Biology subjects

Dadar, M.

Publications and source records attributed to Dadar, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2020

MNI-FTD Templates: Unbiased Average Templates of Frontotemporal Dementia Variants

Standard anatomical templates are widely used in human neuroimaging processing pipelines to facilitate group level analyses and comparisons across different subjects and populations. The MNI-ICBM152 template is the most commonly used standard template, representing an average of 152 healthy young adult brains. However, in patients with neurodegenerative diseases such as frontotemporal dementia (FTD), the high levels of atrophy lead to significant differences between the brain shape of the individuals and the MNI-ICBM152 template. Such differences might inevitably lead to registration errors or subtle biases in downstream analyses and results. Disease-specific templates are therefore desirable to reflect the anatomical characteristics of the populations of interest and to reduce potential registration errors when processing data from such populations. Here, we present MNI-FTD136, MNI-bvFTD70, MNI-svFTD36, and MNI-pnfaFTD30, four unbiased average templates of 136 FTD patients, 70 behavioural variant (bv), 36 semantic variant (sv), and 30 progressive nonfluent aphasia (pnfa) variant FTD patients as well as a corresponding age matched average template of 133 healthy controls (MNI-CN133), along with probabilistic tissue maps for each template. The public availability of these templates will facilitate analyses of FTD cohorts and enable comparisons between different studies in a common standardized space appropriate to FTD populations.

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bioRxiv2020

Amyloid β Impacts Future Freezing of Gait in Parkinson's Disease Via White Matter Hyperintensities

BackgroundFreezing of gait (FOG) is a common symptom in Parkinsons Disease (PD) patients. Previous studies have reported relationships between FOG, substantia nigra (SN) degeneration, dopamine transporter (DAT) concentration, as well as amyloid {beta} deposition. However, there is a paucity of research on the concurrent impact of white matter damage. ObjectivesTo assess the inter-relationships between these different co-morbidities, their impact on future FOG and whether they act independently of each other. MethodsWe used baseline MRI and longitudinal gait data from the Parkinsons Progression Markers Initiative (PPMI). We used deformation based morphometry (DBM) from T1-weighted MRI to measure SN atrophy, and segmentation of white matter hyperintensities (WMH) as a measure of WM pathological load. Putamen and caudate DAT levels from SPECT as well as cerebrospinal fluid (CSF) amyloid {beta} were obtained directly from the PPMI. Following correlation analyses, we investigated whether WMH burden mediates the impact of amyloid {beta} on future FOG. ResultsSN DBM, WMH load, putamen and caudate DAT activity and CSF amyloid {beta} levels were significantly different between PD patients with and without future FOG (p < 0.008). Mediation analysis demonstrated an effect of CSF amyloid {beta} levels on future FOG via WMH load, independent of SN atrophy and striatal DAT activity levels. ConclusionsAmyloid {beta} might impact future FOG in PD patients through an increase in WMH burden, in a pathway independent of Lewy body pathology.

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bioRxiv2020

PhyloBrain atlas: a cortical brain MRI atlas following a phylogenetic approach

Cortical atlases constitute a consistent division of the human cortex into areas that have common structural as well as meaningful and distinctive functional characteristics. The most widely used atlases follow the cytoarchitectonic and myeloarchitectonic characteristics of the cortex and have been combined to the standard anatomical nomenclature of gyri and sulci. More recently, common functional features depicted by resting state functional MRI have also guided the division of the cortical brain in functional regions of interest. However, to date, there are no atlases that divide the cortex considering the common evolutionary changes experienced by the mammalian cortex. Hence, the present study proposes the division of cortical areas into five main regions of interest (ROIs) following a phylogenetic approach: 1- archicortex, 2- paleocortex, 3- peri-archicortex, 4- proisocortex, 5-neocortex, and twelve neocortical sub-ROIs: 5.1.temporopolar, 5.2.post-central, 5.3.pre-central, 5.4.pericalcarine, 5.5.superior temporal, 5.6.middle temporal, 5.7.precuneus, 5.8.insular, 5.9.inferior parietal, 5.10.caudal anterior, 5.11.posterior cingulate, and 5.12.lingual gyrus. The segmentations were done using the T1-weighted MNI-ICBM152 non-linear 6th generation symmetric average brain MRI model.

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bioRxiv2020

Beware of White Matter Hyperintensities Causing Systematic Errors in Grey Matter Segmentations!

IntroductionVolumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer grey matter (GM) structure volumes and its possible bias on functional relationships. MethodsT1-weighted images from 1077 participants (4321 timepoints) from the Alzheimers Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery (FLAIR) images. Mixed effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overal WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. ResultsParticipants with higher WMH volumes had higher overalps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (P < 0.0001). When not corrected for WMHs, caudate volumes increased with age (P < 0.0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimers disease patients. After correcting for WMHs, caudate volumes decreased with age (P < 0.0001), and Alzheimers disease patients had lower caudate volumes than cognitively healthy individuals (P < 0.01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (P < 0.0001). ConclusionsPresence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.

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bioRxiv2020

Obesity impairs cognitive function via metabolic syndrome and cerebrovascular disease: an SEM analysis in 15,000 adults from the UK Biobank.

Chronic obesity is associated with several complications, including cognitive impairment and dementia. However, we have piecemeal knowledge of the mechanisms linking obesity to central nervous system damage. Adiposity leads to the metabolic syndrome, consisting of inflammation, hypertension, dyslipidemia and insulin resistance. In turn, these metabolic abnormalities cause cerebrovascular dysfunction, which may cause white and grey matter tissue loss and consequent cognitive impairment. While there have been several neuroimaging studies linking adiposity to changes in brain morphometry, a comprehensive investigation of the relationship has so far not been done. Here we use structural equation modelling applied to over 15,000 individuals from the UK Biobank to identify the causal chain that links adiposity to cognitive dysfunction. We found that body mass index and waist-to-hip ratio were positively related to higher plasma C-reactive protein, dyslipidemia, occurrence of hypertension and diabetes, all of which were in turn related to cerebrovascular disease as measured by volume of white matter hyperintensities on magnetic resonance imaging. White mater hyperintensities were associated with lower cortical thickness and volume and higher subcortical volumes, which were associated with cognitive deficits on tests of visuospatial memory, fluid intelligence, and working memory among others. In follow-up analyses we found that inflammation, hypertension and diabetes mediated 20% of the relationship between obesity and cerebrovascular disease and that cerebrovascular disease mediated a significant proportion of the relationship between obesity and cortical thickness and volume. We also showed that volume of white matter hyperintensities was related to decreased fractional anisotropy and increased mean diffusivity in the majority of white matter tracts, pointing to white matter dysconnectivity as a major cause of impaired cognition. Our results have clinical implications, supporting a role for the management of adiposity in the prevention of late-life dementia and cognitive decline.

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bioRxiv2020

The Temporal Relationships between White Matter Hyperintensities, Neurodegeneration, Amyloid β, and Cognition

INTRODUCTIONCognitive decline in Alzheimers disease is associated with amyloid-{beta} accumulation, neurodegeneration and cerebral small vessel disease, but the temporal relationships between these factors is not well established. METHODSData included white matter hyperintensity (WMH) load, grey matter (GM) atrophy and Alzheimers Disease Assessment Scale-Cognitive-Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (A{beta}1-42) for 461 participants from the Alzheimers Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships between baseline WMH, GM, and A{beta}1-42 to changes in WMH, GM, A{beta}1-42, and cognition at one-year follow-up. RESULTSBaseline WMHs and A{beta}1-42 predicted WMH increase and GM atrophy. Baseline WMHs, GM, and A{beta}1-42 predicted worsening cognition. Only baseline A{beta}1-42 predicted change in A{beta}1-42. DISCUSSIONBaseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline A{beta}1-42 predicted WMH increase, suggesting a potential role of amyloid in WM damage. Research in ContextO_LISystematic Review: Both amyloid {beta} and neurodegeneration are primary pathologies in Alzheimers disease. White matter hyperintensities (indicative of presence of cerebrovascular disease) might also be part of the pathological changes in Alzheimers. However, the temporal relationship between white matter hyperintensities, amyloid {beta}, neurodegeneration, and cognitive decline is still unclear. C_LIO_LIInterpretation: Our results establish a potential temporal order between white matter hyperintensities, amyloid {beta}, neurodegeneration, and cognitive decline, showing that white matter hyperintensities precede neurodegeneration and cognitive decline. The results provide some evidence that amyloid {beta} deposition, in turn, precedes accumulation of white matter hyperintensities. C_LIO_LIFuture Directions: The current findings reinforce the need for future longitudinal investigations of the mechanisms through which white matter hyperintensities impact the aging population in general and Alzheimers disease patients, in particular. C_LI

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bioRxiv2020

Cognitive and Motor Correlates of Grey and White Matter Pathology in Parkinsons Disease

IntroductionPrevious studies have found associations between grey matter atrophy and white matter hyperintensities (WMH) of vascular origin with cognitive and motor deficits in Parkinsons disease (PD). Here we investigate these relationships in a sample of PD patients and age-matched healthy controls. MethodsData 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 Alzheimers 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 Parkinsons 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. ResultsSubstantia 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). ConclusionWhile 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.

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bioRxiv2020

Proof of concept of a novel ex vivo, in situ method for MRI and histological brain assessment

MRI-histology correlation studies of the ex vivo brain mostly employ fresh, extracted (ex situ) specimens, aldehyde fixed by immersion. This method entails manipulation of the fresh brain during extraction, introducing several disadvantages: deformation of the specimen prior to MRI acquisition; introduction of air bubbles in the sulci, creating artifacts; and uneven or poor fixation of the deeper regions of the brain. We propose a new paradigm to scan the ex vivo brain, exploiting a technique used by anatomists: fixation by whole body perfusion, which implies fixation of the brain in situ. This allows scanning the brain surrounded by fluids, meninges, and skull, thus preserving the structural relationships of the brain in vivo and avoiding the disadvantages of ex situ scanning. Our aims were: 1) to assess whether months of in situ fixation resulted in a loss of fluid around the brain; 2) to evaluate whether in situ fixation modified antigenicity for myelin and neuron specific marker; 3) to assess whether in situ fixation improved the register of ex vivo brain images to standard neuroanatomical templates in pseudo-Talairach space for morphometry studies. Five head specimens fixed with a saturated sodium chloride solution (a non-standard fixative used in our anatomy laboratory for neurosurgical simulation) were employed. We acquired 3D T1-weighted (MPRAGE), 2D fluid-attenuated inversion recovery T2-weighted turbo spin echo (T2w-FLAIR), and 3D gradient-echo (3D-GRE) pulse sequences of all brains on a 1.5T MRI. After brain extraction, sections were processed for binding with myelin basic protein (MBP) and neuronal nuclei (NeuN) primary antibodies by immunofluorescence. This study showed that all but one specimen retained fluids in the subarachnoid and ventricular spaces. The specimen that lost fluid was the oldest one, with the longest interval between the time of death and the MRI scanning day being 403 days. All T1-weighted images were successfully processed through a validated pipeline used with in vivo MRIs. The pipeline did not require any modification to run on the ex vivo-in situ scans. All scans were successfully registered to the brain template, more accurately than an ex vivo-ex situ scan and exhibited positive antigenicity for MBP and NeuN. MRI and histology study of the ex vivo-in situ brain fixed by perfusion is feasible and allows for in situ MRI imaging for of at least 10 months post-mortem prior to histology analyses. Fluids around and inside the brain specimens and antigenicity for myelin and neurons were all well preserved.

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bioRxiv2020

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

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 cross-sectionally and longitudinally, and to compare the findings with a recently proposed disease-staging 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 Brettschneiders 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.

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bioRxiv2020

Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data

BackgroundGray and white matter volume difference and change are important imaging markers of pathology and disease progression in neurology and psychiatry. Such measures are usually estimated from tissue segmentation maps produced by publicly available image processing pipelines. However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different MR scanners and sites. MethodsWe used T1-weighted images of a single individual, scanned in 73 sessions across 27 different sites to assess the robustness of the tissue classification tools. Variability in Dice Kappa values and tissue volumes was assessed for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12. We also estimated the sample size necessary to detect a significant 1% volume reduction based on the variability of the estimates from each method within and across scanner models. ResultsBISON had the lowest overall variability in its volumetric estimates, followed by FreeSurfer, and SPM12. All methods also had significant differences between some of their estimates across different scanner manufacturers (e.g. BISON had significantly higher GM estimates and correspondingly lower WM estimates for GE scans compared to Philips and SIEMENs), and different signal-to-noise ratio (SNR) levels (e.g. FAST and FreeSurfer had significantly higher WM volume estimates for high versus medium and low SNR tertiles as well as correspondingly lower GM volume estimates). BISON also had the smallest sample size requirement across all scanners and tissue types, followed by FreeSurfer, and SPM12. ConclusionsOur comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center datasets and multi-scanner databases.

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bioRxiv2019

CerebrA: Accurate registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template

Accurate anatomical atlases are recognized as important tools in brain-imaging research. They are widely used to estimate disease-specific changes and therefore, are of great relevance in extracting regional information on volumetric variations in clinical cohorts in comparison to healthy populations. The use of high spatial resolution magnetic resonance imaging and the improvement in data preprocessing methods have enabled the study of structural volume changes on a wide range of disorders, particularly in neurodegenerative diseases where different brain morphometry analyses are being broadly used in an effort to improve diagnostic biomarkers. In the present dataset, we introduce the Cerebrum Atlas (CerebrA) along with the MNI-ICBM2009c average template. MNI-ICBM2009c is the most recent version of the MNI-ICBM152 brain average, providing a higher level of anatomical details. Cerebra is based on an accurate non-linear registration of cortical and subcortical labelling from Mindboggle 101 to the symmetric MNI-ICBM2009c atlas, followed by manual editing.

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bioRxiv2019

Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementia

INTRODUCTIONBrain structural imaging is paramount for the diagnosis of behavioral variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODSA total of 515 subjects from two different bvFTD databases (training and validation cohorts) were included to perform voxel-wise deformation-based morphometry analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from morphometric differences in isolation and together with bedside cognitive scores. RESULTSAverage ten-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In a separate validation cohort of genetically confirmed bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added cognitive scores. DISCUSSIONThe random forest classifier developed can accurately predict bvFTD at the individual subject level.

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bioRxiv2019

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Current models of decision-making assume that the brain gradually accumulates evidence and drifts towards a threshold which, once crossed, results in a choice selection. These models have been especially successful in primate research, however transposing them to human fMRI paradigms has proved challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

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bioRxiv2019

Conversion of Diffusely Abnormal White Matter to Focal Lesions is Linked to Progression in Secondary Progressive Multiple Sclerosis

Objectives1) To automatically segment focal white matter lesions (FWML) and Diffusely abnormal white matter (DAWM), i.e. regions of diffuse abnormality observed on conventional (T2-weighted) MRI and characterize their longitudinal volumetric and normalized T1-weighted (T1w) intensity evolution, 2) To assess associations of FWML and DAWM with Expanded Disability Status Scale (EDSS) and confirmed disability progression (CDP).\n\nMethodsData includes 3951 timepoints of 589 SPMS participants followed for three years. FWML and DAWM were automatically segmented using a 2-weighted-intensity thresholding technique. Screening DAWM volumes that transformed into FWML at the last visit (DAWM-to-FWML) and normalized T1w intensities (as a marker of severity of damage) in those voxels were calculated.\n\nResultsFWML volume significantly increased and DAWM volume significantly decreased as disease duration increased (p<0.001). Global EDSS scores were positively associated with FWML volumes (p=0.002), but not with DAWM volumes. Median volume of DAWM-to-FWML was significantly higher in patients who progressed (2.75 vs 1.70 cc; p<0.0001), and represented 14% of the total DAWM volume at screening, compared to 10% in patients who did not progress (p=0.001). Normalized T1w intensity values of DAWM-to-FWML were negatively associated with CDP status (p<0.00001).\n\nConclusionDAWM transformed into FWML over time, and this transformation was significantly associated with clinical progression. DAWM voxels that transformed had greater normalized T1w intensity decrease over time, in keeping with relatively greater tissue damage evolution. Evaluation of DAWM in progressive MS provides a useful measure to evaluate therapies that aim to protect this at-risk tissue with the potential to slow progression.

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bioRxiv2019

The Longitudinal Assessment of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Alzheimer’s disease and their Association with White Matter Hyperintensities in the National Alzheimer’s Coordinating Center’s Uniform Data Set

IntroductionNeuropsychiatric symptoms (NPS) are common in all dementias, including those with Alzheimers disease (AD). NPS contribute to patients distress, caregiver burden, and can lead to institutionalization. White matter hyperintensities (WMH) are a common finding on MRI usually indicative of cerebrovascular disease and have been associated with certain NPS. The aim of this study was two-fold. Firstly, we assessed the relationship between WMH load and NPS severity in MCI due to AD (MCI-AD) and AD. Secondly, we assessed the ability of WMH to predict the development and progression of NPS in these participants. Data was obtained from the National Alzheimers Coordinating Center.\n\nMethodsWMH were obtained from baseline MRIs and quantified using an automated segmentation technique. NPS were measured using the Neuropsychiatric Inventory (NPI). Mixed effect models and correlations were used to determine the relationship between WMH load and NPS severity scores.\n\nResultsCross-sectional analysis showed no significant association between NPS and WMH at baseline. Longitudinal mixed effect models, however, revealed a significant relationship between increase in NPI total scores and baseline WMH load (p=0.014). There was also a significant relationship between increase in irritability severity scores over time and baseline WMH load (p= 0.009). Trends were observed for a relationship between increase in agitation severity scores and baseline WMH load (p=0.058). No other NPS severity scores were significantly associated with baseline WMH load. The correlation plot analysis showed that baseline whole brain WMH predicted change in future NPI total scores (r=0.169, p=0.008). Baseline whole brain WMH also predicted change in future agitation severity scores (r= 0.165, p= 0.009). The temporal lobe WMH (r=0.169, p=0.008) and frontal lobe WMH (r=0.153, p=0.016) contributed most to this this change.\n\nConclusionIrritability and agitation are common NPS and very distressful to patients and caregivers. Our findings of an increase in irritability severity over time as well as higher agitation severity scores at follow-up in participants with MCI-AD and AD with increased WMH loads have important implications for treatment, arguing for aggressive treatment of vascular risk factors in patients with MCI-AD and AD.

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bioRxiv2019

White matter hyperintensities and neuropsychiatric symptoms in Alzheimer’s disease and mild cognitive impairment

Background and Purpose: Neuropsychiatric symptoms (NPS) are frequently encountered in patients with Alzheimers disease (AD). Focal grey matter atrophy has been linked to NPS development. Cerebrovascular disease can cause focal lesions and is common among AD patients. As cerebrovascular disease can be detected on MRI as white matter hyperintensities (WMH), this study evaluated WMH burden in mild cognitive impairment (MCI), AD and normal controls and determined their relationship with NPS. Methods: NPS were assessed using the Neuropsychiatric Inventory and grouped into subsyndromes. WMH were measured using an automatic segmentation technique and mean deformation-based morphometry was used to measure atrophy of grey matter regions. Results: WMHs and grey matter atrophy both contributed significantly to NPS subsyndromes in MCI and AD subjects, however, WMH burden played a greater role. Conclusions: This study could provide a better understanding of the pathophysiology of NPS in AD.

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bioRxiv2019

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

IntroductionAccurate 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.\n\nMethodsBISON 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.\n\nResultsThe 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 {kappa}GM = 0.88 {+/-} 0.03, {kappa}WM = 0.85 {+/-} 0.03, {kappa}CSF = 0.77 {+/-} 0.11, outperforming ANTs Atropos ({kappa}GM = 0.79 {+/-} 0.05, {kappa}WM = 0.84 {+/-} 0.05, {kappa}CSF = 0.64 {+/-} 0.22) as well as test-retest Dice Kappa values of {kappa}GM = 0.94 {+/-} 0.006, {kappa}WM = 0.92 {+/-} 0.006, {kappa}CSF = 0.77 {+/-} 0.11 outperforming both manual ({kappa}GM = 0.92 {+/-} 0.01, {kappa}WM = 0.91 {+/-} 0.01, {kappa}CSF = 0.74 {+/-} 0.03) and ANTs Atropos ({kappa}GM = 0.87 {+/-} 0.001, {kappa}WM = 0.92 {+/-} 0.001, {kappa}CSF = 0.79 {+/-} 0.05). The human phantom dataset validations showed high generalizability for both Atropos ({kappa}GM = 0.97 {+/-} 0.01, {kappa}WM = 0.96 {+/-} 0.01, {kappa}CSF = 0.93 {+/-} 0.02) and BISON ({kappa}GM = 0.95 {+/-} 0.01, {kappa}WM = 0.94 {+/-} 0.01, {kappa}CSF = 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.\n\nConclusionOur 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.

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bioRxiv2019

Automated Separation of Diffusely Abnormal White Matter from Focal White Matter Lesions on MRI in Multiple Sclerosis

BackgroundPrevious histopathology and MRI studies have addressed the differences between focal white matter lesions (FWML) and diffusely abnormal white matter (DAWM) in multiple sclerosis (MS). These two categories of white matter T2-weighted (T2w) hyperintensity show different degrees of demyelination, axonal loss and immune cell density on histopathology, potentially offering distinct correlations with symptoms.\n\nObjectives1) To automate the separation of FWML and DAWM using T2w MRI intensity thresholds and to investigate their differences in magnetization transfer ratios (MTR), which are sensitive to myelin content; 2) to correlate MTR values in FWML and DAWM with normalized signal intensity values on fluid attenuated inversion recovery (FLAIR), T2w, and T1-weighted (T1w) contrasts, as well as with the ratio of T2w/T1w normalized values, in order to determine whether these normalized intensities can be used as myelin-sensitive markers when MTR is not available.\n\nMethodsUsing similar 3T MRI protocols, 2 MS cohorts of 20 participants each were scanned in 2 centers, including: 3D T1w (MP2RAGE), 3D FLAIR, 2D T2w, and 3D magnetization-transfer (MT) contrasts. We used the first dataset to develop an automated technique to separate FWML from DAWM and the second one to validate the automation of the technique. We applied the automatic thresholds to both datasets and assessed the overlap of the manual and the automated masks using Dice kappa. We also assessed differences in mean MTR values between NAWM, DAWM and FWML, using manually and automatically derived masks in both datasets. Finally, we used the mean intensity of manually-traced areas of NAWM on T2w images as the normalization factor for each MRI contrast, and compared these with the normalized-intensity values obtained using automated NAWM (A-NAWM) masks as the normalization factor. Paired t-tests assessed the MTR differences across tissue types. Wilcoxon Signed Rank test and paired t-tests assessed FWML and DAWM differences in manual and automated derived volumes. Pearson correlations assessed the relationship between MTR and normalized intensity values in the manual and automated derived masks.\n\nResultsThe mean Dice-kappa values for dataset 1 were: 0.8 for DAWM masks and 0.7 for FWML masks. In dataset 2, mean dice-kappa values were: 0.8 for DAWM and 0.8 for FWML. Also, manual and automated DAWM and FWML volumes were not significantly different in both datasets. MTR values (expressed as mean and standard deviation, arbitrary units) were significantly lower in manually derived FWML compared with DAWM in both datasets: 1) FWML: 37.1 {+/-} 3.2 vs DAWM: 43.3 {+/-} 2.1; t=13.2; p<0.0001, and 2) FWML: 32.5 {+/-} 3.9 vs DAWM: 38.8 {+/-} 1.7; t=9.8; p<0.0001. Similar results were obtained using the automatic derived masks in both datasets: 1) FWML: 35.8 {+/-} 2.8 vs DAWM: 43.1 {+/-} 1.6; t=15.3; p<0.0001, and 2) FWML: 31.3 {+/-} 3.1 vs DAWM: 38.1 {+/-} 1.5; t=12.8; p<0.0001. MTR was also significantly lower in manually derived DAWM masks compared with normal appearing white matter (NAWM) in both datasets: 1) NAWM: 46.7 {+/-} 1.3; t=10.1; p<0.0001, and 2) NAWM: 39.3 {+/-} 0.8; t=3.1; p=0.003. Similar results were obtained using the automatic derived masks in both datasets: 1) NAWM: 46.3 {+/-} 1.1; t=13.7; p<0.0001, and 2) NAWM: 39.9 {+/-} 1.1; t=9.6; p<0.0001.\n\nIn both datasets, manually derived FWML and DAWM MTR values showed significant correlations with normalized FLAIR (r=-0.35 to -0.67; p=0.06 to <0.0001), T2w (r=-0.60 to -0.72; p=0.003 to <0.0001), T1w/T2w (r=0.63 to 0.77; p=0.002 to <0.0001), and T1w (r=0.77 to 0.92; p<0.0001) intensities. Finally, normalized intensity values obtained using automatic derived masks were significantly correlated with the manually derived values in both datasets.\n\nConclusionsThe separation of FWML and DAWM on MRI scans of MS patients using automated intensity thresholds on T2w images is feasible. MTR values are significantly lower in FWML than DAWM, and DAWM values are significantly lower than NAWM, reflecting potentially greater demyelination within focal lesions. Normalized intensity values of different MRI contrasts exhibit a significant correlation with MTR values in both tissues of interest and could be used as a proxy to assess demyelination when MTR images are not available.

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