White matter in different regions evolves differently during progression to dementia

Collins

2019

Preprint

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Introduction: Accurate differentiation of brain tissue types from T1-weighted magnetic resonance images (MRIs) is a critical requirement in many neuroscience and clinical applications. Accurate automated tissue segmentation is challenging due to the variabilities in the tissue intensity profiles caused by differences in scanner models and acquisition protocols, in addition to the varying age of the subjects and potential presence of pathology.In this paper, we present BISON (Brain tISue segmentatiON), a new pipeline for tissue segmentation.Methods: BISON performs tissue segmentation using a random forests classifier and a set of intensity and location priors obtained based on T1-weighted images. The proposed method has been developed and cross-validated based on multi-center and multi-scanner manual labels of 72 subjects aging from 5-96 years old, ensuring the generalizability of the results to new data from various age ranges. In addition, we assessed the test-retest reliability of BISON on 2 datasets; a. using 20 subjects that had scan/re-scan MRIs and manual segmentations available, and b. using a human phantom dataset including 90 scans from a single individual acquired across 10 years. Results:The results of the proposed method were compared against Atropos, a commonly used tissue classification method from ANTs. The proposed method yielded cross-validation Dice Kappa values of κGM = 0.88 ± 0.03, κWM = 0.85 ± 0.03, κCSF = 0.77 ± 0.11, outperforming ANTs Atropos (κGM = 0.79 ± 0.05, κWM = 0.84 ± 0.05, κCSF = 0.64 ± 0.22) as well as test-retest Dice Kappa values of κGM = 0.94 ± 0.006, κWM = 0.92 ± 0.006, κCSF = 0.77 ± 0.11 outperforming both manual (κGM = 0.92 ± 0.01, κWM = 0.91 ± 0.01, κCSF = 0.74 ± 0.03) andANTs Atropos (κGM = 0.87 ± 0.001, κWM = 0.92 ± 0.001, κCSF = 0.79 ± 0.05). The human phantom dataset validations showed high generalizability for both Atropos (κGM = 0.97 ± 0.01, κWM = 0.96 ± 0.01, κCSF = 0.93 ± 0.02) and BISON (κGM = 0.95 ± 0.01, κWM = 0.94 ± 0.01, κCSF = 0.85 ± 0.03), while Atropos tended to consistently under-segment the cortical CSF. Finally, our assessment of BISON, Atropos, FAST from FSL, and SPM12 segmentations in presence of white matter hyperintensities (WMHs) showed that BISON outperforms the other three methods, correctly detecting WMHs as WM. Conclusion:Our results show that BISON can provide accurate and robust segmentations in data from different age ranges and various scanner models, making it ideal for performing tissue classification in large multi-center and multi-scanner databases.

Section: Introductionmentioning

confidence: 99%

BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier

Collins

2019

Preprint

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“…3T rather than 1.5T), yielding images with better contrast. All the image processing tools used in this study have been developed and extensively validated for use in multi-center studies involving different MRI systems, and have been used in numerous such studies (Zeighami et al, 2015, Dadar et al, 2018bMisquitta et al, 2018;Dadar et al, 2019;Manera et al, 2019;Sanford et al, 2019). In addition, all analyses included center as a categorical random effect to ensure that any residual variability across centers would not bias the results.…”

Section: Discussionmentioning

confidence: 99%

Cerebral Atrophy in Amyotrophic Lateral Sclerosis Parallels the Pathological Distribution of TDP43

Manera

Zinman

et al. 2020

Preprint

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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by a preferential involvement of both upper and lower motor neurons. Evidence from neuroimaging and post-mortem studies confirms additional involvement of brain regions extending beyond the motor cortex. The aim of this study was to assess the extent of cerebral disease in ALS crosssectionally and longitudinally, and to compare the findings with a recently proposed diseasestaging model of ALS pathology. Deformation-based morphometry (DBM) was used to identify the patterns of brain atrophy associated with ALS and to assess their relationship with clinical symptoms. Longitudinal T1-weighted MRI data and clinical measures were acquired at baseline, 4 months, and 8 months, from 66 ALS patients and 43 age-matched controls who participated in the Canadian ALS Neuroimaging Consortium (CALSNIC) study. Whole brain voxel-wise mixed-effects modelling analysis showed extensive atrophy patterns differentiating ALS patients from the normal controls. Cerebral atrophy was present in the motor cortex and corticospinal tract, involving both GM and WM, and to a lesser extent in non-motor regions. More specifically, the results showed significant bilateral atrophy in the motor cortex, the corticospinal tract including the internal capsule and brainstem, with an overall pattern of ventricular enlargement; along with significant progressive longitudinal atrophy in the precentral gyrus, frontal and parietal white matter, accompanied by ventricular and sulcal enlargement. Atrophy in the precentral gyrus was significantly associated with greater disability as quantified with the ALS Functional Rating Scale-Revised (ALSFRS-R) (p<0.0001). The pattern of atrophy observed using DBM was consistent with the Brettschneider's four stage pathological model of the disease. Deformation based morphometry provides a sensitive indicator of atrophy in ALS, and has potential as a biomarker of disease burden, in both gray and white matter.

“…Longitudinal MRI and clinical data were consistently acquired from the patients and age-matched controls across all visits. DBM, SNIPE, and WMH measurements were estimated using extensively validated and widely used automated methods 3,4,11,16,22,[35][36][37] , allowing refined and accurate assessment of the grey matter and white matter pathology in the same population. Relevant medication (both PD and non-PD) was included as covariates in the models.…”

Section: Discussionmentioning

confidence: 99%

“…Hypertension and blood pressure fluctuations are associated with WMHs 9 . In non-PD elderly individuals, WMHs have been associated with global cognitive impairment, executive dysfunction, rigidity, and gait disorders [10][11][12][13][14][15] . Given that some of the symptoms associated with WMHs in non-PD individuals overlap with PD features, one might hypothesize that coexistence of WMHs in PD patients would lead to even greater cognitive and motor deficits.…”

Section: Introductionmentioning

confidence: 99%

Cognitive and Motor Correlates of Grey and White Matter Pathology in Parkinson’s Disease

Gee

Shuaib

et al. 2020

Preprint

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Introduction: Previous studies have found associations between grey matter atrophy and white matter hyperintensities (WMH) of vascular origin with cognitive and motor deficits in Parkinson's disease (PD).Here we investigate these relationships in a sample of PD patients and age-matched healthy controls.Methods: Data included 50 PD patients and 45 age-matched controls with T1-weighted and FLAIR scans at baseline, 18-months, and 36-months follow-up. Deformation-based morphometry was used to measure grey matter atrophy. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure Alzheimer's disease-like textural patterns in the hippocampi. WMHs were segmented using T1-weighted and FLAIR images. The relationship between MRI features and clinical scores was assessed using mixed-effects models. The motor subscore of the Unified Parkinson's Disease Rating Scale (UPDRSIII), number of steps in a walking trial, and Dementia Rating Scale (DRS) were used respectively as measures of motor function, gait, and cognition.Results: Substantia nigra atrophy was significantly associated with motor deficits, with a greater impact in PDs (p<0.05). Hippocampal SNIPE scores were associated with cognitve decline in both PD and controls (p<0.01). WMH burden was significantly associated with cognitive decline and increased motor deficits in the PD group, and gait deficits in both PD and controls (p<0.03). Conclusion:While substantia nigra atrophy and WMH burden were significantly associated with additional motor deficits, WMH burden and hippocampal atrophy were associated with cognitive deficits in PD patients. These results suggest an additive contribution of both grey and white matter damage to the motor and cognitive deficits in PD.