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Intrinsic non-stationarity correction for Fixel-Based Analysis

Conference Paper

Intrinsic non-stationarity correction for Fixel-Based Analysis

Abstract

The Fixel-Based Analysis (FBA) framework enables data-driven statistical inference of effects in diffusion MRI measures that is both sensitive and specific to crossing fibre geometry ("fixel": specific fibre bundle element within a specific voxel). A fundamental building block of this framework is the Connectivity-based Fixel Enhancement (CFE) method, which provides enhancement of fixel-wise statistical measures according to fixel connectivity estimated through tractography (on the basis that genuine biological effects are likely to appear along the lengths of affected white matter pathways). This method can however have much greater sensitivity to effects within the core of major white matter bundles than elsewhere. While non-parametric, permutation-based non-stationarity correction can theoretically be used to mitigate such effects, this incurs additional computational time, and can exhibit undesirable numerical instability. We propose an intrinsic normalisation of the statistical enhancement performed by CFE, which provides superior correction for spatially heterogeneous statistical power without requiring additional permutations prior to statistical testing.
20/12/2018 OHBM
https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&subView=1&abs=1595 1/4
Intrinsic non-stationarity correction for Fixel-Based
Analysis
Submission No:
1712
Submission Type:
Abstract Submission
Authors:
Robert Smith , Dennis Dimond , David Vaughan , Donna Parker , Thijs Dhollander , Graeme Jackson ,
Alan Connelly
Institutions:
The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, The University of Melbourne,
Melbourne, Australia, University of Calgary, Calgary, Canada, Austin Health, Melbourne, Australia
Introduction:
The Fixel-Based Analysis (FBA) framework enables data-driven statistical inference of effects in diffusion MRI
measures that is both sensitive and specic to crossing bre geometry ("xel": specic bre bundle element
within a specic voxel) . A fundamental building block of this framework is the Connectivity-based Fixel
Enhancement (CFE) method , which provides enhancement of xel-wise statistical measures according to
xel connectivity estimated through tractography (on the basis that genuine biological effects are likely to appear
along the lengths of affected white matter pathways). This method can however have much greater sensitivity to
effects within the core of major white matter bundles than elsewhere. While non-parametric, permutation-based
non-stationarity correction can theoretically be used to mitigate such effects, this incurs additional
computational time, and can exhibit undesirable numerical instability. We propose an intrinsic normalisation of the
statistical enhancement performed by CFE, which provides superior correction for spatially heterogeneous
statistical power without requiring additional permutations prior to statistical testing.
Methods:
Non-stationarity correction involves computing an estimate of "expected statistical enhancement in the absence
of an effect", to which values computed during statistical inference can then be normalised. In non-parametric
permutation-based Voxel-Based Analysis (VBA), using e.g. cluster size / cluster mass / Threshold-Free Cluster
Enhancement (TFCE) for statistical enhancement & false positive control , for any particular
voxel in template space it is difcult to accurately estimate a priori the empirical statistical power. In this case the
effects of non-stationarity may instead be estimated in a non-parametric, permutation-based fashion by
computing the "empirical statistic": for some number of permutations, randomly permute the input data, apply
the relevant statistical enhancement algorithm to the resulting statistical map; nally, for each template voxel,
compute some statistic from the values observed across permutations (e.g. the mean of non-zero values)
.
The empirical statistic may also be generated & utilised in the context of FBA. This however compounds the
computational expense of CFE; and, depending on how the data are generated, may in some instances reduce
statistical power.
1,2 3 1,4 1 1,2 1,4,2
1,2
1 2
3 4
Raffelt2017
Raffelt2015
Friston1994;Smith2009
Salimi-
Khorshidi2011
20/12/2018 OHBM
https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&subView=1&abs=1595 2/4
Here we instead make the observation that the xel-xel connectivity matrix utilised by CFE intrinsically captures
the total extent of connected xels by which each individual xel may be statistically enhanced. We therefore
propose a 'normalised' form of the CFE equation, which internally scales the magnitude of statistical
enhancement by the total extent of connectivity present in each xel, providing effective non-stationarity
correction without necessitating permutation-based computation of the empirical statistic.
·Comparison of existing and proposed equations for Connectivity-based Fixel Enhancement (CFE).
Results:
The gure compares the outcomes of FBA comparing 13 patients with right hand side Temporal Lobe Epilepsy
(TLE) with Hippocampal Sclerosis (HS) to 30 matched controls, based on the Fibre Density and Cross-section
(FDC) metric quantied as part of the standard FBA processing pipeline , using different methods to
account for heterogeneous statistical power (no correction; non-parametric permutation-based empirical
statistic; proposed intrinsic normalisation).
Use of the proposed normalised CFE equation yields increases in the length and breadth of statistically signicant
bundles, improved spatial continuity of super-threshold xels, and sensitivity to changes in peripheral white
matter bundles (e.g. ipsilateral parahippocampal WM as indicated by arrows in gure - an expected pathological
effect).
Raffelt2017
20/12/2018 OHBM
https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&subView=1&abs=1595 3/4
·Differences in FBA outcomes for example dataset using various non-stationarity correction techniques; arrows
indicate location of right hemisphere parahippocampal white matter.
Conclusions:
The proposed change to CFE is a simple yet powerful methodological improvement to the FBA framework.
Disorders of the Nervous System:
Epilepsy
Imaging Methods:
Diffusion MRI
Modeling and Analysis Methods:
20/12/2018 OHBM
https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&subView=1&abs=1595 4/4
Diffusion MRI Modeling and Analysis
Methods Development
Keywords:
Epilepsy
MRI
Statistical Methods
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Indicates the priority used for review
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... Whole brain tractogram statistical analyses on fixel images, in study specific template space, were done using an intrinsic normalisation of the connectivity-based fixel enhanced (CFE) method (Raffelt et al., 2015;Smith et al., 2019) within MRtrix. The advantage of this method is that it considers crossing fibres, using tractography, and it includes information along white matter pathways during its statistical inference estimation. ...
Article
Full-text available
The impact of multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) - associated disorders (MOGAD) on brain structure in youth remains poorly understood. Reductions in cortical mantle thickness on structural MRI and abnormal diffusion-based white matter metrics (e.g., diffusion tensor parameters) have been well documented in MS but not in MOGAD. Characterizing structural abnormalities found in children with these disorders can help clarify the differences and similarities in their impact on neuroanatomy. Importantly, while MS and MOGAD affect the entire CNS, the visual pathway is of particular interest in both groups, as most patients have evidence for clinical or subclinical involvement of the anterior visual pathway. Thus, the visual pathway is of key interest in analyses of structural abnormalities in these disorders and may distinguish MOGAD from MS patients. In this study we collected MRI data on 18 MS patients, 14 MOGAD patients and 26 age- and sex-matched typically developing children (TDC). Full-brain group differences in fixel diffusion measures (fibre-bundle populations) and cortical thickness measures were tested using age and sex as covariates. Visual pathway analysis was performed by extracting mean diffusion measures within lesion free optic radiations, cortical thickness within the visual cortex, and retinal nerve fibre layer (RNFL) and ganglion cell layer thickness measures from optical coherence tomography (OCT). Fixel based analysis (FBA) revealed MS patients have widespread abnormal white matter within the corticospinal tract, inferior longitudinal fasciculus, and optic radiations, while within MOGAD patients, non-lesional impact on white matter was found primarily in the right optic radiation. Cortical thickness measures were reduced predominately in the temporal and parietal lobes in MS patients and in frontal, cingulate and visual cortices in MOGAD patients. Additionally, our findings of associations between reduced RNFLT and axonal density in MOGAD and TORT in MS patients in the optic radiations imply widespread axonal and myelin damage in the visual pathway, respectively. Overall, our approach of combining FBA, cortical thickness and OCT measures has helped evaluate similarities and differences in brain structure in MS and MOGAD patients in comparison to TDC.
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