Based on the Network Degeneration Hypothesis: Separating Individual Patients with Different Neurodegenerative Syndromes in a Preliminary Hybrid PET/MR Study

The interplay between insomnia symptoms and Alzheimer's Disease across three main brain networks

STUDY OBJECTIVES

Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network (SN), and central executive network (CEN).

METHODS

We selected 320 subjects from the ADNI database and divided by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores.

RESULTS

Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterised by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were non-significant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD.

CONCLUSIONS

We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.

Cerebello-Parietal Functional Connectivity in Amnestic Mild Cognitive Impairment

The role of the cerebellum in amnestic mild cognitive impairment (aMCI), typically a prodromal stage of Alzheimer's disease, is not fully understood. We studied the lobule-specific cerebello-cerebral connectivity in 15 cognitively normal and 16 aMCI using resting-state functional MRI. Our analysis revealed weaker connectivity between the cognitive cerebellar lobules and parietal lobe in aMCI. However, stronger connectivity was observed in the cognitive cerebellar lobules with certain brain regions, including the precuneus cortex, posterior cingulate gyrus, and caudate nucleus in participants with worse cognition. Leveraging these measurable changes in cerebello-parietal functional networks in aMCI could offer avenues for future therapeutic interventions.

Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging

Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.

Alterations of Limbic Structure Volumes in Patients with Obstructive Sleep Apnea

OBJECTIVES

We investigated the change in limbic structure volumes and intrinsic limbic network in patients with obstructive sleep apnea (OSA) compared to healthy controls.

METHODS

We enrolled 26 patients with OSA and 30 healthy controls. They underwent three-dimensional T1-weighted magnetic resonance imaging (MRI) on a 3 T MRI scanner. The limbic structures were analyzed volumetrically using the FreeSurfer program. We examined the intrinsic limbic network using the Brain Analysis with Graph Theory program and compared the groups' limbic structure volumes and intrinsic limbic network.

RESULTS

There were significant differences in specific limbic structure volumes between the groups. The volumes in the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens in patients with OSA were lower than those in healthy controls (right amygdala, 0.102 vs. 0.113%, p = 0.004; right hippocampus, 0.253 vs. 0.281%, p = 0.002; right hypothalamus, 0.028 vs. 0.032%, p = 0.002; right nucleus accumbens, 0.021 vs. 0.024%, p = 0.019; left amygdala, 0.089 vs. 0.098%, p = 0.007; left basal forebrain, 0.020 vs. 0.022%, p = 0.027; left hippocampus, 0.245 vs. 0.265%, p = 0.021; left hypothalamus, 0.028 vs. 0.031%, p = 0.016; left nucleus accumbens, 0.023 vs. 0.027%, p = 0.002). However, there were no significant differences in network measures between the groups.

CONCLUSION

We demonstrate that the volumes of several limbic structures in patients with OSA are significantly lower than those in healthy controls. However, there are no alterations to the intrinsic limbic network. These findings suggest that OSA is one of the risk factors for cognitive impairments.

Frontotemporal Dementia, Where Do We Stand? A Narrative Review

Frontotemporal dementia (FTD) is a neurodegenerative disease of growing interest, since it accounts for up to 10% of middle-age-onset dementias and entails a social, economic, and emotional burden for the patients and caregivers. It is characterised by a (at least initially) selective degeneration of the frontal and/or temporal lobe, generally leading to behavioural alterations, speech disorders, and psychiatric symptoms. Despite the recent advances, given its extreme heterogeneity, an overview that can bring together all the data currently available is still lacking. Here, we aim to provide a state of the art on the pathogenesis of this disease, starting with established findings and integrating them with more recent ones. In particular, advances in the genetics field will be examined, assessing them in relation to both the clinical manifestations and histopathological findings, as well as considering the link with other diseases, such as amyotrophic lateral sclerosis (ALS). Furthermore, the current diagnostic criteria will be explored, including neuroimaging methods, nuclear medicine investigations, and biomarkers on biological fluids. Of note, the promising information provided by neurophysiological investigations, i.e., electroencephalography and non-invasive brain stimulation techniques, concerning the alterations in brain networks and neurotransmitter systems will be reviewed. Finally, current and experimental therapies will be considered.

The Use of Tau PET to Stage Alzheimer Disease According to the Braak Staging Framework

Amyloid-β plaques and neurofibrillary tangles (NFTs) are the 2 histopathologic hallmarks of Alzheimer disease (AD). On the basis of the pattern of NFT distribution in the brain, Braak and Braak proposed a histopathologic staging system for AD. Braak staging provides a compelling framework for staging and monitoring of NFT progression in vivo using PET imaging. Because AD staging remains based on clinical features, there is an unmet need to translate neuropathologic staging to a biologic clinical staging system. Such a biomarker staging system might play a role in staging preclinical AD or in improving recruitment strategies for clinical trials. Here, we review the literature regarding AD staging with the Braak framework using tau PET imaging, here called PET-based Braak staging. Our aim is to summarize the efforts of implementing Braak staging using PET and assess correspondence with the Braak histopathologic descriptions and with AD biomarkers. Methods: We conducted a systematic literature search in May 2022 on PubMed and Scopus combining the terms "Alzheimer" AND "Braak" AND ("positron emission tomography" OR "PET"). Results: The database search returned 262 results, and after assessment for eligibility, 21 studies were selected. Overall, most studies indicate that PET-based Braak staging may be an efficient method to stage AD since it presents an adequate ability to discriminate between phases of the AD continuum and correlates with clinical, fluid, and imaging biomarkers of AD. However, the translation of the original Braak descriptions to tau PET was done taking into account the limitations of this imaging technique. This led to important interstudy variability in the anatomic definitions of Braak stage regions of interest. Conclusion: Refinements in this staging system are necessary to incorporate atypical variants and Braak-nonconformant cases. Further studies are needed to understand the possible applications of PET-based Braak staging to clinical practice and research. Furthermore, there is a need for standardization in the topographic definitions of Braak stage regions of interest to guarantee reproducibility and methodologic homogeneity across studies.

Discriminating Paradoxical and Psychophysiological Insomnia Based on Structural and Functional Brain Images: A Preliminary Machine Learning Study

Insomnia disorder (ID) is a prevalent mental illness. Several behavioral and neuroimaging studies suggested that ID is a heterogenous condition with various subtypes. However, neurobiological alterations in different subtypes of ID are poorly understood. We aimed to assess whether unimodal and multimodal whole-brain neuroimaging measurements can discriminate two commonly described ID subtypes (i.e., paradoxical and psychophysiological insomnia) from each other and healthy subjects. We obtained T1-weighted images and resting-state fMRI from 34 patients with ID and 48 healthy controls. The outcome measures were grey matter volume, cortical thickness, amplitude of low-frequency fluctuation, degree centrality, and regional homogeneity. Subsequently, we applied support vector machines to classify subjects via unimodal and multimodal measures. The results of the multimodal classification were superior to those of unimodal approaches, i.e., we achieved 81% accuracy in separating psychophysiological vs. control, 87% for paradoxical vs. control, and 89% for paradoxical vs. psychophysiological insomnia. This preliminary study provides evidence that structural and functional brain data can help to distinguish two common subtypes of ID from each other and healthy subjects. These initial findings may stimulate further research to identify the underlying mechanism of each subtype and develop personalized treatments for ID in the future.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and ¹⁸F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

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.

Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer's disease: a simultaneous PET/MR study

PURPOSE

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). However, the effect of AD/MCI on the coupling between regional neural activity (measured by regional fluorodeoxyglucose imaging (rFDG)) and inter-regional FC (measured by resting-state functional magnetic resonance imaging (rs-fMRI)) is poorly understood.

METHODS

We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/rs-fMRI and assessed rFDG and inter-regional FC metrics (i.e., clustering coefficient and degree centrality). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between rFDG and inter-regional FC metrics using hierarchical moderated multiple regression analysis. We also tested this effect by considering interaction between disease status and inter-regional FC metrics, as well as interaction between disease status and rFDG.

RESULTS

Our findings revealed that both rFDG and inter-regional FC metrics were disrupted in MCI and AD. Moreover, AD altered the relationship between rFDG and inter-regional FC metrics. In particular, we found that AD moderated the effect of inter-regional FC metrics of the caudate, parahippocampal gyrus, angular gyrus, supramarginal gyrus, frontal pole, inferior temporal gyrus, middle frontal, lateral occipital, supramarginal gyrus, precuneus, and thalamus on predicting their rFDG. On the other hand, AD moderated the effect of rFDG of the parietal operculum on predicting its inter-regional FC metric.

CONCLUSION

Our findings demonstrated that AD decoupled the link between regional neural activity and functional segregation and global connectivity across particular brain regions.

The role of disrupted functional connectivity in aphasia

Language is one of the most complex and specialized higher cognitive processes. Brain damage to the distributed, primarily left-lateralized language network can result in aphasia, a neurologic disorder characterized by receptive and/or expressive deficits in spoken and/or written language. Most often, aphasia is the consequence of stroke-termed poststroke aphasia (PSA)-yet, aphasia can also manifest due to neurodegenerative disease, specifically, a disorder called primary progressive aphasia (PPA). In recent years, functional connectivity neuroimaging studies have provided emerging evidence supporting theories regarding the relationships between language impairments, structural brain damage, and functional network properties in these two disorders. This chapter reviews the current evidence for the "network phenotype of stroke injury" hypothesis (Siegel et al., 2016) as it pertains to PSA and the "network degeneration hypothesis" (Seeley et al., 2009) as it pertains to PPA. Methodologic considerations for functional connectivity studies, limitations of the current functional connectivity literature in aphasia, and future directions are also discussed.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.

The use of positron emission tomography/magnetic resonance imaging in dementia: A literature review

OBJECTIVES

Positron emission tomography-magnetic resonance imaging (PET/MRI) is an emerging hybrid imaging system in clinical nuclear medicine. Research demonstrates a comparative utility to current unimodal and hybrid methods, including PET-computed tomography (PET/CT), in several medical subspecialities such as neuroimaging. The aim of this review is to critically evaluate the literature from 2016 to 2021 using PET/MRI for the investigation of patients with mild cognitive impairment or dementia, and discuss the evidence base for widening its application into clinical practice.

METHODS

A comprehensive literature search using the PubMed database was conducted to retrieve studies using PET/MRI in relation to the topics of mild cognitive impairment, dementia, or Alzheimer's disease between January 2016 and January 2021. This search strategy enabled studies on all dementia types to be included in the analysis. Studies were required to have a minimum of 10 human subjects and incorporate simultaneous PET/MRI.

RESULTS

A total of 116 papers were retrieved, with 39 papers included in the final selection. These were broadly categorised into reviews (12), technical/methodological papers (11) and new data studies (16). For the current review, discussion focused on findings from the new data studies.

CONCLUSIONS

PET/MRI offers additional insight into the underlying anatomical, metabolic and functional changes associated with dementia when compared with unimodal methods and PET/CT, particularly relating to brain regions including the hippocampus and default mode network. Furthermore, the improved diagnostic utility of PET/MRI, as reported by radiologists, offers improved classification of dementia patients, with important implications for clinical management.

The Clinical Spectrum of Young Onset Dementia Points to Its Stochastic Origins

BACKGROUND

Dementia is a major global health problem and the search for improved therapies is ongoing. The study of young onset dementia (YOD)-with onset prior to 65 years-represents a challenge owing to the variety of clinical presentations, pathology, and gene mutations. The advantage of the investigation of YOD is the lack of comorbidities that complicate the clinical picture in older adults. Here we explore the origins of YOD.

OBJECTIVE

To define the clinical diversity of YOD in terms of its demography, range of presentations, neurological examination findings, comorbidities, medical history, cognitive findings, imaging abnormalities both structural and functional, electroencephagraphic (EEG) data, neuropathology, and genetics.

METHODS

A prospective 20-year study of 240 community-based patients referred to specialty neurology clinics established to elucidate the nature of YOD.

RESULTS

Alzheimer's disease (AD; n = 139) and behavioral variant frontotemporal (bvFTD; n = 58) were the most common causes with a mean age of onset of 56.5 years for AD (±1 SD 5.45) and 57.1 years for bvFTD (±1 SD 5.66). Neuropathology showed a variety of diagnoses from multiple sclerosis, Lewy body disease, FTD-MND, TDP-43 proteinopathy, adult-onset leukoencephalopathy with axonal steroids and pigmented glia, corticobasal degeneration, unexplained small vessel disease, and autoimmune T-cell encephalitis. Non-amnestic forms of AD and alternative forms of FTD were discovered. Mutations were only found in 11 subjects (11/240 = 4.6%). APOE genotyping was not divergent between the two populations.

CONCLUSION

There are multiple kinds of YOD, and most are sporadic. These observations point to their stochastic origins.

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

Standard templates are widely used in human neuroimaging processing pipelines to facilitate group-level analyses and comparisons across subjects/populations. MNI-ICBM152 template is the most commonly used standard template, representing an average of 152 healthy young adult brains. However, in patients with neurodegenerative diseases such as frontotemporal dementia (FTD), high atrophy levels lead to significant differences between individuals' brain shapes and MNI-ICBM152 template. Such differences might inevitably lead to registration errors or subtle biases in downstream analyses and results. Disease-specific templates are therefore desirable to reflect the anatomical characteristics of the populations of interest and reduce potential registration errors. Here, we present MNI-FTD136, MNI-bvFTD70, MNI-svFTD36, and MNI-pnfaFTD30, four unbiased average templates of 136 FTD patients, 70 behavioural variant (bv), 36 semantic variant (sv), and 30 progressive nonfluent aphasia (pnfa) variant FTD patients and a corresponding age-matched template of 133 controls (MNI-CN133), along with probabilistic tissue maps for each template. Public availability of these templates will facilitate analyses of FTD cohorts and enable comparisons between different studies in an appropriate common standardized space.

Indication of retrograde tau spreading along Braak stages and functional connectivity pathways

PURPOSE

Tau pathology progression in Alzheimer's disease (AD) is explained through the network degeneration hypothesis and the neuropathological Braak stages; however, the compatibility of these models remains unclear.

METHODS

We utilized [18F]AV-1451 tau-PET scans of 39 subjects with AD and 39 sex-matched amyloid-negative healthy controls (HC) in the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The peak cluster of tau-tracer uptake was identified in each Braak stage of neuropathological tau deposition and used to create a seed-based functional connectivity network (FCN) using 198 HC subjects, to identify healthy networks unaffected by neurodegeneration.

RESULTS

Voxel-wise tau deposition was both significantly higher inside relative to outside FCNs and correlated significantly and positively with levels of healthy functional connectivity. Within many isolated Braak stages and regions, the correlation between tau and intrinsic functional connectivity was significantly stronger than it was across the whole brain. In this way, each peak cluster of tau was related to multiple Braak stages traditionally associated with both earlier and later stages of disease.

CONCLUSION

We show specificity of healthy FCN topography for AD-pathological tau as well as positive voxel-by-voxel correlations between pathological tau and healthy functional connectivity. We propose a model of "up- and downstream" functional tau progression, suggesting that tau pathology evolves along functional connectivity networks not only "downstream" (i.e., along the expected sequence of the established Braak stages) but also in part "upstream" or "retrograde" (i.e., against the expected sequence of the established Braak stages), with pathology in earlier Braak stages intensified by its functional relationship to later disease stages.

Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility

Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.

How is psychological stress linked to sleep quality? The mediating role of functional connectivity between the sensory/somatomotor network and the cingulo-opercular control network

How the functional connectivity of brain networks affects the relationship between psychological stress and sleep quality remains unclear. To better understand the associations between psychological stress, resting-state functional connectivity (RSFC), and sleep quality, we used the RSFC, Pittsburgh Sleep Quality Index (PSQI), and Psychosomatic Tension Relaxation Inventory (PSTRI) to investigate the relationship between psychological stress, sleep quality, and RSFC in four brain networks, the sensory/somatomotor (SM) network, cigulo-opercular control (CO) network, default mode (DM) network, and dorsal attention (DA) network, in a large healthy sample of 315 college students from Southwest University. Results showed that the brain functional connectivity in the SM, CO, DM, and DA networks was significantly correlated to sleep quality. Meanwhile, we also found that the brain functional connectivity between the SM and CO networks partially mediated the relationship between psychological stress and sleep quality, suggesting that psychological stress has an important effect on individuals' sleep quality, and increased functional connectivity between the SM and CO networks provides a neural basis for the association between psychological stress and poor sleep quality.

Connectome Signatures of Hyperexcitation in Cognitively Intact Middle-Aged Female APOE-ε4 Carriers

Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer's disease, leading to "hyperexcitability" in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation-inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation-inhibition balance within each sex and across an age range of 40-60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual Morris Water task) in three regions: the left pars triangularis, left hippocampus, and left isthmus of cingulate gyrus. Increased excitation of memory-related circuits may be evidence of compensatory recruitment of neuronal resources for memory-focused activities. In sum, our results are consistent with known Alzheimer's disease sex differences; in that female APOE-ε4 carriers have globally disrupted excitation-inhibition balance that may confer greater vulnerability to disease neuropathology.

Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies

OBJECTIVE

Imaging studies of major depressive disorder have reported structural and functional abnormalities in a variety of spatially diverse brain regions. Quantitative meta-analyses of this literature, however, have failed to find statistically significant between-study spatial convergence, other than transdiagnostic-only effects. In the present study, the authors applied a novel multimodal meta-analytic approach to test the hypothesis that major depression exhibits spatially convergent structural and functional brain abnormalities.

METHODS

This coordinate-based meta-analysis included voxel-based morphometry (VBM) studies and resting-state voxel-based pathophysiology (VBP) studies of blood flow, glucose metabolism, regional homogeneity, and amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF). Input data were grouped into three primary meta-analytic classes: gray matter atrophy, increased function, and decreased function in patients with major depression relative to healthy control subjects. In secondary meta-analyses, the data were grouped across primary categories, and in tertiary analyses, by medication status and absence of psychiatric comorbidity. Activation likelihood estimation was used for all analyses.

RESULTS

A total of 92 publications reporting 152 experiments were identified, collectively representing 2,928 patients with major depressive disorder. The primary analyses detected no convergence across studies. The secondary analyses identified portions of the subgenual cingulate cortex, hippocampus, amygdala, and putamen as demonstrating convergent abnormalities. The tertiary analyses (clinical subtypes) showed improved convergence relative to the secondary analyses.

CONCLUSIONS

Coordinate-based meta-analysis identified spatially convergent structural (VBM) and functional (VBP) abnormalities in major depression. The findings suggest replicable neuroimaging features associated with major depression, beyond the transdiagnostic effects reported in previous meta-analyses, and support a continued research focus on the subgenual cingulate and other selected regions' role in depression.

Network degeneration in Parkinson’s disease: multimodal imaging of nigro-striato-cortical dysfunction

The spreading hypothesis of neurodegeneration assumes an expansion of neural pathologies along existing neural pathways. Multimodal neuroimaging studies have demonstrated distinct topographic patterns of cerebral pathologies in neurodegeneration. For Parkinson’s disease the hypothesis so far rests largely on histopathological evidence of α-synuclein spreading in a characteristic pattern and progressive nigrostriatal dopamine depletion. Functional consequences of nigrostriatal dysfunction on cortical activity remain to be elucidated. Our goal was to investigate multimodal imaging correlates of degenerative processes in Parkinson’s disease by assessing dopamine depletion and its potential effect on striatocortical connectivity networks and cortical metabolism in relation to parkinsonian symptoms. We combined 18F-DOPA-PET, 18F-fluorodeoxyglucose (FDG)-PET and resting state functional MRI to multimodally characterize network alterations in Parkinson’s disease. Forty-two patients with mild-to-moderate stage Parkinson’s disease and 14 age-matched healthy control subjects underwent a multimodal imaging protocol and comprehensive clinical examination. A voxel-wise group comparison of 18F-DOPA uptake identified the exact location and extent of putaminal dopamine depletion in patients. Resulting clusters were defined as seeds for a seed-to-voxel functional connectivity analysis. 18F-FDG metabolism was compared between groups at a whole-brain level and uptake values were extracted from regions with reduced putaminal connectivity. To unravel associations between dopaminergic activity, striatocortical connectivity, glucose metabolism and symptom severity, correlations between normalized uptake values, seed-to-cluster β-values and clinical parameters were tested while controlling for age and dopaminergic medication. Aside from cortical hypometabolism, 18F-FDG-PET data for the first time revealed a hypometabolic midbrain cluster in patients with Parkinson’s disease that comprised caudal parts of the bilateral substantia nigra pars compacta. Putaminal dopamine synthesis capacity was significantly reduced in the bilateral posterior putamen and correlated with ipsilateral nigral 18F-FDG uptake. Resting state functional MRI data indicated significantly reduced functional connectivity between the dopamine depleted putaminal seed and cortical areas primarily belonging to the sensorimotor network in patients with Parkinson’s disease. In the inferior parietal cortex, hypoconnectivity in patients was significantly correlated with lower metabolism (left P = 0.021, right P = 0.018). Of note, unilateral network alterations quantified with different modalities corresponded with contralateral motor impairments. In conclusion, our results support the hypothesis that degeneration of nigrostriatal fibres functionally impairs distinct striatocortical connections, disturbing the efficient interplay between motor processing areas and impairing motor control in patients with Parkinson’s disease. The present study is the first to reveal trimodal evidence for network-dependent degeneration in Parkinson’s disease by outlining the impact of functional nigrostriatal pathway impairment on striatocortical functional connectivity networks and cortical metabolism.

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

INTRODUCTION

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

METHODS

We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.

RESULTS

Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).

DISCUSSION

This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

Early-life adversity and neurological disease: age-old questions and novel answers

Neurological illnesses, including cognitive impairment, memory decline and dementia, affect over 50 million people worldwide, imposing a substantial burden on individuals and society. These disorders arise from a combination of genetic, environmental and experiential factors, with the latter two factors having the greatest impact during sensitive periods in development. In this Review, we focus on the contribution of adverse early-life experiences to aberrant brain maturation, which might underlie vulnerability to cognitive brain disorders. Specifically, we draw on recent robust discoveries from diverse disciplines, encompassing human studies and experimental models. These discoveries suggest that early-life adversity, especially in the perinatal period, influences the maturation of brain circuits involved in cognition. Importantly, new findings suggest that fragmented and unpredictable environmental and parental signals comprise a novel potent type of adversity, which contributes to subsequent vulnerabilities to cognitive illnesses via mechanisms involving disordered maturation of brain 'wiring'.

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.

Overlapping and distinct neural metabolic patterns related to impulsivity and hypomania in Parkinson's disease

Impulsivity and hypomania are common non-motor features in Parkinson's disease (PD). The aim of this study was to find the overlapping and distinct neural correlates of these symptoms in PD. Symptoms of impulsivity and hypomania were assessed in 24 PD patients using the Barratt Impulsiveness Scale (BIS-11) and Self-Report Manic Inventory (SRMI), respectively. In addition, fluorodeoxyglucose positron emission tomography (FDG-PET) imaging for each individual was performed. We conducted two separate multiple regression analyses for BIS-11 and SRMI scores with FDG-PET data to identify the brain regions that are associated with both impulsivity and hypomania scores, as well as those exclusive to each symptom. Then, seed-based functional connectivity analyses on healthy subjects identified the areas connected to each of the exclusive regions and the overlapping region, used as seeds. We observed a positive association between BIS-11 and SRMI scores and neural metabolism only in the prefrontal areas. Conjunction analysis revealed an overlapping region in the middle frontal gyrus. Regions exclusive to impulsivity were found in the medial part of the right superior frontal gyrus and regions exclusive to hypomania were in the right superior frontal gyrus, right precentral gyrus and right paracentral lobule. Connectivity patterns of seeds exclusively related to impulsivity were different from those for hypomania in healthy brains. These results provide evidence of both overlapping and distinct regions linked with impulsivity and hypomania scores in PD. The exclusive regions for each characteristic are connected to specific intrinsic functional networks.

Differential intrinsic functional connectivity changes in semantic variant primary progressive aphasia

The semantic variant of primary progressive aphasia (svPPA) is a clinical syndrome characterized by semantic memory deficits with relatively preserved motor speech, syntax, and phonology. There is consistent evidence linking focal neurodegeneration of the anterior temporal lobes (ATL) to the semantic deficits observed in svPPA. Less is known about large-scale functional connectivity changes in this syndrome, particularly regarding the interplay between affected and spared language networks that leads to the unique cognitive dissociations typical of svPPA. Using whole-brain, seed-based connectivity on task-free Magnetic Resonance Imaging (MRI) data, we studied connectivity of networks anchored to three left-hemisphere regions crucially involved in svPPA symptomatology: ATL just posterior to the main atrophic area, opercular inferior frontal gyrus, and posterior inferior temporal lobe. First, in 32 healthy controls, these seeds isolated three networks: a ventral semantic network involving anterior middle temporal and angular gyri, a dorsal articulatory-phonological system involving inferior frontal and supramarginal regions, and a third functional connection between posterior inferior temporal and intraparietal regions likely involved in linking visual and linguistic processes. We then compared connectivity strength of these three networks between 16 svPPA patients and the 32 controls. In svPPA, decreased functional connectivity in the ventral semantic network correlated with weak semantic skills, while connectivity of the network seeded from the posterior inferior temporal lobe, though not significantly different between the two groups, correlated with pseudoword reading skills. Increased connectivity between the inferior frontal gyrus and the superior portion of the angular gyrus suggested possible adaptive changes. Our findings have two main implications. First, they support a functional subdivision of the left IPL based on its connectivity to specific language-related regions. Second, the unique neuroanatomical and linguistic profile observed in svPPA provides a compelling model for the functional interplay of these networks, being either up- or down- regulated in response to disease.

Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability

Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.

Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA

Non-fluent/agrammatic primary progressive aphasia (nfvPPA) is caused by neurodegeneration within the left fronto-insular speech and language production network (SPN). Graph theory is a branch of mathematics that studies network architecture (topology) by quantifying features based on its elements (nodes and connections). This approach has been recently applied to neuroimaging data to explore the complex architecture of the brain connectome, though few studies have exploited this technique in PPA. Here, we used graph theory on functional MRI resting state data from a group of 20 nfvPPA patients and 20 matched controls to investigate topological changes in response to focal neurodegeneration. We hypothesized that changes in the network architecture would be specific to the affected SPN in nfvPPA, while preserved in the spared default mode network (DMN). Topological configuration was quantified by hub location and global network metrics. Our findings showed a less efficiently wired and less optimally clustered SPN, while no changes were detected in the DMN. The SPN in the nfvPPA group showed a loss of hubs in the left fronto-parietal-temporal area and new critical nodes in the anterior left inferior-frontal and right frontal regions. Behaviorally, speech production score and rule violation errors correlated with the strength of functional connectivity of the left (lost) and right (new) regions respectively. This study shows that focal neurodegeneration within the SPN in nfvPPA is associated with network-specific topological alterations, with the loss and gain of crucial hubs and decreased global efficiency that were better accounted for through functional rather than structural changes. These findings support the hypothesis of selective network vulnerability in nfvPPA and may offer biomarkers for future behavioral intervention.

Combined PET/MRI: Global Warming-Summary Report of the 6th International Workshop on PET/MRI, March 27-29, 2017, Tübingen, Germany

The 6th annual meeting to address key issues in positron emission tomography (PET)/magnetic resonance imaging (MRI) was held again in Tübingen, Germany, from March 27 to 29, 2017. Over three days of invited plenary lectures, round table discussions and dialogue board deliberations, participants critically assessed the current state of PET/MRI, both clinically and as a research tool, and attempted to chart future directions. The meeting addressed the use of PET/MRI and workflows in oncology, neurosciences, infection, inflammation and chronic pain syndromes, as well as deeper discussions about how best to characterise the tumour microenvironment, optimise the complementary information available from PET and MRI, and how advanced data mining and bioinformatics, as well as information from liquid biomarkers (circulating tumour cells and nucleic acids) and pathology, can be integrated to give a more complete characterisation of disease phenotype. Some issues that have dominated previous meetings, such as the accuracy of MR-based attenuation correction (AC) of the PET scan, were finally put to rest as having been adequately addressed for the majority of clinical situations. Likewise, the ability to standardise PET systems for use in multicentre trials was confirmed, thus removing a perceived barrier to larger clinical imaging trials. The meeting openly questioned whether PET/MRI should, in all cases, be used as a whole-body imaging modality or whether in many circumstances it would best be employed to give an in-depth study of previously identified disease in a single organ or region. The meeting concluded that there is still much work to be done in the integration of data from different fields and in developing a common language for all stakeholders involved. In addition, the participants advocated joint training and education for individuals who engage in routine PET/MRI. It was agreed that PET/MRI can enhance our understanding of normal and disrupted biology, and we are in a position to describe the in vivo nature of disease processes, metabolism, evolution of cancer and the monitoring of response to pharmacological interventions and therapies. As such, PET/MRI is a key to advancing medicine and patient care.

PET/MR Imaging: New Frontier in Alzheimer's Disease and Other Dementias

Alzheimer's disease (AD) is the most common form of dementia; a progressive neurodegenerative disease that currently lacks an effective treatment option. Early and accurate diagnosis, in addition to quick elimination of differential diagnosis, allows us to provide timely treatments that delay the progression of AD. Imaging plays an important role for the early diagnosis of AD. The newly emerging PET/MR imaging strategies integrate the advantages of PET and MR to diagnose and monitor AD. This review introduces the development of PET/MR imaging systems, technical considerations of PET/MR imaging, special considerations of PET/MR in AD, and the system's potential clinical applications and future perspectives in AD.

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.

Neurologic Applications of PET/MR Imaging

PET/MR imaging benefits neurologic clinical care and research by providing spatially and temporally matched anatomic MR imaging, advanced MR physiologic imaging, and metabolic PET imaging. MR imaging sequences and PET tracers can be modified to target physiology specific to a neurologic disease process, with applications in neurooncology, epilepsy, dementia, cerebrovascular disease, and psychiatric and neurologic research. Simultaneous PET/MR imaging provides efficient acquisition of multiple temporally matched datasets, and opportunities for motion correction and improved anatomic assignment of PET data. Current challenges include optimizing MR imaging-based attenuation correction and necessity for dual expertise in PET and MR imaging.

Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Studies using resting-state functional magnetic resonance imaging (fMRI) to investigate underlying pathophysiology of motor and non-motor symptoms in PD yielded largely inconsistent results. This quantitative neuroimaging meta-analysis aims to identify consistent abnormal intrinsic functional patterns in PD across studies. We used PubMed to retrieve suitable resting-state studies and stereotactic data were extracted from 28 individual between-group comparisons. Convergence across their findings was tested using the activation likelihood estimation (ALE) approach. We found convergent evidence for intrinsic functional disturbances in bilateral inferior parietal lobule (IPL) and the supramarginal gyrus in PD patients compared to healthy subjects. In follow-up task-based and task-independent functional connectivity (FC) analyses using two independent healthy subject data sets, we found that the regions showing convergent aberrations in PD formed an interconnected network mainly with the default mode network (DMN). Behavioral characterization of these regions using the BrainMap database suggested associated dysfunction of perception and executive processes. Taken together, our findings highlight the role of parietal cortex in the pathophysiology of PD.

Functional reorganization in obstructive sleep apnoea and insomnia: A systematic review of the resting-state fMRI

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Resting state functional MRI studies is a promising non-invasive tool for better understanding of the pathophysiology of sleep disorders.

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The salience network is involved in hyperarousal and affective symptoms in insomnia.

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The posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

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Disruption of intrinsic networks have been demonstrated in major depression, which is a common co-morbidity of sleep disorders.

Functional neuroimaging techniques have accelerated progress in the study of sleep disorders. Considering the striking prevalence of these disorders in the general population, however, as well as their strong bidirectional relationship with major neuropsychiatric disorders, including major depressive disorder, their numbers are still surprisingly low. This review examines the contribution of resting state functional MRI to current understanding of two major sleep disorders, insomnia disorder and obstructive sleep apnoea. An attempt is made to learn from parallels of previous resting state functional neuroimaging findings in major depressive disorder. Moreover, shared connectivity biomarkers are suggested for each of the sleep disorders. Taken together, despite some inconsistencies, the synthesis of findings to date highlights the importance of the salience network in hyperarousal and affective symptoms in insomnia. Conversely, dysfunctional connectivity of the posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Purpose

Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms.

Materials & methods

Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis.

Results

Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach.

Conclusion

Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

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Diagnostic criteria for behavioral variant frontotemporal dementia include imaging.

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Study validates MRI's potential to predict diagnosis with machine learning algorithms.

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Support vector machine classification enabled high classification accuracy.

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Accuracy was higher in disease-specific than whole-brain approaches.

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Structural MRI can individually identify behavioral variant frontotemporal dementia.

Structural and functional neural adaptations in obstructive sleep apnea: An activation likelihood estimation meta-analysis

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The right basolateral amygdala, the hippocampus and the right insular cortex are important nodes in obstructive sleep apnea (OSA).

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Functional characterization of these regions suggested associated dysfunction of emotional, sensory, and limbic processes in OSA.

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Connectivity analysis demonstrated that these regions are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus.

Obstructive sleep apnea (OSA) is a common multisystem chronic disorder. Functional and structural neuroimaging has been widely applied in patients with OSA, but these studies have often yielded diverse results. The present quantitative meta-analysis aims to identify consistent patterns of abnormal activation and grey matter loss in OSA across studies. We used PubMed to retrieve task/resting-state functional magnetic resonance imaging and voxel-based morphometry studies. Stereotactic data were extracted from fifteen studies, and subsequently tested for convergence using activation likelihood estimation. We found convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula. Functional characterization of these regions using the BrainMap database suggested associated dysfunction of emotional, sensory, and limbic processes. Assessment of task-based co-activation patterns furthermore indicated that the two regions obtained from the meta-analysis are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus. Taken together, our findings highlight the role of right amygdala, hippocampus and insula in the abnormal emotional and sensory processing in OSA.

Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis

An objective biomarker for early identification and accurate differential diagnosis of amyotrophic lateral sclerosis (ALS) is lacking. (18)F-FDG PET brain imaging with advanced statistical analysis may provide a tool to facilitate this. The objective of this work was to validate volume-of-interest (VOI) and voxel-based (using a support vector machine [SVM] approach) (18)F-FDG PET analysis methods to differentiate ALS from controls in an independent prospective large cohort, using a priori-derived classifiers. Furthermore, the prognostic value of (18)F-FDG PET was evaluated.

METHODS

A prospective cohort of patients with a suspected diagnosis of a motor neuron disorder (n = 119; mean age ± SD, 61 ± 12 y; 81 men and 38 women) was recruited. One hundred five patients were diagnosed with ALS (mean age ± SD, 61.0 ± 12 y; 74 men and 31 women) (group 2), 10 patients with primary lateral sclerosis (mean age ± SD, 55.5 ± 12 y; 3 men and 7 women), and 4 patients with progressive muscular atrophy (mean age ± SD, 59.2 ± 5 y; 4 men). The mean disease duration of all patients was 15.0 ± 13.4 mo at diagnosis, with PET conducted 15.2 ± 13.3 mo after the first symptoms. Data were compared with a previously gathered dataset of 20 screened healthy subjects (mean age ± SD, 62.4 ± 6.4 y; 12 men and 8 women) and 70 ALS patients (mean age ± SD, 62.2 ± 12.5 y; 44 men and 26 women) (group 1). Data were spatially normalized and analyzed on a VOI basis (statistical software (using the Hammers atlas) and voxel basis using statistical parametric mapping. Discriminant analysis and SVM were used to classify new cases based on the classifiers derived from group 1.

RESULTS

Compared with controls, ALS patients showed a nearly identical pattern of hypo- and hypermetabolism in groups 1 and 2. VOI-based discriminant analysis resulted in an 88.8% accuracy in predicting the new ALS cases. For the SVM approach, this accuracy was 100%. Brain metabolism between ALS and primary lateral sclerosis patients was nearly identical and not separable on an individual basis. Extensive frontotemporal hypometabolism was predictive for a lower survival using a Kaplan-Meier survival analysis (P < 0.001).

CONCLUSION

On the basis of a previously acquired training set, (18)F-FDG PET with advanced discriminant analysis methods is able to accurately distinguish ALS from controls and aids in assessing individual prognosis. Further validation on multicenter datasets and ALS-mimicking disorders is needed to fully assess the general applicability of this approach.