Patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia

More Similar than Different: Memory, Executive Functions, Cortical Thickness, and Glucose Metabolism in Biomarker-Positive Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Background

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are typically associated with very different clinical and neuroanatomical presentations; however, there is increasing recognition of similarities.

Objective

To examine memory and executive functions, as well as cortical thickness, and glucose metabolism in AD and bvFTD signature brain regions.

Methods

We compared differences in a group of biomarker-defined participants with Alzheimer's disease and a group of clinically diagnosed participants with bvFTD. These groups were also contrasted with healthy controls (HC).

Results

As expected, memory functions were generally more impaired in AD, followed by bvFTD, and both clinical groups performed more poorly than the HC group. Executive function measures were similar in AD compared to bvFTD for motor sequencing and go/no-go, but bvFTD had more difficulty with a set shifting task. Participants with AD showed thinner cortex and lower glucose metabolism in the angular gyrus compared to bvFTD. Participants with bvFTD had thinner cortex in the insula and temporal pole relative to AD and healthy controls, but otherwise the two clinical groups were similar for other frontal and temporal signature regions.

Conclusions

Overall, the results of this study highlight more similarities than differences between AD and bvFTD in terms of cognitive functions, cortical thickness, and glucose metabolism. Further research is needed to better understand the mechanisms mediating this overlap and how these relationships evolve longitudinally.

Cortical thinning 3 years after ischaemic stroke is associated with cognitive impairment and APOE ε4

Cortical thinning has been described in many neurodegenerative diseases and used for both diagnosis and disease monitoring. The imaging signatures of post-stroke vascular cognitive impairment have not been well described. We investigated the trajectory of cortical thickness over 3 years following ischaemic stroke compared to healthy stroke-free age- and sex-matched controls. We also compared cortical thickness between cognitively normal and impaired stroke survivors, and between APOE ɛ4 carriers and non-carriers. T1-weighted MRI and cognitive data for 90 stroke survivors and 36 controls from the Cognition And Neocortical Volume After Stroke (CANVAS) study were used. Cortical thickness was estimated using FreeSurfer volumetric reconstruction according to the Desikan-Killiany parcellation atlas. Segmentation inaccuracies were manually corrected and infarcted ipsilesional vertices in cortical thickness maps were identified and excluded using stroke lesion masks traced a-priori. Mixed-effects regression was used to compare cortical thickness cross-sectionally between groups and longitudinally between timepoints. Healthy control and stroke groups did not differ on demographics and most clinical characteristics, though controls were less likely to have atrial fibrillation. Age was negatively associated with global mean cortical thickness independent of sex or group, with women in both groups having significantly thicker cortex. Three months post-stroke, cortical thinning was limited and focal. From 3 months to 3 years, the rate of cortical thinning in stroke was faster compared to that in healthy controls. However, this difference in cortical thinning rate could not survive family-wise correction for multiple comparisons. Yet, cortical thinning at 3 years was found more spread especially in ipsilesional hemispheres in regions implicated in motor, sensory, and memory processing and recovery. The cognitively impaired stroke survivors showed greater cortical thinning, compared to controls, than those who were cognitively normal at 3 years. Also, carriers of the APOE ɛ4 allele in stroke exhibited greater cortical thinning independent of cognitive status. The temporal changes of cortical thickness in both healthy and stroke cohorts followed previously reported patterns of cortical thickness asymmetry loss across the human adult life. However, this loss of thickness asymmetry was amplified in stroke. The post-stroke trajectories of cortical thickness reported in this study may contribute to our understanding of imaging signatures of vascular cognitive impairment.

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

BACKGROUND

Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.

PURPOSE

The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.

METHODS

In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.

RESULTS

Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).

CONCLUSION

These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

Gray matter changes related to microglial activation in Alzheimer's disease

Neuroinflammation is increasingly recognized as playing a key pathogenetic role in Alzheimer's disease (AD). We examined the relationship between in vivo neuroinflammation and gray matter (GM) changes. Twenty-eight subjects with clinically probable AD (n = 14) and amyloid-positive mild cognitive impairment (n = 14) (age 71.9 ± 8.4 years, 46% female) and 24 healthy controls underwent structural 3T brain MRI. AD/mild cognitive impairment participants exhibited GM atrophy and cortical thinning in AD-related temporoparietal regions (false discovery rate-corrected p < 0.05). Patients also showed increased microglial activation in temporal cortices. Higher 11C-PK11195 binding in these regions was associated with reduced volume and cortical thickness in parietal, occipital, and cingulate areas (false discovery rate p < 0.05). Hippocampal GM atrophy and parahippocampal cortical thinning were related to worse cognition (p < 0.05), but these effects were not mediated by microglial activation. This study demonstrates an association between in vivo microglial activation and markers of GM damage in AD, positioning neuroinflammation as a potential target for immunotherapeutic strategies.

An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine

Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere with individual daily tasks. Thus, the accurate diagnosis of both AD and the prodromal stage (i.e., mild cognitive impairment (MCI)) is crucial for timely treatment. As AD is inherently dynamic, the relationship between AD indicators is unclear and varies over time. To address this issue, we first aimed at investigating differences in atrophic patterns between individuals with AD and MCI and healthy controls (HCs). Then we utilized multiple biomarkers, along with filter- and wrapper-based feature selection and an extreme learning machine- (ELM-) based approach, with 10-fold cross-validation for classification. Increasing efforts are focusing on the use of multiple biomarkers, which can be useful for the diagnosis of AD and MCI. However, optimum combinations have yet to be identified and most multimodal analyses use only volumetric measures obtained from magnetic resonance imaging (MRI). Anatomical structural MRI (sMRI) measures have also so far mostly been used separately. The full possibilities of using anatomical MRI for AD detection have thus yet to be explored. In this study, three measures (cortical thickness, surface area, and gray matter volume), obtained from sMRI through preprocessing for brain atrophy measurements; cerebrospinal fluid (CSF), for quantification of specific proteins; cognitive score, as a measure of cognitive performance; and APOE ε4 allele status were utilized. Our results show that a combination of specific biomarkers performs well, with accuracies of 97.31% for classifying AD vs. HC, 91.72% for MCI vs. HC, 87.91% for MCI vs. AD, and 83.38% for MCIs vs. MCIc, respectively, when evaluated using the proposed algorithm. Meanwhile, the areas under the curve (AUC) from the receiver operating characteristic (ROC) curves combining multiple biomarkers provided better classification performance. The proposed features combination and selection algorithm effectively classified AD and MCI, and MCIs vs. MCIc, the most challenging classification task, and therefore could increase the accuracy of AD classification in clinical practice. Furthermore, we compared the performance of the proposed method with SVM classifiers, using a cross-validation method with Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.

Motor cortical thickness is related to effort-based decision-making in humans

Although motor cortex is integral in driving physical exertion, how its inherent properties influence decisions to exert is unknown. In this study, we examined how anatomical properties of motor cortex are related to participants' subjective valuations of effort and their decisions to exert effort. We used computational modeling to characterize participants' subjective valuation of physical effort during an effort-based decision-making task in which they made choices about exerting different levels of hand-grip exertion. We also acquired structural MRI data from these participants and extracted anatomical measures of each individual's hand knob, the region of motor cortex recruited during hand-grip exertion. We found that individual participants' cortical thickness of hand knob was associated with their effort-based decisions regarding hand exertion. These data provide evidence that the anatomy of an individual's motor cortex is an important factor in decisions to engage in physical activity.NEW & NOTEWORTHY How effortful a task feels is an integral aspect of human decision-making that influences choices to engage in physical activity. We show that properties of motor cortex (the brain region responsible for physical exertion) are related to assessments of effort and decisions to exert. These findings provide a link between the anatomical properties of motor cortex and the cognitive function of effort-based choice.

Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease

Incorporating with machine learning technology, neuroimaging markers which extracted from structural Magnetic Resonance Images (sMRI), can help distinguish Alzheimer's Disease (AD) patients from Healthy Controls (HC). In the present study, we aim to investigate differences in atrophic regions between HC and AD and apply machine learning methods to classify these two groups. T1-weighted sMRI scans of 158 patients with AD and 145 age-matched HC were acquired from the ADNI database. Five kinds of parameters (i.e. cortical thickness, surface area, gray matter volume, curvature and sulcal depth) were obtained through the preprocessing steps. The recursive feature elimination (RFE) method for support vector machine (SVM) and leave-one-out cross validation (LOOCV) were applied to determine the optimal feature dimensions. Each kind of parameter was trained by SVM algorithm to acquire a classifier, which was used to classify HC and AD ultimately. Moreover, the ROC curves were depicted for testing the classifiers' performance and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The results showed that the decreased cortical thickness and gray matter volume dramatically exhibited the trend of atrophy. The key differences between AD and HC existed in the cortical thickness and gray matter volume of the entorhinal cortex and medial orbitofrontal cortex. In terms of classification results, an optimal accuracy of 90.76% was obtained via multi-parameter combination (i.e. cortical thickness, gray matter volume and surface area). Meanwhile, the receiver operating characteristic (ROC) curves and area under the curve (AUC) were also verified multi-parameter combination could reach a better classification performance (AUC=0.94) after the SVM-RFE method. The results could be well prove that multi-parameter combination could provide more useful classified features from multivariate anatomical structure than single parameter. In addition, as cortical thickness and multi-parameter combination contained more important classified information with fewer feature dimensions after feature selection, it could be optimum to separate HC from AD to take the top two important features of them to construct SVM classifiers in two-dimensional space. The proposed work is a promising approach suggesting an important role for machine-learning based diagnostic image analysis for clinical practice.

Identification of Alzheimer's Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features

Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI.

Regional Gray Matter Atrophy in Vascular Mild Cognitive Impairment

BACKGROUND

The aim of this study was to explore the neuroanatomical bases of vascular mild cognitive impairment (VaMCI) with respect to attention/executive function, memory, language, and visuospatial function.

METHODS

We used voxel-based morphometric analysis to identify brain regions that significantly differed in terms of gray matter volumes (GMVs) between 43 patients with VaMCI and 55 healthy controls. Then, we compared the individual GMVs of the selected regions with the neuropsychological profiles of the VaMCI patients.

RESULTS

The delayed recall component of the Rey-Osterrieth Complex Figure Test (CFT) (74.4%), the Symbol Digit Modalities Test (74.4%), the Boston Naming Test (51.2%), and the CFT-copy (81.4%) shared the highest incidence of impairment in the 4 cognitive domains, respectively. Compared with controls, patients with VaMCI exhibited significantly reduced GMVs. This effect was mainly present in the frontal regions, including the bilateral dorsolateral prefrontal cortex (DLPFC), the orbital portion of the superior frontal gyrus (SFG), and the left supplemental motor area, and was also observed in the bilateral posterior cingulated cortex (PCC). GMVs were significantly correlated with performance in the Trail Making Test, part B, in the bilateral DLPFC and PCC, the clock drawing test in the right orbital portion of the SFG, and CFT-delayed recall in the right PCC.

CONCLUSIONS

These results, from the perspective of brain morphology, uniquely explored the specific cerebral structural changes of VaMCI, thus providing a deeper understanding of the pathophysiology of the disease.

Directed network motifs in Alzheimer's disease and mild cognitive impairment

Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer’s disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer’s disease.

Directed progression brain networks in Alzheimer's disease: properties and classification

This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties.

Advances in MRI biomarkers for the diagnosis of Alzheimer's disease

With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.

Cortical thickness estimation in longitudinal stroke studies: A comparison of 3 measurement methods

There is considerable controversy about the causes of cognitive decline after stroke, with evidence for both the absence and coexistence of Alzheimer pathology. A reduction in cortical thickness has been shown to be an important biomarker for the progression of many neurodegenerative diseases, including Alzheimer's disease (AD). However, brain volume changes following stroke are not well described. Cortical thickness estimation presents an ideal way to detect regional and global post-stroke brain atrophy. In this study, we imaged a group of patients in the first month after stroke and at 3 months. We compared three methods of estimating cortical thickness on unmasked images: one surface-based (FreeSurfer) and two voxel-based methods (a Laplacian method and a registration method, DiRecT). We used three benchmarks for our analyses: accuracy of segmentation (especially peri-lesional performance), reproducibility, and biological validity. We found important differences between these methods in cortical thickness values and performance in high curvature areas and peri-lesional regions, but similar reproducibility metrics. FreeSurfer had less reliance on manual boundary correction than the other two methods, while reproducibility was highest in the Laplacian method. A discussion of the caveats for each method and recommendations for use in a stroke population is included. We conclude that both surface- and voxel-based methods are valid for estimating cortical thickness in stroke populations.

MRI signatures of brain macrostructural atrophy and microstructural degradation in frontotemporal lobar degeneration subtypes

Brain magnetic resonance imaging (MRI) studies have demonstrated regional patterns of brain macrostructural atrophy and white matter microstructural alterations separately in the three major subtypes of frontotemporal lobar degeneration (FTLD), which includes behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA). This study was to investigate to what extent the pattern of white matter microstructural alterations in FTLD subtypes mirrors the pattern of brain atrophy, and to compare the ability of various diffusion tensor imaging (DTI) indices in characterizing FTLD patients, as well as to determine whether DTI measures provide greater classification power for FTLD than measuring brain atrophy. Twenty-five patients with FTLD (13 with bvFTD, 6 with SD, and 6 with PNFA) and 19 healthy age-matched control subjects underwent both structural MRI and DTI scans. Measurements of regional brain atrophy were based on T1-weighted MRI data and voxel-based morphometry. Measurements of regional white matter degradation were based on voxelwise as well as regions-of-interest tests of DTI variations, expressed as fractional anisotropy, axial diffusivity, and radial diffusivity. Compared to controls, bvFTD, SD, and PNFA patients each exhibited characteristic regional patterns of brain atrophy and white matter damage. DTI overall provided significantly greater accuracy for FTLD classification than brain atrophy. Moreover, radial diffusivity was more sensitive in assessing white matter damage in FTLD than other DTI indices. The findings suggest that DTI in general and radial diffusivity in particular are more powerful measures for the classification of FTLD patients from controls than brain atrophy.

A longitudinal study of atrophy in amnestic mild cognitive impairment and normal aging revealed by cortical thickness

In recent years, amnestic mild cognitive impairment (aMCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease. An understanding of the pathology of aMCI may benefit the development of effective clinical treatments for dementia. In this work, we measured the cortical thickness of 109 aMCI subjects and 99 normal controls (NC) twice over two years. The longitudinal changes and the cross-sectional differences between the two types of participants were explored using the vertex thickness values. The thickness of the cortex in aMCI was found significantly reduced in both longitudinal and between-group comparisons, mainly in the temporal lobe, superolateral parietal lobe and some regions of the frontal cortices. Compared to NC, the aMCI showed a significantly high atrophy rate in the left lateral temporal lobe and left parahippocampal gyrus over two years. Additionally, a significant positive correlation between brain atrophy and the decline of Mini-Mental State Examination (MMSE) scores was also found in the left superior and left middle temporal gyrus in aMCI. These findings demonstrated specific longitudinal spatial patterns of cortical atrophy in aMCI and NC. The higher atrophy rate in aMCI might be responsible for the accelerated functional decline in the aMCI progression process.

Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia

Based on the recent literature and collective experience, an international consortium developed revised guidelines for the diagnosis of behavioural variant frontotemporal dementia. The validation process retrospectively reviewed clinical records and compared the sensitivity of proposed and earlier criteria in a multi-site sample of patients with pathologically verified frontotemporal lobar degeneration. According to the revised criteria, 'possible' behavioural variant frontotemporal dementia requires three of six clinically discriminating features (disinhibition, apathy/inertia, loss of sympathy/empathy, perseverative/compulsive behaviours, hyperorality and dysexecutive neuropsychological profile). 'Probable' behavioural variant frontotemporal dementia adds functional disability and characteristic neuroimaging, while behavioural variant frontotemporal dementia 'with definite frontotemporal lobar degeneration' requires histopathological confirmation or a pathogenic mutation. Sixteen brain banks contributed cases meeting histopathological criteria for frontotemporal lobar degeneration and a clinical diagnosis of behavioural variant frontotemporal dementia, Alzheimer's disease, dementia with Lewy bodies or vascular dementia at presentation. Cases with predominant primary progressive aphasia or extra-pyramidal syndromes were excluded. In these autopsy-confirmed cases, an experienced neurologist or psychiatrist ascertained clinical features necessary for making a diagnosis according to previous and proposed criteria at presentation. Of 137 cases where features were available for both proposed and previously established criteria, 118 (86%) met 'possible' criteria, and 104 (76%) met criteria for 'probable' behavioural variant frontotemporal dementia. In contrast, 72 cases (53%) met previously established criteria for the syndrome (P < 0.001 for comparison with 'possible' and 'probable' criteria). Patients who failed to meet revised criteria were significantly older and most had atypical presentations with marked memory impairment. In conclusion, the revised criteria for behavioural variant frontotemporal dementia improve diagnostic accuracy compared with previously established criteria in a sample with known frontotemporal lobar degeneration. Greater sensitivity of the proposed criteria may reflect the optimized diagnostic features, less restrictive exclusion features and a flexible structure that accommodates different initial clinical presentations. Future studies will be needed to establish the reliability and specificity of these revised diagnostic guidelines.

Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia

Based on the recent literature and collective experience, an international consortium developed revised guidelines for the diagnosis of behavioural variant frontotemporal dementia. The validation process retrospectively reviewed clinical records and compared the sensitivity of proposed and earlier criteria in a multi-site sample of patients with pathologically verified frontotemporal lobar degeneration. According to the revised criteria, 'possible' behavioural variant frontotemporal dementia requires three of six clinically discriminating features (disinhibition, apathy/inertia, loss of sympathy/empathy, perseverative/compulsive behaviours, hyperorality and dysexecutive neuropsychological profile). 'Probable' behavioural variant frontotemporal dementia adds functional disability and characteristic neuroimaging, while behavioural variant frontotemporal dementia 'with definite frontotemporal lobar degeneration' requires histopathological confirmation or a pathogenic mutation. Sixteen brain banks contributed cases meeting histopathological criteria for frontotemporal lobar degeneration and a clinical diagnosis of behavioural variant frontotemporal dementia, Alzheimer's disease, dementia with Lewy bodies or vascular dementia at presentation. Cases with predominant primary progressive aphasia or extra-pyramidal syndromes were excluded. In these autopsy-confirmed cases, an experienced neurologist or psychiatrist ascertained clinical features necessary for making a diagnosis according to previous and proposed criteria at presentation. Of 137 cases where features were available for both proposed and previously established criteria, 118 (86%) met 'possible' criteria, and 104 (76%) met criteria for 'probable' behavioural variant frontotemporal dementia. In contrast, 72 cases (53%) met previously established criteria for the syndrome (P < 0.001 for comparison with 'possible' and 'probable' criteria). Patients who failed to meet revised criteria were significantly older and most had atypical presentations with marked memory impairment. In conclusion, the revised criteria for behavioural variant frontotemporal dementia improve diagnostic accuracy compared with previously established criteria in a sample with known frontotemporal lobar degeneration. Greater sensitivity of the proposed criteria may reflect the optimized diagnostic features, less restrictive exclusion features and a flexible structure that accommodates different initial clinical presentations. Future studies will be needed to establish the reliability and specificity of these revised diagnostic guidelines.

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.

Structural abnormalities in the cortex of the rTg4510 mouse model of tauopathy: a light and electron microscopy study

rTg4510 transgenic (TG) mice overexpress mutant (P301L) human tau protein. We have compared the dorsal premotor cortex of TG mice versus non-transgenic (NT) mice at 4, 9, and 13 months of age, using light (LM) and electron microscopy (EM). LM assessment shows that cortical thickness in TG mice is reduced by almost 50% from 4 to 13 months of age, while at the same time layer I thickness is reduced by 80%, with most of the cortical thinning occurring between 4 and 9 months. In TG mice, spherical, empty vacuoles, up to 60 μm in diameter, become increasingly abundant with age and by 9 months, pyramidal and non-pyramidal neurons with large intracellular tangles of tau protein are common throughout the cortex. These tangles occur in the perikarya; we have not observed them entering into cellular processes, nor have we observed ghost tangles in the intercellular matrix. In TG mice, nerve fiber pathology is widespread by 13 months, and split myelin sheaths, ballooned sheaths, and swollen axons containing mitochondrial aggregations are all common. Astrocytes become increasingly filled with glial filaments as TG mice age, and microglial cells almost always contain phagocytic inclusions. However, no glial cells are seen to contain tau in their cytoplasm. These observations add to the base of knowledge available on this commonly employed model of tauopathy.

Understanding higher level gait disturbances in mild dementia in order to improve rehabilitation: 'last in-first out'

Predicting and anticipating disturbances in higher level gait is particularly relevant for patients with dementia as higher level gait appears to be closely related to higher level cognitive functioning. A phenomenon that could contribute to the understanding and prediction of disturbances in higher level gait and gait-related motor activity in the various subtypes of dementia is paraphrased as 'last in-first out'. 'Last in-first out' refers to the principle that neural circuits that mature late in development are the most vulnerable to neurodegeneration. The strength of relating symptoms to the 'last in-first out' principle is that a future symptom can be predicted and anticipated in a therapeutic way, even if the disease process has not already started. Therefore, the aim of this review is to provide new strategies for rehabilitation of higher level gait disturbances in dementia based upon the 'last in-first out' principle. These new strategies emerge from five neural networks: the superior longitudinal fasciculus, the uncinate fasciculus, the fronto-cerebellar and fronto-striatal connections, and the cingulum.

Anterior insula degeneration in frontotemporal dementia

The human anterior insula is anatomically and functionally heterogeneous, containing key nodes within distributed speech–language and viscero-autonomic/social–emotional networks. The frontotemporal dementias selectively target these large-scale systems, leading to at least three distinct clinical syndromes. Examining these disorders, researchers have begun to dissect functions which rely on specific insular nodes and networks. In the behavioral variant of frontotemporal dementia, early-stage frontoinsular degeneration begets progressive “Salience Network” breakdown that leaves patients unable to model the emotional impact of their own actions or inactions. Ongoing studies seek to clarify local microcircuit- and cellular-level factors that confer selective frontoinsular vulnerability. The search for frontotemporal dementia treatments will depend on a rich understanding of insular biology and could help clarify specialized human language, social, and emotional functions.

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.