Atrophy of amygdala and abnormal memory-related alpha oscillations over posterior cingulate predict conversion to Alzheimer's disease

Non-Drug and Non-Invasive Therapeutic Options in Alzheimer's Disease

Despite the massive efforts of modern medicine to stop the evolution of Alzheimer's disease (AD), it affects an increasing number of people, changing individual lives and imposing itself as a burden on families and the health systems. Considering that the vast majority of conventional drug therapies did not lead to the expected results, this review will discuss the newly developing therapies as an alternative in the effort to stop or slow AD. Focused Ultrasound (FUS) and its derived Transcranial Pulse Stimulation (TPS) are non-invasive therapeutic approaches. Singly or as an applied technique to change the permeability of the blood-brain-barrier (BBB), FUS and TPS have demonstrated the benefits of use in treating AD in animal and human studies. Adipose-derived stem Cells (ADSCs), gene therapy, and many other alternative methods (diet, sleep pattern, physical exercise, nanoparticle delivery) are also new potential treatments since multimodal approaches represent the modern trend in this disorder research therapies.

[Effectiveness of brain magnetic resonance imaging in the early diagnosis and characterization of dementias; a systematic review]

INTRODUCTION

Magnetic resonance imaging (MRI) is a frequently used test in the diagnosis of dementia. The objective was to evaluate its effectiveness for the early diagnosis of dementia in patients with mild cognitive impairment (MCI).

MATERIAL AND METHODS

Original studies were selected from systematic reviews between 2011 and 2021, according to PRISMA 2020 criteria. QUADAS-2 and GRADE tools were used, and a meta-analysis was performed.

RESULTS

Final selection of 23 articles. Patient selection and index test had a high probability of bias. The certainty of the evidence was very low. In the hippocampus, sensitivity was 0.62 (95%CI 0.48-0.79) and specificity 0.70 (95%CI 0.55-0.80). In the temporal lobe, sensitivity was 0.65 (range 0.45) and specificity 0.69 (range 0.32).

CONCLUSIONS

There is insufficient evidence to recommend routine brain MRI for the early diagnosis of dementia in patients with MCI.

Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review

Alzheimer disease (AD) remains a significant global health concern. The progression from preclinical stages to overt dementia has become a crucial point of interest for researchers. This paper reviews the potential of neurophysiological biomarkers in predicting AD progression, based on a systematic literature search following PRISMA guidelines, including 55 studies. EEG-based techniques have been predominantly employed, whereas TMS studies are less common. Among the investigated neurophysiological measures, spectral power measurements and event-related potentials-based measures, including P300 and N200 latencies, have emerged as the most consistent and reliable biomarkers for predicting the likelihood of conversion to AD. In addition, TMS-based indices of cortical excitability and synaptic plasticity have also shown potential in assessing the risk of conversion to AD. However, concerns persist regarding the methodological discrepancies among studies, the accuracy of these neurophysiological measures in comparison to established AD biomarkers, and their immediate clinical applicability. Further research is needed to validate the predictive capabilities of EEG and TMS measures. Advancements in this area could lead to cost-effective, reliable biomarkers, enhancing diagnostic processes and deepening our understanding of AD pathophysiology.

FORTCARE-MCI study protocol: evaluation of Fortasyn Connect in the management of mild cognitive impairment in primary care

Background

Neuropsychiatric symptoms are prevalent in patients with mild cognitive impairment (MCI) and are predictive of the conversion to dementia. Fortasyn Connect, a medical food, has shown efficacy in managing cognitive and behavioral symptoms associated with MCI. Early diagnosis and intervention in primary care are essential for managing MCI. However, real-world prospective studies assessing Fortasyn Connect in MCI are still limited.

Methods

This observational, multicenter, prospective study will enroll 150 patients recently diagnosed with MCI by primary care physicians across several regions in Spain. Participants will be followed-up over a 12-month period, with assessments at baseline, 6 months, and 12 months, as per clinical practice. The study aims to evaluate the impact of Fortasyn Connect on neuropsychiatric symptoms, cognition, and health-related quality of life (HRQoL) using validated neuropsychological tests and machine learning (ML) techniques. The primary outcome measure will be changes in neuropsychiatric symptoms using the Neuropsychiatric Inventory Questionnaire (NPI-Q) at 6 months. Secondary outcome measures include further changes in the NPI-Q at 12 months, and changes in cognition (Fototest, and clock-drawing test) and HRQoL (EQ-5D-5L) at 6 and 12 months. Exploratory outcomes will assess speech using an artificial intelligence (AI)-enhanced ML tool, with a correlation analysis of these findings with traditional neuropsychological test results.

Conclusion

This study will provide evidence of the effectiveness of Fortasyn Connect in a real-world setting, exploring its potential to stabilize or improve neuropsychiatric symptoms, cognition, and HRQoL in MCI patients. Results will also contribute to the understanding of AI and ML in identifying early biomarkers of cognitive decline, supporting the timely management of MCI.

Knockdown of microglial iron import gene, Slc11a2, worsens cognitive function and alters microglial transcriptional landscape in a sex-specific manner in the APP/PS1 model of Alzheimer's disease

BACKGROUND

Microglial cell iron load and inflammatory activation are significant hallmarks of late-stage Alzheimer's disease (AD). In vitro, microglia preferentially upregulate the iron importer, divalent metal transporter 1 (DMT1, gene name Slc11a2) in response to inflammatory stimuli, and excess iron can augment cellular inflammation, suggesting a feed-forward loop between iron import mechanisms and inflammatory signaling. However, it is not understood whether microglial iron import mechanisms directly contribute to inflammatory signaling and chronic disease in vivo. These studies determined the effects of microglial-specific knockdown of Slc11a2 on AD-related cognitive decline and microglial transcriptional phenotype.

METHODS

In vitro experiments and RT-qPCR were used to assess a role for DMT1 in amyloid-β-associated inflammation. To determine the effects of microglial Slc11a2 knockdown on AD-related phenotypes in vivo, triple-transgenic Cx3cr1Cre-ERT2;Slc11a2flfl;APP/PS1+or - mice were generated and administered corn oil or tamoxifen to induce knockdown at 5-6 months of age. Both sexes underwent behavioral analyses to assess cognition and memory (12-15 months of age). Hippocampal CD11b+ microglia were magnetically isolated from female mice (15-17 months) and bulk RNA-sequencing analysis was conducted.

RESULTS

DMT1 inhibition in vitro robustly decreased Aβ-induced inflammatory gene expression and cellular iron levels in conditions of excess iron. In vivo, Slc11a2KD APP/PS1 female, but not male, mice displayed a significant worsening of memory function in Morris water maze and a fear conditioning assay, along with significant hyperactivity compared to control WT and APP/PS1 mice. Hippocampal microglia from Slc11a2KD APP/PS1 females displayed significant increases in Enpp2, Ttr, and the iron-export gene, Slc40a1, compared to control APP/PS1 cells. Slc11a2KD cells from APP/PS1 females also exhibited decreased expression of markers associated with subsets of disease-associated microglia (DAMs), such as Apoe, Ctsb, Ly9, Csf1, and Hif1α.

CONCLUSIONS

This work suggests a sex-specific role for microglial iron import gene Slc11a2 in propagating behavioral and cognitive phenotypes in the APP/PS1 model of AD. These data also highlight an association between loss of a DAM-like phenotype in microglia and cognitive deficits in Slc11a2KD APP/PS1 female mice. Overall, this work illuminates an iron-related pathway in microglia that may serve a protective role during disease and offers insight into mechanisms behind disease-related sex differences.

Knockdown of microglial iron import gene, DMT1, worsens cognitive function and alters microglial transcriptional landscape in a sex-specific manner in the APP/PS1 model of Alzheimer's disease

Background

Microglial cell iron load and inflammatory activation are significant hallmarks of late-stage Alzheimer's disease (AD). In vitro, microglia preferentially upregulate the iron importer, divalent metal transporter 1 (DMT1, gene name Slc11a2) in response to inflammatory stimuli, and excess iron can augment cellular inflammation, suggesting a feed-forward loop between iron import mechanisms and inflammatory signaling. However, it is not understood whether microglial iron import mechanisms directly contribute to inflammatory signaling and chronic disease in vivo. These studies determined the effects of microglial-specific knockdown of Slc11a2 on AD-related cognitive decline and microglial transcriptional phenotype.

Methods

In vitro experiments and RT-qPCR were used to assess a role for DMT1 in amyloid-β-associated inflammation. To determine the effects of microglial Slc11a2 knockdown on AD-related phenotypes in vivo, triple-transgenic Cx3cr1 Cre - ERT2 ;Slc11a2 flfl;APP/PS1 + or - mice were generated and administered corn oil or tamoxifen to induce knockdown at 5-6 months of age. Both sexes underwent behavioral analyses to assess cognition and memory (12-15 months of age). Hippocampal CD11b + microglia were magnetically isolated from female mice (15-17 months) and bulk RNA-sequencing analysis was conducted.

Results

DMT1 inhibition in vitro robustly decreased Aβ-induced inflammatory gene expression and cellular iron levels in conditions of excess iron. In vivo, Slc11a2 KD APP/PS1 female, but not male, mice displayed a significant worsening of memory function in Morris water maze and a fear conditioning assay, along with significant hyperactivity compared to control WT and APP/PS1 mice. Hippocampal microglia from Slc11a2 KD APP/PS1 females displayed significant increases in Enpp2, Ttr, and the iron-export gene, Slc40a1, compared to control APP/PS1 cells. Slc11a2 KD cells from APP/PS1 females also exhibited decreased expression of markers associated with disease-associated microglia (DAMs), such as Apoe, Ctsb, Csf1, and Hif1α.

Conclusions

This work suggests a sex-specific role for microglial iron import gene Slc11a2 in propagating behavioral and cognitive phenotypes in the APP/PS1 model of AD. These data also highlight an association between loss of a DAM-like phenotype in microglia and cognitive deficits in Slc11a2 KD APP/PS1 female mice. Overall, this work illuminates an iron-related pathway in microglia that may serve a protective role during disease and offers insight into mechanisms behind disease-related sex differences.

Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits

Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.

Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis

Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.

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.

Amygdala activity and amygdala-hippocampus connectivity: Metabolic diseases, dementia, and neuropsychiatric issues

With rapid aging of the population worldwide, the number of people with dementia is dramatically increasing. Some studies have emphasized that metabolic syndrome, which includes obesity and diabetes, leads to increased risks of dementia and cognitive decline. Factors such as insulin resistance, hyperglycemia, high blood pressure, dyslipidemia, and central obesity in metabolic syndrome are associated with synaptic failure, neuroinflammation, and imbalanced neurotransmitter levels, leading to the progression of dementia. Due to the positive correlation between diabetes and dementia, some studies have called it "type 3 diabetes". Recently, the number of patients with cognitive decline due to metabolic imbalances has considerably increased. In addition, recent studies have reported that neuropsychiatric issues such as anxiety, depressive behavior, and impaired attention are common factors in patients with metabolic disease and those with dementia. In the central nervous system (CNS), the amygdala is a central region that regulates emotional memory, mood disorders, anxiety, attention, and cognitive function. The connectivity of the amygdala with other brain regions, such as the hippocampus, and the activity of the amygdala contribute to diverse neuropathological and neuropsychiatric issues. Thus, this review summarizes the significant consequences of the critical roles of amygdala connectivity in both metabolic syndromes and dementia. Further studies on amygdala function in metabolic imbalance-related dementia are needed to treat neuropsychiatric problems in patients with this type of dementia.

Resting state electroencephalographic rhythms are affected by immediately preceding memory demands in cognitively unimpaired elderly and patients with mild cognitive impairment

Experiments on event-related electroencephalographic oscillations in aged people typically include blocks of cognitive tasks with a few minutes of interval between them. The present exploratory study tested the effect of being engaged on cognitive tasks over the resting state cortical arousal after task completion, and whether it differs according to the level of the participant’s cognitive decline. To investigate this issue, we used a local database including data in 30 healthy cognitively unimpaired (CU) persons and 40 matched patients with amnestic mild cognitive impairment (aMCI). They had been involved in 2 memory tasks for about 40 min and underwent resting-state electroencephalographic (rsEEG) recording after 5 min from the task end. eLORETA freeware estimated rsEEG alpha source activity as an index of general cortical arousal. In the CU but not aMCI group, there was a negative correlation between memory tasks performance and posterior rsEEG alpha source activity. The better the memory tasks performance, the lower the posterior alpha activity (i.e., higher cortical arousal). There was also a negative correlation between neuropsychological test scores of global cognitive status and alpha source activity. These results suggest that engagement in memory tasks may perturb background brain arousal for more than 5 min after the tasks end, and that this effect are dependent on participants global cognitive status. Future studies in CU and aMCI groups may cross-validate and extend these results with experiments including (1) rsEEG recordings before memory tasks and (2) post-tasks rsEEG recordings after 5, 15, and 30 min.

Are there consistent abnormalities in event-related EEG oscillations in patients with Alzheimer's disease compared to other diseases belonging to dementia?

Cerebrospinal and structural-molecular neuroimaging in-vivo biomarkers are recommended for diagnostic purposes in Alzheimer's disease (AD) and other dementias; however, they do not explain the effects of AD neuropathology on neurophysiological mechanisms underpinning cognitive processes. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association reviewed the field literature and reached consensus on the event-related electroencephalographic oscillations (EROs) that show consistent abnormalities in patients with significant cognitive deficits due to Alzheimer's, Parkinson's (PD), Lewy body (LBD), and cerebrovascular diseases. Converging evidence from oddball paradigms showed that, as compared to cognitively unimpaired (CU) older adults, AD patients had lower amplitude in widespread delta (>4 Hz) and theta (4-7 Hz) phase-locked EROs as a function of disease severity. Similar effects were also observed in PD, LBD, and/or cerebrovascular cognitive impairment patients. Non-phase-locked alpha (8-12 Hz) and beta (13-30 Hz) oscillations were abnormally reduced (event-related desynchronization, ERD) in AD patients relative to CU. However, studies on patients with other dementias remain lacking. Delta and theta phase-locked EROs during oddball tasks may be useful neurophysiological biomarkers of cognitive systems at work in heuristic and intervention clinical trials performed in AD patients, but more research is needed regarding their potential role for other dementias.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

The potential role of leukoaraiosis in remodeling the brain network to buffer cognitive decline: a Leukoaraiosis And Disability study from Alzheimer's Disease Neuroimaging Initiative

Background

Leukoaraiosis (LA) is a phenomenon of the brain that is often observed in elderly people. However, little is known about the role of LA in cognitive impairment in neurodegeneration and disease. This cross-sectional, retrospective Leukoaraiosis And Disability (LADIS) study aimed to characterize the relationship between brain white matter connectivity properties with LA ratings in patients with Alzheimer's disease (AD) as compared with age-matched cognitively normal controls.

Methods

Patients with AD (n=76) and elderly individuals with normal cognitive (NC) function (n=82) were classified into 3 groups, LA1, LA2, and LA3, according to the rating of their white matter changes (WMCs). Diffusion tensor imaging (DTI) data were analyzed by quantifying and comparing the white matter connectivity properties and gray matter (GM) volume of brain regions of interest (ROIs).

Results

The rich-club network properties in the AD LA1 and LA2 groups showed significant patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA1 and LA2 groups, respectively. However, the rich-club network properties in the AD LA3 group showed similar patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA3 group, despite there being significant hippocampal and amygdala atrophic differences between AD patients and NC elders. Compared to the NC LA1 group, the characteristic path length of white matter fiber connectivity in the NC LA3 group was significantly increased, and the brain's global efficiency, clustering coefficient, and network connectivity strength were significantly reduced (P<0.05, respectively). However, no significant differences (P>0.05) were observed in characteristic path length, reduced global efficiency, or the clustering coefficient between the NC LA3 and AD LA1 groups, or between the NC LA3 and AD LA2 groups.

Conclusions

Our findings offer some insights into a potential role of LA in cognitive impairment that may predict the development of disability in older adults. The occurrence of LA, an intermediate degenerative change, during neurodegeneration and disease may potentially lead to the remodeling of the brain network through brain plasticity. LA, therefore, representing a possible compensatory mechanism to buffer cognitive decline.

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Background:

Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated.

Objective:

To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline.

Methods:

We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available.

Results:

Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures.

Conclusion:

Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements

OBJECTIVE

This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.

METHODS

For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database.

RESULTS

The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features.

CONCLUSIONS

The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed.

SIGNIFICANCE

The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

Amygdala: Neuroanatomical and Morphophysiological Features in Terms of Neurological and Neurodegenerative Diseases

The amygdala is one of the most discussed structures of the brain. Correlations between its level of activity, size, biochemical organization, and various pathologies are the subject of many studies, and can serve as a marker of existing or future disease. It is hypothesized that the amygdala is not just a structural unit, but includes many other regions in the brain. In this review, we present the updated neuroanatomical and physiological aspects of the amygdala, discussing its involvement in neurodegenerative and neurological diseases. The amygdala plays an important role in the processing of input signals and behavioral synthesis. Lesions in the amygdala have been shown to cause neurological disfunction of ranging severity. Abnormality in the amygdala leads to conditions such as depression, anxiety, autism, and also promotes biochemical and physiological imbalance. The amygdala collects pathological proteins, and this fact can be considered to play a big role in the progression and diagnosis of many degenerative diseases, such as Alzheimer’s disease, chronic traumatic encephalopathy, Lewy body diseases, and hippocampal sclerosis. The amygdala has shown to play a crucial role as a central communication system in the brain, therefore understanding its neuroanatomical and physiological features can open a channel for targeted therapy of neurodegenerative diseases.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease

Background: The early progression continuum of Alzheimer’s disease (AD) has been considered to advance through subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI). Altered functional connectivity (FC) in the default mode network (DMN) is regarded as a hallmark of AD. Furthermore, the DMN can be divided into two subnetworks, the anterior and posterior subnetworks. However, little is known about distinct disruptive patterns in the subsystems of the DMN across the preclinical AD spectrum. This study investigated the connectivity patterns of anterior DMN (aDMN) and posterior DMN (pDMN) across the preclinical AD spectrum.

Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in the DMN subnetworks in 20 healthy controls (HC), eight SCD, 11 naMCI, and 28 aMCI patients. Moreover, a correlation analysis was used to examine associations between the altered connectivity of the DMN subnetworks and the neurocognitive performance.

Results: Compared to the HC, SCD patients showed increased FC in the bilateral superior frontal gyrus (SFG), naMCI patients showed increased FC in the left inferior parietal lobule (IPL), and aMCI patients showed increased FC in the bilateral IPL in the aDMN; while SCD patients showed decreased FC in the precuneus, naMCI patients showed increased FC in the left middle temporal gyrus (MTG), and aMCI patients also showed increased FC in the right middle frontal gyrus (MFG) in the pDMN. Notably, the FC between the ventromedial prefrontal cortex (vmPFC) and the left MFG and the IPL in the aDMN was associated with episodic memory in the SCD and aMCI groups. Interestingly, the FC between the posterior cingulated cortex (PCC) and several regions in the pDMN was associated with other cognitive functions in the SCD and naMCI groups.

Conclusions: This study demonstrates that the three preclinical stages of AD exhibit distinct FC alternations in the DMN subnetworks. Furthermore, the patient group data showed that the altered FC involves cognitive function. These findings can provide novel insights for tailored clinical intervention across the preclinical AD spectrum.

Neuroimmune nexus of depression and dementia: Shared mechanisms and therapeutic targets

Dysfunctional immune activity is a physiological component of both Alzheimer's disease (AD) and major depressive disorder (MDD). The extent to which altered immune activity influences the development of their respective cognitive symptoms and neuropathologies remains under investigation. It is evident, however, that immune activity affects neuronal function and circuit integrity. In both disorders, alterations are present in similar immune networks and neuroendocrine signalling pathways, immune responses persist in overlapping neuroanatomical locations, and morphological and structural irregularities are noted in similar domains. Epidemiological studies have also linked the two disorders, and their genetic and environmental risk factors intersect along immune-activating pathways and can be synonymous with one another. While each of these disorders individually contains a large degree of heterogeneity, their shared immunological components may link distinct phenotypes within each disorder. This review will therefore highlight the shared immune pathways of AD and MDD, their overlapping neuroanatomical features, and previously applied, as well as novel, approaches to pharmacologically manipulate immune pathways, in each neurological condition. LINKED ARTICLES: This article is part of a themed section on Therapeutics for Dementia and Alzheimer's Disease: New Directions for Precision Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.18/issuetoc.

EEG alpha power change during working memory encoding in adults with different memory performance levels

Working memory (WM) capacity affects our daily life in many ways, and its decrease often associates with neural disorders (e.g. Alzheimer's disease). Several studies have confirmed that alpha rhythms play an active role in memory mechanism. In the present study, we designed a digit verbal span experiment to find out that how the alpha power change during working memory encoding, and the relationship between alpha power and individual WM performance. Consistent with previous studies, our results confirm that alpha power is positively related to WM performance. Participants who had higher alpha power during WM encoding achieved better memory performance. We can conclude that the reason is alpha rhythms reflect inhibition of task-irrelevant information. Howbeit, a linear relationship between WM load and alpha power amplitude during memory encoding cannot be deduced in our experiment.