Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems

Wu, Jer-Fang; Ngo, Gia H.; Greve, Douglas N.; Li, J.; He, Tong; Fischl, Bruce; Eickhoff, Simon B.; Yeo, B.T. Thomas

doi:10.1002/hbm.24213

Cited by 97 publications

(98 citation statements)

References 71 publications

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“…To obtain the MNI coordinates of each insular electrode contact, we normalized individual MRI scans to a standard T1 template in MNI space using BrainVoyager 20.2 (Brain Innovation, Maastricht, The Netherlands). To visualize the spatial distributions of the response amplitudes at each contact location in the left and right insula, we then used the MNI coordinates to generate group-level 3D glass brain plots using the iELVis open source Matlab toolbox 35 and a FreeSurfer anatomical atlas ( http://surfer.nmr.mgh.harvard.edu/ ), after performing nonlinear mapping between the MNI volumetric coordinate system and the FreeSurfer surface coordinate system 36 .…”

Section: Methodsmentioning

confidence: 99%

Insular responses to transient painful and non-painful thermal and mechanical spinothalamic stimuli recorded using intracerebral EEG

Liberati

Mulders

Algoet

et al. 2020

Sci Rep

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Brief thermo-nociceptive stimuli elicit low-frequency phase-locked local field potentials (LFPs) and high-frequency gamma-band oscillations (GBOs) in the human insula. Although neither of these responses constitute a direct correlate of pain perception, previous findings suggest that insular GBOs may be strongly related to the activation of the spinothalamic system and/or to the processing of thermal information. To disentangle these different features of the stimulation, we compared the insular responses to brief painful thermonociceptive stimuli, non-painful cool stimuli, mechano-nociceptive stimuli, and innocuous vibrotactile stimuli, recorded using intracerebral electroencephalograpic activity in 7 epileptic patients (9 depth electrodes, 58 insular contacts). All four types of stimuli elicited consistent low-frequency phase-locked LFPs throughout the insula, possibly reflecting supramodal activity. The latencies of thermo-nociceptive and cool low-frequency phase-locked LFPs were shorter in the posterior insula compared to the anterior insula, suggesting a similar processing of thermal input initiating in the posterior insula, regardless of whether the input produces pain and regardless of thermal modality. In contrast, only thermo-nociceptive stimuli elicited an enhancement of insular GBOs, suggesting that these activities are not simply related to the activation of the spinothalamic system or to the conveyance of thermal information.

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Section: Methodsmentioning

confidence: 99%

Insular responses to transient painful and non-painful thermal and mechanical spinothalamic stimuli recorded using intracerebral EEG

Liberati

Mulders

Algoet

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Note, for visualisation of volume normalised data, we utilised an optimal MNI 152 to fsaverage mapping strategy (Wu et al, ). Data from both normalisation streams were additionally displayed on a flattened representation of fsaverage, calculated by making cuts to the cortical surface and using a cost function to metrically optimise (in terms of distance) the flattened representation (available as standard in Freesurfer).…”

Section: Resultsmentioning

confidence: 99%

Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher‐order cognitive tasks: An inter‐sensory task directing attention between visual and somatosensory domains

Aquino

Sokoliuk

Pakenham

et al. 2018

Human Brain Mapping

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Functional MRI at ultra‐high field (UHF, ≥7 T) provides significant increases in BOLD contrast‐to‐noise ratio (CNR) compared with conventional field strength (3 T), and has been exploited for reduced field‐of‐view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher‐order cognitive tasks leads to a number of challenges, in particular how to perform robust group‐level statistical analyses. This study addresses these challenges using an inter‐sensory cognitive task which modulates top‐down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalisation and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal‐lateral‐prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second‐level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multivariate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.

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“…Cortical reconstruction, volumetric segmentation and cortical thickness extraction used recon-all , and the output was inspected for quality assurance. The individual cortical thickness (CT) maps were then transformed into MNI space using the non-linear mapping method proposed by Wu et al (2018) .…”

Section: Methodsmentioning

confidence: 99%

Towards accurate and unbiased imaging-based differentiation of Parkinson’s disease, progressive supranuclear palsy and corticobasal syndrome

Correia

Rittman

Barnes

et al. 2020

Brain Communications

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The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, we compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion. Our sample was drawn from a cohort of 69 healthy controls, and patients with idiopathic Parkinson’s disease (n = 35), progressive supranuclear palsy Richardson’s syndrome (n = 52) and corticobasal syndrome (n = 36). Participants underwent standardized T1-weighted and diffusion-weighted magnetic resonance imaging. Strict data quality control and group matching reduced the control and patient numbers to 43, 32, 33 and 26, respectively. We compared two different methods for feature selection and dimensionality reduction: whole-brain principal components analysis, and an anatomical region-of-interest based approach. In both cases, support vector machines were used to construct a statistical model for pairwise classification of healthy controls and patients. The accuracy of each model was estimated using a leave-two-out cross-validation approach, as well as an independent validation using a different set of subjects. Our cross-validation results suggest that using principal components analysis for feature extraction provides higher classification accuracies when compared to a region-of-interest based approach. However, the differences between the two feature extraction methods were significantly reduced when an independent sample was used for validation, suggesting that the principal components analysis approach may be more vulnerable to overfitting with cross-validation. Both T1-weighted and diffusion magnetic resonance imaging data could be used to successfully differentiate between subject groups, with neither modality outperforming the other across all pairwise comparisons in the cross-validation analysis. However, features obtained from diffusion magnetic resonance imaging data resulted in significantly higher classification accuracies when an independent validation cohort was used. Overall, our results support the use of statistical classification approaches for differential diagnosis of parkinsonian disorders. However, classification accuracy can be affected by group size, age, sex and movement artefacts. With appropriate controls and out-of-sample cross validation, diagnostic biomarker evaluation including magnetic resonance imaging based classifiers may be an important adjunct to clinical evaluation.

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Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems

Cited by 97 publications

References 71 publications

Insular responses to transient painful and non-painful thermal and mechanical spinothalamic stimuli recorded using intracerebral EEG

Insular responses to transient painful and non-painful thermal and mechanical spinothalamic stimuli recorded using intracerebral EEG

Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher‐order cognitive tasks: An inter‐sensory task directing attention between visual and somatosensory domains

Towards accurate and unbiased imaging-based differentiation of Parkinson’s disease, progressive supranuclear palsy and corticobasal syndrome

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