Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease

Three dimensional and four dimensional live imaging to study mechanisms of progressive neurodegeneration

Neurodegenerative diseases are complex and progressive, posing challenges to their study and understanding. Recent advances in microscopy imaging technologies have enabled the exploration of neurons in three spatial dimensions (3D) over time (4D). When applied to 3D cultures, tissues, or animals, these technologies can provide valuable insights into the dynamic and spatial nature of neurodegenerative diseases. This review focuses on the use of imaging techniques and neurodegenerative disease models to study neurodegeneration in 4D. Imaging techniques such as confocal microscopy, two-photon microscopy, miniscope imaging, light sheet microscopy, and robotic microscopy offer powerful tools to visualize and analyze neuronal changes over time in 3D tissue. Application of these technologies to in vitro models of neurodegeneration such as mouse organotypic culture systems and human organoid models provide versatile platforms to study neurodegeneration in a physiologically relevant context. Additionally, use of 4D imaging in vivo, including in mouse and zebrafish models of neurodegenerative diseases, allows for the investigation of early dysfunction and behavioral changes associated with neurodegeneration. We propose that these studies have the power to overcome the limitations of two-dimensional monolayer neuronal cultures and pave the way for improved understanding of the dynamics of neurodegenerative diseases and the development of effective therapeutic strategies.

Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature

BACKGROUND

Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS

We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS

We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS

fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Functional Organization of Auditory and Reward Systems in Aging

The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (n = 24) and older (n = 24) adults, using whole-brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared with older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was higher when listening to music selected by the participant. These results highlight the roles of aging and reward sensitivity on auditory and reward networks. Results may inform the design of music-based interventions for older adults and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.

The use of neuroimaging techniques in the early and differential diagnosis of dementia

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Introduction

Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers.

Methods

We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses.

Results

Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia.

Discussion

Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Functional connectivity in behavioral variant frontotemporal dementia

INTRODUCTION

Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD.

METHODS

Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD.

RESULTS

(1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread.

CONCLUSIONS

FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.

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.

Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

An important goal in neuroscience is to elucidate the causal relationships between the brain's different regions. This can help reveal the brain's functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer's disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.

Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging

The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning

Alzheimer’s disease (AD) is the most common type of dementia, and depression is a risk factor for developing AD. Epidemiological studies provide a clinical correlation between late-life depression (LLD) and AD. Depression patients generally remit with no residual symptoms, but LLD patients demonstrate residual cognitive impairment. Due to the lack of effective treatments, understanding how risk factors affect the course of AD is essential to manage AD. Advances in neuroimaging, including resting-state functional MRI (fMRI), have been used to address neural systems that contribute to clinical symptoms and functional changes across various psychiatric disorders. Resting-state fMRI studies have contributed to understanding each of the two diseases, but the link between LLD and AD has not been fully elucidated. This review focuses on three crucial and well-established networks in AD and LLD and discusses the impacts on cognitive decline, clinical symptoms, and prognosis. Three networks are the (1) default mode network, (2) executive control network, and (3) salience network. The multiple properties emphasized here, relevant for the hypothesis of the linkage between LLD and AD, will be further developed by ongoing future studies.

Distinct network topology in Alzheimer's disease and behavioral variant frontotemporal dementia

Background

Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits.

Methods

In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled.

Results

Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes.

Conclusions

Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

Concussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

Resting State Functional Connectivity Is Decreased Globally Across the C9orf72 Mutation Spectrum

A repeat expansion mutation in the C9orf72 gene causes amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), or symptoms of both, and has been associated with gray and white matter changes in brain MRI scans. We used graph theory to examine the network properties of brain function at rest in a population of mixed-phenotype C9orf72 mutation carriers (C9+). Twenty-five C9+ subjects (pre-symptomatic, or diagnosed with ALS, behavioral variant FTD (bvFTD), or both ALS and FTD) and twenty-six healthy controls underwent resting state fMRI. When comparing all C9+ subjects with healthy controls, both global and connection-specific decreases in resting state connectivity were observed, with no substantial reorganization of network hubs. However, when analyzing subgroups of the symptomatic C9+ patients, those with bvFTD (with and without comorbid ALS) show remarkable reorganization of hubs compared to patients with ALS alone (without bvFTD), indicating that subcortical regions become more connected in the network relative to other regions. Additionally, network connectivity measures of the right hippocampus and bilateral thalami increased with increasing scores on the Frontal Behavioral Inventory, indicative of worsening behavioral impairment. These results indicate that while C9orf72 mutation carriers across the ALS-FTD spectrum have global decreased resting state brain connectivity, phenotype-specific effects can also be observed at more local network levels.

Multi-dimensional persistent feature analysis identifies connectivity patterns of resting-state brain networks in Alzheimer's disease

OBJECTIVE

The characterization of functional brain network is crucial to understanding the neural mechanisms associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Some studies have shown that graph theoretical analysis could reveal changes of the disease-related brain networks by thresholding edge weights. But the choice of threshold depends on ambiguous cognitive conditions, which leads to the lack of interpretability. Recently, persistent homology (PH) was proposed to record the persistence of topological features of networks across every possible thresholds, reporting a higher sensitivity than graph theoretical features in detecting network-level biomarkers of AD. However, most research on PH focused on 0-dimensional features (persistence of connected components) reflecting the intrinsic topology of the brain network, rather than 1-dimensional features (persistence of cycles) with an interesting neurobiological communication pattern. Our aim is to explore the multi-dimensional persistent features of brain networks in the AD and MCI patients, and further to capture valuable brain connectivity patterns.

APPROACH

We characterized the change rate of the connected component numbers across graph filtration using the functional derivative curves, and examined the persistence landscapes that vectorize the persistence of cycle structures. After that, the multi-dimensional persistent features were validated in disease identification using a K-nearest neighbor algorithm. Furthermore, a connectivity pattern mining framework was designed to capture the disease-specific brain structures.

MAIN RESULTS

We found that the multi-dimensional persistent features can identify statistical group differences, quantify subject-level distances, and yield disease-specific connectivity patterns. Relatively high classification accuracies were received when compared with graph theoretical features.

SIGNIFICANCE

This work represents a conceptual bridge linking complex brain network analysis and computational topology. Our results can be beneficial for providing a complementary objective opinion to the clinical diagnosis of neurodegenerative diseases.

Neuroimaging in genetic frontotemporal dementia and amyotrophic lateral sclerosis

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) have a strong clinical, genetic and pathological overlap. This review focuses on the current understanding of structural, functional and molecular neuroimaging signatures of genetic FTD and ALS. We overview quantitative neuroimaging studies on the most common genes associated with FTD (MAPT, GRN), ALS (SOD1), and both (C9orf72), and summarize visual observations of images reported in the rarer genes (CHMP2B, TARDBP, FUS, OPTN, VCP, UBQLN2, SQSTM1, TREM2, CHCHD10, TBK1).

Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia

Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.

Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.

Covariate Assisted Principal regression for covariance matrix outcomes

In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics.

Association Between Social Cognition Changes and Resting State Functional Connectivity in Frontotemporal Dementia, Alzheimer's Disease, Parkinson's Disease, and Healthy Controls

OBJECTIVE

To determine the relationship between alterations in resting state functional connectivity and social cognition dysfunction among patients with frontotemporal dementia (FTD), Alzheimer's disease (AD), Parkinson's disease (PD), and healthy controls (HC).

METHODS

Fifty-seven participants (FTD = 10, AD = 18, PD = 19, and HC = 10) underwent structural and functional imaging and completed the Awareness of Social Inference Test-Emotion Evaluation Test (TASIT-EET), Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scale, Revised Self-Monitoring Scale (RSMS), Interpersonal Reactivity Index (IRI), and Social Norms Questionnaire (SNQ). A multi-variate pattern analysis (MVPA) was carried out to determine activation differences between the groups. The clusters from the MVPA were used as seeds for the ROI-to-voxel analysis. Relationship between social cognition deficits and uncinate integrity was also investigated.

RESULTS

BOLD signal activation differed among the four groups of AD, PD, FTD, and HC in the left inferior temporal gyrus-anterior division [L-ITG (ant)], right central opercular cortex (R-COp), right supramarginal gyrus, posterior division (R-SMG, post), right angular gyrus (R-AG), and R-ITG. The BOLD co-activation of the L-ITG (ant) with bilateral frontal pole (FP) and paracingulate gyrus was positively associated with IRI-perspective taking (PT) (r = 0.38, p = 0.007), SNQ total (r = 0.37, p = 0.009), and TASIT-EET (r = 0.47, p < 0.001).

CONCLUSION

Patients with neurodegenerative diseases showed alterations in connectivity in brain regions important for social cognition compared with HCs. Functional connectivity correlated with performance on social cognition tasks and alterations could be responsible for some of the social cognition deficits observed in all neurodegenerative diseases.

EEG time signature in Alzheimer´s disease: Functional brain networks falling apart

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EEG microstate topographies differ between controls and memory clinic patients.

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Microstate parameters differ in a gradient-like manner in SCD, MCI and AD patients.

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Changes in topography of microstate class C correlate with CSF Aβ42 levels.

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Changes in topography of microstate class B correlate with CSF p-tau levels.

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EEG microstates detect early disruption of neurocognitive networks in AD.

Spontaneous mental activity is characterized by dynamic alterations of discrete and stabile brain states called functional microstates that are thought to represent distinct steps of human information processing. Electroencephalography (EEG) directly reflects functioning of brain synapses with a uniquely high temporal resolution, necessary for investigation of brain network dynamics. Since synaptic dysfunction is an early event and best correlate of cognitive status and decline in patients along Alzheimer's disease (AD) continuum, EEG microstates might serve as valuable early markers of AD. The present study investigated differences in EEG microstate topographies and parameters (duration, occurrence and contribution) between a large cohort of healthy elderly (n = 308) and memory clinic patients: subjective cognitive decline (SCD, n = 210); mild cognitive impairment (MCI, n = 230) and AD (n = 197) and how they correlate to conventional cerebrospinal fluid (CSF) markers of AD. Four most representative microstate maps assigned as classes A, B (asymmetrical), C and D (symmetrical) were computed from the resting state EEGs since it has been shown previously that this is sufficient to explain most of the resting state EEG data. Statistically different topography of microstate maps were found between the controls and the patient groups for microstate classes A, C and D. Changes in the topography of microstate class C were associated with the CSF Aβ42 levels, whereas changes in the topography of class B were linked with the CSF p-tau levels. Gradient-like increase in the contribution of asymmetrical (A and B) and gradient-like decrease in the contribution of symmetrical (C and D) maps were observed with the more severe stage of cognitive impairment. Our study demonstrated extensive relationship of resting state EEG microstates topographies and parameters with the stage of cognitive impairment and AD biomarkers. Resting state EEG microstates might therefore serve as functional markers of early disruption of neurocognitive networks in patients along AD continuum.

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.

Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.

Differential effects of Down's syndrome and Alzheimer's neuropathology on default mode connectivity

Down's syndrome is a chromosomal disorder that invariably results in both intellectual disability and Alzheimer's disease neuropathology. However, only a limited number of studies to date have investigated intrinsic brain network organisation in people with Down's syndrome, none of which addressed the links between functional connectivity and Alzheimer's disease. In this cross‐sectional study, we employed ¹¹C‐Pittsburgh Compound‐B (PiB) positron emission tomography in order to group participants with Down's syndrome based on the presence of fibrillar beta‐amyloid neuropathology. We also acquired resting state functional magnetic resonance imaging data to interrogate the connectivity of the default mode network; a large‐scale system with demonstrated links to Alzheimer's disease. The results revealed widespread positive connectivity of the default mode network in people with Down's syndrome (n = 34, ages 30–55, median age = 43.5) and a stark lack of anti‐correlation. However, in contrast to typically developing controls (n = 20, ages 30–55, median age = 43.5), the Down's syndrome group also showed significantly weaker connections in localised frontal and posterior brain regions. Notably, while a comparison of the PiB‐negative Down's syndrome group (n = 19, ages 30–48, median age = 41.0) to controls suggested that alterations in default mode connectivity to frontal brain regions are related to atypical development, a comparison of the PiB‐positive (n = 15, ages 39–55, median age = 48.0) and PiB‐negative Down's syndrome groups indicated that aberrant connectivity in posterior cortices is associated with the presence of Alzheimer's disease neuropathology. Such distinct profiles of altered connectivity not only further our understanding of the brain physiology that underlies these two inherently linked conditions but may also potentially provide a biomarker for future studies of neurodegeneration in people with Down's syndrome.

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.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

BACKGROUND/OBJECTIVE

Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.

METHODS

Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).

RESULTS

Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).

CONCLUSION

Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

Exploring quantitative group-wise differentiation of Alzheimer's disease and behavioural variant frontotemporal dementia using tract-specific microstructural white matter and functional connectivity measures at multiple time points

Objectives

This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD).

Methods

Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared.

Results

Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD.

Conclusions

This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD.

Key Points

• The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach.

• Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer’s disease.

• Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer’s disease.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06061-7) contains supplementary material, which is available to authorized users.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

BACKGROUND

Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification.

METHODS

Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC).

RESULTS

The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004).

CONCLUSIONS

FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Structural and functional changes of the visual cortex in early Huntington's disease

Huntington's disease (HD) is an autosomal-dominant inherited neurodegenerative disorder characterized by motor disturbances, psychiatric disturbances, and cognitive impairment. Visual cognitive deficits and atrophy of the posterior cerebral cortex are additionally present in early disease stages. This study aimed to assess the extent of structural and functional brain alterations of the visual cortex in HD gene carriers using different neuroimaging modalities. Structural and functional magnetic resonance imaging data were acquired from 18 healthy controls, 21 premanifest, and 20 manifest HD gene carriers. Voxel-based morphometry (VBM) analysis and cortical thickness measurements were performed to assess structural changes in the visual cortex. Brain function was measured by assessing neuronal connectivity changes in response to visual stimulation and at rest in visual resting-state networks. Multiple linear regression analyses were performed to examine the relationship between visual cognitive function and structural imaging measures. Compared to controls, pronounced atrophy and decreased neuronal function at rest were present in associative visual cortices in manifest HD. The primary visual cortex did not show group differences in cortical thickness and in vascular activity after visual stimulation. Thinning of the associative visual cortex was related to worse visual perceptual function. Premanifest HD gene carriers did not show any differences in brain structure or function compared to controls. This study improves the knowledge on posterior brain changes in HD, as our findings suggest that the primary visual cortex remains preserved, both structurally and functionally, while atrophy of associative visual cortices is present in early HD and linked to clinical visual deficits.

Weighted Symbolic Dependence Metric (wSDM) for fMRI resting-state connectivity: A multicentric validation for frontotemporal dementia

The search for biomarkers of neurodegenerative diseases via fMRI functional connectivity (FC) research has yielded inconsistent results. Yet, most FC studies are blind to non-linear brain dynamics. To circumvent this limitation, we developed a “weighted Symbolic Dependence Metric” (wSDM) measure. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependence measure by symbolic similarity, capturing both linear and non-linear associations. We compared this measure with a linear connectivity metric (Pearson’s R) in its capacity to identify patients with behavioral variant frontotemporal dementia (bvFTD) and controls based on resting-state data. We recruited participants from two international centers with different MRI recordings to assess the consistency of our measure across heterogeneous conditions. First, a seed-analysis comparison of the salience network (a specific target of bvFTD) and the default-mode network (as a complementary control) between patients and controls showed that wSDM yields better identification of resting-state networks. Moreover, machine learning analysis revealed that wSDM yielded higher classification accuracy. These results were consistent across centers, highlighting their robustness despite heterogeneous conditions. Our findings underscore the potential of wSDM to assess fMRI-derived FC data, and to identify sensitive biomarkers in bvFTD.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

Background

Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification.

Methods

Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC).

Results

The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005).

Conclusions

FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks.

Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease.

Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients (n = 107) with control subjects (n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms.

Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found.

Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Functional Connectivity Changes in Behavioral, Semantic, and Nonfluent Variants of Frontotemporal Dementia

Frontotemporal dementia (FTD) affects behavior, language, and personality. This study aims to explore functional connectivity changes in three FTD variants: behavioral (bvFTD), semantic (svPPA), and nonfluent variant (nfvPPA). Seventy-six patients diagnosed with FTD by international criteria and thirty-two controls were investigated. Functional connectivity from resting functional magnetic resonance imaging (fMRI) was estimated for the whole brain. Two types of analysis were done: network basic statistic and topological measures by graph theory. Several hubs in the limbic system and basal ganglia were compromised in the behavioral variant apart from frontal networks. Nonfluent variants showed a major disconnection with respect to the behavioral variant in operculum and parietal inferior. The global efficiency had lower coefficients in nonfluent variants than behavioral variants and controls. Our results support an extensive disconnection among frontal, limbic, basal ganglia, and parietal hubs.

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.

Brain network connectivity differs in early-onset neurodegenerative dementia

OBJECTIVE

To investigate functional brain network architecture in early-onset Alzheimer disease (EOAD) and behavioral variant frontotemporal dementia (bvFTD).

METHODS

Thirty-eight patients with bvFTD, 37 patients with EOAD, and 32 age-matched healthy controls underwent 3D T1-weighted and resting-state fMRI. Graph analysis and connectomics assessed global and local functional topologic network properties, regional functional connectivity, and intrahemispheric and interhemispheric between-lobe connectivity.

RESULTS

Despite similarly extensive cognitive impairment relative to controls, patients with EOAD showed severe global functional network alterations (lower mean nodal strength, local efficiency, clustering coefficient, and longer path length), while patients with bvFTD showed relatively preserved global functional brain architecture. Patients with bvFTD demonstrated reduced nodal strength in the frontoinsular lobe and a relatively focal altered functional connectivity of frontoinsular and temporal regions. Functional connectivity breakdown in the posterior brain nodes, particularly in the parietal lobe, differentiated patients with EOAD from those with bvFTD. While EOAD was associated with widespread loss of both intrahemispheric and interhemispheric functional correlations, bvFTD showed a preferential disruption of the intrahemispheric connectivity.

CONCLUSIONS

Disease-specific patterns of functional network topology and connectivity alterations were observed in patients with EOAD and bvFTD. Graph analysis and connectomics may aid clinical diagnosis and help elucidate pathophysiologic differences between neurodegenerative dementias.

Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r² = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r² = 0.62 (p < 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD.

OCT in Alzheimer's disease: thinning of the RNFL and superior hemiretina

BACKGROUND

Peripapillary retinal nerve fiber layer (pRNFL) and internal macular layer thinning have been demonstrated in Alzheimer's disease (AD) with optical coherence tomography (OCT) studies. The purpose of this study is to compare the pRNFL thickness and overall retinal thickness (RT) in AD patients with non-AD patients, using spectral domain optical coherence tomography (SD-OCT) and determine the sectors most characteristically affected in AD.

METHODS

A cross-sectional study was performed to determine the pRNFL and overall macular RT thicknesses in AD and non-AD patients, attending a tertiary hospital center. For pRNFL, the global and six peripapillary quadrants were calculated, and for overall RT values, the nine Early Treatment Diabetic Retinopathy Study (ETDRS) areas were used. A multiple regression analysis was applied to assess the effects of disease, age, gender, spherical equivalent, visual acuity, intraocular pressure, axial length and blood pressure on pRNFL and overall macular RT.

RESULTS

A total of 202 subjects, including 50 eyes of 50 patients with mild AD (mean age 73.10; SD = 5.36 years) and 152 eyes of 152 patients without AD (mean age 71.03; SD = 4.62 years). After Bonferroni correction, the pRNFL was significantly thinner for the AD group globally and in the temporal superior quadrant (10.76 μm and 20.09 μm mean decrease, respectively). The RT thickness was also decreased in superior sectors S3 and S6 (mean thinning of 9.92 μm and 11.65 μm, respectively). Spearman's correlation coefficient showed a direct association between pRNFL in the temporal superior quadrant and RT in superior S6 and S3 sectors (r_S = 0.41; p < 0.001 and r_S = 0.28; p < 0.001, respectively).

CONCLUSIONS

Patients with AD showed a significant thickness reduction in global and temporal superior quadrants in pRNFL and in superior pericentral and peripheral sectors of RT. These findings may reflect a peripapillary and retinal changes characteristic of AD, suggesting the importance of SD-OCT as a potential adjuvant in early diagnosis of AD. Further studies are needed to understand which retinal layers and macular sectors are more useful as potential ocular biomarker over time in AD.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

Imaging and fluid biomarkers in frontotemporal dementia

Frontotemporal dementia (FTD), the second most common type of presenile dementia, is a heterogeneous neurodegenerative disease characterized by progressive behavioural and/or language problems, and includes a range of clinical, genetic and pathological subtypes. The diagnostic process is hampered by this heterogeneity, and correct diagnosis is becoming increasingly important to enable future clinical trials of disease-modifying treatments. Reliable biomarkers will enable us to better discriminate between FTD and other forms of dementia and to predict disease progression in the clinical setting. Given that different underlying pathologies probably require specific pharmacological interventions, robust biomarkers are essential for the selection of patients with specific FTD subtypes. This Review emphasizes the increasing availability and potential applications of structural and functional imaging biomarkers, and cerebrospinal fluid and blood fluid biomarkers in sporadic and genetic FTD. The relevance of new MRI modalities - such as voxel-based morphometry, diffusion tensor imaging and arterial spin labelling - in the early stages of FTD is discussed, together with the ability of these modalities to classify FTD subtypes. We highlight promising new fluid biomarkers for staging and monitoring of FTD, and underline the importance of large, multicentre studies of individuals with presymptomatic FTD. Harmonization in the collection and analysis of data across different centres is crucial for the implementation of new biomarkers in clinical practice, and will become a great challenge in the next few years.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Gray matter network differences between behavioral variant frontotemporal dementia and Alzheimer's disease

We set out to study whether single-subject gray matter (GM) networks show disturbances that are specific for Alzheimer's disease (AD; n = 90) or behavioral variant frontotemporal dementia (bvFTD; n = 59), and whether such disturbances would be related to cognitive deficits measured with mini-mental state examination and a neuropsychological battery, using subjective cognitive decline subjects as reference. AD and bvFTD patients had a lower degree, connectivity density, clustering, path length, betweenness centrality, and small world values compared with subjective cognitive decline. AD patients had a lower connectivity density than bvFTD patients (F = 5.79, p = 0.02; mean ± standard deviation bvFTD 16.10 ± 1.19%; mean ± standard deviation AD 15.64 ± 1.02%). Lasso logistic regression showed that connectivity differences between bvFTD and AD were specific to 23 anatomical areas, in terms of local GM volume, degree, and clustering. Lower clustering values and lower degree values were specifically associated with worse mini-mental state examination scores and lower performance on the neuropsychological tests. GM showed disease-specific alterations, when comparing bvFTD with AD patients, and these alterations were associated with cognitive deficits.