Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Cardoso, M. Jorge; Modat, Marc; Wolz, Robin; Melbourne, Andrew; Cash, David M.; Rueckert, Daniel; Ourselin, Sébastien

doi:10.1109/tmi.2015.2418298

Cited by 296 publications

(313 citation statements)

References 34 publications

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“…GIF uses an atlas propagation and label fusion strategy to calculate the voxel probabilities of GM, white matter, and CSF31; this method has been previously used in MS and other neurodegenerative disorders 33, 34. The template library had 95 MRI brain scans (HCs and patients with AD) with neuroanatomic labels (http://www.neuromorphometrics.com/).…”

Section: Methodsmentioning

confidence: 99%

Deep gray matter volume loss drives disability worsening in multiple sclerosis

Eshaghi

Prados

Brownlee

et al. 2018

Annals of Neurology

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329

284

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ObjectiveGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.MethodsWe analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing‐remitting [RRMS], 128 secondary‐progressive [SPMS], and 125 primary‐progressive [PPMS]), over an average follow‐up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow‐up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time‐to‐EDSS progression.ResultsSPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time‐to‐EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow‐up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (–1.45%), PPMS (–1.66%), and RRMS (–1.34%) than CIS (–0.88%) and HCs (–0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (–1.21%) was significantly faster than RRMS (–0.76%), CIS (–0.75%), and HCs (–0.51%). Similarly, the rate of parietal GM atrophy in SPMS (–1.24‐%) was faster than CIS (–0.63%) and HCs (–0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).InterpretationThis large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210–222

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Section: Methodsmentioning

confidence: 99%

Deep gray matter volume loss drives disability worsening in multiple sclerosis

Eshaghi

Prados

Brownlee

et al. 2018

Annals of Neurology

Self Cite

329

284

View full text Add to dashboard Cite

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“…MRI images were analyzed using two methods: region of interest (ROI) analysis and voxel‐based morphometry (VBM). For ROI analysis, scans were parcellated into brain regions as previously described,23 using an atlas propagation and label fusion strategy,24 combining bilateral ROIs to calculate grey matter cortical (frontal, temporal, parietal, occipital, cingulate, insular), subcortical (hippocampus, amygdala, caudate, putamen, thalamus), and cerebellar volumes 25, 26. Whole brain volumes were calculated by combining all grey and white matter regions extracted from the automated brain segmentation method.…”

Section: Methodsmentioning

confidence: 99%

Poly(GP), neurofilament and grey matter deficits in C9orf72 expansion carriers

Meeter

Gendron

Sias

et al. 2018

Ann Clin Transl Neurol

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ObjectiveTo evaluate poly(GP), a dipeptide repeat protein, and neurofilament light chain (NfL) as biomarkers in presymptomatic C9orf72 repeat expansion carriers and patients with C9orf72‐associated frontotemporal dementia. Additionally, to investigate the relationship of poly(GP) with indicators of neurodegeneration as measured by NfL and grey matter volume.MethodsWe measured poly(GP) and NfL levels in cerebrospinal fluid (CSF) from 25 presymptomatic C9orf72 expansion carriers, 64 symptomatic expansion carriers with dementia, and 12 noncarriers. We explored associations with grey matter volumes using region of interest and voxel‐wise analyses.ResultsPoly(GP) was present in C9orf72 expansion carriers and absent in noncarriers (specificity 100%, sensitivity 97%). Presymptomatic carriers had lower poly(GP) levels than symptomatic carriers. NfL levels were higher in symptomatic carriers than in presymptomatic carriers and healthy noncarriers. NfL was highest in patients with concomitant motor neuron disease, and correlated with disease severity and survival. Associations between poly(GP) levels and small grey matter regions emerged but did not survive multiple comparison correction, while higher NfL levels were associated with atrophy in frontotemporoparietal cortices and the thalamus.InterpretationThis study of C9orf72 expansion carriers reveals that: (1) poly(GP) levels discriminate presymptomatic and symptomatic expansion carriers from noncarriers, but are not associated with indicators of neurodegeneration; and (2) NfL levels are associated with grey matter atrophy, disease severity, and shorter survival. Together, poly(GP) and NfL show promise as complementary biomarkers for clinical trials for C9orf72‐associated frontotemporal dementia, with poly(GP) as a potential marker for target engagement and NfL as a marker of disease activity and progression.

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“…The Geodesic Information Flows (GIF) software framework17 was used to segment and parcellate cortical and subcortical volumes. The GIF framework provides a more robust segmentation than other state‐of‐the‐art methods such as Freesurfer (http://surfer.nmr.mgh.harvard.edu/), which has been shown to produce noisy segmentations in some regions, for example, the putamen 18, 19…”

Section: Methodsmentioning

confidence: 99%

An image‐based model of brain volume biomarker changes in Huntington's disease

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

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ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

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Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Cited by 296 publications

References 34 publications

Deep gray matter volume loss drives disability worsening in multiple sclerosis

Deep gray matter volume loss drives disability worsening in multiple sclerosis

Poly(GP), neurofilament and grey matter deficits in C9orf72 expansion carriers

An image‐based model of brain volume biomarker changes in Huntington's disease

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