Evidence map›Paper›PMID 27547726›Full record

ArticleNeuroImage. Clinical2016

Data-driven regions of interest for longitudinal change in frontotemporal lobar degeneration.

Aleksandr Pankov, Richard J Binney, Adam M Staffaroni, John Kornak, Suneth Attygalle, Norbert Schuff, Michael W Weiner, Joel H Kramer, Bradford C Dickerson, Bruce L Miller and 1 more

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. A multicenter comparison of [European journal of nuclear medicine and molecular imaging · 2021
    Article
  7. Baseline neuropsychological profiles in prion disease predict survival time.Annals of clinical and translational neurology · 2020
    Article
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Cerebrospinal fluid biomarkers predict frontotemporal dementia trajectory.Annals of clinical and translational neurology · 2018
    Article
  13. Article
  14. Article
  15. Neuroimaging in Dementia.Seminars in neurology · 2017
    Review
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Aleksandr PankovDepartment of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Richard J BinneyDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
Adam M StaffaroniDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
John KornakDepartment of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Suneth AttygalleDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
Norbert SchuffDepartment of Radiology, University of California, San Francisco, San Francisco, CA, USA.
Michael W WeinerDepartment of Radiology, University of California, San Francisco, San Francisco, CA, USA.
Joel H KramerDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
Bradford C DickersonDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Bruce L MillerDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
Howard J RosenDepartment of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.

Funding

TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Jennifer Merrilees · 2002 to 2026
$67.2M
PrPSc SPECIFIC INTERACTION WITH NOVEL PrP-Fc FUSION PROTEINSP50AG023501 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MILLER, BRUCE L · 2004 to 2018
$27.1M
The Frontotemporal Lobar Degeneration Neuroimaging InitiativeR01AG032306 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROSEN, HOWARD J · 2009 to 2013
$10.0M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
Multimodal Imaging in Frontotemporal DegenerationK24AG045333 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROSEN, HOWARD J · 2013 to 2023
$1.9M
NIA NIH HHS K24 AG045333NIA NIH HHS P01 AG019724NIA NIH HHS P50 AG023501NIA NIH HHS R01 AG032306NIGMS NIH HHS T32 GM067547
6 · The paper itself

Abstract

Current research is investigating the potential utility of longitudinal measurement of brain structure as a marker of drug effect in clinical trials for neurodegenerative disease. Recent studies in Alzheimer's disease (AD) have shown that measurement of change in empirically derived regions of interest (ROIs) allows more reliable measurement of change over time compared with regions chosen a-priori based on known effects of AD on brain anatomy. Frontotemporal lobar degeneration (FTLD) is a devastating neurodegenerative disorder for which there are no approved treatments. The goal of this study was to identify an empirical ROI that maximizes the effect size for the annual rate of brain atrophy in FTLD compared with healthy age matched controls, and to estimate the effect size and associated power estimates for a theoretical study that would use change within this ROI as an outcome measure. Eighty six patients with FTLD were studied, including 43 who were imaged twice at 1.5 T and 43 at 3 T, along with 105 controls (37 imaged at 1.5 T and 67 at 3 T). Empirically-derived maps of change were generated separately for each field strength and included the bilateral insula, dorsolateral, medial and orbital frontal, basal ganglia and lateral and inferior temporal regions. The extent of regions included in the 3 T map was larger than that in the 1.5 T map. At both field strengths, the effect sizes for imaging were larger than for any clinical measures. At 3 T, the effect size for longitudinal change measured within the empirically derived ROI was larger than the effect sizes derived from frontal lobe, temporal lobe or whole brain ROIs. The effect size derived from the data-driven 1.5 T map was smaller than at 3 T, and was not larger than the effect size derived from a-priori ROIs. It was estimated that measurement of longitudinal change using 1.5 T MR systems requires approximately a 3-fold increase in sample size to obtain effect sizes equivalent to those seen at 3 T. While the results should be confirmed in additional datasets, these results indicate that empirically derived ROIs can reduce the number of subjects needed for a longitudinal study of drug effects in FTLD compared with a-priori ROIs. Field strength may have a significant impact on the utility of imaging for measuring longitudinal change.

Indexed as

AgedAtrophyBrainBrain MappingFemaleFrontotemporal Lobar DegenerationHumansImage Processing, Computer-AssistedLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedSignal Processing, Computer-AssistedFrontotemporal dementiaMagnetic resonance imaging

Identifiers

PMID27547726
PMCPMC4983147

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.