Biology subjects

Collins, D. L.

Publications and source records attributed to Collins, D. L..

At least 37 records · Page 2Linked to original sources

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

neuroscience↗

bioRxiv2020

Creation of an Open Science Dataset from PREVENT-AD, a Longitudinal Cohort Study of Pre-symptomatic Alzheimer's Disease.

To move Alzheimer Disease (AD) research forward it is essential to collect data from large cohorts, but also make such data available to the global research community. We describe the creation of an open science dataset from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) cohort, composed of cognitively unimpaired older individuals with a parental or multiple-sibling history of AD. From 2011 to 2017, 386 participants were enrolled (mean age 63 years old {+/-} 5) for sustained investigation among whom 349 have retrospectively agreed to share their data openly. Repositories are findable through the unified interface of the Canadian Open Neuroscience Platform (https://portal.conp.ca/) and contain up to five years of longitudinal imaging data, cerebral fluid biochemistry, neurosensory capacities, cognitive, genetic, and medical information. Imaging data can be accessed openly at https://openpreventad.loris.ca while most of the other information, sensitive by nature, is accessible by qualified researchers at https://registeredpreventad.loris.ca. In addition to being a living resource for continued data acquisition, PREVENT-AD offers opportunities to facilitate understanding of AD pathogenesis.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2019

A sub+ cortical fMRI-based surface parcellation

Both the cortex and the subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with mapping results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation. We extend this surface-based approach to include also the subcortical deep gray-matter structures. Using the life-span data from the Enhanced Nathan Klein Institute - Rockland Sample, comprised of data from 590 individuals from 6 to 85 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that our extended functional parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels. We also show that our subcortical parcels align with known subnuclei. Further, we show that this parcellation is appropriate for use with data from other modalities; we generate cortical and subcortical white/gray contrast measures for this same dataset, and draw on the fact that areal differences are evident in the relation of white/gray contrast to age, to sex, to brain volume, and to interactions of these terms; we show that our extended functional parcellation provides an improved fit to the complexity of the life-span changes in the white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels. We provide our extended functional parcellation for the use of the neuroimaging community.

neuroscience↗

bioRxiv2019

Unsupervised domain adaptation for the automated segmentation of neuroanatomy in MRI: a deep learning approach

Neuroanatomical segmentation in T1-weighted magnetic resonance imaging of the brain is a prerequisite for quantitative morphological measurements, as well as an essential element in general pre-processing pipelines. While recent fully automated segmentation methods based on convolutional neural networks have shown great potential, these methods nonetheless suffer from severe performance degradation when there are mismatches between training (source) and testing (target) domains (e.g. due to different scanner acquisition protocols or due to anatomical differences in the respective populations under study). This work introduces a new method for unsupervised domain adaptation which improves performance in challenging cross-domain applications without requiring any additional annotations on the target domain. Using a previously validated state-of-the-art segmentation method based on a context-augmented convolutional neural network, we first demonstrate that networks with better domain generalizability can be trained using extensive data augmentation with label-preserving transformations which mimic differences between domains. Second, we incorporate unlabelled target domain samples into training using a self-ensembling approach, demonstrating further performance gains, and further diminishing the performance gap in comparison to fully-supervised training on the target domain.

bioinformatics↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2019

DEFORMATION BASED MORPHOMETRY STUDY OF LONGITUDINAL MRI CHANGES IN BEHAVIORAL VARIANT FRONTOTEMPORAL DEMENTIA

ObjectiveTo objectively quantify how cerebral volume loss could assist with clinical diagnosis and clinical trial design in the behavioural variant of frontotemporal dementia (bvFTD).\n\nMethodsWe applied deformation-based morphometric analyses with robust registration to precisely quantify the magnitude and pattern of atrophy in patients with bvFTD as compared to cognitively normal controls (CNCs), to assess the progression of atrophy over one year follow up and to generate clinical trial sample size estimates to detect differences for the structures most sensitive to change. This study included 203 subjects - 70 bvFTD and 133 CNCs - with a total of 482 timepoints from the Frontotemporal Lobar Degeneration Neuroimaging Initiative.\n\nResultsDeformation based morphometry (DBM) revealed significant atrophy in the frontal lobes, insula, medial and anterior temporal regions bilaterally in bvFTD subjects compared to controls with outstanding subcortical involvement. We provide detailed information on regional changes per year. In both cross-sectional analysis and over a one-year follow-up period, ventricle expansion was the most prominent differentiator of bvFTD from controls and a sensitive marker of disease progression.\n\nConclusionsAutomated measurement of ventricular expansion is a sensitive and reliable marker of disease progression in bvFTD to be used in clinical trials for potential disease modifying drugs, as well as possibly to implement in clinical practice. Ventricular expansion measured with DBM provides the lowest published estimated sample size for clinical trial design to detect significant differences over one and two years.

neuroscience↗

bioRxiv2019

MRI and cognitive scores complement each other to accurately predict Alzheimer’s dementia 2 to 7 years before clinical onset

BackgroundPredicting cognitive decline and the eventual onset of dementia in patients with Mild Cognitive Impairment (MCI) is of high value for patient management and potential cohort enrichment in pharmaceutical trials. We used cognitive scores and MRI biomarkers from a single baseline visit to predict the onset of dementia in an MCI population over a nine-year follow-up period.\n\nMethodAll MCI subjects from ADNI1, ADNI2, and ADNI-GO with available baseline cognitive scores and T1w MRI were included in the study (n=756). We built a Naive Bayes classifier for every year over a 9-year follow-up period and tested each one with Leave one out cross validation.\n\nResultsWe reached 87% prediction accuracy at five years follow-up with an AUC>0.85 from two to seven years (peaking at 0.92 at five years). Both cognitive test scores and MR biomarkers were needed to make the prognostic models highly sensitive and specific, especially for longer follow-ups. MRI features are more sensitive, while cognitive features bring specificity to the prediction.\n\nConclusionCombining cognitive scores and MR biomarkers yield accurate prediction years before onset of dementia. Such a tool may be helpful in selecting patients that would most benefit from lifestyle changes, and eventually early treatments that would slow cognitive decline and delay the onset of dementia.

neuroscience↗

bioRxiv2019

Bridging micro and macro: accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases

Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, and hippocampus for all mentioned atlases. The dataset intends to provide the bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: nist.mni.mcgill.ca/?p=1209.

bioengineering↗

bioRxiv2019

SNIPE score can capture prodromal Alzheimer's in cognitively normal subjects

Capturing early changes in the brain related to Alzheimers disease may lead to models that successfully predict cognitive decline and the eventual onset of dementia, well ahead of onset of clinical symptoms. In this study we used both hippocampal volume and our hippocampal driven SNIPE score to show which marker better captures Alzheimers related changes in a large dataset of normal controls (N=515) from the ADNI study, comparing controls that remain cognitively stable and controls that progress to either MCI or Alzheimers dementia during 10 years of follow-up (median follow-up: 30 months). We measured hippocampal volume and SNIPE score and found that the effect size to differentiate between stable and progressor groups was significantly larger for SNIPE score than for volume. Our results also show that there is a significant age-related difference between groups for both markers, and the difference is greater with the SNIPE score. Our experiments show that considering high sensitivity of our SNIPE score regarding to early AD-related brain changes, this marker is a better candidate in comparison to hippocampal volume for predicting the future onset of dementia.

neuroscience↗

bioRxiv2019

Cortical and subcortical T1 white/gray contrast, chronological age, and cognitive performance

The maturational schedule of typical brain development is tightly constrained; deviations from it are associated with cognitive atypicalities, and are potentially predictive of developmental disorders. Previously, we have shown that the white/gray contrast at the inner border of the cortex is a good predictor of chronological age, and is sensitive to aspects of brain development that reflect cognitive performance. Here we extend that work to include the contrast at the white/gray border of subcortical structures. We show that cortical and subcortical contrast together yield better age-predictions than any non-kernel-based method based on a single image-type, and that the residuals of the improved predictions provide new insight into unevenness in cognitive performance. We demonstrate the improvement in age predictions in two large datasets: the NIH Pediatric Data, with 831 scans of typically developing individuals between 4 and 22 years of age; and the Pediatric Imaging, Neurocognition, and Genetics data, with 909 scans of individuals in a similar age-range. Assessment of the relation of the residuals of these age predictions to verbal and performance IQ revealed correlations in opposing directions, and a principal component analysis of the residuals of the model that best fit the contrast data produced components related to either performance IQ or verbal IQ. Performance IQ was associated with the first principle component, reflecting increased cortical contrast, broadly, with almost no subcortical presence; verbal IQ was associated with the second principle component, reflecting reduced contrast in the basal ganglia and increased contrast in the bilateral arcuate fasciculi.

neuroscience↗

bioRxiv2019

An MRI Atrophy Biomarker Predicts Global Prognosis in Early De Novo Parkinson's Disease

BackgroundCommonly used neuroimaging biomarkers in Parkinsons disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naive early stage, could predict PD prognosis. Methods362 de novo PD patients with T1-weighted MRI (n=222 for the main analysis, 140 for the validation analysis) were recruited from the Parkinsons Progression Markers Initiative (PPMI). We investigated a previously identified PD-specific network atrophy pattern as a potential biomarker of disease severity and prognosis. Progression trajectories of motor function (MDS-UPDRS-part III), cognition (Montreal Cognitive Assessment (MoCA)), and a global composite outcome measure were compared between atrophy tertiles using mixed effect models. The prognostic value of the MRI atrophy measure was compared with 123I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers. FindingsAfter 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r=-0.197, p=0.003), MoCA (r=0.253, p=0.0002) and global composite outcome (r=-0.249, p=0.0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p=0.012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p<0.0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p=0.0372) and a +0.38 (p=0.0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis. InterpretationA PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a stronger predictor of prognosis than any of the other tested biomarkers. Therefore, it has considerable potential as a prognostic biomarker for clinical trials of early PD.

neuroscience↗