29
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Lessons of ALS imaging: Pitfalls and future directions — A critical review

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          While neuroimaging in ALS has gained unprecedented momentum in recent years, little progress has been made in the development of viable diagnostic, prognostic and monitoring markers.

          Objectives

          To identify and discuss the common pitfalls in ALS imaging studies and to reflect on optimal study designs based on pioneering studies.

          Methods

          A “PubMed”-based literature search on ALS was performed based on neuroimaging-related keywords. Study limitations were systematically reviewed and classified so that stereotypical trends could be identified.

          Results

          Common shortcomings, such as relatively small sample sizes, statistically underpowered study designs, lack of disease controls, poorly characterised patient cohorts and a large number of conflicting studies, remain a significant challenge to the field. Imaging data of ALS continue to be interpreted at a group-level, as opposed to meaningful individual-patient inferences.

          Conclusions

          A systematic, critical review of ALS imaging has identified stereotypical shortcomings, the lessons of which should be considered in the design of future prospective MRI studies. At a time when large multicentre studies are underway a candid discussion of these factors is particularly timely.

          Highlights

          • Stereotypical shortcomings can be identified in ALS neuroimaging studies.

          • A systematic discussion of ALS study limitations is particularly timely.

          • Individual patient data meta-analyses and multicentre studies are urgently required.

          • The gaps identified in ALS imaging indicate exciting research opportunities.

          Related collections

          Most cited references102

          • Record: found
          • Abstract: found
          • Article: not found

          Demyelination increases radial diffusivity in corpus callosum of mouse brain.

          Myelin damage, as seen in multiple sclerosis (MS) and other demyelinating diseases, impairs axonal conduction and can also be associated with axonal degeneration. Accurate assessments of these conditions may be highly beneficial in evaluating and selecting therapeutic strategies for patient management. Recently, an analytical approach examining diffusion tensor imaging (DTI) derived parameters has been proposed to assess the extent of axonal damage, demyelination, or both. The current study uses the well-characterized cuprizone model of experimental demyelination and remyelination of corpus callosum in mouse brain to evaluate the ability of DTI parameters to detect the progression of myelin degeneration and regeneration. Our results demonstrate that the extent of increased radial diffusivity reflects the severity of demyelination in corpus callosum of mouse brain affected by cuprizone treatment. Subsequently, radial diffusivity decreases with the progression of remyelination. Furthermore, radial diffusivity changes were specific to the time course of changes in myelin integrity as distinct from axonal injury, which was detected by betaAPP immunostaining and shown to be most extensive prior to demyelination. Radial diffusivity offers a specific assessment of demyelination and remyelination, as distinct from acute axonal damage.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

            Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions. Copyright © 2012 Elsevier Ltd. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses.

              Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80% power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power. Copyright 2002 Elsevier Science B.V.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                27 February 2014
                27 February 2014
                2014
                : 4
                : 436-443
                Affiliations
                [a ]Academic Unit of Neurology, Trinity College Dublin, Room 5.43, Biomedical Sciences Building, Pearse Street, Dublin 2, Ireland
                Author notes
                []Correspondence to: Peter Bede, Tel.: +353 1 8964497; fax: +353 1 2604787. bedepeter@ 123456hotmail.com
                Article
                S2213-1582(14)00029-1
                10.1016/j.nicl.2014.02.011
                3950559
                24624329
                a9c4a3a4-f48a-4c91-82f0-096738e55067
                © 2014 The Authors
                History
                : 22 December 2013
                : 23 February 2014
                : 23 February 2014
                Categories
                Review Article

                amyotrophic lateral sclerosis,biomarker,mri,pet,spectroscopy,ad, axial diffusivity,c9orf72, chromosome 9 open reading frame 72,dti, diffusion tensor imaging,fa, fractional anisotropy,md, mean diffusivity,meg, magnetoencephalography,mrs, magnetic resonance spectroscopy,mune, motor unit number estimation,pet, positron emission tomography,pns, peripheral nervous system,rd, radial diffusivity,roi, region of interest,spect, single photon emission computed tomography,tms, transcranial magnetic stimulation,vbm, voxel-based morphometry

                Comments

                Comment on this article