Optimized Brain Extraction for Pathological Brains (optiBET)

Lutkenhoff, Evan S.; Rosenberg, Matthew; Chiang, Jeffrey N.; Zhang, Kunyu; Pickard, John D.; Owen, Adrian M.; Monti, Martin M.

doi:10.1371/journal.pone.0115551

Cited by 202 publications

(165 citation statements)

References 18 publications

(27 reference statements)

Supporting

Mentioning

154

Contrasting

Order By: Relevance

“…This was achieved using optiBET [29] and FMRIB software library (FSL)-FAST [30] respectively. The second step is to, also automatically, extract all subcortical structures, which was achieved using other tools from the same FSL as is described in [28].…”

Section: Image Preprocessingmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

View full text Add to dashboard Cite

Correspondence: Víctor González-Castro (victor.gonzalez@ed.ac.uk) or María del C. Valdés Hernández (M. Valdes-Hernan@ed.ac.uk) In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

show abstract

Section: Image Preprocessingmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

View full text Add to dashboard Cite

show abstract

“…First, segmentation of the brain image of DOC patients is complicated by distortions in different types of tissues (e.g. white matter, CSF) during automated segmentation38. To overcome this challenge, DOC patients with larger hematoncus and malacostic foci, moderate to severe hydrocephalus, or severe brain atrophy were excluded.…”

Section: Discussionmentioning

confidence: 99%

Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness

Long

Xie

et al. 2016

Sci Rep

View full text Add to dashboard Cite

Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state.

show abstract

“…13 Prior to analysis, 3 preprocessing steps were performed. First, data were brain-extracted, using optiBET, 14 to remove from the images extraneous nonbrain tissue (eg, eyes, neck, skull). Second, subcortical structures of interest were segmented, on an individual basis, and reconstructed into 3-dimensional vertex meshes (as implemented in FSL FIRST).…”

Section: Data Preprocessingmentioning

confidence: 99%

Thalamic and extrathalamic mechanisms of consciousness after severe brain injury

Lutkenhoff

Chiang

Tshibanda

et al. 2015

Annals of Neurology

Self Cite

157

151

View full text Add to dashboard Cite

Objective: What mechanisms underlie the loss and recovery of consciousness after severe brain injury? We sought to establish, in the largest cohort of patients with disorders of consciousness (DOC) to date, the link between gold standard clinical measures of awareness and wakefulness, and specific patterns of local brain pathology-thereby possibly providing a mechanistic framework for patient diagnosis, prognosis, and treatment development. Methods: Structural T1-weighted magnetic resonance images were collected, in a continuous sample of 143 severely brain-injured patients with DOC (and 96 volunteers), across 2 tertiary expert centers. Brain atrophy in subcortical regions (bilateral thalamus, basal ganglia, hippocampus, basal forebrain, and brainstem) was assessed across (1) healthy volunteers and patients, (2) clinical entities (eg, vegetative state, minimally conscious state), (3) clinical measures of consciousness (Coma Recovery Scale-Revised), and (4) injury etiology. Results: Compared to volunteers, patients exhibited significant atrophy across all structures (p < 0.05, corrected). Strikingly, we found almost no significant differences across clinical entities. Nonetheless, the clinical measures of awareness and wakefulness upon which differential diagnosis rely were systematically associated with tissue atrophy within thalamic and basal ganglia nuclei, respectively; the basal forebrain was atrophied in proportion to patients' response to sensory stimulation. In addition, nontraumatic injuries exhibited more extensive thalamic atrophy. Interpretation: These findings provide, for the first time, a grounding in pathology for gold standard behavior-based clinical measures of consciousness, and reframe our current models of DOC by stressing the different links tying thalamic mechanisms to willful behavior and extrathalamic mechanisms to behavioral (and electrocortical) arousal.ANN NEUROL 2015;00:000-000 T he mechanisms supporting consciousness, as well as its loss and recovery after severe brain injury, remain largely unknown. In the context of disorders of consciousness (DOC) 1 such as the vegetative state (VS) and the minimally conscious state (MCS), the lack of a mechanistic understanding of the relationship between brain damage and neurological condition has direct consequences for our ability to make accurate diagnoses, prognoses, and to develop targeted interventions, thereby raising complicated medical and ethical questions. 2Although information concerning the nature and extent of a patient's brain damage is generally taken into consideration during clinical assessments, current differential diagnosis procedures rely exclusively-as per international guidelines-on behavioral presentation. 3-5 Consequently, although our understanding of DOC is continuously increasing, 6,7 little is known about the connection between behaviorally defined clinical entities and the underlying brain damage, [8][9][10] or the degree to which standard behavior-based clinical assessments (eg, JFK View this article online at wil...

show abstract

Optimized Brain Extraction for Pathological Brains (optiBET)

Cited by 202 publications

References 18 publications

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness

Thalamic and extrathalamic mechanisms of consciousness after severe brain injury

Contact Info

Product

Resources

About