Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease

Fronto-striatal alterations correlate with apathy severity in behavioral variant frontotemporal dementia

Structural and functional changes in cortical and subcortical regions have been reported in behavioral variant frontotemporal dementia (bvFTD), however, a multimodal approach may provide deeper insights into the neural correlates of neuropsychiatric symptoms. In this multicenter study, we measured cortical thickness (CTh) and subcortical volumes to identify structural abnormalities in 37 bvFTD patients, and 37 age- and sex-matched healthy controls. For seed regions with significant structural changes, whole-brain functional connectivity (FC) was examined in a sub-cohort of N = 22 bvFTD and N = 22 matched control subjects to detect complementary alterations in brain network organization. To explore the functional significance of the observed structural and functional deviations, correlations with clinical and neuropsychological outcomes were tested where available. Significantly decreased CTh was observed in the bvFTD group in caudal middle frontal gyrus, left pars opercularis, bilateral superior frontal and bilateral middle temporal gyrus along with subcortical volume reductions in bilateral basal ganglia, thalamus, hippocampus, and amygdala. Resting-state functional magnetic resonance imaging showed decreased FC in bvFTD between: dorsal striatum and left caudal middle frontal gyrus; putamen and fronto-parietal regions; pallidum and cerebellum. Conversely, bvFTD showed increased FC between: left middle temporal gyrus and paracingulate gyrus; caudate nucleus and insula; amygdala and parahippocampal gyrus. Additionally, cortical thickness in caudal, lateral and superior frontal regions as well as caudate nucleus volume correlated negatively with apathy severity scores of the Neuropsychiatry Inventory Questionnaire. In conclusion, multimodal structural and functional imaging indicates that fronto-striatal regions have a considerable influence on the severity of apathy in bvFTD.

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.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

The mediation role of sleep quality in the relationship between cognitive decline and depression

Objectives

Associations between cognitive decline and depression have been inconclusive. We examined 1) whether sleep quality mediates these relationships and 2) which factor of sleep quality mediates these relationships.

Methods

This study utilized baseline data from the 2018 West China Health and Aging Trend study (WCHAT), a large cohort data-set that including participants aged over 50 years old. We defined depression using the 15-item Geriatric Depression Scale (GDS-15). Cognitive status was measured using the Short Portable Mental Status Questionnaire (SPMSQ) and sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). Direct relationships between cognitive decline, sleep quality and depression were assessed using multiple linear regression. Mediation models and structural equation model (SEM) pathway analysis were used to test the mediating role of specific aspects of sleep (e.g., quality, duration) in the relationship between cognitive decline and depression.

Results

Of 6828 participants aged 50 years old or older, the proportion of depression was 17.4%. Regression analysis indicated a total association between cognitive scores (β = 0.251, 95% CI 0.211 to 0.290, p < 0.001) and depression status. After adjusted PSQI scores, the association between cognitive scores and depression status was still significant (β = 0.242, 95% CI 0.203 to 0.281, p < 0.001), indicating a partial mediation effect of sleep quality. Mediation analysis verified sleep quality partially mediate the associations between cognitive decline and depression (indirect effect estimate = 0.0308, bootstrap 95% CI 0.023 to 0.040; direct effect estimate = 0.3124, bootstrap 95% CI 0.269 to 0.350). And daytime dysfunction had a highest mediation effect with a proportion of mediation up to 14.6%.

Conclusions

Sleep quality partially mediated the relationship between cognitive decline and depression. Daytime dysfunction had a highest mediation effect. Further research is necessary to examine the effects of sleep quality on the relationship of cognitive decline and depression.

The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation

INTRODUCTION

While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.

AREAS COVERED

This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models.

EXPERT OPINION

Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.

Try to see it my way - Examining the relationship between visual perspective taking and theory of mind in frontotemporal dementia

The behavioural variant of frontotemporal dementia (bvFTD) is characterised by pronounced alterations in social functioning, including the understanding of others' thoughts and feelings via theory of mind. The emergence of such impairments in other social disorders such as autism and schizophrenia is suggested to reflect an inability to imagine the other person's visual perspective of the world. To our knowledge, relationships between visual perspective taking and theory of mind have not previously been explored in bvFTD. Here, we sought to examine the capacity for visual perspective taking and theory of mind in bvFTD, and to establish their inter-relationships and underlying neural correlates. Fifteen bvFTD patients and 15 healthy Controls completed a comprehensive battery of perspective taking measures, comprising Level 1 ('what') and Level 2 ('how') visual perspective taking tasks, a cartoon task capturing theory of mind, and a questionnaire assessing subjective perspective taking in daily life. Compared with Controls, bvFTD patients displayed significant impairments across all perspective taking measures. These perspective taking impairments, however, were not correlated with one another in bvFTD. Region-of-interest voxel-based morphometry analyses suggested distinct neural correlates for visual perspective taking (inferior frontal gyrus) versus theory of mind (medial prefrontal cortex, precuneus), which appeared to partially overlap with those implicated in subjective perspective taking (inferior frontal gyrus, precuneus, temporoparietal junction). Despite pervasive impairments in all aspects of perspective taking in bvFTD, these did not appear to relate to one another at the behavioural or neural level in our study. Future large-scale studies manipulating discrete aspects of the tasks will help to clarify the neurocognitive mechanisms of, and relationships between, visual perspective taking and theory of mind in bvFTD, along with their real-world implications.

Mechanisms of the Rapid Effects of Ketamine on Depression and Sleep Disturbances: A Narrative Review

Recently, sleep has been recognized as a crucial factor for health and longevity. The daily sleep/wake cycle provides the basis of biorhythm, which controls whole-body homeostasis and homeodynamics. Sleep disturbances can contribute to several physical and psychological disorders, including cardiovascular disease, obesity, depression, and cognitive dysfunction. The clinical use of the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine began in the 1970s. Over the years, physicians have used it as a short-acting anesthetic, analgesic, and antidepressant; however, in-depth research has revealed new possible applications for ketamine, such as for treating sleep disturbances and circadian rhythm disorders. The aim of this narrative review is to examine the literature on the mechanistic role of the antidepressant ketamine in affecting sleep disturbance. Additionally, we discuss the pharmacologic and pharmacokinetic mechanisms of ketamine as an antidepressant and the predictive biomarkers for ketamine’s effect on sleep and cognitive function.

Computationally derived anatomic subtypes of behavioral variant frontotemporal dementia show temporal stability and divergent patterns of longitudinal atrophy

INTRODUCTION

Behavioral variant frontotemporal dementia (bvFTD) can be computationally divided into four distinct anatomic subtypes based on patterns of frontotemporal and subcortical atrophy. To more precisely predict disease trajectories of individual patients, the temporal stability of each subtype must be characterized.

METHODS

We investigated the longitudinal stability of the four previously identified anatomic subtypes in 72 bvFTD patients. We also applied a voxel-wise mixed effects model to examine subtype differences in atrophy patterns across multiple timepoints.

RESULTS

Our results demonstrate the stability of the anatomic subtypes at baseline and over time. While they had common salience network atrophy, each subtype showed distinctive baseline and longitudinal atrophy patterns.

DISCUSSION

Recognizing these anatomically heterogeneous subtypes and their different patterns of atrophy progression in early bvFTD will improve disease course prediction in individual patients. Longitudinal volumetric predictions based on these anatomic subtypes may be used as a more accurate endpoint in treatment trials.

A Narrative Review of Cerebellar Malfunctions and Sleep Disturbances

Cerebellar malfunctions significantly impact the regulation of the sleep–wakefulness transition. The possible mechanism for this effect is still unknown. Evidence on the role of cerebellar processing in the sleep–wake cycle is derived mainly from animal studies, and clinical management of the sleep–wake cycle is also challenging. The purpose of this review is to investigate the role of cerebellar activity during normal sleep and the association between cerebellar dysfunction and sleep disorders. Large-scale, multicenter trials are still needed to confirm these findings and provide early identification and intervention strategies to improve cerebellar function and the sleep quality of patients.

Cortical Complexity Analyses and Their Cognitive Correlate in Alzheimer's Disease and Frontotemporal Dementia

Background:

The changes of cortical structure in Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are usually described in terms of atrophy. However, neurodegenerative diseases may also affect the complexity of cortical shape, such as the fractal dimension of the brain surface.

Objective:

In this study, we aimed at assessing the regional patterns of cortical thickness and fractal dimension changes in a cross-sectional cohort of patients with AD and FTD.

Methods:

Thirty-two people with symptomatic AD-pathology (clinically probable AD, n = 18, and amyloid-positive mild cognitive impairment, n = 14), 24 with FTD and 28 healthy controls underwent high-resolution 3T structural brain MRI. Using surface-based morphometry, we created vertex-wise cortical thickness and fractal dimension maps for group comparisons and correlations with cognitive measures in AD and FTD.

Results:

In addition to the well-established pattern of cortical thinning encompassing temporoparietal regions in AD and frontotemporal areas in FTD, we observed reductions of fractal dimension encompassing cingulate areas and insula for both conditions, but specifically involving orbitofrontal cortex and paracentral gyrus for FTD (FDR p < 0.05). Correlational analyses between fractal dimension and cognition showed that these regions were particularly vulnerable with regards to memory and language impairment, especially in FTD.

Conclusion:

While the present study demonstrates globally similar patterns of fractal dimension changes in AD and FTD, we observed distinct cortical complexity correlates of cognitive domains impairment. Further studies are required to assess cortical complexity measures at earlier disease stages (e.g., in prodromal/asymptomatic carriers of FTD-related gene mutations) and determine whether fractal dimension represents a sensitive imaging marker for prevention and diagnostic strategies.

Generalizing Longitudinal Age Effects on Brain Structure - A Two-Study Comparison Approach

Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults (n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present.

Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions

Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.

More Than Words: Extra-Sylvian Neuroanatomic Networks Support Indirect Speech Act Comprehension and Discourse in Behavioral Variant Frontotemporal Dementia

Indirect speech acts—responding “I forgot to wear my watch today” to someone who asked for the time—are ubiquitous in daily conversation, but are understudied in current neurobiological models of language. To comprehend an indirect speech act like this one, listeners must not only decode the lexical-semantic content of the utterance, but also make a pragmatic, bridging inference. This inference allows listeners to derive the speaker’s true, intended meaning—in the above dialog, for example, that the speaker cannot provide the time. In the present work, we address this major gap by asking non-aphasic patients with behavioral variant frontotemporal dementia (bvFTD, n = 21) and brain-damaged controls with amnestic mild cognitive impairment (MCI, n = 17) to judge simple question-answer dialogs of the form: “Do you want some cake for dessert?” “I’m on a very strict diet right now,” and relate the results to structural and diffusion MRI. Accuracy and reaction time results demonstrate that subjects with bvFTD, but not MCI, are selectively impaired in indirect relative to direct speech act comprehension, due in part to their social and executive limitations, and performance is related to caregivers’ judgment of communication efficacy. MRI imaging associates the observed impairment in bvFTD to cortical thinning not only in traditional language-associated regions, but also in fronto-parietal regions implicated in social and executive cerebral networks. Finally, diffusion tensor imaging analyses implicate white matter tracts in both dorsal and ventral projection streams, including superior longitudinal fasciculus, frontal aslant, and uncinate fasciculus. These results have strong implications for updated neurobiological models of language, and emphasize a core, language-mediated social disorder in patients with bvFTD.

Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework

Alzheimer’s disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer’s disease.

FTD spectrum: Neuroimaging across the FTD spectrum

Frontotemporal dementia is a complex and heterogeneous neurodegenerative disease that encompasses many clinical syndromes, pathological diseases, and genetic mutations. Neuroimaging has played a critical role in our understanding of the underlying pathophysiology of frontotemporal dementia and provided biomarkers to aid diagnosis. Early studies defined patterns of neurodegeneration and hypometabolism associated with the clinical, pathological and genetic aspects of frontotemporal dementia, with more recent studies highlighting how the breakdown of structural and functional brain networks define frontotemporal dementia. Molecular positron emission tomography ligands allowing the in vivo imaging of tau proteins have also provided important insights, although more work is needed to understand the biology of the currently available ligands.

Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease

Background

Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy.

Methods

We selected 523 individuals from ADNI (100 preclinical AD, 288 prodromal AD, 135 AD dementia) with abnormal baseline amyloid PET/CSF and ≥1 year of MRI follow-up. We calculated total and 90 regional GM volumes for 2281 MRI scans (median [IQR]; 4 [3–5] scans per individual over 2 [1.6–4] years) and used linear mixed models to investigate atrophy as a function of time, aging and decline on MMSE. Analyses included clinical stage as interaction with the predictor and were corrected for baseline age, sex, education, field strength and total intracranial volume. We repeated analyses for a sample of participants with normal amyloid (n = 387) to assess whether associations were specific for amyloid pathology.

Results

Using time or aging as predictors, amyloid abnormal participants annually declined −1.29 ± 0.08 points and − 0.28 ± 0.03 points respectively on the MMSE and −12.23 ± 0.47 cm³ and −8.87 ± 0.34 respectively in total GM volume (p < .001). For the time and age models atrophy was widespread and preclinical and prodromal AD showed similar atrophy patterns. Comparing prodromal AD to AD dementia, AD dementia showed faster atrophy mostly in temporal lobes as modeled with time, while prodromal AD showed faster atrophy in mostly frontoparietal areas as modeled with age (p_FDR < 0.05). Modeling change in GM volume as a function of decline on MMSE, slopes were less steep compared to those based on time and aging (−4.1 ± 0.25 cm³ per MMSE point decline; p < .001) and showed steeper atrophy for prodromal AD compared to preclinical AD in the right inferior temporal gyrus (p < .05) and compared to AD dementia mostly in temporal areas (p_FDR < 0.05). Associations with time, aging and MMSE remained when accounting for these effects in the other models, suggesting that all measures explain part of the variance in GM atrophy. Repeating analyses in amyloid normal individuals, effects for time and aging showed similar widespread anatomical patterns, while associations with MMSE were largely reduced.

Conclusion

Effects of time, aging and MMSE all explained variance in GM atrophy slopes within individuals. Associations with MMSE were weaker than those for time or age, but specific for amyloid pathology. This suggests that at least some of the atrophy observed in time or age models may not be specific to AD.

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Modeling atrophy as a function of time or aging show similar anatomical patterns.

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GM atrophy as a function of time or aging seems nonspecific for amyloid pathology.

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GM atrophy as a function of MMSE shows involvement of different anatomical patterns.

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Atrophy modeled based on time or age was steeper than modeled based on MMSE.

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Atrophy patterns based on MMSE were specific for amyloid pathology.

Neurocognitive mechanisms of theory of mind impairment in neurodegeneration: a transdiagnostic approach

Much of human interaction is predicated upon our innate capacity to infer the thoughts, beliefs, emotions, and perspectives of others, in short, to possess a "theory of mind" (ToM). While the term has evolved considerably since its inception, ToM encompasses our unique ability to apprehend the mental states of others, enabling us to anticipate and predict subsequent behavior. From a developmental perspective, ToM has been a topic of keen research interest, with numerous studies seeking to explicate the origins of this fundamental capacity and its disruption in developmental disorders such as autism. The study of ToM at the opposite end of the lifespan, however, is paradoxically new born, emerging as a topic of interest in its own right comparatively recently. Here, we consider the unique insights afforded by studying ToM capacity in neurodegenerative disorders. Arguing from a novel, transdiagnostic perspective, we consider how ToM vulnerability reflects the progressive degradation of neural circuits specialized for an array of higher-order cognitive processes. This mechanistic approach enables us to consider the common and unique neurocognitive mechanisms that underpin ToM dysfunction across neurodegenerative disorders and for the first time examine its relation to behavioral disturbances across social, intimate, legal, and criminal settings. As such, we aim to provide a comprehensive overview of ToM research in neurodegeneration, the resultant challenges for family members, clinicians, and the legal profession, and future directions worthy of exploration.

Biomarkers for diseases with TDP-43 pathology

The discovery that aggregated transactive response DNA-binding protein 43 kDa (TDP-43) is the major component of pathological ubiquitinated inclusions in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) caused seminal progress in the unveiling of the genetic bases and molecular characteristics of these now so-called TDP-43 proteinopathies. Substantial increase in the knowledge of clinic-pathological coherencies, especially for FTLD variants, could be made in the last decade, but also revealed a considerable complexity of TDP-43 pathology and often a poor correlation of clinical and molecular disease characteristics. To date, an underlying TDP-43 pathology can be predicted only for patients with mutations in the genes C9orf72 and GRN, but is dependent on neuropathological verification in patients without family history, which represent the majority of cases. As etiology-specific therapies for neurodegenerative proteinopathies are emerging, methods to forecast TDP-43 pathology at patients' lifetime are highly required. Here, we review the current status of research pursued to identify specific indicators to predict or exclude TDP-43 pathology in the ALS-FTLD spectrum disorders and findings on candidates for prognosis and monitoring of disease progression in TDP-43 proteinopathies with a focus on TDP-43 with its pathological forms, neurochemical and imaging biomarkers.

Amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD) are characterised by differential activation of ER stress pathways: focus on UPR target genes

The endoplasmic reticulum (ER) plays an important role in maintenance of proteostasis through the unfolded protein response (UPR), which is strongly activated in most neurodegenerative disorders. UPR signalling pathways mediated by IRE1α and ATF6 play a crucial role in the maintenance of ER homeostasis through the transactivation of an array of transcription factors. When activated, these transcription factors induce the expression of genes involved in protein folding and degradation with pro-survival effects. However, the specific contribution of these transcription factors to different neurodegenerative diseases remains poorly defined. Here, we characterised 44 target genes strongly influenced by XBP1 and ATF6 and quantified the expression of a subset of genes in the human post-mortem spinal cord from amyotrophic lateral sclerosis (ALS) cases and in the frontal and temporal cortex from frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD) cases and controls. We found that IRE1α-XBP1 and ATF6 pathways were strongly activated both in ALS and AD. In ALS, XBP1 and ATF6 activation was confirmed by a substantial increase in the expression of both known and novel target genes involved particularly in co-chaperone activity and ER-associated degradation (ERAD) such as DNAJB9, SEL1L and OS9. In AD cases, a distinct pattern emerged, where targets involved in protein folding were more prominent, such as CANX, PDIA3 and PDIA6. These results reveal that both overlapping and disease-specific patterns of IRE1α-XBP1 and ATF6 target genes are activated in AD and ALS, which may be relevant to the development of new therapeutic strategies. Graphical abstract The endoplasmic reticulum (ER) plays an important role in maintenance of proteostasis through the unfolded protein response (UPR). Two major UPR signalling pathways are mediated by IRE1α and ATF6. Here, we demonstrate that these pathways activate differential gene sets in human post-mortem tissues derived from amyotrophic lateral sclerosis (ALS) compared to Alzheimer's disease (AD) cases. Our results identify IRE1α and ATF6 specific targets that can have major implications in the development of new therapeutic strategies and potential biomarkers.

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.

Amsterdam Dementia Cohort: Performing Research to Optimize Care

The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.

Altered cerebral hemodyamics and cortical thinning in asymptomatic carotid artery stenosis

Cortical thinning is a potentially important biomarker, but the pathophysiology in cerebrovascular disease is unknown. We investigated the association between regional cortical blood flow and regional cortical thickness in patients with asymptomatic unilateral high-grade internal carotid artery disease without stroke. Twenty-nine patients underwent high resolution anatomical and single-delay, pseudocontinuous arterial spin labeling magnetic resonance imaging with partial volume correction to assess gray matter baseline flow. Cortical thickness was estimated using Freesurfer software, followed by co-registration onto each patient's cerebral blood flow image space. Paired t-tests assessed regional cerebral blood flow in motor cortex (supplied by the carotid artery) and visual cortex (indirectly supplied by the carotid) on the occluded and unoccluded side. Pearson correlations were calculated between cortical thickness and regional cerebral blood flow, along with age, hypertension, diabetes and white matter hyperintensity volume. Multiple regression and generalized estimating equation were used to predict cortical thickness bilaterally and in each hemisphere separately. Cortical blood flow correlated with thickness in motor cortex bilaterally (p = 0.0002), and in the occluded and unoccluded sides individually; age (p = 0.002) was also a predictor of cortical thickness in the motor cortex. None of the variables predicted cortical thickness in visual cortex. Blood flow was significantly lower on the occluded versus unoccluded side in the motor cortex (p<0.0001) and in the visual cortex (p = 0.018). On average, cortex was thinner on the side of occlusion in motor but not in visual cortex. The association between cortical blood flow and cortical thickness in carotid arterial territory with greater thinning on the side of the carotid occlusion suggests that altered cerebral hemodynamics is a factor in cortical thinning.

Spatial gene expression analysis of neuroanatomical differences in mouse models

MRI is a powerful modality to detect neuroanatomical differences that result from mutations and treatments. Knowing which genes drive these differences is important in understanding etiology, but candidate genes are often difficult to identify. We tested whether spatial gene expression data from the Allen Brain Institute can be used to inform us about genes that cause neuroanatomical differences. For many single-gene-mutation mouse models, we found that affected neuroanatomy was not strongly associated with the spatial expression of the altered gene and there are specific caveats for each model. However, among models with significant neuroanatomical differences from their wildtype controls, the mutated genes had preferential spatial expression in affected neuroanatomy. In mice exposed to environmental enrichment, candidate genes could be identified by a genome-wide search for genes with preferential spatial expression in the altered neuroanatomical regions. These candidates have functions related to learning and plasticity. We demonstrate that spatial gene expression of single-genes is a poor predictor of altered neuroanatomy, but altered neuroanatomy can identify candidate genes responsible for neuroanatomical phenotypes.

Sleep Disturbance, Cognitive Decline, and Dementia: A Review

Approximately half of older people report sleep disturbances, which are associated with various health conditions, including neurodegenerative disease and dementia. Indeed, 60 to 70% of people with cognitive impairment or dementia have sleep disturbances, which are linked to poorer disease prognosis. Sleep disturbances in people with dementia have long been recognized and studied; however, in the past 10 years, researchers have begun to study disturbed sleep, including sleep fragmentation, abnormal sleep duration, and sleep disorders, as risk factors for dementia. In this review the authors summarize evidence linking sleep disturbance and dementia. They describe how specific aspects of sleep (e.g., quality, duration) and the prevalence of clinical sleep disorders (e.g., sleep-disordered breathing, rapid eye movement sleep behavior disorder) change with age; how sleep parameters and sleep disorders are associated with the risk of dementia; how sleep can be disturbed in dementia; and how disturbed sleep affects dementia prognosis. These findings highlight the potential importance of identifying and treating sleep problems and disorders in middle-aged and older adults as a strategy to prevent cognitive decline and dementia. The authors also review recent evidence linking sleep disturbances to the pathophysiology underlying dementing conditions, and briefly summarize available treatments for sleep disorders in people with dementia.

Advances in neuroimaging in frontotemporal dementia

Frontotemporal dementia (FTD) is a clinically and neuroanatomically heterogeneous neurodegenerative disorder with multiple underlying genetic and pathological causes. Whilst initial neuroimaging studies highlighted the presence of frontal and temporal lobe atrophy or hypometabolism as the unifying feature in patients with FTD, more detailed studies have revealed diverse patterns across individuals, with variable frontal or temporal predominance, differing degrees of asymmetry, and the involvement of other cortical areas including the insula and cingulate, as well as subcortical structures such as the basal ganglia and thalamus. Recent advances in novel imaging modalities including diffusion tensor imaging, resting-state functional magnetic resonance imaging and molecular positron emission tomography imaging allow the possibility of investigating alterations in structural and functional connectivity and the visualisation of pathological protein deposition. This review will cover the major imaging modalities currently used in research and clinical practice, focusing on the key insights they have provided into FTD, including the onset and evolution of pathological changes and also importantly their utility as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. Validating neuroimaging biomarkers that are able to accomplish these tasks will be crucial for the ultimate goal of powering upcoming clinical trials by correctly stratifying patient enrolment and providing sensitive markers for evaluating the effects and efficacy of disease-modifying therapies. This review describes the key insights provided by research into the major neuroimaging modalities currently used in research and clinical practice, including what they tell us about the onset and evolution of FTD and how they may be used as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. This article is part of the Frontotemporal Dementia special issue.

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

BACKGROUND

In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data.

METHODS

3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD.

RESULTS

The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, ηp2 = 0.070) and p = 0.001 (β = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks.

CONCLUSIONS

We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

Thinner temporal and parietal cortex is related to incident clinical progression to dementia in patients with subjective cognitive decline

INTRODUCTION

We aimed to investigate if thinner cortex of the Alzheimer's disease (AD)-signature region was related to clinical progression in patients with subjective cognitive decline (SCD).

METHODS

We included 302 SCD patients with clinical follow-up (≥1 year) and three-dimensional T1 magnetic resonance imaging. We estimated AD-signature cortical thickness, consisting of nine frontal, parietal, and temporal gyri and hippocampal volume. We used Cox proportional hazard models (hazard ratios and 95% confidence intervals) to evaluate cortical thickness in relation to clinical progression to mild cognitive impairment (MCI) or dementia.

RESULTS

After a follow-up of the mean (standard deviation) 3 (2) years, 49 patients (16%) showed clinical progression to MCI (n = 32), AD (n = 9), or non-AD dementia (n = 8). Hippocampal volumes, thinner cortex of the AD-signature (hazard ratio [95% confidence interval], 5 [2-17]) and various AD-signature subcomponents were associated with increased risk of clinical progression. Stratified analyses showed that thinner AD-signature cortex was specifically predictive for clinical progression to dementia but not to MCI.

DISCUSSION

In SCD patients, thinner regional cortex is associated with clinical progression to dementia.