Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use

Tahedl, Marlene; Chipika, Rangariroyashe H.; Lope, Jasmin; Shing, Stacey Li Hi; Hardiman, Orla; Bede, Peter

doi:10.1007/s00415-020-10368-7

Cited by 24 publications

(18 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We specifically investigated the frequently used design of three acquisition time points (see e.g., Cardenas-Blanco et al, 2016;Tahedl et al, 2021), i.e., time point 1 at baseline and time points 2 and 3 at follow-up 1 and 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…There are generally three potential sources of variability which influence the correlation structure in longitudinal data, i.e., (1) between-subject variation, (2) inherent within-subject biological change, and (3) measurement error (Fitzmaurice et al, 2011). Complex fit models for harmonizing longitudinal imaging data based on empirical Bayesian methods (combining batches -ComBat) (Beer et al, 2020), conditional growth models (Tahedl et al, 2021) or mixed effect models (Cardenas-Blanco et al, 2016) are used for analysis to take these influences into account. The linear mixed effects model for longitudinal data (Bernal-Rusiel et al, 2013) follows:…”

Section: Fit Approach By Linear Mixed Effects Modelsmentioning

confidence: 99%

“…In longitudinal MRI studies of neurodegenerative diseases, due to a balance between costs and timeline and the validity of the results, a common concept is to plan with one baseline and two follow-up studies with (almost) identical time intervals, i.e., bisectioning the observation time period. Examples for such a study design can be found in amyotrophic lateral sclerosis (ALS) (Cardenas-Blanco et al, 2016;Kassubek et al, 2018;Kalra et al, 2020;Steinbach et al, 2021;Tahedl et al, 2021), in Huntington's disease (Tabrizi et al, 2009;Hobbs et al, 2015;Müller et al, 2019). This study design usually allows for a linear fit to obtain longitudinal trends of resulting parameters in the respective imaging domain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

et al. 2021

View full text Add to dashboard Cite

BackgroundLongitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans.MethodsDifferent analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV).ResultsFor the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis.ConclusionsThis theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Fit Approach By Linear Mixed Effects Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Relatively few studies have focussed on the classification of individual patient imaging data in ALS [17,18]. A variety of innovative approaches have been explored [19] spanning from z-score based approaches, through support vector machine frameworks, discriminant function analyses, to regression models, with varying degree of classification accuracy [16,[20][21][22][23][24]. Several studies have reported excellent 'area under the curve' (AUC) values with reference to the discriminatory potential of a specific measure between patients and healthy controls, but binary classification into 'ALS' versus 'healthy' does not mirror real-life diagnostic dilemmas.…”

Section: Introductionmentioning

confidence: 99%

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

2021

Self Cite

View full text Add to dashboard Cite

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

show abstract

“…The combination of white matter measures with grey matter metrics is likely to improve the diagnostic classification of individual subjects further. [58] The testing of this white matter rating scheme in presymptomatic mutation carriers would lend further credence to the validity of this approach. [4,59] A natural expansion of this strategy is the inclusion of non-ALS neurodegenerative cohorts and disease mimics, which is beyond the scope of this present study.…”

Section: Discussionmentioning

confidence: 94%

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

Tahedl¹,

Murad²,

Lope³

et al. 2021

Journal of the Neurological Sciences

Self Cite

View full text Add to dashboard Cite

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a 'blinded diagnosis' as likely 'ALS' or 'control'. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.

show abstract

Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use

Cited by 24 publications

References 85 publications

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

Contact Info

Product

Resources

About