LISA improves statistical analysis for fMRI

Lohmann, Gabriele; Stelzer, Johannes; Lacosse, Eric; Kumar, Vinod; Mueller, Karsten; Kuehn, Esther; Grodd, Wolfgang; Scheffler, Klaus

doi:10.1038/s41467-018-06304-z

Cited by 40 publications

(36 citation statements)

References 67 publications

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“…These include steerable filters (Knutsson et al, 1983), which enable directionally-adaptive spatial smoothing (Friman et al, 2003;Eklund et al, 2011;Zhuang et al, 2017;Abramian et al, 2020b), wavelet transforms (Mallat, 1989;Bullmore et al, 2004), which try to strike a balance between localization in space and frequency domain (Ruttimann et al, 1998;Van De Ville et al, 2004;Breakspear et al, 2006), and non-linear filters (e.g. bilateral filters) that locally adapt to various features of adjacent voxels (Smith and Brady, 1997;Rydell et al, 2008;Lohmann et al, 2018). While such methods have been successfully applied to GM, their adaptive properties rely on the spatial features manifested by the BOLD contrast.…”

Section: Spatial Smoothing Tailored To Fmri Data In White Mattermentioning

confidence: 99%

“…In the absence of any DW-MRI data, it would be possible to adapt the proposed method to use a structure tensor representation (Knutsson, 1989) derived from T1-weighted MRI images as the complementary contrast (Abramian et al, 2020b), wherein the proposed filtering scheme could be extended to function across the entire brain mask. The resulting morphology-based spatial smoothing could then be seen as a GSP-based alternative to non-linear filtering algorithms which enable spatial smoothing within similar anatomical compartments (Smith and Brady, 1997;Weickert and Scharr, 2002;Ding et al, 2005;Rydell et al, 2008;Lohmann et al, 2018), but will not provide adaptation to WM fiber orientations.…”

Section: Outlook; Potential Extensions and Other Applicationsmentioning

confidence: 99%

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Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

Abramian

Larsson

Eklund

et al. 2020

Preprint

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Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing WM fMRI data that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the WM BOLD signal is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the WM BOLD signal as they are incapable of adapting to the underlying domain of the BOLD signal in white matter. The fundamental element in the proposed method is a graph-based description of the WM that encodes the underlying anisotropy observed across WM, derived from diffusion MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth WM fMRI data, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing the capacity of the proposed method for detecting streamline-like activations within axonal bundles.

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Section: Spatial Smoothing Tailored To Fmri Data In White Mattermentioning

confidence: 99%

Section: Outlook; Potential Extensions and Other Applicationsmentioning

confidence: 99%

Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

Abramian

Larsson

Eklund

et al. 2020

Preprint

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“…For each voxel within the mask, we correlated single-participant behavioural predictability gain and neural parameter estimates across conditions, using a custom Matlab script. After applying Fisher’s z-transformation to all Pearson product-moment correlation coefficients, single-participant maps were submitted to the Local Indicators of Spatial Association (LISA) group-level one-sample t -test (Lohmann et al 2018). LISA is a threshold-free framework that allows to find consistent effects in small brain regions by applying a non-linear filter to statistical maps before controlling for multiple comparisons at a false discovery rate (FDR) < 0.05.…”

Section: Methodsmentioning

confidence: 99%

Neural modelling of the semantic predictability gain under challenging listening conditions

Rysop

Schmitt

Obleser

et al. 2019

Preprint

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When speech intelligibility is reduced, listeners exploit constraints posed by semantic context to facilitate comprehension. The left angular gyrus (AG) has been argued to drive this semantic predictability gain. Taking a network perspective, we ask how the connectivity within languagespecific and domain-general networks flexibly adapts to the predictability and intelligibility of speech. Participants repeated sentences, which varied in semantic predictability of the final word and in acoustic intelligibility. At the neural level, increasing intelligibility of highly predictable sentences led to stronger activation of left-hemispheric semantic regions including subregions of the AG (PGa, PGp) and posterior middle temporal gyrus. The behavioural predictability gain of single participants mapped onto the same regions but was complemented by increased activity in frontal and medial regions. The facilitatory influence of PGp on PGa (quantified by dynamic causal modelling) increased for highly predictable sentences. In contrast, cingulo-opercular regions showed stronger activation with increasing intelligibility only for low predictability. Critically, better individual performance in highly predictable sentences correlated with a stronger inhibitory influence of pre-SMA on right insula. These results suggest that successful comprehension in noisy listening conditions relies on an interplay of semantic regions and concurrent inhibition of cognitive control regions when semantic cues are available. Keywords: angular gyrus, cingulo-opercular network, semantic network, predictability gain, speech in noise comprehension. Rysop, Schmitt, et al. · Page 2 of 38 Hartwigsen and colleagues (2015) used transcranial magnetic stimulation to demonstrate that the ventral portion of left AG is functionally relevant for the behavioural comprehension gain.Together, these studies provide converging evidence for a role of the left AG in the topdown use of semantic information when the incoming acoustic signal is degraded. Yet, the functional interplay of left AG with other regions in the semantic network are unclear. Moreover, no network-level descriptors of the semantic predictability gain to comprehension are known.

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“…While this form of intracortical smoothing requires accurate knowledge of the local layer geometry within the cortex, there are alternative approaches to apply layer-specific smoothing in preferred layer-directions without the need to have pre-defined layer labels available. For example, it has been suggested to apply intracortical non-isotropic smoothing based on the local functional activation (Lohmann et al 2018;Smith and Brady 1997) or based on the non-isotropic MRI signal intensity across the cortical depth. In LayNii, this form of smoothing is implemented in the program LN_GRADSMOOTH by taking advantage of the fact that highresolution EPI intensities contain informative anatomical information too.…”

Section: Layer-specific Smoothingmentioning

confidence: 99%

LayNii: A software suite for layer-fMRI

Huber

Poser

Bandettini³

et al. 2020

Preprint

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Highlights• A new software toolbox is introduced for layer-specific functional MRI: LAYNII.• LAYNII is a suite of command-line executable C++ programs for Linux, Windows, and macOS.• LAYNII is designed for layer-fMRI data that suffer from SNR and coverage constraints.• LAYNII performs layerification in the native voxel space of functional data.• LAYNII performs layer-smoothing, GE-BOLD vein removal, QA, and VASO analysis. AbstractHigh-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient performance accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LAYNII. This toolbox is a collection of commandline executable programs written in C/C++ and is distributed open-source and as pre-compiled binaries for Linux, Windows, and macOS. LAYNII is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analysed in alternative software packages. Some of the most popular programs of LAYNII contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layerspecific smoothing, vein-removal of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data. layer-fMRI | layer fmri | laminar fMRI | columnar fMRI | submillimeter fMRI | fMRI QA | cortical depth-dependent fMRI Correspondence: renzohuber@gmail.com Graphical abstract Huber et al. | bioRχiv | June 12, 2020 | 1-20

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LISA improves statistical analysis for fMRI

Cited by 40 publications

References 67 publications

Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

Neural modelling of the semantic predictability gain under challenging listening conditions

LayNii: A software suite for layer-fMRI

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