Longitudinal rates of lobar atrophy in frontotemporal dementia, semantic dementia, and Alzheimer's disease

Quantifying change in individual subjects affected by frontotemporal lobar degeneration using automated longitudinal MRI volumetry

A novel method of automated MRI volumetry was used to study regional atrophy and disease progression in repeated MRI measurements of patients with frontotemporal lobar degeneration (FTLD). Fifty-nine structural MRI data sets of 17 clinically diagnosed FTLD patients were acquired over up to 30 months in intervals of 6 months and compared with data of 30 age-matched healthy controls. Patients were further subgrouped into behavioral variant FTLD (bvFTLD), progressive nonfluent aphasia (PNFA), and semantic dementia (SemD). Gray matter (GM) volumes of frontal lobes (FL) and temporal lobes (TL) were determined by voxel-based volumetry based on SPM5 algorithms and a probabilistic brain atlas. MRI volumetry revealed frontal and temporal GM atrophy across FTLD patients, with further progression over time. Significant side asymmetry of TL volumes was found in SemD. The ratio of TL to FL volumes was significantly reduced in SemD and increased in bvFTLD. Using this ratio, 6/7 SemD patients and 5/6 bvFTLD patients could be correctly differentiated. TL/FL ratios in bvFTLD and SemD further diverged significantly over a time span of only 6 months. Rates of temporal GM loss per 6 months were 3-4% in SemD, and 2.5% for frontal GM loss in bvFTLD, and thereby clearly exceeded published cerebral volume loss in healthy elderly subjects. The study presents a fully automated, observer-independent volumetric assessment of regional atrophy which allows differentiation of FTLD subgroups. Its sensitivity for atrophy progression--even in such short intervals like 6 months--might benefit future clinical trials as treatment outcome measure.

Lempel-Ziv complexity in schizophrenia: a MEG study

OBJECTIVE

The neurodevelopmental-neurodegenerative debate is a basic issue in the field of the neuropathological basis of schizophrenia (SCH). Neurophysiological techniques have been scarcely involved in such debate, but nonlinear analysis methods may contribute to it.

METHODS

Fifteen patients (age range 23-42 years) matching DSM IV-TR criteria for SCH, and 15 sex- and age-matched control subjects (age range 23-42 years) underwent a resting-state magnetoencephalographic evaluation and Lempel-Ziv complexity (LZC) scores were calculated.

RESULTS

Regression analyses indicated that LZC values were strongly dependent on age. Complexity scores increased as a function of age in controls, while SCH patients exhibited a progressive reduction of LZC values. A logistic model including LZC scores, age and the interaction of both variables allowed the classification of patients and controls with high sensitivity and specificity.

CONCLUSIONS

Results demonstrated that SCH patients failed to follow the "normal" process of complexity increase as a function of age. In addition, SCH patients exhibited a significant reduction of complexity scores as a function of age, thus paralleling the pattern observed in neurodegenerative diseases.

SIGNIFICANCE

Our results support the notion of a progressive defect in SCH, which does not contradict the existence of a basic neurodevelopmental alteration.

Neuroimaging findings in frontotemporal lobar degeneration spectrum of disorders

Frontotemporal lobar degeneration (FTLD) is a clinically and pathologically heterogeneous spectrum of disorders. In the last few years, neuroimaging has contributed to the phenotypic characterisation of these patients. Complementary to the clinical and neuropsychological evaluations, structural magnetic resonance imaging (MRI) and functional techniques provide important pieces of information for the diagnosis of FTLD. They also appear to be useful in distinguishing FTLD from patients with Alzheimer's disease (AD). Preliminary studies in pathologically proven cases suggested that distinct patterns of tissue loss could assist in predicting in vivo the pathological subtype. Recent years have also witnessed impressive advances in the development of novel imaging approaches. Diffusion tensor MRI and functional MRI have improved our understanding of the pathophysiology of the disease, and this should lead to the identification of additional useful markers of disease progression. This reviews discusses comprehensively the state-of-the-art of neuroimaging in the study of FTLD spectrum of disorders, and attempts to envisage which will be new neuroimaging biomarkers that could serve as surrogate measures of the underlying pathology. This will be central in the design of treatment trials of experimental drugs, which are likely to emerge in the near future, to target the pathological processes associated with this condition.

Quantitative structural MRI for early detection of Alzheimer's disease

Alzheimer's disease (AD) is a common progressive neurodegenerative disorder that is not currently diagnosed until a patient reaches the stage of dementia. There is a pressing need to identify AD at an earlier stage, so that treatment, when available, can begin early. Quantitative structural MRI is sensitive to the neurodegeneration that occurs in mild and preclinical AD, and is predictive of decline to dementia in individuals with mild cognitive impairment. Objective evidence of ongoing brain atrophy will be critical for risk/benefit decisions once potentially aggressive, disease-modifying treatments become available. Recent advances have paved the way for the use of quantitative structural MRI in clinical practice, and initial clinical use has been promising. However, further experience with these measures in the relatively unselected patient populations seen in clinical practice is needed to complete translation of the recent enormous advances in scientific knowledge of AD into the clinical realm.

Occupation attributes relate to location of atrophy in frontotemporal lobar degeneration

Frontotemporal lobar degeneration (FTLD) often presents with asymmetric atrophy. We assessed whether premorbid occupations in FTLD patients were associated with these hemispheric asymmetries. In a multi-center chart review of 588 patients, occupation information was related to location of tissue loss or dysfunction. Patients with atrophy lateralized to the right had professions more dependent on verbal abilities than patients with left-lateralized or symmetrical atrophy. In a subgroup of 96 well-characterized patients with quantified neuroimaging data, the lateralization effect was localized to the temporal lobes and included verbal and mathematical ability. Patients whose professions placed high demands on language and mathematics had relatively preserved left temporal relative to right temporal volumes. Thus, occupation selection occurring in early adulthood is related to lateralized brain asymmetry in patients who develop FTLD decades later in the relatively deficient hemisphere. The finding suggests that verbal and mathematical occupations may have been pursued due to developmental right-lateralized functional impairment that precedes the neurodegenerative process. Alternatively, long-term engagement of activities associated with these occupations contributed to left-lateralized reserve, right-lateralized dysfunction, or both.

Fully automated analysis using BRAINS: AutoWorkup

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.

Structure-Function-Based Quantitative Brain Image Analysis

Many neurodegenerative dementias produce significant alterations in the brain that are often not detectable by neurologic tests or with structural imaging. PET is ideally suited for monitoring cell/molecular events early in the course of a disease as well as during pharmacologic therapy. During the past 2 decades, molecular neuroimaging using PET and magnetic resonance (MR) has advanced elegantly and steadily gained importance in the clinical and research arenas. Software- and hardware-based multimodality brain imaging allowing the correlation between anatomic and molecular information has revolutionized clinical diagnosis and now offers unique capabilities for the clinical neuroimaging community and neuroscience researchers at large.

Progression of atrophy in Alzheimer's disease and related disorders

Longitudinal MRI is a powerful tool that allows the assessment of progression of brain changes over multiple imaging time-points and has been increasingly employed in the study of neurodegenerative dementias, particularly Alzheimer's disease (AD). Early studies demonstrated that AD was associated with increased rates of whole brain loss and hippocampal atrophy. A number of sophisticated voxel-level techniques have now been developed that have provided additional information describing regional atrophy over time in the temporal, parietal, and frontal lobes in AD. Studies have also focused on subjects in the prodromal phase of AD in order to describe the earliest changes that are occurring in the brain. Atrophy has been shown to start in the medial temporal lobes and fusiform gyrus at least 3 years before subjects reach a diagnosis of AD, and then spread to the posterior temporal lobes and parietal lobes, and then eventually the frontal lobes. These patterns of atrophy correlate well with the progression of neurofibrillary tangles observed on pathology. Rates of atrophy have also been shown to accelerate over the course of the disease as a subject progresses from cognitively normal to a diagnosis of AD. Similar techniques have also been applied to other neurodegenerative diseases, such as frontotemporal dementia which show higher rates of atrophy and different patterns of progression to those observed in AD. Hence, longitudinal MRI shows promise as a biomarker of disease progression in neurodegenerative disease.

Measuring disease progression in frontotemporal lobar degeneration: a clinical and MRI study

OBJECTIVES

There is currently much interest in biomarkers of disease activity in frontotemporal lobar degeneration (FTLD). We assessed MRI and behavioral measures of progression in a longitudinal FTLD cohort.

METHODS

Thirty-two patients with FTLD (11 behavioral variant frontotemporal dementia [bvFTD], 11 semantic dementia [SemD], 10 progressive nonfluent aphasia [PNFA]) and 24 age-matched healthy controls were assessed using volumetric brain MRI and standard behavioral measures (Mini-Mental State Examination, Frontal Assessment Battery, Clinical Dementia Rating Scale, Neuropsychiatric Inventory with Caregiver Distress scale) at baseline and 1 year later. A semi-automated image registration protocol was used to calculate annualized rates of brain atrophy (brain boundary shift integral [BBSI]) and ventricular expansion (ventricular boundary shift integral [VBSI]). Associations between these rates and changes in behavioral indices were investigated.

RESULTS

Rates of whole brain atrophy were greater in the entire FTLD cohort and in each subgroup compared with controls (all p < or = 0.004). Rates of ventricular expansion were greater in the entire cohort (p < 0.001) and the SemD (p = 0.002) and PNFA (p = 0.05) subgroups compared with controls. Changes in Mini-Mental State Examination, Frontal Assessment Battery, and Clinical Dementia Rating Scale scores were associated with MRI measures of progression, though not uniformly across FTLD subgroups. Both BBSI and VBSI yielded feasible sample size estimates for detecting meaningful treatment effects in SemD and PNFA language subgroups. Sample sizes were substantially larger using MRI biomarkers for the bvFTD subgroup, and using behavioral biomarkers in general.

CONCLUSIONS

Semi-automated MRI atrophy measures are potentially useful objective biomarkers of progression in frontotemporal lobar degeneration (FTLD); however, careful stratification of FTLD subtypes will be important in future clinical trials of disease-modifying therapies.

Dementia induces correlated reductions in white matter integrity and cortical thickness: a multivariate neuroimaging study with sparse canonical correlation analysis

We use a new, unsupervised multivariate imaging and analysis strategy to identify related patterns of reduced white matter integrity, measured with the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI), and decreases in cortical thickness, measured by high resolution T1-weighted imaging, in Alzheimer's disease (AD) and frontotemporal dementia (FTD). This process is based on a novel computational model derived from sparse canonical correlation analysis (SCCA) that allows us to automatically identify mutually predictive, distributed neuroanatomical regions from different imaging modalities. We apply the SCCA model to a dataset that includes 23 control subjects that are demographically matched to 49 subjects with autopsy or CSF-biomarker-diagnosed AD (n=24) and FTD (n=25) with both DTI and T1-weighted structural imaging. SCCA shows that the FTD-related frontal and temporal degeneration pattern is correlated across modalities with permutation corrected p<0.0005. In AD, we find significant association between cortical thinning and reduction in white matter integrity within a distributed parietal and temporal network (p<0.0005). Furthermore, we show that-within SCCA identified regions-significant differences exist between FTD and AD cortical-connective degeneration patterns. We validate these distinct, multimodal imaging patterns by showing unique relationships with cognitive measures in AD and FTD. We conclude that SCCA is a potentially valuable approach in image analysis that can be applied productively to distinguishing between neurodegenerative conditions.

Sparse unbiased analysis of anatomical variance in longitudinal imaging

We present a new algorithm for reliable, unbiased, multivariate longitudinal analysis of cortical and white matter atrophy rates with penalized statistical methods. The pipeline uses a step-wise approach to transform and personalize template information first to a single-subject template (SST) and then to the individual's time series data. The first stream of information flows from group template to the SST; the second flows from the SST to the individual time-points and provides unbiased, prior-based segmentation and measurement of cortical thickness. MRI-bias correction, consistent longitudinal segmentation, cortical parcellation and cortical thickness estimation are all based on strong use of the subject-specific priors built from initial diffeomorphic mapping between the SST and optimal group template. We evaluate our approach with both test-retest data and with application to a driving biological problem. We use test-retest data to show that this approach produces (a) zero change when the retest data contains the same image content as the test data and (b) produces normally distributed, low variance estimates of thickness change centered at zero when test-retest data is collected near in time to test data. We also show that our approach--when combined with sparse canonical correlation analysis--reveals plausible, significant, annualized decline in cortical thickness and white matter volume when contrasting frontotemporal dementia and normal aging.