VentRa: distinguishing frontotemporal dementia from psychiatric disorders.

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

Racial differences in white matter hyperintensity burden in older adults.

White matter hyperintensities (WMHs) may be one of the earliest pathological changes in aging. Race differences in WMH burden has been conflicting. This study examined if race influences WMHs and whether these differences are influenced by vascular risk factors. Alzheimer's Disease Neuroimaging Initiative participants were included if they had a baseline MRI, diagnosis, and WMH measurements. Ninety-one Blacks and 1937 Whites were included. Using bootstrap re-sampling, 91 Whites were randomly sampled and matched to Blacks based on age, sex, education, and diagnosis 1000 times. Linear models examined the influence of race on baseline WMHs, and change of WMHs over time, with and without vascular factors. Vascular risk factors had higher prevalence in Blacks than Whites. When not including vascular factors, Blacks had greater frontal, parietal, deep, and total WMH burden compared to Whites. There were no race differences in longitudinal progression of WMH accumulation. After controlling for vascular factors, only overall longitudinal parietal WMH group differences remained significant, suggesting that vascular factors contribute to racial group differences observed in WMHs.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia.

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Ventricular features as reliable differentiators between bvFTD and other dementias.

INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. METHODS: Using 678 subjects -69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer's Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. RESULTS: Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left-right lateral ventricle ratios), and 76% accuracy using only the single APR feature. DISCUSSION: Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

MNI-FTD templates, unbiased average templates of frontotemporal dementia variants.

Standard templates are widely used in human neuroimaging processing pipelines to facilitate group-level analyses and comparisons across subjects/populations. 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), high atrophy levels lead to significant differences between individuals' brain shapes and 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 reduce potential registration errors. 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 and a corresponding age-matched template of 133 controls (MNI-CN133), along with probabilistic tissue maps for each template. Public availability of these templates will facilitate analyses of FTD cohorts and enable comparisons between different studies in an appropriate common standardized space.

MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia.

INTRODUCTION: Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODS: A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. RESULTS: Average 10-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 the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. CONCLUSION: Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.

CerebrA, 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.

Deformation based morphometry study of longitudinal MRI changes in behavioral variant frontotemporal dementia.

OBJECTIVE: To objectively quantify how cerebral volume loss could assist with clinical diagnosis and clinical trial design in the behavioural variant of frontotemporal dementia (bvFTD). METHODS: We 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. RESULTS: Deformation 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. CONCLUSIONS: Automated 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.