Comparison of memory fMRI response among normal, MCI, and Alzheimer's patients

Cerebrovascular Dysfunction in Alzheimer's Disease and Transgenic Rodent Models

Alzheimer's Disease (AD) and Alzheimer's Disease-Related Dementia (ADRD) are the primary causes of dementia that has a devastating effect on the quality of life and is a tremendous economic burden on the healthcare system. The accumulation of extracellular beta-amyloid (Aβ) plaques and intracellular hyperphosphorylated tau-containing neurofibrillary tangles (NFTs) in the brain are the hallmarks of AD. They are also thought to be the underlying cause of inflammation, neurodegeneration, brain atrophy, and cognitive impairments that accompany AD. The discovery of APP, PS1, and PS2 mutations that increase Aβ production in families with early onset familial AD led to the development of numerous transgenic rodent models of AD. These models have provided new insight into the role of Aβ in AD; however, they do not fully replicate AD pathology in patients. Familial AD patients with mutations that elevate the production of Aβ represent only a small fraction of dementia patients. In contrast, those with late-onset sporadic AD constitute the majority of cases. This observation, along with the failure of previous clinical trials targeting Aβ or Tau and the modest success of recent trials using Aβ monoclonal antibodies, has led to a reappraisal of the view that Aβ accumulation is the sole factor in the pathogenesis of AD. More recent studies have established that cerebral vascular dysfunction is one of the earliest changes seen in AD, and 67% of the candidate genes linked to AD are expressed in the cerebral vasculature. Thus, there is an increasing appreciation of the vascular contribution to AD, and the National Institute on Aging (NIA) and the Alzheimer's Disease Foundation recently prioritized it as a focused research area. This review summarizes the strengths and limitations of the most commonly used transgenic AD animal models and current views about the contribution of Aβ accumulation versus cerebrovascular dysfunction in the pathogenesis of AD.

Brain Representation of Animal and Non-Animal Images in Patients with Mild Cognitive Impairment and Alzheimer's Disease

Background

In early Alzheimer's disease (AD), high-level visual functions and processing speed are impacted. Few functional magnetic resonance imaging (fMRI) studies have investigated high-level visual deficits in AD, yet none have explored brain activity patterns during rapid animal/non-animal categorization tasks.

Objective

To address this, we utilized the previously known Integrated Cognitive Assessment (ICA) to collect fMRI data and compare healthy controls (HC) to individuals with mild cognitive impairment (MCI) and mild AD.

Methods

The ICA encompasses a rapid visual categorization task that involves distinguishing between animals and non-animals within natural scenes. To comprehensively explore variations in brain activity levels and patterns, we conducted both univariate and multivariate analyses of fMRI data.

Results

The ICA task elicited activation across a range of brain regions, encompassing the temporal, parietal, occipital, and frontal lobes. Univariate analysis, which compared responses to animal versus non-animal stimuli, showed no significant differences in the regions of interest (ROIs) across all groups, with the exception of the left anterior supramarginal gyrus in the HC group. In contrast, multivariate analysis revealed that in both HC and MCI groups, several regions could differentiate between animals and non-animals based on distinct patterns of activity. Notably, such differentiation was absent within the mild AD group.

Conclusions

Our study highlights the ICA task's potential as a valuable cognitive assessment tool designed for MCI and AD. Additionally, our use of fMRI pattern analysis provides valuable insights into the complex changes in brain function associated with AD. This approach holds promise for enhancing our understanding of the disease's progression.

Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer's disease using electrophysiological methods

With an aging population, cognitive decline and neurodegenerative disorders are an emerging public health crises with enormous, yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.

APOE ɛ4, but not polygenic Alzheimer's disease risk, is related to longitudinal decrease in hippocampal brain activity in non-demented individuals

The hippocampus is affected early in Alzheimer's disease (AD) and altered hippocampal functioning influences normal cognitive aging. Here, we used task-based functional MRI to assess if the APOE ɛ4 allele or a polygenic risk score (PRS) for AD was linked to longitudinal changes in memory-related hippocampal activation in normal aging (baseline age 50-95, n = 292; n = 182 at 4 years follow-up, subsequently non-demented for at least 2 years). Mixed-models were used to predict level and change in hippocampal activation by APOE ɛ4 status and PRS based on gene variants previously linked to AD at p ≤ 1, p < 0.05, or p < 5e-8 (excluding APOE). APOE ɛ4 and PRS_p<5e-8 significantly predicted AD risk in a larger sample from the same study population (n = 1542), while PRS_p≤1 predicted memory decline. APOE ɛ4 was linked to decreased hippocampal activation over time, with the most prominent effect in the posterior hippocampi, while PRS was unrelated to hippocampal activation at all p-thresholds. These results suggests a link for APOE ɛ4, but not for AD genetics in general, on functional changes of the hippocampi in normal aging.

The Role of Ryanodine Receptors in Regulating Neuronal Activity and Its Connection to the Development of Alzheimer's Disease

Research into the early impacts of Alzheimer's disease (AD) on synapse function is one of the most promising approaches to finding a treatment. In this context, we have recently demonstrated that the Abeta42 peptide, which builds up in the brain during the processing of the amyloid precursor protein (APP), targets the ryanodine receptors (RyRs) of mouse hippocampal neurons and potentiates calcium (Ca²⁺) release from the endoplasmic reticulum (ER). The uncontrolled increase in intracellular calcium concentration ([Ca²⁺]_i), leading to the development of Ca²⁺ dysregulation events and related excitable and synaptic dysfunctions, is a consolidated hallmark of AD onset and possibly other neurodegenerative diseases. Since RyRs contribute to increasing [Ca²⁺]_i and are thought to be a promising target for AD treatment, the goal of this review is to summarize the current level of knowledge regarding the involvement of RyRs in governing neuronal function both in physiological conditions and during the onset of AD.

Effect of online tDCS to left somatomotor cortex on neuropsychiatric symptoms among older adults at risk for dementia

BACKGROUND

Neuropsychiatric symptoms (NPS) in mild cognitive impairment (MCI) cause distress to patients and caregivers, and accelerate progression to dementia. Transcranial direct current stimulation (tDCS) is a promising non-invasive treatment for NPS.

OBJECTIVE/HYPOTHESIS

This pilot study assessed behavioral and neural effects of a 4-week anodal tDCS intervention targeting left sensorimotor cortex (LSMC: left precentral/postcentral gyri) during visual attention (compared to online sham tDCS), in 40 older adults (24 females, mean age = 71) with MCI.

METHODS

A phase 0 double-blinded randomized control trial was conducted. NPS (patient-reported mood symptoms plus a caregiver-reported questionnaire) and fMRI were measured at baseline and immediately post-intervention.

RESULTS

Generalized Estimating Equations found no significant group by time interactions for either NPS measure. However, there was evidence of decreased patient-reported NPS (Wald's χ2 = 3.80, p = .051), decreased LSMC activation during visual attention (Wald's χ2 = 2.93, p = .087), and increased LSMC-amygdala resting-state functional connectivity (rsFC; Wald's χ2 = 3.13, p = .077) in intervention group from pre-to post-intervention. Decrease in LSMC activation (Wald's χ2 = 9.20, p = .002) and increase in LSMC-amygdala rsFC (Wald's χ2 = 4.72, p = .030) related to decreased patient-reported NPS. Increased positive valence across sessions was significantly associated with intervention-related NPS improvement (Wald's χ2 = 22.92, p < .001). There were no findings for caregiver-reported NPS. Effects were stronger for left postcentral compared to left precentral gyrus.

CONCLUSION

We found tentative evidence that tDCS applied to LSMC during visual attention in older adults with MCI improved NPS via changes in LSMC activation and LSMC-amygdala rsFC, suggesting improved emotion regulation. Patient-reported NPS was more sensitive to these changes than caregiver-reports, and effects were strongest for left postcentral gyrus. Follow-up studies should perform precise mechanistic investigation and efficacy testing.

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.

Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.

Episodic Memory, Hippocampal Volume, and Function for Classification of Mild Cognitive Impairment Patients Regarding Amyloid Pathology

Background:

Previous studies of hippocampal function and volume related to episodic memory deficits in patients with amnestic mild cognitive impairment (aMCI) have produced mixed results including increased or decreased activity and volume. However, most of them have not included biomarkers, such as amyloid-β (Aβ) deposition which is the hallmark for early identification of the Alzheimer’s disease continuum.

Objective:

We investigated the role of Aβ deposition, functional hippocampal activity and structural volume in aMCI patients and healthy elderly controls (HC) using a new functional MRI (fMRI) ecological episodic memory task.

Methods:

Forty-six older adults were included, among them Aβ PET PIB positive (PIB+) aMCI (N = 17), Aβ PET PIB negative (PIB–) aMCI (N = 15), and HC (N = 14). Hippocampal volume and function were analyzed using Freesurfer v6.0 and FSL for news headlines episodic memory fMRI task, and logistic regression for group classification in conjunction with episodic memory task and traditional neuropsychological tests.

Results:

The aMCI PIB+ and PIB–patients showed significantly worse performance in relation to HC in most traditional neuropsychological tests and within group difference only on story recall and the ecological episodic memory fMRI task delayed recall. The classification model reached a significant accuracy (78%) and the classification pattern characterizing the PIB+ included decreased left hippocampal function and volume, increased right hippocampal function and volume, and worse episodic memory performance differing from PIB–which showed increased left hippocampus volume.

Conclusion:

The main findings showed differential neural correlates, hippocampal volume and function during episodic memory in aMCI patients with the presence of Aβ deposition.

Novelty-Related fMRI Responses of Precuneus and Medial Temporal Regions in Individuals at Risk for Alzheimer Disease

Background and Objectives

We assessed whether novelty-related fMRI activity in medial temporal lobe regions and the precuneus follows an inverted U-shaped pattern across the clinical spectrum of increased Alzheimer disease (AD) risk as previously suggested. Specifically, we tested for potentially increased activity in individuals with a higher AD risk due to subjective cognitive decline (SCD) or mild cognitive impairment (MCI). We further tested whether activity differences related to diagnostic groups were accounted for by CSF markers of AD or brain atrophy.

Methods

We studied 499 participants aged 60–88 years from the German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study (DELCODE) who underwent task-fMRI. Participants included 163 cognitively normal (healthy control, HC) individuals, 222 SCD, 82 MCI, and 32 patients with clinical diagnosis of mild AD. CSF levels of β-amyloid 42/40 ratio and phosphorylated-tau181 were available from 232 participants. We used region-based analyses to assess novelty-related activity (novel > highly familiar scenes) in entorhinal cortex, hippocampus, and precuneus as well as whole-brain voxel-wise analyses. First, general linear models tested differences in fMRI activity between participant groups. Complementary regression models tested quadratic relationships between memory impairment and activity. Second, relationships of activity with AD CSF biomarkers and brain volume were analyzed. Analyses were controlled for age, sex, study site, and education.

Results

In the precuneus, we observed an inverted U-shaped pattern of novelty-related activity across groups, with higher activity in SCD and MCI compared with HC, but not in patients with AD who showed relatively lower activity than MCI. This nonlinear pattern was confirmed by a quadratic relationship between memory impairment and precuneus activity. Precuneus activity was not related to AD biomarkers or brain volume. In contrast to the precuneus, hippocampal activity was reduced in AD dementia compared with all other groups and related to AD biomarkers.

Discussion

Novelty-related activity in the precuneus follows a nonlinear pattern across the clinical spectrum of increased AD risk. Although the underlying mechanism remains unclear, increased precuneus activity might represent an early signature of memory impairment. Our results highlight the nonlinearity of activity alterations that should be considered in clinical trials using functional outcome measures or targeting hyperactivity.

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

Assessment of Brain Function in Patients With Cognitive Impairment Based on fNIRS and Gait Analysis

Background

Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer’s disease (AD). This study aims to explore the changes in gait and brain co-functional connectivity between cognitively healthy and cognitively impaired groups under dual-task walking through the functional near-infrared spectroscopy (fNIRS) and gait analysis devices.

Method

This study used fNIRS device and gait analysis devices to collect the data of 54 older adults. According to the Mini-mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scales, the older adults were cognitively healthy (control group) and cognitively impaired (experimental group), of which 38 were in the control group and 16 were in the experimental group. The experiment was divided into a total of three sets of task experiments: a walking-only experiment, a dual-task walking-easy (DTW-easy) experiment, and a dual-task walking-difficult (DTW-difficult) experiment.

Main Result

For the cognitively impaired and cognitively healthy populations, there were no significant differences in overall functional connectivity, region of interest (ROI) connection strength, and gait performance during single-task walking between the two groups.Whereas the performances of DTW differed significantly from the single-task walking in terms of between-group variability of functional connectivity strength change values, and ROI connection strength change values in relation to the dual-task cost of gait. Finally, the cognitively impaired group was significantly more affected by DTW-difficult tasks than the cognitively healthy group.

Conclusion

This study provides a new approach to assist in the diagnosis of people with cognitive impairment and provides a new research pathway for the identification of cognitive impairment.

The roles of executive functioning, simple attention, and medial temporal lobes in early learning, late learning, and delayed recall

Fractionating performance of a verbal list-learning test can provide a nuanced interpretation of the relationship between brain networks and learning and memory abilities. Within older adult samples, including those with mild cognitive impairment and Alzheimer’s disease, cortical volumes for attention and executive functioning networks correlate more strongly with neuropsychological performance measures of early learning trials relative to late learning and delayed recall. In contrast, medial temporal lobe (MTL) structures, such as the hippocampus, are more strongly correlated to performance on late learning and delayed recall measures relative to early learning. We sought to extend these findings by evaluating the contributions of simple attention, executive function (EF), and MTL structures to learning and recall in a cognitively heterogeneous sample of older adults that included healthy controls (n = 54), adults with MCI (n = 63), and those with dementia (n = 13). We used canonical correlation analyses to test the hypotheses that the contributions of EF, simple attention, and the MTL to verbal memory would differ across phases of learning and recall. Results showed that relationships between the MTL and memory were the only ones to demonstrate a graded pattern of association, ranging from r = .46 to .57 across early learning, late learning, and delayed recall. Simple attention and EF were both significantly and moderately related to learning and recall, but those relationships did not vary across phases as hypothesized. We explore alternative interpretations for our discrepant findings, including the influence of sample characteristics and methodology, advocating for multivariate approaches.

Altered linear coupling between stimulus-evoked blood flow and oxygen metabolism in the aging human brain

Neural-vascular coupling (NVC) is the process by which oxygen and nutrients are delivered to metabolically active neurons by blood vessels. Murine models of NVC disruption have revealed its critical role in healthy neural function. We hypothesized that, in humans, aging exerts detrimental effects upon the integrity of the neural-glial-vascular system that underlies NVC. To test this hypothesis, calibrated functional magnetic resonance imaging (cfMRI) was used to characterize age-related changes in cerebral blood flow (CBF) and oxygen metabolism during visual cortex stimulation. Thirty-three younger and 27 older participants underwent cfMRI scanning during both an attention-controlled visual stimulation task and a hypercapnia paradigm used to calibrate the blood-oxygen-level-dependent signal. Measurement of stimulus-evoked blood flow and oxygen metabolism permitted calculation of the NVC ratio to assess the integrity of neural-vascular communication. Consistent with our hypothesis, we observed monotonic NVC ratio increases with increasing visual stimulation frequency in younger adults but not in older adults. Age-related changes in stimulus-evoked cerebrovascular and neurometabolic signal could not fully explain this disruption; increases in stimulus-evoked neurometabolic activity elicited corresponding increases in stimulus-evoked CBF in younger but not in older adults. These results implicate age-related, demand-dependent failures of the neural-glial-vascular structures that comprise the NVC system.

Near-Lifespan Tracking of Cerebral Microvascular Degeneration in Aging to Alzheimer’s Continuum

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions of people worldwide and is currently incurable. As the population ages, AD and related dementia are becoming the biggest epidemic in medical history: the number of people aged 65 and older with AD is projected to increase between two- and three-fold by 2050. Imaging and biomarker studies suggest that the pathophysiological processes of AD begin more than a decade before the diagnosis of dementia, opening the possibility of early, preemptive prediction. For accurate prediction, it is important although challenging to fully understand how multiple etiologies and age-related prodromal processes contribute to the onset of Alzheimer’s continuum, across a long period comparable to the lifespan. Addressing this challenge was one of the overarching transformative concepts at the 2015 AD Research Summit, “to develop new programs on systems biology and integrative physiology to gain a deeper understanding of the complex biology of the disease.”

Among other factors, cerebral microvascular degeneration (CMD) may play a key role in the onset and development of Alzheimer’s continuum, potentially prior to, along with, or independently of the beta-amyloid (Aβ) accumulation. Despite its importance for early detection and as a therapeutic target for early intervention, it is unknown whether CMD is a causal factor for AD pathogenesis or an early consequence of multifactorial conditions that lead to AD at a later stage. Here, this Viewpoint suggests that we should fill two critical knowledge gaps: (1) Temporal relationships between various CMDs and other key factors before/during/after the onset of Alzheimer’s continuum have not been established; (2) Little integrative study down to the capillary vessel level has been conducted on how individual defects in various microvascular structural and flow properties distinctly correlate with and/or contribute to neuronal degeneration. As the first step toward filling these gaps, I propose utilizing recent advances in microscopic imaging and image analysis techniques to longitudinally track a comprehensive set of CMDs over the lifespan in model animals, along with Aβ, tau, neuronal degeneration, and cognitive impairment when possible.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Retinal Vasculopathy in Alzheimer's Disease

The retina has been increasingly investigated as a site of Alzheimer’s disease (AD) manifestation for over a decade. Early reports documented degeneration of retinal ganglion cells and their axonal projections. Our group provided the first evidence of the key pathological hallmarks of AD, amyloid β-protein (Aβ) plaques including vascular Aβ deposits, in the retina of AD and mild cognitively impaired (MCI) patients. Subsequent studies validated these findings and further identified electroretinography and vision deficits, retinal (p)tau and inflammation, intracellular Aβ accumulation, and retinal ganglion cell-subtype degeneration surrounding Aβ plaques in these patients. Our data suggest that the brain and retina follow a similar trajectory during AD progression, probably due to their common embryonic origin and anatomical proximity. However, the retina is the only CNS organ feasible for direct, repeated, and non-invasive ophthalmic examination with ultra-high spatial resolution and sensitivity. Neurovascular unit integrity is key to maintaining normal CNS function and cerebral vascular abnormalities are increasingly recognized as early and pivotal factors driving cognitive impairment in AD. Likewise, retinal vascular abnormalities such as changes in vessel density and fractal dimensions, blood flow, foveal avascular zone, curvature tortuosity, and arteriole-to-venule ratio were described in AD patients including early-stage cases. A rapidly growing number of reports have suggested that cerebral and retinal vasculopathy are tightly associated with cognitive deficits in AD patients and animal models. Importantly, we recently identified early and progressive deficiency in retinal vascular platelet-derived growth factor receptor-β (PDGFRβ) expression and pericyte loss that were associated with retinal vascular amyloidosis and cerebral amyloid angiopathy in MCI and AD patients. Other studies utilizing optical coherence tomography (OCT), retinal amyloid-fluorescence imaging and retinal hyperspectral imaging have made significant progress in visualizing and quantifying AD pathology through the retina. With new advances in OCT angiography, OCT leakage, scanning laser microscopy, fluorescein angiography and adaptive optics imaging, future studies focusing on retinal vascular AD pathologies could transform non-invasive pre-clinical AD diagnosis and monitoring.

Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer's Disease Using Susceptibility-Weighted Imaging

The presence of iron is an important factor for normal brain functions, whereas excessive deposition of iron may impair normal cognitive function in the brain and lead to Alzheimer’s disease (AD). MRI has been widely applied to characterize brain structural and functional changes caused by AD. However, the effectiveness of using susceptibility-weighted imaging (SWI) for the analysis of brain iron deposition is still unclear, especially within the context of early AD diagnosis. Thus, in this study, we aim to explore the relationship between brain iron deposition measured by SWI with the progression of AD using various feature selection and classification methods. The proposed model was evaluated on a 69-subject SWI imaging dataset consisting of 24 AD patients, 21 mild cognitive impairment patients, and 24 normal controls. The identified AD progression-related regions were then compared with the regions reported from previous genetic association studies, and we observed considerable overlap between these two. Further, we have identified a new potential AD-related gene (MEF2C) closely related to the interaction between iron deposition and AD progression in the brain.

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.

A quadratic function of activation in individuals at risk of Alzheimer's disease

INTRODUCTION

Brain activation is hypothesized to form an inverse U-shape in prodromal Alzheimer's disease (AD), with hyperactivation in the early phase, followed by hypoactivation.

METHODS

Using task-related functional magnetic resonance imaging (fMRI), we tested the inverse U-shape hypothesis with polynomial regressions and between-group comparisons in individuals with subjective cognitive decline plus (SCD+; smaller hippocampal volumes compared to a group of healthy controls without SCD and/or apolipoprotein E [APOE] ε4 allele) or mild cognitive impairment (MCI).

RESULTS

A quadratic function modeled the relationship between proxies of disease severity (neurodegeneration, memory performance) and left superior parietal activation. Linear negative functions modeled the relationship between neurodegeneration and left hippocampal/right inferior temporal activation. Group comparison indicated presence of hyperactivation in SCD+ and hypoactivation in MCI in the left superior parietal lobule, relative to healthy controls.

DISCUSSION

These findings support the presence of an inverse U-shape model of activation and suggest that hyperactivation might represent a biomarker of the early AD stages.

Deficits in recent but not lifetime familiarity in amnestic mild cognitive impairment

People with amnestic mild cognitive impairment (aMCI) repeat questions, seemingly without any sense of familiarity (i.e., recognition of prior occurrence without recollection of episodic context). Accumulation of neurofibrillary tau in preclinical Alzheimer's disease begins in perirhinal cortex, a medial temporal lobe region linked to familiarity. Both observations would predict impaired familiarity assessment in aMCI; however, the extant evidence is mixed. To reveal familiarity impairments, it may be necessary to minimize the influence of recollection. In the current study, older adults with aMCI and healthy controls were administered two tasks on which a well-characterized patient (NB) with selective familiarity impairments due to surgical left temporal lobe excision sparing the hippocampus showed abnormal performance: frequency judgments for words exposed to in a recent study phase and judgments of cumulative lifetime familiarity for object concepts denoted by words. We also administered a process dissociation procedure (PDP) task that previously revealed spared familiarity in aMCI. We predicted that familiarity would be spared in aMCI on the PDP task, but impaired when assessed by frequency judgments for recent laboratory exposures and lifetime familiarity judgments. Familiarity was spared on the PDP task, but was impaired when probed with frequency judgments for recently exposed words in aMCI. Lifetime familiarity was also not impaired in aMCI. These results highlight the benefits of studying familiarity under conditions that minimize recollection and the value of frequency judgments in revealing familiarity deficits, and suggest that perirhinal cortex may not be necessary for accessing familiarity accumulated over a lifetime of experience.

Entorhinal Tau Predicts Hippocampal Activation and Memory Deficits in Alzheimer's Disease

BACKGROUND

To date, it remains unclear how amyloid plaques and neurofibrillary tangles are related to neural activation and, consequently, cognition in Alzheimer's disease (AD). Recent findings indicate that tau accumulation may drive hippocampal hyperactivity in cognitively normal aging, but it remains to be elucidated how tau accumulation is related to neural activation in AD.

OBJECTIVE

To determine whether the association between tau accumulation and hippocampal hyperactivation persists in mild cognitive impairment (MCI) and mild dementia or if the two measures dissociate with disease progression, we investigated the relationship between local tau deposits and memory-related neural activation in MCI and mild dementia due to AD.

METHODS

Fifteen patients with MCI or mild dementia due to AD underwent a neuropsychological assessment and performed an item memory task during functional magnetic resonance imaging. Cerebral tau accumulation was assessed using positron emission tomography and [18F]-AV-1451.

RESULTS

Entorhinal, but not global tau accumulation, was highly correlated with hippocampal activation due to visual item memory encoding and predicted memory loss over time. Neural activation in the posterior cingulate cortex and the fusiform gyrus was not significantly correlated with tau accumulation.

CONCLUSION

These findings extend previous observations in cognitively normal aging, demonstrating that entorhinal tau continues to be closely associated with hippocampal hyperactivity and memory performance in MCI and mild dementia due to AD. Furthermore, data suggest that this association is strongest in medial temporal lobe structures. In summary, our data provide novel insights into the relationship of tau accumulation to neural activation and memory in AD.

Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network

Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.

Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias

Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.

Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer's disease

INTRODUCTION

Large-scale brain networks are disrupted in the early stages of Alzheimer's disease (AD). Electroencephalography microstate analysis, a promising method for studying brain networks, parses EEG signals into topographies representing discrete, sequential network activations. Prior studies indicate that patients with AD show a pattern of global microstate disorganization. We investigated whether any specific microstate changes could be found in patients with AD and mild cognitive impairment (MCI) compared to healthy controls (HC).

MATERIALS AND METHODS

Standard EEGs were obtained from 135 HC, 117 patients with MCI, and 117 patients with AD from six Nordic memory clinics. We parsed the data into four archetypal microstates.

RESULTS

There was significantly increased duration, occurrence, and coverage of microstate A in patients with AD and MCI compared to HC. When looking at microstates in specific frequency bands, we found that microstate A was affected in delta (1-4 Hz), theta (4-8 Hz), and beta (13-30 Hz), while microstate D was affected only in the delta and theta bands. Microstate features were able to separate HC from AD with an accuracy of 69.8% and HC from MCI with an accuracy of 58.7%.

CONCLUSIONS

Further studies are needed to evaluate whether microstates represent a valuable disease classifier. Overall, patients with AD and MCI, as compared to HC, show specific microstate alterations, which are limited to specific frequency bands. These alterations suggest disruption of large-scale cortical networks in AD and MCI, which may be limited to specific frequency bands.

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.

NEW METHODS

Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.

RESULTS

The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.

COMPARISON WITH EXISTING METHODS

Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.

CONCLUSIONS

These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.

Alzheimer's Disease: The Link Between Amyloid-β and Neurovascular Dysfunction

While prevailing evidence supports that the amyloid cascade hypothesis is a key component of Alzheimer's disease (AD) pathology, many recent studies indicate that the vascular system is also a major contributor to disease progression. Vascular dysfunction and reduced cerebral blood flow (CBF) occur prior to the accumulation and aggregation of amyloid-β (Aβ) plaques and hyperphosphorylated tau tangles. Although research has predominantly focused on the cellular processes involved with Aβ-mediated neurodegeneration, effects of Aβ on CBF and neurovascular coupling are becoming more evident. This review will describe AD vascular disturbances as they relate to Aβ, including chronic cerebral hypoperfusion, hypertension, altered neurovascular coupling, and deterioration of the blood-brain barrier. In addition, we will describe recent findings about the relationship between these vascular defects and Aβ accumulation with emphasis on in vivo studies utilizing rodent AD models.

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging

The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment

Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer's disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

Functional Connectivity between the Resting-State Olfactory Network and the Hippocampus in Alzheimer's Disease

Olfactory impairment is associated with prodromal Alzheimer's disease (AD) and is a risk factor for the development of dementia. AD pathology is known to disrupt brain regions instrumental in olfactory information processing, such as the primary olfactory cortex (POC), the hippocampus, and other temporal lobe structures. This selective vulnerability suggests that the functional connectivity (FC) between the olfactory network (ON), consisting of the POC, insula and orbital frontal cortex (OFC) (Tobia et al., 2016), and the hippocampus may be impaired in early stage AD. Yet, the development trajectory of this potential FC impairment remains unclear. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate FC changes between the ON and hippocampus in four groups: aged-matched cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. FC was calculated using low frequency fMRI signal fluctuations in the ON and hippocampus (Tobia et al., 2016). We found that the FC between the ON and the right hippocampus became progressively disrupted across disease states, with significant differences between EMCI and LMCI groups. Additionally, there were no significant differences in gray matter hippocampal volumes between EMCI and LMCI groups. Lastly, the FC between the ON and hippocampus was significantly correlated with neuropsychological test scores, suggesting that it is related to cognition in a meaningful way. These findings provide the first in vivo evidence for the involvement of FC between the ON and hippocampus in AD pathology. Results suggest that functional connectivity (FC) between the olfactory network (ON) and hippocampus may be a sensitive marker for Alzheimer's disease (AD) progression, preceding gray matter volume loss.

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

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

Evidence of parietal hyperactivation in individuals with mild cognitive impairment who progressed to dementia: A longitudinal fMRI study

Hyperactivation, which is defined as a higher level of activation in patients compared to cognitively unimpaired older adults (controls; CTL), might represent an early signature of Alzheimer's Disease (AD). The goal of this study was to assess the presence and location of hyperactivation in individuals with mild cognitive impairment (MCI) who were later diagnosed with dementia, examine how hyperactivation changes longitudinally, and whether it is related to time before dementia. Forty participants, 26 with MCI and 14 CTL were enrolled in the study. Magnetic resonance imaging was used to measure functional activation while participants encoded word-pairs as well as cortical thickness and regional brain volume at study entry (Y0) and two years later (Y2). Clinical follow-up was completed every two years following study entry to identify progressors (pMCI), that is, individuals who later received a diagnosis of dementia. Task-related activation was assessed in pMCI in both hippocampi and in regions showing greater cortical thinning from Y0 to Y2 compared to CTLs. Hyperactivation was found in pMCI individuals in the right supramarginal gyrus. Persons with pMCI also showed hypoactivation in the left hippocampus and left pars opercularis. Both hyper- and hypoactivation were present at Y0 and Y2 and did not change longitudinally. Activation was not associated with time before dementia diagnosis. Smaller volume and thinner cortical thickness were associated with shorter time to diagnosis in the left hippocampus and left pars opercularis. In conclusion, hyperactivation was found in individuals who later progressed to dementia, confirming that it might represent an early biomarker to identify individuals in the prodromal phase of AD and that its understanding could contribute to elucidate the key brain mechanisms that precede dementia.

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Hyperactivation of the right parietal region was found in MCI progressors.

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Hypoactivation was found in the hippocampal and frontal regions.

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This overall pattern was specific to MCI who progressed to dementia.

Cerebral changes improved by physical activity during cognitive decline: A systematic review on MRI studies

Current treatment in late-life cognitive impairment and dementia is still limited, and there is no cure for brain tissue degeneration or reversal of cognitive decline. Physical activity represents a promising non-pharmacological interventional approach in many diseases causing cognitive impairment, but its effect on brain integrity is still largely unknown. Especially research of cerebral alterations in disease state that goes beyond observations of clinical improvement is crucial to understand disease processes and possible effective treatments. In this systematic review, we address the question how physical activity and fitness in mild cognitive impairment (MCI) and Alzheimer's disease (AD) influences brain architecture compared to cognitively healthy elderly. We review both interventional studies comprising aerobic, coordinative and resistance exercises and observational studies on fitness and physical activity combined with Magnetic Resonance imaging (MRI). Different MRI approaches were included such as volumetric and structural analyses, Diffusion Tensor Imaging (DTI), functional MRI and Cerebral Blood Flow (CBF). We evaluate MRI results for different exercise modalities and performed a methodological evaluation of interventional studies in cognitive decline compared to normal aging. According to our results, among 12 interventions in AD/MCI, aerobic exercise is most frequently applied (9 studies). Interventions in AD/MCI altogether reveal a higher methodological quality compared to interventions in healthy elderly (8.33 ± 2.19 vs. 6.25 ± 2.36 out of 13 points), with most frequent missing aspects related to descriptions of complications, lack of intention-to-treat and statistical power analyses. Effects of aerobic exercise and fitness seem to mainly impact brain structures sensitive to neurodegeneration, which especially comprise frontal, temporal and parietal regions, such as the hippocampal/parahippocampal region, precuneus, anterior cingulate and prefrontal cortex, which are reported by several studies. General fitness measured via an objective fitness assessment and questionnaires seems to have a more global cerebral effect, probably due to its long-term application, whereas distinct intervention effects of durations between 3 and 6 months seem to concentrate on more local brain regions as the hippocampus, which can also be influenced by region of interest analyses. There is still a lack of evidence on other or combined types of intervention modalities, such as resistance, coordinative as well as multicomponent exercise during cognitive decline, and complex interventions as dancing. Future research should examine their beneficial effect on brain integrity, since several non-MRI studies already point to their advantageous impact. As a further future prospect, combination and application of newly developed imaging methods such as metabolic imaging should be envisaged to understand physical activity and its cerebral influence under its many-sided facets.

A study on changes of the resting-state brain function network in patients with amnestic mild cognitive impairment

This study aimed to explore the structural and functional characteristics of the neural network of resting-state brain activities in patients with amnestic mild cognitive impairment (aMCI) by functional magnetic resonance imaging (fMRI) technology. Resting state fMRI scanning was performed on 10 clinically diagnosed aMCI patients and 10 healthy volunteers, and the difference in the resting-state brain activities between aMCI patients and healthy volunteers was compared using the brain function network regional homogeneity (ReHo) analysis method. Results of the ReHo analysis of aMCI patients and healthy volunteers revealed that the ReHo value significantly increased in the posterior cingulate gyrus region, medial frontal lobe, medial cortex of the prefrontal lobe, and part of the parietal lobe. Compared with the normal elderlies, ReHo decreased in aMCI patients in the left temporal lobe (middle temporal gyrus and inferior temporal gyrus), left parahippocampal gyrus, occipital lobe, lingual gyrus, precuneus, and other regions while ReHo increased in regions of the right frontal lobe (inferior frontal gyrus), left superior temporal gyrus, precentral gyrus (frontal lobe), right thalamus, left fusiform gyrus, and other regions. In the resting state, there may be regional abnormalities in brain functional areas in aMCI patients, which may be associated with cognitive impairment.

Multivariate group-level analysis for task fMRI data with canonical correlation analysis

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Executive Functions in Alzheimer Disease: A Systematic Review

Alzheimer's disease is a severe irreversible syndrome, characterized by a slow and progressive cognitive decline that interferes with the standard instrumental and essential functions of daily life. Promptly identifying the impairment of particular cognitive functions could be a fundamental condition to limit, through preventive or therapeutic interventions, the functional damages found in this degenerative dementia. This study aims to analyse, through a systematic review of the studies, the sensitivity of four experimental paradigms (Wisconsin Card Sorting Test, Stroop Task, Go/No-Go Task, and Flanker Task) considered as golden standard instruments for executive functions assessment in elderly subjects affected by Alzheimer dementia. This review was carried out according to the PRISMA method. Forty-five studies comparing the executive performance of patients with Alzheimer's dementia (diagnosed according to different classification criteria for dementia) and healthy elderly patients both over the age of sixty, were selected. For the research, PubMed, PsycINFO, PsycArticles databases were used. The study highlighted the importance of using standard protocols to evaluate executive dysfunction in Alzheimer's disease. The Stroop task allows discriminating better between healthy and pathological aging.

Neuroimaging and Neurolaw: Drawing the Future of Aging

Human brain-aging is a complex, multidimensional phenomenon. Knowledge of the numerous aspects that revolve around it is therefore essential if not only the medical issues, but also the social, psychological, and legal issues related to this phenomenon are to be managed correctly. In the coming decades, it will be necessary to find solutions to the management of the progressive aging of the population so as to increase the number of individuals that achieve successful aging. The aim of this article is to provide a current overview of the physiopathology of brain aging and of the role and perspectives of neuroimaging in this context. The progressive development of neuroimaging has opened new perspectives in clinical and basic research and it has modified the concept of brain aging. Neuroimaging will play an increasingly important role in the definition of the individual's brain aging in every phase of the physiological and pathological process. However, when the process involved in age-related brain cognitive diseases is being investigated, factors that might affect this process on a clinical and behavioral level (genetic susceptibility, risks factors, endocrine changes) cannot be ignored but must, on the contrary, be integrated into a neuroimaging evaluation to ensure a correct and global management, and they are therefore discussed in this article. Neuroimaging appears important to the correct management of age-related brain cognitive diseases not only within a medical perspective, but also legal, according to a wider approach based on development of relationship between neuroscience and law. The term neurolaw, the neologism born from the relationship between these two disciplines, is an emerging field of study, that deals with various issues in the impact of neurosciences on individual rights. Neuroimaging, enhancing the detection of physiological and pathological brain aging, could give an important contribution to the field of neurolaw in elderly where the full control of cognitive and volitional functions is necessary to maintain a whole series of rights linked to legal capacity. For this reason, in order to provide the clinician and researcher with a broad view of the brain-aging process, the role of neurolaw will be introduced into the brain-aging context.

Functional magnetic resonance imaging with encoding task in patients with mild cognitive impairment and different severity of leukoaraiosis

Leukoaraiosis is one of the main contributors to mild cognitive impairment due to vascular damage (vascular MCI, VMCI), whose pathophysiology has not been fully elucidated yet. We aimed to shed light on such issue using functional MRI (fMRI). Sixteen patients with VMCI were enrolled and compared with twenty-five patients with MCI but without significant vascular damage (non-vascular MCI, NVMCI) and with fifteen healthy controls (HC). They all underwent fMRI with incidental verbal learning paradigm, using a 3T scanner. Differently from cases with NVMCI (versus HC), VMCI patients presented a higher BOLD activation in the right parieto-occipital cortex and a lower activation in the left superior and middle frontal gyri, anterior cingulum and in left fronto-opercular area when compared to HC. Cortical activation evaluated by fMRI may reflect specific patterns of damage and attempt of compensation in patients with MCI and different severity of leukoaraiosis.

Effects of Different Cognitive Trainings on Amnestic Mild Cognitive Impairment in the Elderly: A One-Year Longitudinal Functional Magnetic Resonance Imaging (MRI) Study

BACKGROUND Cognitive training has been focused on the interventions of amnestic mild cognitive impairment (aMCI) in recent years, with poor understanding. MATERIAL AND METHODS The study participants with aMCI were screened in a previous intervention trial. Functional magnetic resonance imaging (fMRI) was adopted to investigate effects of single-domain cognitive training (SDCT) and multi-domain cognitive training (MDCT) on aMCI and to explore potential mechanisms. RESULTS There were significant differences in the grey matter volume of the middle frontal gyrus, superior parietal lobule, inferior temporal gyrus, fusiform gyrus, and ventral V3 between the MDCT/SDCT group and the control group (P<0.05). Regional homogeneity (ReHo) increased significantly in the right and left inferior frontal gyrus as well as in the left and right precentral gyrus after intervention in the MDCT group and the SDCT group. ReHo increased significantly in the right and left lingual gyrus of the MDCT group and the control group. ReHo reduced significantly in the right middle temporal gyrus of the MDCT group but increased significantly in the left middle temporal gyrus in the SDCT group and the control group. The voxel of grey matter in the precuneus was positively related to the language scores on RBANS (Repeatable Battery for the Assessment of Neuropsychological Status), and amygdala, fusiform gyrus, and hippocampus also had a positive relationship with delayed memory scores in RBANS of the MDCT group. In the MDCT group, the attention and reasoning scores were also positively related to the ReHo of middle temporal gyrus. CONCLUSIONS Both MDCT and SDCT may improve the aMCI at brain functional and structural levels; however, the MDCT group exhibited higher ReHo values in middle temporal gyrus and superior occipital gyrus. Also, it was confirmed that MDCT leads to better results than SDCT, showing a significant correlation of cognitive functions such as attention, memory, reasoning, and visual-spatial ability.

Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer's disease

Alzheimer's disease (AD) is caused by a cascade of changes to brain integrity. Neuroimaging biomarkers are important in diagnosis and monitoring the effects of interventions. As memory impairments are among the first symptoms of AD, the relationship between imaging findings and memory deficits is important in biomarker research. The most established magnetic resonance imaging (MRI) finding is hippocampal atrophy, which is related to memory decline and currently used as a diagnostic criterion for AD. While the medial temporal lobes are impacted early by the spread of neurofibrillary tangles, other networks and regional changes can be found quite early in the progression. Atrophy in several frontal and parietal regions, cortical thinning, and white matter alterations correlate with memory deficits in early AD. Changes in activation and connectivity have been detected by functional MRI (fMRI). Task-based fMRI studies have revealed medial temporal lobe hypoactivation, parietal hyperactivation, and frontal hyperactivation in AD during memory tasks, and activation patterns of these regions are also altered in preclinical and prodromal AD. Resting state fMRI has revealed alterations in default mode network activity related to memory in early AD. These studies are limited in part due to the historic inclusion of patients who had suspected AD but likely did not have the disorder. Modern biomarkers allow for more diagnostic certainty, allowing better understanding of neuroimaging markers in true AD, even in the preclinical stage. Larger patient cohorts, comparison of candidate imaging biomarkers to more established biomarkers, and inclusion of more detailed neuropsychological batteries to assess multiple aspects of memory are needed to better understand the memory deficit in AD and help develop new biomarkers. This article reviews MRI findings related to episodic memory impairments in AD and introduces a new study with multimodal imaging and comprehensive neuropsychiatric evaluation to overcome current limitations.

Predicted sequence of cortical tau and amyloid-β deposition in Alzheimer disease spectrum

We investigated sequential order between tau and amyloid-β (Aβ) deposition in Alzheimer disease spectrum using a conditional probability method. Two hundred twenty participants underwent ¹⁸F-flortaucipir and ¹⁸F-florbetaben positron emission tomography scans and neuropsychological tests. The presence of tau and Aβ in each region and impairment in each cognitive domain were determined by Z-score cutoffs. By comparing pairs of conditional probabilities, the sequential order of tau and Aβ deposition were determined. Probability for the presence of tau in the entorhinal cortex was higher than that of Aβ in all cortical regions, and in the medial temporal cortices, probability for the presence of tau was higher than that of Aβ. Conversely, in the remaining neocortex above the inferior temporal cortex, probability for the presence of Aβ was always higher than that of tau. Tau pathology in the entorhinal cortex may appear earlier than neocortical Aβ and may spread in the absence of Aβ within the neighboring medial temporal regions. However, Aβ may be required for massive tau deposition in the distant cortical areas.

Effects of pharmacological and nonpharmacological treatments on brain functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment: a critical review

BACKGROUND

A growing number of pharmacological and nonpharmacological trials have been performed to test the efficacy of approved or experimental treatments in Alzheimer disease (AD) and mild cognitive impairment (MCI). In this context, functional magnetic resonance imaging (fMRI) may be a good candidate to detect brain changes after a short period of treatment.

MAIN BODY

This critical review aimed to identify and discuss the available studies that have tested the efficacy of pharmacological and nonpharmacological treatments in AD and MCI cases using task-based or resting-state fMRI measures as primary outcomes. A PubMed-based literature search was performed with the use of the three macro-areas: 'disease', 'type of MRI', and 'type of treatment'. Each contribution was individually reviewed according to the Cochrane Collaboration's tool for assessing risk of bias. Study limitations were systematically detected and critically discussed. We selected 34 pharmacological and 13 nonpharmacological articles. According to the Cochrane Collaboration's tool for assessing risk of bias, 40% of these studies were randomized but only a few described clearly the randomization procedure, 36% declared the blindness of participants and personnel, and only 21% reported the blindness of outcome assessment. In addition, 28% of the studies presented more than 20% drop-outs at short- and/or long-term assessments. Additional common shortcomings of the reviewed works were related to study design, patient selection, sample size, choice of outcome measures, management of drop-out cases, and fMRI methods.

CONCLUSION

There is an urgent need to obtain efficient treatments for AD and MCI. fMRI is powerful enough to detect even subtle changes over a short period of treatment; however, the soundness of methods should be improved to enable meaningful data interpretation.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

A study of feature extraction for Alzheimer's disease based on resting-state fMRI

The Alzheimer's Disease (AD) has become a major threat of human health with its incidence rate ascending year by year. Early diagnosis of AD is very important for AD patients to keep life quality. The resting-state fMRI (rs-fMRI) which precisely reflects the brain changes on the resting state of individuals provides a quantitative approach, which has been introduced to distinguish AD patients from normal population. In this study, we proposed a method to find the most distinctive features identifying AD patients from rs-fMRI images. The ALFF and ReHo parameters based on pre-processed rs-fMRI data were extracted, and some key parameters of the brain functional network based on graph theory were calculated. Then we tested the recognition performance of different classifiers, and the best classification algorithm, that is, Support Vector Machine (SVM) with linear-kernel are selected. Finally through a recursive feature selection procedure, we got the most distinctive feature set. Additionally, this study also implies that there may be several changes in some particular ROIs of the brain during the AD development.

Subjective memory complaints are associated with brain activation supporting successful memory encoding

Subjective memory complaints, the perceived decline in cognitive abilities in the absence of clinical deficits, may precede Alzheimer's disease. Individuals with subjective memory complaints show differential brain activation during memory encoding; however, whether such differences contribute to successful memory formation remains unclear. Here, we investigated how subsequent memory effects, activation which is greater for hits than misses during an encoding task, differed between healthy older adults aged 50 to 85 years with (n = 23) and without (n = 41) memory complaints. Older adults with memory complaints, compared to those without, showed lower subsequent memory effects in the occipital lobe, superior parietal lobe, and posterior cingulate cortex. In addition, older adults with more memory complaints showed a more negative subsequent memory effects in areas of the default mode network, including the posterior cingulate cortex, precuneus, and ventromedial prefrontal cortex. Our findings suggest that for successful memory formation, older adults with subjective memory complaints rely on distinct neural mechanisms which may reflect an overall decreased task-directed attention.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Alzheimer's Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy

Widely researched Alzheimer's disease (AD) biomarkers include in vivo brain imaging with PET and MRI, imaging of amyloid plaques, and biochemical assays of Aβ 1 - 42, total tau, and phosphorylated tau (p-tau-181) in cerebrospinal fluid (CSF). In this review, we critically evaluate these biomarkers and discuss their clinical utility for the differential diagnosis of AD. Current AD biomarker tests are either highly invasive (requiring CSF collection) or expensive and labor-intensive (neuroimaging), making them unsuitable for use in the primary care, clinical office-based setting, or to assess drug efficacy in clinical trials. In addition, CSF and neuroimaging biomarkers continue to face challenges in achieving required sensitivity and specificity and minimizing center-to-center variability (for CSF-Aβ 1 - 42 biomarkers CV = 26.5% ; http://www.alzforum.org/news/conference-coverage/paris-standardization-hurdle-spinal-fluid-imaging-markers). Although potentially useful for selecting patient populations for inclusion in AD clinical trials, the utility of CSF biomarkers and neuroimaging techniques as surrogate endpoints of drug efficacy needs to be validated. Recent trials of β- and γ-secretase inhibitors and Aβ immunization-based therapies in AD showed no significant cognitive improvements, despite changes in CSF and neuroimaging biomarkers. As we learn more about the dysfunctional cellular and molecular signaling processes that occur in AD, and how these processes are manifested in tissues outside of the brain, new peripheral biomarkers may also be validated as non-invasive tests to diagnose preclinical and clinical AD.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.