Widespread disruption of functional brain organization in early-onset Alzheimer's disease

Diverging Relationships among Amyloid, Tau, and Brain Atrophy in Early-Onset and Late-Onset Alzheimer's Disease

PURPOSE

Alzheimer's disease (AD) dementia may not be a single disease entity. Early-onset AD (EOAD) and late-onset AD (LOAD) have been united under the same eponym of AD until now, but disentangling the heterogeneity according to the age of sonset has been a major tenet in the field of AD research.

MATERIALS AND METHODS

Ninety-nine patients with AD (EOAD, n=54; LOAD, n=45) and 66 cognitively normal controls completed both [18F]THK5351 and [18F]flutemetamol (FLUTE) positron emission tomography scans along with structural magnetic resonance imaging and detailed neuropsychological tests.

RESULTS

EOAD patients had higher THK retention in the precuneus, parietal, and frontal lobe, while LOAD patients had higher THK retention in the medial temporal lobe. Intravoxel correlation analyses revealed that EOAD presented narrower territory of local FLUTE-THK correlation, while LOAD presented broader territory of correlation extending to overall parieto-occipito-temporal regions. EOAD patients had broader brain areas which showed significant negative correlations between cortical thickness and THK retention, whereas in LOAD, only limited brain areas showed significant correlation with THK retention. In EOAD, most of the cognitive test results were correlated with THK retention. However, a few cognitive test results were correlated with THK retention in LOAD.

CONCLUSION

LOAD seemed to show gradual increase in tau and amyloid, and those two pathologies have association to each other. On the other hand, in EOAD, tau and amyloid may develop more abruptly and independently. These findings suggest LOAD and EOAD may have different courses of pathomechanism.

Distinguishable neural circuit mechanisms associated with the clinical efficacy of rTMS in aMCI patients

Repetitive transcranial magnetic stimulation is used in early-stage Alzheimer's disease to slow progression, but heterogeneity in response results in different treatment outcomes. The mechanisms underlying this heterogeneity are unclear. This study used resting-state neuroimaging to investigate the variability in episodic memory improvement from angular gyrus repetitive transcranial magnetic stimulation and tracked the neural circuits involved. Thirty-four amnestic mild cognitive impairment patients underwent angular gyrus repetitive transcranial magnetic stimulation (4 weeks, 20 Hz, 100% resting motor threshold) and were divided into high-response and low-response groups based on minimal clinically important differences in auditory verbal learning test scores. Baseline and pre/post-treatment neural circuit activities were compared. Results indicated that the orbital middle frontal gyrus in the orbitofrontal cortex network and the precuneus in the default mode network had higher local activity in the low-response group. After treatment, changes in local and remote connectivity within brain regions of the orbitofrontal cortex, default mode network, visual network, and sensorimotor network showed opposite trends and were related to treatment effects. This suggests that the activity states of brain regions within the orbitofrontal cortex and default mode network could serve as imaging markers for early cognitive compensation in amnestic mild cognitive impairment patients and predict the aftereffects of repetitive transcranial magnetic stimulation response.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.

Cerebral organoids derived from patients with Alzheimer's disease with PSEN1/2 mutations have defective tissue patterning and altered development

During the past two decades, induced pluripotent stem cells (iPSCs) have been widely used to study human neural development and disease. Especially in the field of Alzheimer's disease (AD), remarkable effort has been put into investigating molecular mechanisms behind this disease. Then, with the advent of 3D neuronal cultures and cerebral organoids (COs), several studies have demonstrated that this model can adequately mimic familial and sporadic AD. Therefore, we created an AD-CO model using iPSCs derived from patients with familial AD forms and explored early events and the progression of AD pathogenesis. Our study demonstrated that COs derived from three AD-iPSC lines with PSEN1(A246E) or PSEN2(N141I) mutations developed the AD-specific markers in vitro, yet they also uncover tissue patterning defects and altered development. These findings are complemented by single-cell sequencing data confirming this observation and uncovering that neurons in AD-COs likely differentiate prematurely.

The Sporadic Early-onset Alzheimer's Disease Signature Of Atrophy: Preliminary Findings From The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) Cohort

INTRODUCTION

Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early-onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker.

METHODS

We analyzed MRI scans to define the sporadic EOAD-signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early-onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment.

RESULTS

The EOAD-signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD-signature atrophy was associated with the severity of cognitive impairment.

DISCUSSION

The EOAD-signature atrophy is a reliable and clinically valid biomarker of AD-related neurodegeneration that could be used in clinical trials for EOAD.

HIGHLIGHTS

We developed an early-onset Alzheimer's disease (EOAD)-signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD-signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.

Lower masticatory function relates to cognitive health and intrinsic brain network in older adults

OBJECTIVES

Mastication is associated with brain activation at the primary somatosensory cortex (S1) and the primary motor cortex (M1). Masticatory functions differ between patients with cognitive impairment (CI) and cognitively healthy older adults (non-CI). The association between cognitive health, brain network of functional connectivity, and mastication has remained unknown. The study investigated the association between masticatory performance (MP) and the topological feature of the functional network at the M1 and S1 in the CI and non-CI groups.

SUBJECTS AND METHODS

Forty-nine non-CI and 15 CI subjects received resting-state (rs) fMRI and assessment of MP. The topological feature of the M1 and S1 was quantified by eigenvector centrality (EC), an index that reflects a brain region as a functional "hub" of brain network.

RESULTS

In the non-CI group, MP was significantly correlated with EC of the left M1 and the right M1. The correlation was not statistically significant in the CI group. Cognitive status (CI or non-CI) and EC of the left M1 and the right M1, respectively, were statistically significant predictors to individual MP.

CONCLUSION

Cognitive status and the topological feature of the M1 in the intrinsic functional network may contribute to the individual difference in masticatory function.

Triple-network analysis of Alzheimer's disease based on the energy landscape

Introduction

Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state.

Methods

Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state.

Results

AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index.

Discussion

The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.

The Current State of Functional MR Imaging for Trauma Prognostication

In this review, we discuss the basics of functional MRI (fMRI) techniques including task-based and resting state fMRI, and overview the major findings in patients with traumatic brain injury. We summarize the studies that have longitudinally evaluated the changes in brain connectivity and task-related activation in trauma patients during different phases of trauma. We discuss how these data may potentially be used for prognostication, treatment planning, or monitoring and management of trauma patients.

Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments

Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer's disease (AD) mild cognitive impairment (MCI), compared with normal control (NC) persons, using the zoom-in neural network (ZNN) deep-learning algorithm. ZNN stacks a set of zoom-in learning units (ZLUs) in a feedforward hierarchy without backpropagation. The resting-state fMRI (rs-fMRI) dataset for AD assessments was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The Automated Anatomical Labeling (AAL-90) atlas, which provides 90 neuroanatomical functional regions, was used to assess and detect the implicated regions in the course of AD. The features of the ZNN are extracted from the 140-time series rs-fMRI voxel values in a region of the brain. ZNN yields the three classification accuracies of AD versus MCI and NC, NC versus AD and MCI, and MCI versus AD and NC of 97.7%, 84.8%, and 72.7%, respectively, with the seven discriminative regions of interest (ROIs) in the AAL-90.

Differential Involvement of the Locus Coeruleus in Early- and Late-Onset Alzheimer's Disease: A Potential Mechanism of Clinical Differences?

Sporadic early-onset Alzheimer's disease (sEOAD) is often associated with atypical clinical features, yet the cause of this heterogeneity remains unclear. This study investigated post-mortem atrophy of the locus coeruleus (LC) in sEOAD and late-onset Alzheimer's disease (LOAD). Levels of LC atrophy, as estimated by pathologist-rating of hypopigmentation, were compared between sEOAD (n = 115) and LOAD (n = 672) participants while controlling for other measures of pathological progression. Subsequent analyses compared low vs. high LC atrophy sEOAD subgroups on neuropsychological test performance. Results show nearly 4 times greater likelihood of higher LC atrophy in sEOAD as compared to LOAD (p < .005). sEOAD participants with greater LC atrophy displayed significantly worse performance on various baseline measures of attentional functioning (p < .05), despite similar global cognition (p = .25). These findings suggest the LC is an important potential driver of clinical and pathological heterogeneity in sEOAD.

Dissecting the clinical heterogeneity of early-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) is a rare but particularly devastating form of AD. Though notable for its high degree of clinical heterogeneity, EOAD is defined by the same neuropathological hallmarks underlying the more common, late-onset form of AD. In this review, we describe the various clinical syndromes associated with EOAD, including the typical amnestic phenotype as well as atypical variants affecting visuospatial, language, executive, behavioral, and motor functions. We go on to highlight advances in fluid biomarker research and describe how molecular, structural, and functional neuroimaging can be used not only to improve EOAD diagnostic acumen but also enhance our understanding of fundamental pathobiological changes occurring years (and even decades) before the onset of symptoms. In addition, we discuss genetic variation underlying EOAD, including pathogenic variants responsible for the well-known mendelian forms of EOAD as well as variants that may increase risk for the much more common forms of EOAD that are either considered to be sporadic or lack a clear autosomal-dominant inheritance pattern. Intriguingly, specific pathogenic variants in PRNP and MAPT-genes which are more commonly associated with other neurodegenerative diseases-may provide unexpectedly important insights into the formation of AD tau pathology. Genetic analysis of the atypical clinical syndromes associated with EOAD will continue to be challenging given their rarity, but integration of fluid biomarker data, multimodal imaging, and various 'omics techniques and their application to the study of large, multicenter cohorts will enable future discoveries of fundamental mechanisms underlying the development of EOAD and its varied clinical presentations.

Functional Connectivity of the Brain Across Rodents and Humans

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease

Alzheimer's disease (AD) is characterized by different clinical entities. Although AD phenotypes share a common molecular substrate (i.e., amyloid beta and tau accumulation), several clinicopathological differences exist. Brain functional networks might provide a macro-scale scaffolding to explain this heterogeneity. In this review, we summarize the evidence linking different large-scale functional network abnormalities to distinct AD phenotypes. Specifically, executive deficits in early-onset AD link with the dysfunction of networks that support sustained attention and executive functions. Posterior cortical atrophy relates to the breakdown of visual and dorsal attentional circuits, while the primary progressive aphasia variant of AD may be associated with the dysfunction of the left-lateralized language network. Additionally, network abnormalities might provide in vivo signatures for distinguishing proteinopathies that mimic AD, such as TAR DNA binding protein 43 related pathologies. These network differences vis-a-vis clinical syndromes are more evident in the earliest stage of AD. Finally, we discuss how these findings might pave the way for new tailored interventions targeting the most vulnerable brain circuit at the optimal time window to maximize clinical benefits.

It Is Time to Study Overlapping Molecular and Circuit Pathophysiologies in Alzheimer's and Lewy Body Disease Spectra

Alzheimer's disease (AD) is the leading cause of dementia due to neurodegeneration and is characterized by extracellular senile plaques composed of amyloid β1 - 42 (Aβ) as well as intracellular neurofibrillary tangles consisting of phosphorylated tau (p-tau). Dementia with Lewy bodies constitutes a continuous spectrum with Parkinson's disease, collectively termed Lewy body disease (LBD). LBD is characterized by intracellular Lewy bodies containing α-synuclein (α-syn). The core clinical features of AD and LBD spectra are distinct, but the two spectra share common cognitive and behavioral symptoms. The accumulation of pathological proteins, which acquire pathogenicity through conformational changes, has long been investigated on a protein-by-protein basis. However, recent evidence suggests that interactions among these molecules may be critical to pathogenesis. For example, Aβ/tau promotes α-syn pathology, and α-syn modulates p-tau pathology. Furthermore, clinical evidence suggests that these interactions may explain the overlapping pathology between AD and LBD in molecular imaging and post-mortem studies. Additionally, a recent hypothesis points to a common mechanism of prion-like progression of these pathological proteins, via neural circuits, in both AD and LBD. This suggests a need for understanding connectomics and their alterations in AD and LBD from both pathological and functional perspectives. In AD, reduced connectivity in the default mode network is considered a hallmark of the disease. In LBD, previous studies have emphasized abnormalities in the basal ganglia and sensorimotor networks; however, these account for movement disorders only. Knowledge about network abnormalities common to AD and LBD is scarce because few previous neuroimaging studies investigated AD and LBD as a comprehensive cohort. In this paper, we review research on the distribution and interactions of pathological proteins in the brain in AD and LBD, after briefly summarizing their clinical and neuropsychological manifestations. We also describe the brain functional and connectivity changes following abnormal protein accumulation in AD and LBD. Finally, we argue for the necessity of neuroimaging studies that examine AD and LBD cases as a continuous spectrum especially from the proteinopathy and neurocircuitopathy viewpoints. The findings from such a unified AD and Parkinson's disease (PD) cohort study should provide a new comprehensive perspective and key data for guiding disease modification therapies targeting the pathological proteins in AD and LBD.

Exploitation of Marine Molecules to Manage Alzheimer's Disease

Neurodegenerative diseases are sociosanitary challenges of today, as a result of increased average life expectancy, with Alzheimer’s disease being one of the most prevalent. This pathology is characterized by brain impairment linked to a neurodegenerative process culminating in cognitive decline and behavioral disorders. Though the etiology of this pathology is still unknown, it is usually associated with the appearance of senile plaques and neurofibrillary tangles. The most used prophylaxis relies on anticholinesterase drugs and NMDA receptor antagonists, whose main action is to relieve symptoms and not to treat or prevent the disease. Currently, the scientific community is gathering efforts to disclose new natural compounds effective against Alzheimer’s disease and other neurodegenerative pathologies. Marine natural products have been shown to be promising candidates, and some have been proven to exert a high neuroprotection effect, constituting a large reservoir of potential drugs and nutraceutical agents. The present article attempts to describe the processes of extraction and isolation of bioactive compounds derived from sponges, algae, marine bacteria, invertebrates, crustaceans, and tunicates as drug candidates against AD, with a focus on the success of pharmacological activity in the process of finding new and effective drug compounds.

Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum

Understanding the interrelationships of clinical manifestations of Alzheimer's disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n = 80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease.

Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls

OBJECTIVES

The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC.

METHODS

Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted.

RESULTS

Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881.

CONCLUSIONS

EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully.

SIGNIFICANCE

This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.

Distinct fiber-specific white matter reductions pattern in early- and late-onset Alzheimer's disease

Background: The underlying white matter impairment in patients with early and late-onset Alzheimer’s disease (EOAD and LOAD) is still unclear, and this might due to the complex AD pathology.

Methods: We included 31 EOAD, 45 LOAD, and 64 younger, 46 elder controls in our study to undergo MRI examinations. Fiber density (FD) and fiber bundle cross-section (FC) were measured using fixel-based analysis based on diffusion weighted images. On whole brain and tract-based level, we compared these parameters among different groups (p<0.05, FWE corrected). Moreover, we verified our results in another independent dataset using the same analyses.

Results: Compared to young healthy controls, EOAD had significantly lower FD in the splenium of corpus callosum, limbic tracts, cingulum bundles, and posterior thalamic radiation, and higher FC in the splenium of corpus callosum, dorsal cingulum and posterior thalamic radiation. On the other hand, LOAD had lower FD and FC as well. Importantly, a similar pattern was found in the independent validation dataset. Among all groups, both the FD and FC were associated with cognitive function. Furthermore, FD of fornix column and body, and FC of ventral cingulum were associated with composite amyloid and tau level (r=-0.34 and -0.53, p<0.001) respectively.

Conclusions: EOAD and LOAD were characterized by distinct white matter impairment patterns, which may be attributable to their different neuropathologies.

Distinct impaired patterns of intrinsic functional network centrality in patients with early- and late-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) involves multiple cognitive domains and shows more rapid progression than late-onset Alzheimer's disease (LOAD). However, the difference in pathogenesis between EOAD and LOAD is still unclear. Accordingly, we applied intrinsic network analysis to explore the potential neuropathological mechanism underlying distinct clinical phenotypes. According to the cut-off age of 65, we included 20 EOAD patients, 20 LOAD patients, and 36 age-matched controls (19 young and 17 old controls). We employed resting-state functional MRI and network centrality analysis to explore the local (degree centrality (DC)) and global (eigenvector centrality (EC)) functional integrity. Two-sample t-test analysis was performed, with gray matter volume, age, gender, and education as covariates. Furthermore, we performed a correlation analysis between network metrics and cognition. Compared to young controls, EOAD patients exhibited lower DC in the middle temporal gyrus (MTG), parahippocampal gyrus (PHG), superior temporal gyrus (STG), and lower EC in the MTG, PHG, and postcentral gyrus. In contrast, LOAD patients exhibited lower DC in the STG and anterior cingulum gyrus and higher DC in the middle frontal gyrus compared to old controls. No significant difference in EC was observed in LOAD patients. Furthermore, both DC and EC correlated with cognitive performance. Our study demonstrated divergent functional network impairments in EOAD and LOAD patients. EOAD patients showed more complex network damage involving both local and global centrality properties, while LOAD patients mainly featured local functional connectivity changes. Such centrality impairments are related to poor cognition, especially regarding memory performance.

Age at onset reveals different functional connectivity abnormalities in prodromal Alzheimer's disease

Age at symptom onset (AAO) underlies different Alzheimer's disease (AD) clinical variants: late-onset AD (LOAD) is characterized by memory deficits, while early-onset AD (EOAD) presents predominantly with non-memory symptoms. The involvement of different neural networks may explain these distinct clinical phenotypes. In this study, we tested the hypothesis of an early and selective involvement of neural networks based on AAO in AD. Twenty memory clinic patients with prodromal AD (i.e., mild cognitive impairment with an AD-like cerebrospinal fluid profile) and 30 healthy controls underwent a cognitive evaluation and a resting state functional MRI exam. Independent component analysis was performed to assess functional connectivity (FC) in the following networks: default mode, frontoparietal, limbic, visual, and sensorimotor. Patients were stratified into late-onset (pLOAD) and early-onset (pEOAD) prodromal AD according to the AAO and controls were stratified into younger and older groups accordingly. Decreased FC within the default mode and the limbic networks was observed in pLOAD, while pEOAD showed lower FC in the frontoparietal and visual networks. The sensorimotor network did not show differences between groups. A significant association was found between memory and limbic network FC in pLOAD, and between executive functions and frontoparietal network FC in pEOAD, although the latter association did not survive multiple comparison correction. Our findings indicate that aberrant connectivity in memory networks is associated with pLOAD, while networks underlying executive and visuo-spatial functions are affected in pEOAD. These findings are in line with the hypothesis that the pathophysiological mechanisms underlying EOAD and LOAD are distinct.

Effects of Alzheimer's and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer's Disease

Early- and late-onset Alzheimer's disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [¹⁸F]-THK5351 PET, [¹⁸F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study

The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

Differential Involvement of the Locus Coeruleus in Early- and Late-Onset Alzheimer's Disease: A Potential Mechanism of Clinical Differences?

Early-onset Alzheimer's disease (EOAD) has been associated with an increased likelihood of atypical clinical manifestations such as attentional impairment, yet the cause of this heterogeneity remains unclear. The locus coeruleus (LC) is implicated early in Alzheimer's disease pathology and is associated with attentional functioning. This study investigated post-mortem atrophy of the LC in EOAD and late-onset Alzheimer's disease (LOAD) in a large, well-characterized sample. Results show nearly four times greater likelihood of higher LC atrophy in EOAD as compared to LOAD after controlling for other measures of pathological progression ( p < .005). Follow-up analyses within the EOAD group revealed that compared to those who displayed mild or no LC atrophy at autopsy, those with moderate-severe atrophy of the LC displayed significantly worse performance on various baseline measures of attentional functioning ( p < .05), despite similar overall cognition ( p = .25). These findings suggest the LC is an important potential driver of clinical and pathological heterogeneity in EOAD.

Neuroimaging-Based Scalp Acupuncture Locations for Dementia

Scalp acupuncture is a modality of acupuncture in which acupuncture needles are inserted into a certain layer of the scalp in order to affect the function of corresponding areas of the cerebral cortex and relieve symptoms. Clinical studies have demonstrated the potential of scalp acupuncture as a non-pharmacological treatment for dementia. Unfortunately, recent findings from brain neuroimaging studies on dementia have not been incorporated into scalp acupuncture. This study aims to integrate meta-analysis, resting-state functional connectivity, and diffusion tensor imaging (DTI) to identify potential locations of scalp acupuncture for treatment of dementia. We found that the prefrontal cortex, the medial prefrontal cortex, the middle and superior temporal gyrus, the temporal pole, the supplementary motor area, the inferior occipital gyrus, and the precuneus are involved in the pathophysiology of dementia and, therefore, may be the target areas of scalp acupuncture for dementia treatment. The neuroimaging-based scalp acupuncture protocol developed in this study may help to refine the locations for the treatment of dementia. Integrating multidisciplinary methods to identify key surface cortical areas associated with a certain disorder may shed light on the development of scalp acupuncture and other neuromodulation methods such as transcranial electrical current stimulation, particularly in the domain of identifying stimulation locations.

Disconnection of the right superior parietal lobule from the precuneus is associated with memory impairment in oldest-old Alzheimer's disease patients

There is a wide range of onset age in Alzheimer's disease (AD). Emerging evidence indicates variation of AD manifestations in oldest-old AD (OOAD); however, the pattern of cognitive dysfunctions remains unclear. We aimed to reveal cognitive performance characteristics and changes in brain functional connectivity in OOAD patients by a resting-state fMRI (rs-fMRI) study. We enrolled AD patients who had been referred to Kyushu University Hospital (KUH) or Sanno Hospital, and classified them into middle-old AD (MOAD) (65-79 years old) and OOAD (≥80 years old) according to the age of onset. Our subjects consisted of 19 OOAD, 17 MOAD, and 8 normal subjects. Cognitive performance was evaluated using Mini Mental State Examination-Japanese (MMSE-J) and Clinical Dementia Rating (CDR). rs-fMRI scanning and independent component analysis (ICA) were performed on Sanno Hospital patients and MOAD vs. OOAD patients were compared. The resulting significant regions were used as seeds for ROI-to-ROI analysis of the KUH dataset. Collectively, MMSE-J delayed recall sub-scores were significantly lower in OOAD patients compared with MOAD patients. ICA of the Sanno Hospital data indicated significant connectivity decrease in the default mode network (DMN) in the OOAD group compared with the MOAD group in the right superior parietal lobule (SPL). ROI-to-ROI analysis of the KUH dataset indicated significant disconnection in the OOAD group of the right SPL from the precuneus (p < 0.01). The functional connectivity from the right SPL to the precuneus was positively correlated with the MMSE-J delayed recall sub-score (p = 0.03) and negatively correlated with the CDR memory sub-scale (p = 0.04). These findings indicate that disconnection between the right SPL and the precuneus may contribute to worse memory capability in OOAD compared with MOAD.

Mid age APOE ε4 carriers show memory-related functional differences and disrupted structure-function relationships in hippocampal regions

Carriers of the APOE e4 allele are at higher risk of age-related cognitive decline and Alzheimer’s disease (AD). The underlying neural mechanisms are uncertain, but genotype differences in medial temporal lobe (MTL) functional activity and structure at mid-age might contribute. We tested 16 non-e4 and 16 e4 carriers (aged 45–55) on a subsequent memory task in conjunction with MRI to assess how hippocampal volume (from T1 structural) and microstructure (neurite orientation-dispersion, from NODDI) differs by genotype and in relation to memory encoding. No previous study has investigated APOE effects on hippocampal microstructure using NODDI. Recall performance did not differ by genotype. A genotype by condition interaction in left parahippocampus indicated that in e4 carriers activity did not differentiate subsequently remembered from forgotten words. Hippocampal volumes and microstructure also did not differ by genotype but hippocampal volumes correlated positively with recognition performance in non-e4 carriers only. Similarly, greater hippocampal neurite orientation-dispersion was linked to better recall but only in non-e4s. Thus, we suggest that mid-age e4 carriers show a breakdown of normal MTL activation and structure-performance relationships. This could reflect an inability to utilise compensatory mechanisms, and contribute to higher risk of cognitive decline and AD in later life.

A core transcriptional signature of human microglia: Derivation and utility in describing region-dependent alterations associated with Alzheimer's disease

Growing recognition of the pivotal role microglia play in neurodegenerative and neuroinflammatory disorders has accentuated the need to characterize their function in health and disease. Studies in mouse have applied transcriptome-wide profiling of microglia to reveal key features of microglial ontogeny, functional profile, and phenotypic diversity. While similar, human microglia exhibit clear differences to their mouse counterparts, underlining the need to develop a better understanding of the human microglial profile. On examining published microglia gene signatures, limited consistency was observed between studies. Hence, we sought to derive a core microglia signature of the human central nervous system (CNS), through a comprehensive analysis of existing transcriptomic datasets. Nine datasets derived from cells and tissues, isolated from various regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified, with 249 genes highly conserved between them. This core signature included known microglial markers, and compared with other signatures provides a gene set specific to microglia in the context of the CNS. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression. We believe this resource and the analyses described here, will support further investigations to the contribution of human microglia in CNS health and disease.

Disrupted structural and functional brain networks in Alzheimer's disease

Studies have demonstrated that the clinical manifestations of Alzheimer's disease (AD) are associated with abnormal connections in either functional connectivity networks (FCNs) or structural connectivity networks (SCNs). However, the FCN and SCN of AD have usually been examined separately, and the results were inconsistent. In this multimodal study, we collected resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging data from 46 patients with AD and 39 matched healthy controls (HCs). Graph-theory analysis was used to investigate the topological organization of the FCN and SCN simultaneously. Compared with HCs, both the FCN and SCN of patients with AD showed disrupted network integration (i.e., increased characteristic path length) and segregation (i.e., decreased intramodular connections in the default mode network). Moreover, the FCN, but not the SCN, exhibited a reduced clustering coefficient and reduced rich club connections in AD. The coupling (i.e., correlation) of the FCN and SCN in AD was increased in connections of the default mode network and the rich club. These findings demonstrated overlapping and distinct network disruptions in the FCN and SCN and a strengthened correlation between FCNs and SCNs in AD, which provides a novel perspective for understanding the pathophysiological mechanisms underlying AD.

Learning Pairwise-Similarity Guided Sparse Functional Connectivity Network for MCI Classification

Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.

Involvement of the Precuneus/Posterior Cingulate Cortex Is Significant for the Development of Alzheimer's Disease: A PET (THK5351, PiB) and Resting fMRI Study

Background: Imaging studies in Alzheimer’s disease (AD) have yet to answer the underlying questions concerning the relationship among tau retention, neuroinflammation, network disruption and cognitive decline. We compared the spatial retention patterns of ¹⁸F-THK5351 and resting state network (RSN) disruption in patients with early AD and healthy controls.

Methods: We enrolled 23 ¹¹C-Pittsburgh compound B (PiB)-positive patients with early AD and 24 ¹¹C-PiB-negative participants as healthy controls. All participants underwent resting state functional MRI and ¹⁸F-THK5351 PET scans. We used scaled subprofile modeling/principal component analysis (SSM/PCA) to reduce the complexity of multivariate data and to identify patterns that exhibited the largest statistical effects (variances) in THK5351 concentration in AD and healthy controls.

Findings: SSM/PCA identified a significant spatial THK5351 pattern composed by mainly three clusters including precuneus/posterior cingulate cortex (PCC), right and left dorsolateral prefrontal cortex (DLPFC) which accounted for 23.6% of the total subject voxel variance of the data and had 82.6% sensitivity and 79.1% specificity in discriminating AD from healthy controls. There was a significant relationship between the intensity of the ¹⁸F-THK5351 covariation pattern and cognitive scores in AD. The spatial patterns of ¹⁸F-THK5351 uptake showed significant similarity with intrinsic functional connectivity, especially in the PCC network. Seed-based connectivity analysis from the PCC showed significant decrease in connectivity over widespread brain regions in AD patients. An evaluation of an autopsied AD patient with Braak V showed that ¹⁸F-THK5351 retention corresponded to tau deposition, monoamine oxidase-B (MAO-B) and astrogliosis in the precuneus/PCC.

Interpretation: We identified an AD-specific spatial pattern of ¹⁸F-THK5351 retention in the precuneus/PCC, an important connectivity hub region in the brain. Disruption of the functional connections of this important network hub may play an important role in developing dementia in AD.

Distinct Patterns of Interhemispheric Connectivity in Patients With Early- and Late-Onset Alzheimer's Disease

Background: Early-onset Alzheimer’s disease (EOAD) presents a different clinical profile than late-onset Alzheimer’s disease (LOAD). Neuroimaging studies have demonstrated that patients with EOAD present more atrophy and functional disconnection than LOAD patients. However, it remains unknown whether the interhemispheric functional disconnection or its underlying structural impairment contributes to the different clinical profiles of EOAD and LOAD.

Methods: According to the arbitrary cut-off age of 65, we included 22 EOAD patients, 27 LOAD patients and 38 healthy controls (further divided into 21 relatively young and 17 old controls). Participants underwent resting-state functional MRI, diffusion tensor imaging (DTI) and comprehensive neuropsychological assessments. We used voxel-mirrored homotopic connectivity (VMHC) to examine interhemispheric functional connectivity. Then, we calculated the diffusion index based on tract-based spatial statistics (TBSS). Two-sample t-tests were used to assess the interhemispheric connectivity differences between each patient group and its corresponding control group.

Results: We found that the EOAD patients had lower VMHC in the hippocampus, parahippocampal gyrus (PHG), superior temporal gyrus (STG) and inferior parietal cortex (IPC) than did controls. Consistently, the EOAD patients exhibited white matter (WM) tract impairment in the posterior regions. On the other hand, the LOAD patients displayed increased VMHC and impaired WM tracts in the frontal region. Correlation analyses showed that VMHC in the IPC was related to executive function in the EOAD patients (r = −0.67, P < 0.05).

Conclusion: In contrast to the LOAD patients, patients with EOAD exhibited more widely disrupted interhemispheric functional and structural connectivity, which overlapped well across brain regions. In addition, for the EOAD patients, decreased interhemispheric connectivity related to executive deficits. Our study suggested that different interhemispheric connectivity damage patterns may contribute to the distinct clinical profiles in EOAD and LOAD.

Multiple Frequency Bands Analysis of Large Scale Intrinsic Brain Networks and Its Application in Schizotypal Personality Disorder

The human brain is a complex system composed by several large scale intrinsic networks with distinct functions. The low frequency oscillation (LFO) signal of blood oxygen level dependent (BOLD), measured through resting-state fMRI, reflects the spontaneous neural activity of these networks. We propose to characterize these networks by applying the multiple frequency bands analysis (MFBA) to the LFO time courses (TCs) resulted from the group independent component analysis (ICA). Specifically, seven networks, including the default model network (DMN), dorsal attention network (DAN), control executive network (CEN), salience network, sensorimotor network, visual network and limbic network, are identified. After the power spectral density (PSD) analysis, the amplitude of low frequency fluctuation (ALFF) and the fractional amplitude of low frequency fluctuation (fALFF) is determined in three bands: <0.1 Hz; slow-5; and slow-4. Moreover, the MFBA method is applied to reveal the frequency-dependent alternations of fALFF for seven networks in schizotypal personality disorder (SPD). It is found that seven networks can be divided into three categories: the advanced cognitive networks, primary sensorimotor networks and limbic networks, and their fALFF successively decreases in both slow-4 and slow-5 bands. Comparing to normal control group, the fALFF of DMN, DAN and CEN in SPD tends to be higher in slow-5 band, but lower in slow-4. Higher fALFF of sensorimotor and visual networks in slow-5, higher fALFF of limbic network in both bands have been observed for SPD group. The results of ALFF are consistent with those of fALFF. The proposed MFBA method may help distinguish networks or oscillators in the human brain, reveal subtle alternations of networks through locating their dominant frequency band, and present potential to interpret the neuropathology disruptions.

Noradrenergic Dysfunction in Alzheimer's and Parkinson's Diseases-An Overview of Imaging Studies

Noradrenergic dysfunction contributes to cognitive impairment in Alzheimer's Disease (AD) and Parkinson's Disease (PD). Conventional therapeutic strategies seek to enhance cholinergic and dopaminergic neurotransmission in AD and PD, respectively, and few studies have examined noradrenergic dysfunction as a target for medication development. We review the literature of noradrenergic dysfunction in AD and PD with a focus on human imaging studies that implicate the locus coeruleus (LC) circuit. The LC sends noradrenergic projections diffusely throughout the cerebral cortex and plays a critical role in attention, learning, working memory, and cognitive control. The LC undergoes considerable degeneration in both AD and PD. Advances in magnetic resonance imaging have facilitated greater understanding of how structural and functional alteration of the LC may contribute to cognitive decline in AD and PD. We discuss the potential roles of the noradrenergic system in the pathogenesis of AD and PD with an emphasis on postmortem anatomical studies, structural MRI studies, and functional MRI studies, where we highlight changes in LC connectivity with the default mode network (DMN). LC degeneration may accompany deficient capacity in suppressing DMN activity and increasing saliency and task control network activities to meet behavioral challenges. We finish by proposing potential and new directions of research to address noradrenergic dysfunction in AD and PD.

Early-Onset Alzheimer Disease (EOAD) With Aphasia: A Case Report

Background: Alzheimer's disease (AD) is traditionally subdivided into early onset (EOAD) and late onset (LOAD). EOAD has an onset before age 65 years and accounts for 1-5% of all cases. Two main presentation types of AD are familial and sporadic. Case presentation: The authors present the case of a 68-year-old retired white man, with a college level educational background. At 55 years of age, the patient presented cognitive decline with short-term memory impairment and slowed, hesitant speech. At 57 years, he was unable to remember the way to work, exhibiting spatial disorientation. PET-CT: revealed hypometabolism and atrophy in the left temporal lobe and posterior region of the parietal lobes. Disease course: Evolving with difficulties in comprehension and sentence repetition over past 3 years and with global aphasia in past 6 months, beyond progressive memory impairment. Discussion: Possibly due to the young age and atypical presentation, and the diagnosis of EOAD is often delayed. To the best of our knowledge, this case can be classified as a sporadic EOAD with aphasia. Clinical variant and neuroimaging findings were crucial to the diagnosis and treatment of this atypical presentation of AD.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis

Introduction

We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging.

Methods

Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.

Results

Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network.

Discussion

Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders

Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD

Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

Synaptosomal bioenergetic defects are associated with cognitive impairment in a transgenic rat model of early Alzheimer's disease

Synaptic bioenergetic deficiencies may be associated with early Alzheimer's disease (AD). To explore this concept, we assessed pre-synaptic mitochondrial function in hemizygous (+/-)TgMcGill-R-Thy1-APP rats. The low burden of Aβ and the wide array of behavioral and cognitive impairments described in 6-month-old hemizygous TgMcGill-R-Thy1-APP rats (Tg(+/-)) support their use to investigate synaptic bioenergetics deficiencies described in subjects with early Alzheimer's disease (AD). In this report, we show that pre-synaptic mitochondria from Tg(+/-) rats evidence a decreased respiratory control ratio and spare respiratory capacity associated with deficits in complex I enzymatic activity. Cognitive impairments were prevented and bioenergetic deficits partially reversed when Tg(+/-) rats were fed a nutritionally complete diet from weaning to 6-month-old supplemented with pyrroloquinoline quinone, a mitochondrial biogenesis stimulator with antioxidant and neuroprotective effects. These results provide evidence that, as described in AD brain and not proven in Tg mice models with AD-like phenotype, the mitochondrial bioenergetic capacity of synaptosomes is not conserved in the Tg(+/-) rats. This animal model may be suitable for understanding the basic biochemical mechanisms involved in early AD.

A genetic screen of the mutations in the Korean patients with early-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) has distinct clinical characteristics in comparison to late-onset Alzheimer's disease (LOAD). The genetic contribution is suggested to be more potent in EOAD. However, the frequency of causative mutations in EOAD could be variable depending on studies. Moreover, no mutation screening study has been performed yet employing large population in Korea. Previously, we reported that the rate of family history of dementia in EOAD patients was 18.7% in a nationwide hospital-based cohort study, the Clinical Research Center for Dementia of South Korea (CREDOS) study. This rate is much lower than in other countries and is even comparable to the frequency of LOAD patients in our country. To understand the genetic characteristics of EOAD in Korea, we screened the common Alzheimer's disease (AD) mutations in the consecutive EOAD subjects from the CREDOS study from April 2012 to February 2014. We checked the sequence of APP (exons 16-17), PSEN1 (exons 3-12), and PSEN2 (exons 3-12) genes. We identified different causative or probable pathogenic AD mutations, PSEN1 T116I, PSEN1 L226F, and PSEN2 V214L, employing 24 EOAD subjects with a family history and 80 without a family history of dementia. PSEN1 T116I case demonstrated autosomal dominant trait of inheritance, with at least 11 affected individuals over 2 generations. However, there was no family history of dementia within first-degree relation in PSEN1 L226F and PSEN2 V214L cases. Approximately, 55.7% of the EOAD subjects had APOE ε4 allele, while none of the mutation-carrying subjects had the allele. The frequency of genetic mutation in this study is lower compared to the studies from other countries. The study design that was based on nationwide cohort, which minimizes selection bias, is thought to be one of the contributors to the lower frequency of genetic mutation. However, the possibility of the greater likeliness of earlier onset of sporadic AD in Korea cannot be excluded. We suggest early AD onset and not carrying APOE ε4 allele are more reliable factors for predicting an induced genetic mutation than the presence of the family history in Korean EOAD population.

Neuropsychiatric subsyndromes and brain metabolic network dysfunctions in early onset Alzheimer's disease

Neuropsychiatric symptoms (NPSs) often occur in early-age-of-onset Alzheimer's disease (EOAD) and cluster into sub-syndromes (SSy). The aim of this study was to investigate the association between 18 F-FDG-PET regional and connectivity-based brain metabolic dysfunctions and neuropsychiatric SSy. NPSs were assessed in 27 EOAD using the Neuropsychiatric Inventory and further clustered into four SSy (apathetic, hyperactivity, affective, and psychotic SSy). Eighty-five percent of EOAD showed at least one NPS. Voxel-wise correlations between SSy scores and brain glucose metabolism (assessed with 18 F-FDG positron emission tomography) were studied. Interregional correlation analysis was used to explore metabolic connectivity in the salience (aSN) and default mode networks (DMN) in a larger sample of EOAD (N = 51) and Healthy Controls (N = 57). The apathetic, hyperactivity, and affective SSy were highly prevalent (>60%) as compared to the psychotic SSy (33%). The hyperactivity SSy scores were associated with increase of glucose metabolism in frontal and limbic structures, implicated in behavioral control. A comparable positive correlation with part of the same network was found for the affective SSy scores. On the other hand, the apathetic SSy scores were negatively correlated with metabolism in the bilateral orbitofrontal and dorsolateral frontal cortex known to be involved in motivation and decision-making processes. Consistent with these SSy regional correlations with brain metabolic dysfunction, the connectivity analysis showed increases in the aSN and decreases in the DMN. Behavioral abnormalities in EOAD are associated with specific dysfunctional changes in brain metabolic activity, in particular in the aSN that seems to play a crucial role in NPSs in EOAD. Hum Brain Mapp 37:4234-4247, 2016. © 2016 Wiley Periodicals, Inc.

Identifying the Alteration Patterns of Brain Functional Connectivity in Progressive Mild Cognitive Impairment Patients: A Longitudinal Whole-Brain Voxel-Wise Degree Analysis

Patients with mild cognitive impairment (MCI) are at high risk for developing Alzheimer’s disease (AD), while some of them may remain stable over decades. The underlying mechanism is still not fully understood. In this study, we aimed to explore the connectivity differences between progressive MCI (PMCI) and stable MCI (SMCI) individuals on a whole-brain scale and on a voxel-wise basis, and we also aimed to reveal the differential dynamic alteration patterns between these two disease subtypes. The resting-state functional magnetic resonance images of PMCI and SMCI patients at baseline and year-one were obtained from the Alzheimer’s Disease Neuroimaging Initiative dataset, and the progression was determined based on a 3-year follow-up. A whole-brain voxel-wise degree map that was calculated based on graph-theory was constructed for each subject, and then the cross-sectional and longitudinal analyses on the degree maps were performed between PMCI and SMCI patients. In longitudinal analyses, compared with SMCI group, PMCI group showed decreased long-range degree in the left middle occipital/supramarginal gyrus, while the short-range degree was increased in the left supplementary motor area and middle frontal gyrus and decreased in the right middle temporal pole. A significant longitudinal alteration of decreased short-range degree in the right middle occipital was found in PMCI group. Taken together with previous evidence, our current findings may suggest that PMCI, compared with SMCI, might be a “severe” presentation of disease along the AD continuum, and the rapidly reduced degree in the right middle occipital gyrus may have indicative value for the disease progression. Moreover, the cross-sectional comparison results and corresponding receiver-operator characteristic-curves analyses may indicate that the baseline degree difference is not a good predictor of disease progression in MCI patients. Overall, these findings may provide objective evidence and an indicator to characterize the progression-related brain connectivity changes in MCI patients.

Glucose Metabolic Brain Networks in Early-Onset vs. Late-Onset Alzheimer's Disease

Objective: Early-onset Alzheimer's disease (EAD) shows distinct features from late-onset Alzheimer's disease (LAD). To explore the characteristics of EAD, clinical, neuropsychological, and functional imaging studies have been conducted. However, differences between EAD and LAD are not clear, especially in terms of brain connectivity and networks. In this study, we investigated the differences in metabolic connectivity between EAD and LAD by adopting graph theory measures.

Methods: We analyzed ¹⁸F-fluorodeoxyglucose-positron emission tomography (FDG-PET) images to investigate the distinct features of metabolic connectivity between EAD and LAD. Using metabolic connectivity and graph theory analysis, metabolic network differences between LAD and EAD were explored.

Results: Results showed the decreased connectivity centered in the cingulate gyri and occipital regions in EAD, whereas decreased connectivity in the occipital and temporal regions as well as increased connectivity in the supplementary motor area were observed in LAD when compared with age-matched control groups. Global efficiency and clustering coefficients were decreased in EAD but not in LAD. EAD showed progressive network deterioration as a function of disease severity and clinical dementia rating (CDR) scores, mainly in terms of connectivity between the cingulate gyri and occipital regions. Global efficiency and clustering coefficients were also decreased along with disease severity.

Conclusion: These results indicate that EAD and LAD have distinguished features in terms of metabolic connectivity, with EAD demonstrating more extensive and progressive deterioration.

Gray matter network disruptions and amyloid beta in cognitively normal adults

Gray matter networks are disrupted in Alzheimer's disease (AD). It is unclear when these disruptions start during the development of AD. Amyloid beta 1-42 (Aβ42) is among the earliest changes in AD. We studied, in cognitively healthy adults, the relationship between Aβ42 levels in cerebrospinal fluid (CSF) and single-subject cortical gray matter network measures. Single-subject gray matter networks were extracted from structural magnetic resonance imaging scans in a sample of cognitively healthy adults (N = 185; age range 39-79, mini-mental state examination >25, N = 12 showed abnormal Aβ42 < 550 pg/mL). Degree, clustering coefficient, and path length were computed at whole brain level and for 90 anatomical areas. Associations between continuous Aβ42 CSF levels and single-subject cortical gray matter network measures were tested. Smoothing splines were used to determine whether a linear or nonlinear relationship gave a better fit to the data. Lower Aβ42 CSF levels were linearly associated at whole brain level with lower connectivity density, and nonlinearly with lower clustering values and higher path length values, which is indicative of a less-efficient network organization. These relationships were specific to medial temporal areas, precuneus, and the middle frontal gyrus (all p < 0.05). These results suggest that mostly within the normal spectrum of amyloid, lower Aβ42 levels can be related to gray matter networks disruptions.

Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.

Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing–remitting multiple sclerosis

Background and objective:

To define the pathological substrate underlying disability in multiple sclerosis by evaluating the relationship of resting-state functional connectivity with microstructural brain damage, as assessed by diffusion tensor imaging, and clinical impairments.

Methods:

Thirty relapsing–remitting patients and 24 controls underwent 3T-MRI; motor abilities were evaluated by using measures of walking speed, hand dexterity and balance capability, while information processing speed was evaluated by a paced auditory serial addiction task. Independent component analysis and tract-based spatial statistics were applied to RS-fMRI and diffusion tensor imaging data using FSL software. Group differences, after dual regression, and clinical correlations were modelled with General-Linear-Model and corrected for multiple comparisons.

Results:

Patients showed decreased functional connectivity in 5 of 11 resting-state-networks (cerebellar, executive-control, medial-visual, basal ganglia and sensorimotor), changes in inter-network correlations and widespread white matter microstructural damage. In multiple sclerosis, corpus callosum microstructural damage positively correlated with functional connectivity in cerebellar and auditory networks. Moreover, functional connectivity within the medial-visual network inversely correlated with information processing speed. White matter widespread microstructural damage inversely correlated with both the paced auditory serial addiction task and hand dexterity.

Conclusions:

Despite the within-network functional connectivity decrease and the widespread microstructural damage, the inter-network functional connectivity changes suggest a global brain functional rearrangement in multiple sclerosis. The correlation between functional connectivity alterations and callosal damage uncovers a link between functional and structural connectivity. Finally, functional connectivity abnormalities affect information processing speed rather than motor abilities.