Correcting for Superficial Bias in 7T Gradient Echo fMRI

Huang, Pei; Correia, Marta; Rua, Catarina; Rodgers, Christopher T.; Henson, Richard; Carlin, Johan D.

doi:10.3389/fnins.2021.715549

Cited by 8 publications

(14 citation statements)

References 60 publications

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“…Although we demonstrate ground-truth simulations in this paper, studying bias is not limited to such situations. On the one hand, ground-truth simulations can deliver many valuable insights [17,18]. However, ground-truth simulations are susceptible to the criticism that they may not capture the full complexity of real empirical data.…”

Section: We Should Study and Quantify Biasmentioning

confidence: 99%

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The risk of bias in denoising methods: Examples from neuroimaging

Kay

2022

PLoS ONE

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Experimental datasets are growing rapidly in size, scope, and detail, but the value of these datasets is limited by unwanted measurement noise. It is therefore tempting to apply analysis techniques that attempt to reduce noise and enhance signals of interest. In this paper, we draw attention to the possibility that denoising methods may introduce bias and lead to incorrect scientific inferences. To present our case, we first review the basic statistical concepts of bias and variance. Denoising techniques typically reduce variance observed across repeated measurements, but this can come at the expense of introducing bias to the average expected outcome. We then conduct three simple simulations that provide concrete examples of how bias may manifest in everyday situations. These simulations reveal several findings that may be surprising and counterintuitive: (i) different methods can be equally effective at reducing variance but some incur bias while others do not, (ii) identifying methods that better recover ground truth does not guarantee the absence of bias, (iii) bias can arise even if one has specific knowledge of properties of the signal of interest. We suggest that researchers should consider and possibly quantify bias before deploying denoising methods on important research data.

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Section: We Should Study and Quantify Biasmentioning

confidence: 99%

“…In surveying the literature, we find extensive discussion and consideration of denoising methods and how they fare in specific scientific paradigms. However, we think that, aside from a few notable exceptions [17,18], there has been insufficient emphasis on the issue of statistical bias.…”

Section: Introductionmentioning

confidence: 99%

The risk of bias in denoising methods: Examples from neuroimaging

Kay

2022

PLoS ONE

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“…However, as described in above, the BOLD signals detected with the GE sequences contain the contribution not only from the local microvasculature but also from the macrovasculature, leading to the phenomenon of functional signals detected at the superficial layers being larger than those at the deeper layers. 11,33,50 The two major mechanisms that create this superficial bias are as follows: 1) crosstalk introduced by ascending draining veins that carry the deoxygenated hemoglobin from the deep to the superficial layers (i.e. leakage effect); and 2) different physiological parameters (eg baseline CBV or T 2 *) across the cortical depth (i.e.…”

Section: D Ge-epimentioning

confidence: 99%

Submillimeter fMRI Acquisition Techniques for Detection of Laminar and Columnar Level Brain Activation

Yun,

Küppers,

Shah

2023

Magnetic Resonance Imaging

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Since the first demonstration in the early 1990s, functional MRI (fMRI) has emerged as one of the most powerful, noninvasive neuroimaging tools to probe brain functions. Subsequently, fMRI techniques have advanced remarkably, enabling the acquisition of functional signals with a submillimeter voxel size. This innovation has opened the possibility of investigating subcortical neural activities with respect to the cortical depths or cortical columns. For this purpose, numerous previous works have endeavored to design suitable functional contrast mechanisms and dedicated imaging techniques. Depending on the choice of the functional contrast, functional signals can be detected with high sensitivity or with improved spatial specificity to the actual activation site, and the pertaining issues have been discussed in a number of earlier works. This review paper primarily aims to provide an overview of the subcortical fMRI techniques that allow the acquisition of functional signals with a submillimeter resolution. Here, the advantages and disadvantages of the imaging techniques will be described and compared. We also summarize supplementary imaging techniques that assist in the analysis of the subcortical brain activation for more accurate mapping with reduced geometric deformation. This review suggests that there is no single universally accepted method as the gold standard for subcortical fMRI. Instead, the functional contrast and the corresponding readout imaging technique should be carefully determined depending on the purpose of the study. Due to the technical limitations of current fMRI techniques, most subcortical fMRI studies have only targeted partial brain regions. As a future prospect, the spatiotemporal resolution of fMRI will be pushed to satisfy the community's need for a deeper understanding of whole‐brain functions and the underlying connectivity in order to achieve the ultimate goal of a time‐resolved and layer‐specific spatial scale.Evidence Level1Technical EfficacyStage 1

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“…In addition to morphological studies, MRI sequences can provide other information about brain changes in patients with RTT; for example, the underlying tissue microstructure can be examined using diffusion MRI (dMRI) ( 55 – 57 ), functional information can be gathered using functional MRI (fMRI) ( 57 ), metabolic differences can be identified using magnetic resonance spectroscopy (MRS) ( 46 ), and cerebral blood flow (CBF) can be assessed using arterial spin labeling (ASL) ( 58 , 59 ).…”

Section: Morphologic Mri In Rtt With Mecp2 Mutationmentioning

confidence: 99%

Multimodal Neuroimaging in Rett Syndrome With MECP2 Mutation

Zhao-hong

et al. 2022

Front. Neurol.

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Rett syndrome (RTT) is a rare neurodevelopmental disorder characterized by severe cognitive, social, and physical impairments resulting from de novo mutations in the X-chromosomal methyl-CpG binding protein gene 2 (MECP2). While there is still no cure for RTT, exploring up-to date neurofunctional diagnostic markers, discovering new potential therapeutic targets, and searching for novel drug efficacy evaluation indicators are fundamental. Multiple neuroimaging studies on brain structure and function have been carried out in RTT-linked gene mutation carriers to unravel disease-specific imaging features and explore genotype-phenotype associations. Here, we reviewed the neuroimaging literature on this disorder. MRI morphologic studies have shown global atrophy of gray matter (GM) and white matter (WM) and regional variations in brain maturation. Diffusion tensor imaging (DTI) studies have demonstrated reduced fractional anisotropy (FA) in left peripheral WM areas, left major WM tracts, and cingulum bilaterally, and WM microstructural/network topology changes have been further found to be correlated with behavioral abnormalities in RTT. Cerebral blood perfusion imaging studies using single-photon emission CT (SPECT) or PET have evidenced a decreased global cerebral blood flow (CBF), particularly in prefrontal and temporoparietal areas, while magnetic resonance spectroscopy (MRS) and PET studies have contributed to unraveling metabolic alterations in patients with RTT. The results obtained from the available reports confirm that multimodal neuroimaging can provide new insights into a complex interplay between genes, neurotransmitter pathway abnormalities, disease-related behaviors, and clinical severity. However, common limitations related to the available studies include small sample sizes and hypothesis-based and region-specific approaches. We, therefore, conclude that this field is still in its early development phase and that multimodal/multisequence studies with improved post-processing technologies as well as combined PET–MRI approaches are urgently needed to further explore RTT brain alterations.

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Correcting for Superficial Bias in 7T Gradient Echo fMRI

Cited by 8 publications

References 60 publications

The risk of bias in denoising methods: Examples from neuroimaging

The risk of bias in denoising methods: Examples from neuroimaging

Submillimeter fMRI Acquisition Techniques for Detection of Laminar and Columnar Level Brain Activation

Multimodal Neuroimaging in Rett Syndrome With MECP2 Mutation

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