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• “Region of interest” approach in cerebellar meta-analysis
  Helena M Gellersen, Christine C Guo, Claire O'Callaghan, Rachel H Tan, Saber Sami and Michael Hornberger
  Published on: 17 August 2017
• Does Cerebellar Atrophy in Neurodegeneration?
  Li-Qin Sheng, Ping-Lei Pan
  Published on: 17 August 2017

• Published on: 17 August 2017

  “Region of interest” approach in cerebellar meta-analysis

  • Helena M Gellersen, Doctoral Candidate Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge, UK
  • Other Contributors:
    • Christine C Guo, Team Head
    • Claire O'Callaghan, Clinical Research Fellow
    • Rachel H Tan, Senior Research Fellow
    • Saber Sami, Lecturer in Dementia Research
    • Michael Hornberger, Head of Department of Medicine and Professor of Applied Dementia Research

  In our previous meta-analysis of cerebellar atrophy in seven major neurodegenerative conditions (Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Huntington’s disease (HD), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP)) we investigated studies that reported grey matter (GM) loss in the cerebellum [1]. Consistent regions of atrophy were found in AD, ALS, FTD, MSA, and PSP but not HD or PD. In their comment on our meta-analysis, Sheng and Pan have argued that our method of selectively investigating studies that found cerebellar atrophy, rather than adopting a whole-brain approach, is “not optimal in a coordinate-based meta-analysis” [2]. They further cite previous whole-brain meta-analyses that did not identify clusters of cerebellar grey matter loss in patients [3-6].
  Here, we argue that our approach was justified given our aim, which was to focus on cases where cerebellar atrophy was found in the respective disease groups in order to determine 1) if such atrophy followed a consistent, robust pattern, and 2) if atrophy patterns were disease-specific or generic, where possible relating them to symptomatology.
  There are several reasons why we chose to focus on the cerebellum rather than adopting a whole-brain approach. First, the cerebellum is hardly a “region of interest” in the classical sense given its marked heterogeneity in terms of function and connectivity as we...

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  In our previous meta-analysis of cerebellar atrophy in seven major neurodegenerative conditions (Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Huntington’s disease (HD), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP)) we investigated studies that reported grey matter (GM) loss in the cerebellum [1]. Consistent regions of atrophy were found in AD, ALS, FTD, MSA, and PSP but not HD or PD. In their comment on our meta-analysis, Sheng and Pan have argued that our method of selectively investigating studies that found cerebellar atrophy, rather than adopting a whole-brain approach, is “not optimal in a coordinate-based meta-analysis” [2]. They further cite previous whole-brain meta-analyses that did not identify clusters of cerebellar grey matter loss in patients [3-6].
  Here, we argue that our approach was justified given our aim, which was to focus on cases where cerebellar atrophy was found in the respective disease groups in order to determine 1) if such atrophy followed a consistent, robust pattern, and 2) if atrophy patterns were disease-specific or generic, where possible relating them to symptomatology.
  There are several reasons why we chose to focus on the cerebellum rather than adopting a whole-brain approach. First, the cerebellum is hardly a “region of interest” in the classical sense given its marked heterogeneity in terms of function and connectivity as well as its large cell count [7,8]. Using an ROI approach on the cerebellum is therefore not vastly different from conducting an analysis restricted to the neocortex, a practice that is abundant and well accepted. The vast majority of coordinates used in our analysis stemmed from whole-brain analyses and omitting the few studies with an ROI approach for the cerebellum is highly unlikely to affect the main clusters we identified.
  Second, there is an abundance of coordinate-based meta-analyses using ROI approaches (see http://www.brainmap.org/pubs/), including one of the most influential papers into the functions of the cerebellum [8].
  Third, the systematic literature search of papers investigating the diseases in question has revealed that many analyses were inherently biased against the cerebellum. As shown in our PRISMA flowchart, there was a tendency towards excluding the cerebellum (n=64) or not reporting or discussing cerebellar findings in text even when figures indicated the cerebellum was affected (n=13 for which we could not obtain data even after contacting authors). Twenty-seven studies further indicated potential cerebellar effects but did not report coordinates. These numbers made it clear that this bias in investigating the diseases of interest and in reporting results would inadvertently be translated into a bias in the meta-analytic results when choosing a whole-brain approach.
  Given this abundance of studies excluding the cerebellum and underreporting cerebellar findings, it is not surprising that previous meta-analyses did not find robust clusters of cerebellar atrophy. Furthermore, there are additional factors that can explain the apparent lack of GM loss in the cerebellum in the AD, ALS, and FTD meta-analyses.
  First, the majority of studies in AD and ALS investigated early stages, during which cerebellar atrophy may not yet be apparent. This tendency to investigate early structural changes may contribute to the belief that the cerebellum is typically spared. Given that clinical assessment using magnetic resonance imaging is carried out early in disease progression and that follow-up scans in clinical practice are rare, later GM loss often remains unexplored. However, neuropathological investigations have found the cerebellum to be affected in AD, ALS, and PSP [9-11]. Second, in our systematic literature search, we contacted authors to obtain additional coordinate data, which was not done in most of the other meta-analyses. The following should emphasize these arguments:
  • AD meta-analysis [3]: did not include seven studies present in our analysis; of the 46 AD studies listed in Table 1, n=23 included exclusively early-stage/mild AD, and n=9 studies mild to moderate AD. One study with preclinical AD cases and another with a sample of MSA with dementia would not have met our inclusion criteria [12,13].
  • FTD meta-analysis [4]: did not include nine studies from our analysis. 3/9 studies in the whole-brain analysis showed cerebellar atrophy; of the remaining six, n=3 included only patients with mild FTD severity, while n=1 included mild to moderate.
  • ALS meta-analysis [5]: did not include two of the studies in our paper. 5/20 studies reported disease-related cerebellar grey matter (GM) or white matter integrity loss; of the other 15, n=10 focussed on patients with mild symptom severity. Interestingly, Minnerop et al. showed a correlation of cerebellar GM loss and disease duration, suggesting that the cerebellum is affected later in the course of ALS [14].
  • We would like to point out that the PSP meta-analysis [6], which was cited by Sheng and Pan as not having detected cerebellar GM loss, did find a cluster of atrophy in right declive (see Table 2). This meta-analysis also did not include two of the studies incorporated in our investigation. Cerebellar GM loss was found in 5/12 studies in this meta-analysis, while n=4 of the remaining seven found white matter loss in cerebellar peduncles in the absence of GM differences. Of the three studies with no cerebellar findings, n=2 were in mild PSP.

  Finally, we would like to emphasise that we are not claiming that cerebellar atrophy is necessarily present in the majority of patients in all of the investigated diseases. Given that we only selected studies with findings in the cerebellum, we did not seek to determine the likelihood of the cerebellum being affected in a given disease. For our purpose, a focus on studies with cerebellar findings is justified. We conclude that if cerebellar atrophy is present, it follows a disease-specific pattern that tends to correlate with symptomatology in many of the included studies. Our findings demonstrate the importance of future research into the role of the cerebellum in neurodegeneration given the two major issues in the literature which are the tendency to disregard the cerebellum and the vast heterogeneity of patient groups regarding disease stages and subtypes.

  References
  1. Gellersen HM, Guo CC, O’Callaghan C, et al. Cerebellar atrophy in neurodegeneration—a meta-analysis. J Neurol, Neurosurg Psychiatry 2017, in press.
  2. Sheng L-Q, Pan P-L. Does Cerebellar Atrophy in Neurodegeneration? J Neurol, Neurosurg Psychiatry 2017. http://jnnp.bmj.com/content/early/2017/05/27/jnnp-2017-315607.responses
  3. Chapleau M, Aldebert J, Montembeault M, Brambati SM. Atrophy in Alzheimer's Disease and Semantic Dementia: An ALE Meta-Analysis of Voxel-Based Morphometry Studies. J Alzheimers Dis 2016;54(3):941-55.
  4. Schroeter ML, Laird AR, Chwiesko C, et al. Conceptualizing neuropsychiatric diseases with multimodal data-driven meta-analyses - the case of behavioral variant frontotemporal dementia. Cortex 2014;57:22-37.
  5. Sheng L, Ma H, Zhong J, et al. Motor and extra-motor gray matter atrophy in amyotrophic lateral sclerosis: quantitative meta-analyses of voxel-based morphometry studies. Neurobiol Aging 2015;36(12):3288-99.
  6. Yu F, Barron DS, Tantiwongkosi B, Fox P. Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta-analysis. Brain Behav 2015;5(6):e00329.
  7. Balsters JH, Laird AR, Fox PT, Eickhoff SB. Bridging the gap between functional and anatomical features of cortico-cerebellar circuits using meta-analytic connectivity modeling. Hum Brain Mapp 2014;35(7):3152-69.
  8. Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage 2009;44(2):489-501.
  9. Kanazawa M, Shimohata T, Toyoshima Y, et al. Cerebellar involvement in progressive supranuclear palsy: A clinicopathological study. Mov Disord 2009;24(9):1312-8.
  10. Larner AJ. The Cerebellum in Alzheimer's Disease. Dement Geriatr Cogn Disord 1997;8(4):203-9.
  11. Prell T, Grosskreutz J. The involvement of the cerebellum in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degen 2013;14(7-8):507-15.
  12. Hämäläinen A, Pihlajamäki M, Tanila H, et al. Increased fMRI responses during encoding in mild cognitive impairment. Neurobiol Aging 2007;28(12):1889-903.
  13. Tondelli M, Wilcock GK, Nichelli P, et al. Structural MRI changes detectable up to ten years before clinical Alzheimer's disease. Neurobiol Aging 2012;33(4):825.e25-36.
  14. Minnerop M, Specht K, Ruhlmann J, et al. In vivo voxel-based relaxometry in amyotrophic lateral sclerosis. J Neurol 2009;256(1):28-34.

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  Conflict of Interest:
  None declared.

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• Published on: 17 August 2017

  Does Cerebellar Atrophy in Neurodegeneration?

  • Li-Qin Sheng, Neurologist Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
  • Other Contributors:
    • Ping-Lei Pan, Neurologist

  Does Cerebellar Atrophy in Neurodegeneration?

  Li-Qin Sheng1, Ping-Lei Pan2
  1 Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
  2 Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China

  Correspondence:
  PingLei Pan, Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province, 224001, PR China. E-mail: panpinglei@163.com, Telephone: +8618361146977
   
  Coordinate-based meta-analysis is a powerful way for neuroimaging studies to identify the most consistent and replicable differences in brain activity or structure in neurodegenerative disorders. In their JNNP publication, Gellersen et al 1 conducted coordinate-based meta-analyses of 54 voxel-based morphometry (VBM) studies in Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), behavioral variant frontotemporal dementia (bvFTD), amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). In this study, they solely focused on cerebellar grey matter (GM) atrophy.1 Marked cerebellar atrophy in AD, ALS, bvFTD, PSP and MSA, but not in PD or HD, was identified in the meta-analyses.1
  These findings are of interest.1 However, the procedure of the meta-analyses had a major limitation. Coordin...

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  Does Cerebellar Atrophy in Neurodegeneration?

  Li-Qin Sheng1, Ping-Lei Pan2
  1 Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
  2 Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China

  Correspondence:
  PingLei Pan, Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province, 224001, PR China. E-mail: panpinglei@163.com, Telephone: +8618361146977
   
  Coordinate-based meta-analysis is a powerful way for neuroimaging studies to identify the most consistent and replicable differences in brain activity or structure in neurodegenerative disorders. In their JNNP publication, Gellersen et al 1 conducted coordinate-based meta-analyses of 54 voxel-based morphometry (VBM) studies in Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), behavioral variant frontotemporal dementia (bvFTD), amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). In this study, they solely focused on cerebellar grey matter (GM) atrophy.1 Marked cerebellar atrophy in AD, ALS, bvFTD, PSP and MSA, but not in PD or HD, was identified in the meta-analyses.1
  These findings are of interest.1 However, the procedure of the meta-analyses had a major limitation. Coordinate-based meta-analysis is usually used for whole-brain imaging findings. Gellersen et al1 only included the studies that reported coordinates of the cerebellar regions, which resulted in study selection bias in the analyses. This bias may increase the statistical power and the risk of false positive findings in their study. The view is supported by previous meta-analyses in these disorders. Robust cerebellar atrophy was not detected in AD 2, ALS 3, bvFTD 4, PSP 5, PD 6, or HD 7 by previous meta-analyses that included many more whole-brain VBM studies. In contrast, cerebellar atrophy was consistently identified in MSA 5, which was accordant with the meta-analysis by Gellersen et al.1 Thus, selective use of a paticular brain region is not optimal in a coordinate-based meta-analysis.
  Based on the current conflicting imaging evidence, it is debatable to consider the existence of cerebellar atrophy in AD, ALS, bvFTD and PSP in VBM studies. As clinical heterogeneity in these disorders and limited by the imaging techniques, further research is warranted to validate cerebellar atrophy in neurodegeneration.

  Funding: This work was supported in part by the National Natural Science Foundation of China (Grant No. 81601161).
  Competing interests: None.
  Contributors: LQS wrote the draft. PLP revised the manuscript.

  References:
  1. Gellersen HM, Guo CC, O'Callaghan C, et al. Cerebellar atrophy in neurodegeneration-a meta-analysis. J Neurol Neurosurg Psychiatry 2017, in press.
  2. Chapleau M, Aldebert J, Montembeault M, et al. Atrophy in Alzheimer's Disease and Semantic Dementia: An ALE Meta-Analysis of Voxel-Based Morphometry Studies. J Alzheimers Dis 2016;54(3):941-55.
  3. Sheng L, Ma H, Zhong J, et al. Motor and extra-motor gray matter atrophy in amyotrophic lateral sclerosis: quantitative meta-analyses of voxel-based morphometry studies. Neurobiol Aging 2015;36(12):3288-99.
  4. Schroeter ML, Laird AR, Chwiesko C, et al. Conceptualizing neuropsychiatric diseases with multimodal data-driven meta-analyses - the case of behavioral variant frontotemporal dementia. Cortex 2014;57:22-37.
  5. Yu F, Barron DS, Tantiwongkosi B, et al. Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta-analysis. Brain Behav 2015;5(6):e00329.
  6. Shao N, Yang J, Shang H. Voxelwise meta-analysis of gray matter anomalies in Parkinson variant of multiple system atrophy and Parkinson's disease using anatomic likelihood estimation. Neurosci Lett 2015;587:79-86.
  7. Dogan I, Eickhoff SB, Schulz JB, et al. Consistent neurodegeneration and its association with clinical progression in Huntington's disease: a coordinate-based meta-analysis. Neurodegener Dis 2013;12(1):23-35.

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  Conflict of Interest:
  None declared.

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