Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD

인지 노쇠에서 FDG PET과 휴지상태 fMRI를 이용한 뇌 신경 활동과 기능적 연결성 패턴의 동적 변화 연구

학위논문(박사)--서울대학교 대학원 :의과대학 의과학과,2019. 8. 이재성.Cognitive frailty is a recently defined clinical condition characterized by concurrent appearance of physical frailty and mild cognitive impairments (MCI). Literature suggests common neuropathophysiological processes underlying physical and cognitive deficits, and physical dysfunction promotes cognitive decline, eventually leading to the emergence of Alzheimers disease dementia. It remains to be discovered how neural activities and brain network reconfigurations are altered in the presence of physical frailty in MCI. In the present study, [18F]FDG PET and resting state fMRI scans were examined in 21 MCI patients without physical frailty (robust group: mean age = 74.7 ± 5.8 years) and 27 MCI with physical frailty (at-risk group: mean age = 75.5 ± 7.3 years). The first part of the study aimed to investigate changes in glucose metabolism and regional homogeneity in cognitive frailty. Regional cerebral hypometabolism was observed in right frontal cortex, anterior cingulate, and bilateral superior parietal cortex in at-risk group, and the metabolic changes in left superior parietal cortex were associated with poorer performances in handgrip strength and executive function. Brain regional homogeneity was reduced in bilateral caudate, right medial and lateral frontal cortex, right superior temporal cortex, and cerebellum, and was increased in right precuneus and cerebellum. Decreased regional homogeneity in bilateral caudate and right superior temporal cortex showed correlations with weaker grip strength, slower gait speed, and lower physical activity, and the regional changes were also linked to cognitive performances in language and visuospatial function. The results demonstrated that the metabolic and functional alterations in cognitive frailty resembled Alzheimer's disease related pattern. The second part of the study aimed to explore alterations in dynamic functional connectivity states and the temporal properties. Dynamic functional connectivity was measured using a sliding-window approach, and certain connectivity configurations (states) were estimated using k-means clustering method. Four distinguishing patterns of functional connectivity were found during the resting state scan time in our MCI cohorts. The most frequently occurring state (State 1) displayed mostly within-network connections, and the less occurring states (States 2, 3 and 4) displayed stronger between-network connections in both positive and negative fashions. The alterations in the temporal properties of dynamic states such as the number of transition, fractional windows, and mean dwell time of states did not reach the significance level, however, at-risk group appeared to have less reoccurrence of within-network State 1 and more reoccurrences of between-network States 2 and 3. Reduced reoccurrence and shorter dwell time of within-network State 1 were significantly correlated with weaker handgrip strength, and the abnormally reduced within-network State 1 may reflect reduced functional network segregation coupled with physical deficits. On the other hand, higher reoccurrence and longer dwell time of State 2, which was characterized by heightened default mode network within-connectivity and increased interactions between default mode network and sensorimotor networks were associated with poorer MMSE-K score. The overexpression of interactions between default mode and sensorimotor networks may interfere with network functional specializations, leading to poor cognitive function. Furthermore, the functional connectivity strengths between sensorimotor and cognitive networks and within cognitive control network were altered in at-risk individuals. The neuroimaging outcomes present that aberrant functional changes in frontal, temporal and parietal cortex may indicate advanced pathological process in the presence of physical frailty in MCI. The time-varying network reconfigurations indicating decreased functional segregation of brain networks may also serve as a potential biomarker in cognitive frailty.인지 노쇠는 신체적 노쇠와 경도 인지 장애가 동시에 존재하는 것이 특징적인 최근 정의된 임상적 질환이다. 기존 연구에 의하면 신체적 노쇠와 인지 기능 저하는 공통적인 신경 병리적 기전을 가지는 것으로 알려져 있다. 또한, 신체적 기능 장애는 인지 기능 감소를 촉진하고, 나아가 알츠하이머병 치매 발병까지 연결된다. 신체적 노쇠를 보이는 경도 인지 장애 환자에서의 신경 활동 변화와 동적 뇌 네트워크 재구성 변화에 대한 연구는 아직까지 보고된 바가 없으며, 본 연구 결과를 통하여 뇌 영상 데이터가 인지 노쇠의 중요한 바이오마커의 역할을 할 것으로 기대된다. 본 연구에서는 [18F]FDG PET과 rs-fMRI 뇌 영상 검사를 총 48명의 경도 인지 기능 장애 환자 (신체적 노쇠가 없는 대조군: 21명, 평균 연령 = 74.7 ± 5.8세; 신체적 노쇠가 있는 위험군: 27명, 평균 연령 = 75.5 ± 7.3세)를 대상으로 진행하였다. 본 연구의 첫번째 부분에서는 위험군에서의 뇌 영역의 포도당 대사의 변화와 regional homogeneity를 이용한 뇌 활동 변화를 조사하였다. 위험군에서는 우측 전두피질, 전대상피질, 양측 상두정소엽에서 뇌 대사 감소가 나타났고, 상두정소엽에서의 대사 변화와 악력, 집행 기능과 양의 상관 관계를 보였다. 뇌 영역의 regional homogeneity 변화는 양측 꼬리핵, 우측 내측과 가측 전두피질, 우측 상측두피질, 소뇌에서 감소하는 것으로 보였고, 꼬리핵과 상측두피질에서의 regional homogeneity 감소는 악력, 보행 속도, 신체 활동 감소 수치와 높은 상관성을 보였다. 또한, 언어 및 시공간 기능의 인지 수행 능력과 높은 상관 관계를 보이는 것을 관찰하였다. 본 연구의 두번째 부분은 위험군에서의 뇌 네트워크의 동적 기능적 연결성과 특징 변화를 살펴보았다. 동적 기능적 연결 분석은 sliding-window 방법과 k-means clustering 방법을 사용하여 뇌 네트워크 연결 구성 상태 (State)를 추정하였다. 휴지기 상태의 영상 촬영 시간 동안 총 4개의 기능적 연결성 패턴이 발견되었고, 가장 자주 발생하는 State 1은 주로 뇌 네트워크 내부 연결성을 보이고, 네트워크 간의 연결성은 약한 것이 특징으로 나타났다. 그 다음으로 자주 나타난 State 2, 3, 4는 네트워크 간의 양과 음의 연결성 모두 강하게 나타났다. State의 전환 수, fractional windows, mean dwell time과 같은 시간적 속성의 그룹 비교 결과는 유의한 수준에 도달하지 못했지만, 위험군에서 State1의 재발현이 감소되고 State 2, State 3의 발현 증가가 나타나는 것으로 확인되었다. State 1의 속성 (fractional windows, mean dwell time)이 악력 및 신체 활동량과 양의 상관 관계를 나타냈고, 반면에, 디폴트 모드 네트워크 기능 연결성 증가가 특징적이었던 State 2의 발현 증가 및 dwell time 증가는 낮은 MMSE-K 점수 저하와 상관이 있는 것을 관찰하였다. 네트워크 변화는 노화현상에서 관측되는 네트워크 기능적 분리 (functional segregation)의 감소를 나타내는 것으로 생각되며, 신체적 기능 저하가 이러한 현상을 촉진하는 것으로 보여진다. 뿐만 아니라, 위험군에서 감각운동 네트워크와 인지 기능 네트워크 간의 기능적 연결성과 인지 조절 네트워크 내부 연결성 세기에도 변화가 나타나는 것을 발견하였다. 결론으로는, 뇌 영상 분석 결과에서 인지 노쇠 위험군 환자에서 전두엽, 측두엽 그리고 두정엽에서 기능적 활동 변화가 나타나는 것을 관찰하였고, 이는 신체적 노쇠가 존재할 경우 병리학적 과정이 가속화되는 것으로 추정된다. 뇌 네트워크의 동적 기능적 연결성 분석을 통해 악력 감소와 함께 네트워크 기능적 분리 감소 현상이 두드러지는 것을 확인하였으며, 따라서, 뇌 영상 데이터가 인지 노쇠의 생체 표지자 역할을 할 수 있는 것으로 기대된다.Chapter 1. Introduction 1 1.1 Frailty and cognition 1 1.2 Purpose of the study 3 Chapter 2. Methodological Background 5 2.1 Measurement of cerebral glucose metabolism using [18F]FDG PET. 5 2.2 Measurements of brain functional activity and intrinsic network using rsfMRI 6 2.2.1 Regional homogeneity using rs-fMRI 6 2.2.2 Group independent component analysis . 7 2.3 Dynamic functional connectivity analysis 10 Chapter 3. Subjects and Methods. 13 3.1 Participants . 13 3.1.1 Criteria of participants 13 3.1.2 Neuropsychological tests 14 3.1.3 Physical frailty definition . 14 3.1.4 Acquisition of [18F]FDG PET and rs-fMR images 19 3.1.5 Statistical analysis . 19 3.2 [18F]FDG PET image analysis . 20 3.2.1 Preprocessing steps of [18F]FDG PET . 20 3.2.2 Statistical analysis . 22 3.3 Resting state functional MRI analysis 22 3.3.1 Preprocessing steps of rs-fMRI 22 3.3.2 Calculations of regional homogeneity 23 3.3.3 Statistical analysis . 23 3.4 Functional connectivity analysis using rs-fMRI. 23 3.4.1 Group independent component analysis . 23 3.4.2 Dynamic functional network connectivity analysis 24 3.4.3 Reproducibility analyses of dynamic functional connectivity states. 26 3.4.4 Graph theory-based topological analysis: network efficiency . 27 3.4.5 Statistical analysis . 28 Chapter 4. Results. 30 4.1 Demographic and clinical characteristics of robust and at-risk groups 30 4.2 Glucose metabolism using [18F]FDG PET . 32 4.2.1 Group comparison in glucose metabolism 32 4.2.2 Relationships between cerebral glucose metabolism and physical and cognitive performances. 35 4.3 Resting-state functional activity using regional homogeneity. 38 4.3.1 Group comparison in regional homogeneity. 38 4.3.2 Relationships between regional homogeneity and physical and cognitive performances 41 4.4 Dynamic functional connectivity of brain networks . 45 4.4.1 Functional connectivity networks . 45 4.4.2 Dynamic functional connectivity states. 53 4.4.3 Validation results of dynamic functional connectivity states 57 4.4.4 Temporal properties of dynamic functional connectivity states . 62 4.4.5 Relationships between dynamic functional connectivity measures and physical and cognitive performances 66 4.4.6 Functional connectivity strengths in states . 70 4.4.7 Dynamic changes of global and local network efficiency 72 Chapter 5. Discussion 75 5.1 Metabolic and functional abnormalities in cognitive frailty 75 5.2 Alterations of dynamic functional connectivity states in cognitive frailty. 79 5.3 Conclusion and limitations of the study . 84 References . 88 국문 초록. 102Docto

Heritability of the Effective Connectivity in the Resting-State Default Mode Network

The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity. Model comparison favored a full-connected model. Structural equal modeling was used to estimate the additive genetics (A), common environment (C) and unique environment (E) contributions to variance for the DMN effective connectivity. The ACE model was preferred in the comparison of structural equation models. Heritability of DMN effective connectivity was 0.54, suggesting that the genetic made a greater contribution to the effective connectivity within DMN. Establishing the heritability of default-mode effective connectivity endorses the use of resting-state networks as endophenotypes or intermediate phenotypes in the search for the genetic basis of psychiatric or neurological illnesses

Novel non-linear approaches to understanding the dynamic brain: knowledge from rsfMRI and EEG studies

Advances in neuroimaging techniques have been critical to identifying new biomarkers for brain diseases. Resting State Functional Magnetic Resonance Imaging (rsfMRI) non-invasively quantifies the Blood Oxygen Level Dependent (BOLD) signal across brain regions with high spatial resolution; whilst temporal resolution of Electroencephalography (EEG) in measuring the brain's electrical response is unsurpassed. Most of the statistical and machine learning methods used to analyze rsfMRI and EEG data, are static and linear, fail to capture the dynamics and complexity of the brain, and are prone to residual noise. The general goals of this thesis dissertation are i) to provide methodological insight by proposing a statistical method namely point process analysis (PPA) and a machine learning (ML) multiband non-linear EEG method. These methods are especially useful to investigate the brain configuration of older participants and individuals with neurodegenerative diseases, and to predict age and sleep quality; and ii) to share biological insights about synchronization between brain regions (i.e., functional connectivity and dynamic functional connectivity) in different stages of mild cognitive impairment and in Alzheimer's disease. The findings, reported and discussed in this thesis, open a path for new research ideas such as applying PPA to EEG data, adjusting the non-linear ML algorithm to apply it to rsfMRI and use these methods to better understand other neurological diseases. Los avances en las técnicas de neuroimagen han sido fundamentales para identificar nuevos biomarcadores de enfermedades cerebrales. La resonancia magnética funcional en estado de reposo (rsfMRI) cuantifica de forma no invasiva la señal dependiente del nivel de oxígeno en sangre (BOLD) en todas las regiones del cerebro con una alta resolución espacial, mientras que la resolución temporal de la electroencefalografía (EEG) para medir la respuesta eléctrica del cerebro es insuperable. La mayoría de los métodos estadísticos y de aprendizaje automático utilizados para analizar datos de rsfMRI y EEG son estáticos y lineales, no captan el dinamismo y la complejidad del cerebro y son propensos al ruido residual. Los objetivos generales de esta tesis doctoral son i) proporcionar una visión metodológica proponiendo un método estadístico, llamado análisis por proceso de puntos (PPA), y un método de aprendizaje automático (ML) multibanda no lineal de EEG. Estos métodos son especialmente útiles para investigar la configuración cerebral de participantes de edad avanzada y de individuos con enfermedades neurodegenerativas, y para predecir la edad y la calidad del sueño; y ii) compartir conocimientos biológicos sobre la sincronización entre regiones cerebrales (es decir, la conectividad funcional y la conectividad funcional dinámica) en diferentes etapas del deterioro cognitivo leve y en la enfermedad de Alzheimer. Los hallazgos, comunicados y discutidos en esta tesis, abren un camino para nuevas ideas de investigación, como la aplicación de PPA a datos de EEG, el ajuste del algoritmo ML no lineal para aplicarlo a rsfMRI y el uso de estos métodos para comprender mejor otras enfermedades neurológicas

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network

Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development

Increased functional connectivity patterns in mild Alzheimer’s disease: a rsfMRI study

Background: Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer’s disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. Methods: In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer’s disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson’s correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. Results: Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. Conclusion: The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.info:eu-repo/semantics/publishedVersio