Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease.

Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer's disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training

THE DEFAULT MODE NETWORK AND EXECUTIVE FUNCTION: INFLUENCE OF AGE, WHITE MATTER CONNECTIVITY, AND ALZHEIMER’S PATHOLOGY

The default mode network (DMN) consists of a set of interconnected brain regions supporting autobiographical memory, our concept of the self, and the internal monologue. These processes must be maintained at all times and consume the highest amount of the brain’s energy during its baseline state. However, when faced with an active, externally-directed cognitive task, the DMN shows a small, but significant, decrease in activity. The reduction in DMN activity during the performance of an active, externally-directed task compared to a baseline state is termed task-induced deactivation (TID), which is thought to ‘free-up’ resources required to respond to external demands. However, older adults show a reduced level of TID in the DMN. Recently, it has begun to be appreciated that this decrease in TID may be associated with poorer cognitive performance, especially during tasks placing high demands on executive function (EF). Diminished DMN TID has not only been associated with increasing age but also with multiple age-related neurobiological correlates such as accumulating Alzheimer’s disease (AD) pathology and reductions in white matter (WM) connectivity. However, these biological factors—age, WM connectivity reductions and increasing AD pathology—are themselves related. Based on the literature, we hypothesized that declining WM connectivity may represent a common pathway by which both age and AD pathology contribute to diminished DMN TID. Further, we hypothesized that declines in DMN function and WM connectivity would predict poorer in EF. Three experiments were carried out to test these hypotheses. Experiment 1 tested whether WM connectivity predicted the level of DMN TID during a task requiring a high level of EF. Results from 117 adults (ages 25-83) showed that WM connectivity declined with increasing age, and that this decline in WM connectivity was directly associated with reduced DMN TID during the task. Experiment 2 tested whether declines in WM connectivity explained both age-related and AD pathology-related declines in DMN TID. Results from 29 younger adults and 35 older adults showed that declining WM connectivity was associated with increasing age and AD pathology, and that this decline in WM connectivity was a common pathway for diminished DMN TID associated with either aging or AD pathology. Experiment 3 investigated whether measures of WM connectivity and DMN TID at baseline could predict EF measured using clinically-used tests. Results from 29 older adults from Experiment 2 showed that less DMN TID predicted poorer EF at baseline and diminished WM connectivity at baseline predicted a greater decline in EF after 3 years. Further, WM connectivity explained reductions in EF predicted by baseline AD pathology, as well as further reductions in EF not predicted by baseline AD pathology. Together the results of these studies suggest that WM connectivity is a key pathway for age-related and AD pathology-related patterns of diminished DMN TID associated with poorer EF. Further, WM connectivity may represent a potential therapeutic target for interventions attempting to prevent future declines in EF occurring in aging and AD

Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure

Fact, Fiction, or Evolution: Mechanism Hypothesis of Alzheimer’s Disease

The metabolism hypothesis of Alzheimer’s disease (AD) was first proposed in 1975. In normal aging and very mild AD, the cerebral metabolic rate for oxygen (CMRO2) and cerebral blood flow (CBF) remained approximately constant, but the metabolism of glucose (CMRglu) declined markedly. This decline in CMRglu identified a specific and primary metabolic defect that triggered downstream cellular cascades evolving into AD and its characteristic neuropathological lesions. These findings led research about AD into the role of insulin resistance that foresaw modern trials of insulin for AD treatment. The metabolism hypothesis evolved over subsequent decades with improved in-vivo measurement of metabolic parameters and AD biomarkers in humans. A more recent model highlights the interrelationships between the default mode network (DMN) and biomarkers such as CMRglu, amyloid, and tau. In other words, metabolic conditions related to sustained cortical activity during aging throughout the lifetime are conducive to the deposition of amyloid. This activity is thought to underlie the “autobiographical self.” These ideas and findings motivate aging and AD-research focus on the biochemistry and cell biology of cerebral metabolism

Multimodal MRI Neuroimaging Biomarkers for Cognitive Normal Adults, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease

Multimodal magnetic resonance imaging (MRI) techniques have been developed to noninvasively measure structural, metabolic, hemodynamic and functional changes of the brain. These advantages have made MRI an important tool to investigate neurodegenerative disorders, including diagnosis, disease progression monitoring, and treatment efficacy evaluation. This paper discusses recent findings of the multimodal MRI in the context of surrogate biomarkers for identifying the risk for AD in normal cognitive (NC) adults, brain anatomical and functional alterations in amnestic mild cognitive impairment (aMCI), and Alzheimer's disease (AD) patients. Further developments of these techniques and the establishment of promising neuroimaging biomarkers will enhance our ability to diagnose aMCI and AD in their early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials

Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations.

Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment

Decreased Default Mode Network connectivity correlates with age-associated structural and cognitive changes

Ageing entails cognitive and motor decline as well as brain changes such as loss of gray (GM) and white matter (WM) integrity, neurovascular and functional connectivity alterations. Regarding connectivity, reduced resting-state fMRI connectivity between anterior and posterior nodes of the Default Mode Network (DMN) relates to cognitive function and has been postulated to be a hallmark of ageing. However, the relationship between age-related connectivity changes and other neuroimaging-based measures in ageing is fragmentarily investigated. In a sample of 116 healthy elders we aimed to study the relationship between antero-posterior DMN connectivity and measures of WM integrity, GM integrity and cerebral blood flow (CBF), assessed with an arterial spin labeling sequence. First, we replicated previous findings demonstrating DMN connectivity decreases in ageing and an association between antero-posterior DMN connectivity and memory scores. The results showed that the functional connectivity between posterior midline structures and the medial prefrontal cortex was related to measures of WM and GM integrity but not to CBF. Gray and WM correlates of anterio-posterior DMN connectivity included, but were not limited to, DMN areas and cingulum bundle. These results resembled patterns of age-related vulnerability which was studied by comparing the correlates of antero-posterior DMN with age-effect maps. These age-effect maps were obtained after performing an independent analysis with a second sample including both young and old subjects. We argue that antero-posterior connectivity might be a sensitive measure of brain ageing over the brain. By using a comprehensive approach, the results provide valuable knowledge that may shed further light on DMN connectivity dysfunctions in ageing

The influence of diet and metabolism on hippocampus and hypothalamus connectivity across the lifespan

The high prevalence of unhealthy dietary patterns, obesity, and related brain disorders such as dementia emphasise the importance of research that examines the effect of dietary and metabolic factors on brain health. Using magnetic resonance imaging (MRI) to assess brain grey matter functional connectivity (FC) and volumes, this thesis aimed to examine the relationship between measures of diet and metabolism and the brain over the adult lifespan. First, a systematic review was conducted, to examine the relationship between dietary and metabolic health in relation to a wide range of brain MRI markers. The reviewed evidence suggested that lower dietary and metabolic health quality was related to reduced brain volume and connectivity, especially in the default mode network and the frontal and temporal lobes, although there were contrasting trends for each of these associations. To address the gaps identified by the review, we examined the association between dietary and metabolic health in relation to the hippocampus and hypothalamus FC and volumes in the cross-sectional Human Connectome Project cohort of 400 younger adults and in the longitudinal Whitehall II cohort of 775 midlife-older aged adults. The Whitehall cohort had longitudinal measures of diet/metabolic markers collected every 5 years throughout their midlife (40-70 years old). First, we note that different dietary and metabolic markers have unique patterns of longitudinal trajectories from mid-to-old-age. Our findings supported the hypothesis that better dietary and metabolic health is associated with volumetric and FC differences of the hippocampus and the hypothalamus both in younger and older cohorts. Specifically, dietary and metabolic health was linked to (1) hippocampal FC with the frontal lobe, precentral gyrus, and occipital lobe and (2) hypothalamic FC with the brainstem and the basal forebrain. These findings contribute to a growing understanding of the brain networks associated with dietary and metabolic health. The thesis provides insights into when in life dietary and metabolic health measures are related to brain health. Our findings indicated that in order to promote brain health in older age, some metabolic factors may be better targeted in midlife (e.g., cholesterol, diet, abdominal fat), while other factors should be targeted as early as possible (blood pressure, body composition/BMI). This may have implications for preventative lifestyle interventions to reduce the risk of developing dementia and to maintain overall brain health