioning

confidence: 99%

When three is greater than five: EEG and fMRI signatures of errors in numerical and physical comparisons

et al. 2017

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Unravelling the neural mechanisms, which determine performance accuracy, is one of the key concepts in cognitive neuroscience. When compared to correct responses, shorter reaction times are commonly observed behavioural feature of errors committed in typical conflict tasks. Yet, little is known about the origins of this phenomenon. In this study, EEG and fMRI experiments were conducted using the numerical version of the Stroop paradigm, which yielded unique behavioural outcomes. Particularly, errors in numerical comparison had shorter reaction times than correct trials, whereas physical comparison resulted in the opposite pattern. This criss-crossing interaction effect was used as a marker when exploring time-courses of brain activity. Group independent component analysis was applied to neurophysiological data and event-related analysis was conducted on the components' time-courses. Results revealed one centro-parietal EEG component and one temporo-parietal fMRI neural network, which exhibited significant task and accuracy interactions. Showing linear increase that peaked right after the response onset, the activity of centro-parietal EEG component was linked to the decision variable signal, which reflects a process of accumulating evidence until reaching an actiontriggering threshold. Both amplitude measurements and linear fits to the signal provided evidence for distinctive characteristics between numerical and physical comparisons, thereby explaining the behavioural outcomes: errors are committed due to accumulation of evidence in favour of the other (wrong) task instruction. The architecture of the temporo-parietal network, which comprises bilateral inferior temporal and intraparietal regions, is highly consistent with the recently established core ''number network''. These findings link perceptual decisions with the generalized magnitude system and impart novel insights into the neural determinants of errors in humans.

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“…At this point, we performed independent component analysis of each functional run with FSL's MELODIC. Components were visually inspected to identify noise components following published guidelines (42). Noise components were regressed out of the functional runs using FSL's fsl_regfilt command.…”

Section: Methodsmentioning

confidence: 99%

Human hippocampus represents space and time during retrieval of real-world memories

Nielson

Smith

Sreekumar

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

216

213

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Memory stretches over a lifetime. In controlled laboratory settings, the hippocampus and other medial temporal lobe brain structures have been shown to represent space and time on the scale of meters and seconds. It remains unclear whether the hippocampus also represents space and time over the longer scales necessary for human episodic memory. We recorded neural activity while participants relived their own experiences, cued by photographs taken with a custom lifelogging device. We found that the left anterior hippocampus represents space and time for a month of remembered events occurring over distances of up to 30 km. Although previous studies have identified similar drifts in representational similarity across space or time over the relatively brief time scales (seconds to minutes) that characterize individual episodic memories, our results provide compelling evidence that a similar pattern of spatiotemporal organization also exists for organizing distinct memories that are distant in space and time. These results further support the emerging view that the anterior, as opposed to posterior, hippocampus integrates distinct experiences, thereby providing a scaffold for encoding and retrieval of autobiographical memories on the scale of our lives.hippocampus | representational similarity analysis | lifelogging | episodic memory T he hippocampus plays a critical role in remembering the events of our lives (1). Direct evidence from single-neuron recordings in rats indicates that cells in the hippocampus fire in specific spatial locations (2-6) or at specific times during a temporal delay (7,8). Single-neuron and functional MRI (fMRI) studies in individuals navigating virtual environments have confirmed that cells coding for spatial location are also present in the human hippocampus (9-11). Similarly, place-responsive cell activity recorded in the hippocampus of patients with epilepsy during navigation of a virtual town was shown to reinstate during episodic memory retrieval of the previous virtual navigation (12). Together, these studies provide evidence that the same neurons in the medial temporal lobe (MTL) that are active during an experience also help represent the memory for that experience. These results, however, are limited to simple events in laboratory settings that occur on the scale of minutes and meters, thereby leaving unanswered whether we harness similar mechanisms in more natural settings and over larger temporal and spatial scales.Recent studies have used more naturalistic designs with incidentally acquired memories recorded via lifelogging devices that automatically capture photographs from the participants' lives (13,14). The typical finding is increased hippocampal activation when participants view images from their cameras as opposed to images from other participants' cameras (15-17), and this activation decays over the course of months (14). Still, there is no evidence to date that the hippocampus or other MTL structures actually represent space or time of autobiographical experiences. We a...

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Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

Cited by 324 publications

References 59 publications

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

When three is greater than five: EEG and fMRI signatures of errors in numerical and physical comparisons

Human hippocampus represents space and time during retrieval of real-world memories

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