ArticleFrontiers in neurology2017
A Fully Automated Pipeline for Normative Atrophy in Patients with Neurodegenerative Disease.
Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.Scientific reports · 2026Article
- Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL-Based Cortical Thickness Estimates.Human brain mapping · 2026Article
- Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods.NeuroImage. Clinical · 2024Article
- LearningBrain sciences · 2023Article
- A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.Frontiers in neurology · 2022Article
- Monro-Kellie Hypothesis: Increase of Ventricular CSF Volume after Surgical Closure of a Spinal Dural Leak in Patients with Spontaneous Intracranial Hypotension.AJNR. American journal of neuroradiology · 2020Article
- Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields.Quantitative imaging in medicine and surgery · 2020Article
- Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.Frontiers in neurology · 2020Article
- Can we predict cognitive decline after initial diagnosis of multiple sclerosis? Results from the German National early MS cohort (KKNMS).Journal of neurology · 2019Article
- Navigating choice in multiple sclerosis management.Neurological research and practice · 2019Review
- Personalized structural image analysis in patients with temporal lobe epilepsy.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVolumetric image analysis to detect progressive brain tissue loss in patients with multiple sclerosis (MS) has recently been suggested as a promising marker for "no evidence of disease activity." Software packages for longitudinal whole-brain volume analysis in individual patients are already in clinical use; however, most of these methods have omitted region-based analysis. Here, we suggest a fully automatic analysis pipeline based on the free software packages FSL and FreeSurfer. MATERIALS AND
methodsFifty-five T1-weighted magnetic resonance imaging (MRI) datasets of five patients with confirmed relapsing-remitting MS and mild to moderate disability were longitudinally analyzed compared to a morphometric reference database of 323 healthy controls (HCs). After lesion filling, the volumes of brain segmentations and morphometric parameters of cortical parcellations were automatically screened for global and regional abnormalities. Error margins and artifact probabilities of regional morphometric parameters were estimated. Linear models were fitted to the series of follow-up MRIs and checked for consistency with cross-sectional aging in HCs.
resultsAs compared to leave-one-out cross-validation in a subset of the control dataset, anomaly detection rates were highly elevated in MRIs of two patients. We detected progressive volume changes that were stronger than expected compared to normal aging in 4/5 patients. In individual patients, we also identified stronger than expected regional decreases of subcortical gray matter, of cortical thickness, and areas of reducing gray-white contrast over time.
conclusionStatistical comparison with a large normative database may provide complementary and rater independent quantitative information about regional morphological changes related to disease progression or drug-related disease modification in individual patients. Regional volume loss may also be detected in clinically stable patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.