A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

Cousineau, Martin; Jodoin, Pierre-Marc; Garyfallidis, Eleftherios; Côté, Marc-Alexandre; Morency, Félix C.; Rozanski, Verena E.; Grand’Maison, Marilyn; Bedell, Barry J.; Descoteaux, Maxime

doi:10.1016/j.nicl.2017.07.020

Cited by 129 publications

(193 citation statements)

References 72 publications

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“…Next, for anatomical fiber tract parcellation, we leveraged the White Matter Query Language (WMQL; https://github.com/demianw/tract_querier; Wassermann et al, ), an automated method to delineate anatomical fiber tracts based on the Freesurfer anatomical regions they intersect (Figure b1). In our study, we applied WMQL because it enables identification of a relatively large number of fiber tracts (45 tracts) and it has been used in multiple works to study white matter parcellation retest–retest reproducibility (Cousineau et al, ; Ning et al, ; Roy et al, ). For a given anatomical fiber tract, subject‐specific parcellation was performed by identifying the fibers from the whole brain tractography that met the tract's anatomical definition (Figure b2).…”

Section: Methodsmentioning

confidence: 99%

“…For a geometrical measure, we computed the volumetric overlap between the parcellated white matter structures to investigate if they had the same volume and shape. We applied the weighted Dice (wDice) coefficient that was designed specifically for measuring volumetric overlap of fiber tracts (Cousineau et al, ). wDice extends the standard Dice coefficient (Dice, ) taking account of the number of fibers per voxel so that it gives higher weighting to voxels with dense fibers, as follows:

italicwDice ((),, P 1, P 2) = \frac{\sum_{υ^{'}} W_{1, υ^{'}} + \sum_{υ^{'}} W_{2, υ^{'}}}{\sum_{υ} W_{1, υ} + \sum_{υ} W_{2, υ}}

where P1 and P2 represent two corresponding parcellated white matter structures from the test–retest data, v’ indicates the set of voxels that are within the intersection of the volumes of P1 and P2 , v indicates the set of voxels that are within the union of the volumes of P1 and P2 , and W is the fraction of the fibers passing through a voxel.…”

Section: Methodsmentioning

confidence: 99%

“…Test–retest reproducibility is considered to be a good indicator of the reliability of white matter parcellation for potential clinical applications (Besseling et al, ; Jovicich et al, ; Keihaninejad et al, ; Kristo et al, ; Lin et al, ). To measure test–retest reproducibility, previous works have used geometrical measures such as volume, volumetric overlap, fiber length and shape, and number of fibers (Cheng et al, ; Cousineau et al, ; Kristo et al, ; Lin et al, ; Owen, Chang, & Mukherjee, ; Smith, Tournier, Calamante, & Connelly, ; Wang et al, ; Zhao et al, ). Other groups have used diffusion measures such as fractional anisotropy (FA) and mean diffusivity (MD) computed from the voxels through which the parcellated fibers pass (Besseling et al, ; Ciccarelli et al, ; Duan, Zhao, He, & Shu, ; Kristo et al, ; Papinutto, Maule, & Jovicich, ; Pfefferbaum, Adalsteinsson, & Sullivan, ; Vollmar et al, ; Yendiki, Reuter, Paul, Diana Rosas, & Fischl, ).…”

Section: Introductionmentioning

confidence: 99%

“…Other groups have used diffusion measures such as fractional anisotropy (FA) and mean diffusivity (MD) computed from the voxels through which the parcellated fibers pass (Besseling et al, ; Ciccarelli et al, ; Duan, Zhao, He, & Shu, ; Kristo et al, ; Papinutto, Maule, & Jovicich, ; Pfefferbaum, Adalsteinsson, & Sullivan, ; Vollmar et al, ; Yendiki, Reuter, Paul, Diana Rosas, & Fischl, ). While multiple studies have assessed test–retest reproducibility of white matter parcellations using cortical‐parcellation‐based strategies, for example, on the brain connectome network (Besson, Lopes, Leclerc, Derambure, & Tyvaert, ; Bonilha et al, ; Buchanan, Pernet, Gorgolewski, Storkey, & Bastin, ; Dennis et al, ; Duda, Cook, & Gee, ; Schumacher et al, ; Smith et al, ; Vaessen et al, ; Zhao et al, ; Zhang, Descoteaux, et al, ) and on anatomical fiber tracts (Besseling et al, ; Cousineau et al, ; Heiervang, Behrens, Mackay, Robson, & Johansen‐Berg, ; Kristo et al, ; Lin et al, ; Papinutto et al, ; Tensaouti, Lahlou, Clarisse, Lotterie, & Berry, ; Wang et al, ; Yendiki et al, ), there are no existing studies of fiber clustering, to our knowledge. Studies have suggested that fiber clustering approaches have advantages in parcellating the white matter in a highly consistent way, aiming to reconstruct fiber parcels/tracts corresponding to the white matter anatomy (Ge et al, ; Sydnor et al, ; Zhang et al, ; Zhang, Wu, Norton, et al, ; Ziyan, Sabuncu, Eric, Grimson, & Westin, ), while cortical‐parcellation‐based methods could be less consistent considering factors such as the variability of intersubject cortical anatomy, the dependence on registration between dMRI and structural images, and the presence of false positive/negative connections (Amunts et al, ; Fischl, Sereno, Tootell, & Dale, ; Maier‐Hein et al, ; Sinke et al, ; Zhang, Descoteaux, et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

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Section: Methodsmentioning

confidence: 99%

italicwDice ((),, P 1, P 2) = \frac{\sum_{υ^{'}} W_{1, υ^{'}} + \sum_{υ^{'}} W_{2, υ^{'}}}{\sum_{υ} W_{1, υ} + \sum_{υ} W_{2, υ}}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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“…Finally, once extracted, these WM bundles were processed through an additional pipeline as described in(Cousineau et al, 2017), available as part of the Sherbrooke Connectivity Imaging Lab (SCIL) python toolbox (SCILPY), with elimination of spurious fascicles using a pruning and outlier-rejection step(Cote et al, 2013) after which the means and standard deviations of each metric were computed. In addition, a control fasciculus, the bilateral uncinate fasciculus (UF), was also added to test for the specificity of potential relationships between age, speech perception and the fasciculi of interest (AF, MdLF).…”

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confidence: 99%

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

Tremblay

Perron²,

Deschamps

et al. 2018

Human Brain Mapping

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In this article, we used High Angular Resolution Diffusion Imaging (HARDI) with advanced anatomically constrained particle filtering tractography to investigate the role of the arcuate fasciculus (AF) and the middle longitudinal fasciculus (MdLF) in speech perception in noise in younger and older adults. Fourteen young and 15 elderly adults completed a syllable discrimination task in the presence of broadband masking noise. Mediation analyses revealed few effects of age on white matter (WM) in these fascicles but broad effects of WM on speech perception, independently of age, especially in terms of sensitivity and criterion (response bias), after controlling for individual differences in hearing sensitivity and head size. Indirect (mediated) effects of age on speech perception through WM microstructure were also found, after controlling for individual differences in hearing sensitivity and head size, with AF microstructure related to sensitivity, response bias and phonological priming, and MdLF microstructure more strongly related to response bias. These findings suggest that pathways of the perisylvian region contribute to speech processing abilities, with relatively distinct contributions for the AF (sensitivity) and MdLF (response bias), indicative of a complex contribution of both phonological and cognitive processes to age‐related speech perception decline. These results provide new and important insights into the roles of these pathways as well as the factors that may contribute to elderly speech perception deficits. They also highlight the need for a greater focus to be placed on studying the role of WM microstructure to understand cognitive aging.

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Altered fornix integrity is associated with sleep apnea‐related hypoxemia in mild cognitive impairment

Marchi,

Daneault,

André

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONThe limbic system is critical for memory function and degenerates early in the Alzheimer's disease continuum. Whether obstructive sleep apnea (OSA) is associated with alterations in the limbic white matter tracts remains understudied.METHODSPolysomnography, neurocognitive assessment, and brain magnetic resonance imaging (MRI) were performed in 126 individuals aged 55–86 years, including 70 cognitively unimpaired participants and 56 participants with mild cognitive impairment (MCI). OSA measures of interest were the apnea‐hypopnea index and composite variables of sleep fragmentation and hypoxemia. Microstructural properties of the cingulum, fornix, and uncinate fasciculus were estimated using free water‐corrected diffusion tensor imaging.RESULTSHigher levels of OSA‐related hypoxemia were associated with higher left fornix diffusivities only in participants with MCI. Microstructure of the other white matter tracts was not associated with OSA measures. Higher left fornix diffusivities correlated with poorer episodic verbal memory.DISCUSSIONOSA may contribute to fornix damage and memory dysfunction in MCI.Highlights Sleep apnea‐related hypoxemia was associated with altered fornix integrity in MCI. Altered fornix integrity correlated with poorer memory function. Sleep apnea may contribute to fornix damage and memory dysfunction in MCI.

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A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

Cited by 129 publications

References 72 publications

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

Altered fornix integrity is associated with sleep apnea‐related hypoxemia in mild cognitive impairment

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