Prodromal Stage Of Alzheimer Research Articles

The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease.

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), where neurodegeneration is not as considerable, thereby potentially increasing the effect of treatments. Therefore, highly sensitive and specific classification of subjects with MCI is necessary, where various MRI modalities have displayed promise. Structural, diffusion, and resting-state (RS) functional MRI analyses were performed on the AD (n = 26), MCI (n = 5), and healthy control (HC) (n = 14) group. Structural analysis was performed via voxel-based morphometry (VBM) and volumetric subcortical segmentation analysis. Fractional anisotropy and mean diffusivity were estimated during the diffusion analysis. RS analysis investigated seed-based functional connectivity. Classification via support vector machine was performed to evaluate which MRI modality most accurately differentiated the groups. Multiple linear regression was conducted to evaluate the MRI modalities correlation with clinical assessment scores. Classification of MCI and HC displayed highest accuracy based on diffusion MRI, which besides demonstrated high correlation with clinical scores. Classification was equally accurate in AD, when using VBM or diffusion tensor imaging measures. Yet, more variance was explained by VBM measures in the clinical assessment scores of the AD group. This study highlights the potential of diffusion MRI in differentiating MCI from HC and AD. However, the results need to be interpreted with caution as sample size and artifacts in the MRI data probably influenced the results.

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a systematic review

BackgroundAn increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia.MethodsWe conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis.ResultsMost studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance.ConclusionsAlthough the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

Novel Plasma miRNAs as Biomarkers and Therapeutic Targets of Alzheimer’s Disease at the Prodromal Stage

Background: Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer’s disease (AD) involving imbalanced beta-site amyloid precursor protein cleaving enzyme 1 (BACE1). MicroRNAs (miRNAs) are associated with AD. Objective: This study aimed to investigated whether plasma miRNAs can predict prodromal AD or are associated with AD pathology. Methods: Participants in the discovery set (n = 10), analysis set (n = 30), and validation set (n = 80) were screened from the China Longitudinal Aging Study. RNA was extracted from the participants’ plasma. Microarray sequencing provided miRNA profiles and differentially expressed miRNAs (DEmiRNAs) in the discovery set included patients with 18F-Flutemetamol positron emission tomography scan-confirmed aMCI. Potential biomarkers were screened in the analysis set. The predict capability of candidate miRNAs was assessed in the validation set. Candidate miRNAs modulation of BACE1 expression was explored in rat and human hippocampal neurons in vitro. Results: We verified 46 significant DEmiRNAs between the aMCI and NC groups (p < 0.05), among which 33 were downregulated. In the analysis set, miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-134-3p levels decreased significantly in the aMCI group. These miRNAs and previously identified miR-107 were selected as potential biomarkers. A prediction model comprising these five miRNAs showed outstanding accuracy (81.25%) to discriminate aMCI at cut-off value of 0.174. Except for miR-134-3p, the other four miRNAs significantly suppressed Bace1 expression in rat hippocampal neurons in vitro. BACE1 modulation of miR-1185-2-3p, miR-1909-3p, and miR-134-3p was confirmed in human hippocampal neurons in vitro. Conclusion: A predictive model consisting of five BACE1-related plasma miRNAs could be a novel biomarker for aMCI.

Detection of Mild Cognitive Impairment Using Kernel Density Estimation Based Texture Analysis of the Corpus Callosum in Brain MR Images

ObjectivesMild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer's disease (AD), which is a progressive and fatal neurodegenerative disorder. Detection of MCI condition can enable early diagnosis resulting in timely intervention to delay the disease progression. Onset of MCI causes tissue alterations in Corpus Callosum (CC) of the brain. Texture analysis of brain Magnetic Resonance (MR) images aids in characterising these imperceptible changes. In this study, Kernel Density Estimation (KDE) technique is used to analyse the textural variations in CC to detect MCI condition. Materials and methodThe pre-processed brain MR images are obtained from a public access database. Reaction Diffusion level set is employed to segment CC from sagittal slices of the images. Kernel density estimation method is applied to study the local intensity variations within the segmented CC. Statistical features quantifying these variations are extracted from the KDE values. These features are used to differentiate MCI condition using linear classifiers based on discriminant analysis and support vector machine. The results are compared with conventional Grey Level Co-occurrence Matrix (GLCM) features for validation. ResultsThe KDE-based texture features extracted from CC show significant variation between normal and MCI classes. Results demonstrate that this approach can differentiate MCI condition with high accuracy and specificity of 81.3% and 82.7%, respectively. The KDE-based features perform better when compared with GLCM features for distinguishing MCI. ConclusionsThe KDE-based texture features are able to capture the subtle changes occurring in CC at the MCI stage. This technique achieves comparable performance to other state-of-the-art methods with reduced number of features. Efficiency of the KDE-based texture analysis confirms that the proposed computer assisted technique can be used for mass screening of MCI, which can aid in handling the disease severity.

Aberrant cerebral perfusion pattern in amnestic mild cognitive impairment and Parkinson's disease with mild cognitive impairment: a comparative arterial spin labeling study.

Mild cognitive impairment (MCI) has been defined as the prodromal stage of Alzheimer's disease and Parkinson's disease (PD) with dementia. We investigated the differences in regional perfusion properties among MCI subtypes and healthy control (HC) subjects by using arterial spin labeling (ASL). Regional normalized CBF (z-CBF) and CBF-connectivity were analyzed from ASL data in 44 amnestic MCI (aMCI) patients, 42 PD-MCI patients, and 50 matched HC participants. The correlations between these significant regions and clinical performance were investigated separately using Spearman correlation analysis. Receiver operating characteristic analysis was generated to determine the differentiating ability of z-CBF values. z-CBF values in disease-related specific regions were extracted for group comparison. MCI subgroups showed overlapped impaired regions, aMCI group seemed more extensive than the PD-MCI group. PD-MCI patients had reduced z-CBF in the bilateral putamen, left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus compared to aMCI group. Correlations to executive performance and motor severity were found in PD-MCI group, and correlations were to memory performance found in aMCI group. CBF-connectivity in left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus were significantly altered. All of the significant clusters had good discriminatory ability. Normalized CBF as measured by ASL revealed different patterns of perfusion between aMCI and PD-MCI, which were probably linked to distinct neural mechanisms. The present study indicates that z-CBF can provide specific perfusion information for further pathological and neuropsychological studies.

Reaction Time Intraindividual Variability Reveals Inhibitory Deficits in Single- and Multiple-Domain Amnestic Mild Cognitive Impairment.

Amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease and other dementias, is characterized by episodic memory impairment. Recent evidence has shown inhibitory control deficits in aMCI, but the extent of these deficits across inhibitory domains (i.e., response inhibition and interference control) and aMCI subtypes (i.e., single vs multiple domain) remains unclear. Few studies have included reaction time intraindividual variability (RT IIV) in these efforts. The aim of this study was to compare response inhibition and interference control between aMCI subtypes using measures of accuracy, mean RT, and RT IIV. We report data from 34 individuals with single-domain aMCI (sdaMCI, 66-86 years), 20 individuals with multiple-domain aMCI (mdaMCI, 68-88 years), and 52 healthy controls (HC, 64-88 years) who completed tasks of response inhibition (Go-NoGo) and interference control (Flanker). Group differences in accuracy, mean RT, and RT IIV were examined for both tasks. Individuals with mdaMCI had higher RT IIV than the other groups on both tasks. In RT IIV, we observed an interference control deficit in mdaMCI and sdaMCI relative to healthy controls, a finding not observed through accuracy or mean RT. RT IIV may detect subtle differences in inhibition deficits between aMCI subtypes that may not be evident with conventional behavioral measures. Findings support the supplementary use of RT IIV when assessing early executive function deficits.

Targeting hippocampal hyperactivity with real-time fMRI neurofeedback: protocol of a single-blind randomized controlled trial in mild cognitive impairment

BackgroundSeveral fMRI studies found hyperactivity in the hippocampus during pattern separation tasks in patients with Mild Cognitive Impairment (MCI; a prodromal stage of Alzheimer’s disease). This was associated with memory deficits, subsequent cognitive decline, and faster clinical progression. A reduction of hippocampal hyperactivity with an antiepileptic drug improved memory performance. Pharmacological interventions, however, entail the risk of side effects. An alternative approach may be real-time fMRI neurofeedback, during which individuals learn to control region-specific brain activity. In the current project we aim to test the potential of neurofeedback to reduce hippocampal hyperactivity and thereby improve memory performance.MethodsIn a single-blind parallel-group study, we will randomize n = 84 individuals (n = 42 patients with MCI, n = 42 healthy elderly volunteers) to one of two groups receiving feedback from either the hippocampus or a functionally independent region. Percent signal change of the hemodynamic response within the respective target region will be displayed to the participant with a thermometer icon. We hypothesize that only feedback from the hippocampus will decrease hippocampal hyperactivity during pattern separation and thereby improve memory performance.DiscussionResults of this study will reveal whether real-time fMRI neurofeedback is able to reduce hippocampal hyperactivity and thereby improve memory performance. In addition, the results of this study may identify predictors of successful neurofeedback as well as the most successful regulation strategies.Trial registrationThe study has been registered with clinicaltrials.gov on the 16th of July 2019 (trial identifier: NCT04020744).

NeuroExercise: The Effect of a 12-Month Exercise Intervention on Cognition in Mild Cognitive Impairment-A Multicenter Randomized Controlled Trial.

Exercise intervention studies in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), have demonstrated inconsistent yet promising results. Addressing the limitations of previous studies, this trial investigated the effects of a 12-month structured exercise program on the progression of MCI. The NeuroExercise study is a multicenter randomized controlled trial across three European countries (Ireland, Netherlands, Germany). Hundred and eighty-three individuals with amnestic MCI were included and were randomized to a 12-month exercise intervention (3 units of 45 min) of either aerobic exercise (AE; n = 60), stretching and toning exercise (ST; n = 65) or to a non-exercise control group (CG; n = 58). The primary outcome, cognitive performance, was determined by an extensive neuropsychological test battery. For the primary complete case (CC) analyses, between-group differences were analyzed with analysis of covariance under two conditions: (1) the exercise group (EG = combined AE and ST groups) compared to the CG and (2) AE compared to ST. Primary analysis of the full cohort (n = 166, 71.5 years; 51.8% females) revealed no between-group differences in composite cognitive score [mean difference (95% CI)], 0.12 [(−0.03, 0.27), p = 0.13] or in any cognitive domain or quality of life. VO2 peak was significantly higher in the EG compared to the CG after 12 months [−1.76 (−3.39, −0.10), p = 0.04]. Comparing the two intervention groups revealed a higher VO2peak level in the aerobic exercise compared to the stretching and toning group, but no differences for the other outcomes. A 12-month exercise intervention did not change cognitive performance in individuals with amnestic MCI in comparison to a non-exercise CG. An intervention effect on physical fitness was found, which may be an important moderator for long term disease progression and warrants long-term follow-up investigations.Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02913053, identifier: NCT02913053.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach.

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

Maximal aerobic capacity is associated with hippocampal cognitive reserve in older adults with amnestic mild cognitive impairment.

Maximal aerobic capacity (MAC) has been associated with preserved neural tissue or brain maintenance (BM) in healthy older adults, including the hippocampus. Amnestic mild cognitive impairment (aMCI) is considered a prodromal stage of Alzheimer's disease. While aMCI is characterized by hippocampal deterioration, the MAC-hippocampal relationship in these patients is not well understood. In contrast to healthy individuals, neurocognitive protective effects in neurodegenerative populations have been associated with mechanisms of cognitive reserve (CR) altering the neuropathology-cognition relationship. We investigated the MAC-hippocampal relationship in aMCI (n = 29) from the perspectives of BM and CR mechanistic models with structural MRI and a memory fMRI paradigm using both group-level (higher-fit patients vs. lower-fit patients) and individual level (continuous correlation) approaches. While MAC was associated with smaller hippocampal volume, contradicting the BM model, higher-fit patients demonstrated statistically significant lower correlation between hippocampal volume and memory performance compared with the lower-fit patients, supporting the model of CR. In addition, while there was no difference in brain activity between the groups during low cognitive demand (encoding of familiar stimuli), higher MAC level was associated with increased cortical and sub-cortical activation during increased cognitive demand (encoding of novel stimuli) and also with bilateral hippocampal activity even when controlling for hippocampal volume, suggesting for an independent effect of MAC. Our results suggest that MAC may be associated with hippocampal-related cognitive reserve in aMCI through altering the relationship between hippocampal-related structural deterioration and cognitive function. In addition, MAC was found to be associated with increased capacity to recruit neural resources during increased cognitive demands.

Atrophy and cognitive profiles in older adults with temporal lobe epilepsy are similar to mild cognitive impairment.

Epilepsy incidence and prevalence peaks in older adults yet systematic studies of brain ageing and cognition in older adults with epilepsy remain limited. Here, we characterize patterns of cortical atrophy and cognitive impairment in 73 older adults with temporal lobe epilepsy (>55 years) and compare these patterns to those observed in 70 healthy controls and 79 patients with amnestic mild cognitive impairment, the prodromal stage of Alzheimer's disease. Patients with temporal lobe epilepsy were recruited from four tertiary epilepsy surgical centres; amnestic mild cognitive impairment and control subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Whole brain and region of interest analyses were conducted between patient groups and controls, as well as between temporal lobe epilepsy patients with early-onset (age of onset <50 years) and late-onset (>50 years) seizures. Older adults with temporal lobe epilepsy demonstrated a similar pattern and magnitude of medial temporal lobe atrophy to amnestic mild cognitive impairment. Region of interest analyses revealed pronounced medial temporal lobe thinning in both patient groups in bilateral entorhinal, temporal pole, and fusiform regions (all P < 0.05). Patients with temporal lobe epilepsy demonstrated thinner left entorhinal cortex compared to amnestic mild cognitive impairment (P = 0.02). Patients with late-onset temporal lobe epilepsy had a more consistent pattern of cortical thinning than patients with early-onset epilepsy, demonstrating decreased cortical thickness extending into the bilateral fusiform (both P < 0.01). Both temporal lobe epilepsy and amnestic mild cognitive impairment groups showed significant memory and language impairment relative to healthy control subjects. However, despite similar performances in language and memory encoding, patients with amnestic mild cognitive impairment demonstrated poorer delayed memory performances relative to both early and late-onset temporal lobe epilepsy. Medial temporal lobe atrophy and cognitive impairment overlap between older adults with temporal lobe epilepsy and amnestic mild cognitive impairment highlights the risks of growing old with epilepsy. Concerns regarding accelerated ageing and Alzheimer's disease co-morbidity in older adults with temporal lobe epilepsy suggests an urgent need for translational research aimed at identifying common mechanisms and/or targeting symptoms shared across a broad neurological disease spectrum.

Differences in the characteristics, neuropsychiatric comorbidities, and psychometric features of subjective cognitive decline in Hispanics versus non‐Hispanic whites

AbstractBackgroundSubjective cognitive decline (SCD) is thought to be a prodromal stage of Alzheimer’s disease (AD), yet has been primarily studied in non‐Hispanic whites. This study aimed to understand the differences in SCD presentation between Hispanics (of any race) and non‐Hispanic whites.MethodA cross‐sectional analysis was performed on participants seen at the NYU Alzheimer’s Disease Center with subjective cognitive decline and who identified as Hispanic (of any race, n=52) and non‐Hispanic white (n=265). Demographics, SCD characteristics, mood/behavioral symptoms, and performance on psychometrics tests were analyzed, with statistical analysis by t‐test, ANOVA, or Fisher’s exact test where appropriate.ResultHispanic participants with SCD were younger (64.3 vs. 68.2, p=0.0008) and had less education (14.3 vs. 17.6, p&lt;0.0001). The Cognitive Change Index (CCI), a measure of SCD, did not distinguish the two groups (total CCI score nor amnestic and non‐amnestic subgroups). In contrast, on the Brief Cognitive Rating Scale (BCRS), another SCD metric, Hispanic participants showed increased severity in concentration (2.62 vs. 1.93), praxis (2.60 vs. 2.13) and calculation (2.96 vs. 2.10), all p&lt;0.001. Study partner observations on the Neuropsychiatric Inventory‐Questionnaire (NPIQ) revealed an increased prevalence of depression (27% vs. 14%, p&lt;0.001) and appetite changes (10% vs. 0%, p&lt;0.0001) in Hispanic SCD participants. When self‐rated via the Geriatric Depression Scale, there were also notable differences in the quality of depression, with increased prevalence in Hispanics of dropping activities (33% vs. 6%), emptiness (19% vs. 3%), boredom (19% vs. 3%), fear (27% vs. 6%), preference to stay home (31% vs. 8%), and helplessness (15% vs. 4%), all p&lt;0.01. On psychometric testing there were no differences in MoCA or MMSE scores. However, Hispanic participants showed lower performance on the Digit Symbol Substitution Test (DSST) (39.2 vs. 56.1, p&lt;0.0001), which can be influenced by mood and has previously shown to be a marker of decline in SCD.ConclusionThese findings support that the characteristics, neuropsychiatric comorbidities, and psychometric features of SCD are notably different between Hispanics and non‐Hispanic whites. In particular, depression in Hispanics warrants further study as a particularly significant variable in determining SCD conversion to AD.

High-dimensional brain-wide functional connectivity mapping in magnetoencephalography

BackgroundBrain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. New methodWe removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis. ResultsWe found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1−4 Hz) and higher-theta (6−8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer’s disease. ConclusionsOur approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer’s disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.

Steady-state cerebral autoregulation in older adults with amnestic mild cognitive impairment: linear mixed model analysis.

We examined whether the efficacy of steady-state cerebral autoregulation (CA) is reduced in older adults with amnestic mild cognitive impairment (aMCI), a prodromal stage of clinical Alzheimer disease (AD). Forty-two patients with aMCI and 24 cognitively normal older adults (NC) of similar age, sex, and education underwent stepwise decreases and increases in mean arterial pressure (MAP) induced by intravenous infusion of sodium nitroprusside and phenylephrine, respectively. Changes in cerebral blood flow (CBF) were measured repeatedly in the internal carotid and vertebral artery. Linear mixed modeling, including random effects of both individual intercept and regression slope, was used to quantify the MAP-CBF relationship accounting for nonindependent, repeated CBF measures. Changes in end-tidal CO2 (EtCO2) associated with changes in MAP were also included in the model to account for their effects on CBF. Marginal mean values of MAP were reduced by 13-14 mmHg during sodium nitroprusside and increased by 20-24 mmHg during phenylephrine infusion in both groups with similar doses of drug infusion. A steeper slope of changes in CBF in response to changes in MAP was observed in aMCI relative to NC, indicating reduced efficacy of CA (MAP × Group, P = 0.040). These findings suggest that cerebrovascular dysfunction may occur early in the development of AD.NEW & NOTEWORTHY Cerebral autoregulation is a fundamental regulatory mechanism to protect brain perfusion against changes in blood pressure that, if impaired, may contribute to the development of Alzheimer's disease. Using a linear mixed model, we demonstrated that the efficacy of cerebral autoregulation, assessed during stepwise changes in arterial pressure, was reduced in individuals with amnestic mild cognitive impairment, a prodromal stage of Alzheimer's disease. These findings support the hypothesis that cerebrovascular dysfunction may be an important underlying pathophysiological mechanism for the development of clinical Alzheimer's disease.

Effect Of Exercise Training On Cerebrovascular Impedance In Amnestic Mild Cognitive Impairment Patients

Cerebrovascular hypoperfusion is associated with cognitive impairment in older adults. PURPOSE: To test the hypotheses that 1) patients with amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer’s disease, have higher cerebrovascular impedance than age-matched cognitively normal individuals; 2) 1-year endurance exercise training reduces cerebrovascular impedance in aMCI patients. METHODS: In the cross-sectional study arm, cerebrovascular impedance was estimated in 58 patients with aMCI (67±7 years) and 25 normal control subjects (65±6 years) with cross-spectral analysis between dynamic changes in cerebral blood flow velocity (CBFV) in the middle cerebral artery (via transcranial Doppler) and carotid arterial blood pressure (via applanation tonometry). In the longitudinal study arm, cerebrovascular impedance was evaluated in randomly-assigned 37 aMCI patients who completed 1-year endurance (n=17) or stretching exercise (n=20). RESULTS: After adjustment for age and sex, aMCI patients exhibited higher impedance modulus in the range of the first harmonic oscillations (0.78-1.56 Hz, Z1) than NC (1.18±0.34 vs. 1.01±0.35 mmHg/cm/s, P=0.037). There was an inverse correlation between Z1 and mean CBFV (r=-0.673, P<0.0001). Linear mixed model analysis of exercise training revealed that Z1 was significantly decreased after 1-year exercise intervention irrespective of exercise modes (time effect: P=0.001; interaction between time and exercise modes: P=0.410). CONCLUSION: Our findings suggest that aMCI is associated with higher cerebrovascular impedance when compared to cognitively normal older adults, and that regular physical activity ameliorates cerebrovascular impedance in patients with aMCI. Supported by the NIH (5R01AG033106-01, RZ) and JSPS (16KK0011, JS).

The Efficacy of COGnitive tRaining in patiEnts with Amnestic mild coGnitive impairmENT (COG-REAGENT): Protocol for a Multi-Center Randomized Controlled Trial.

Amnestic mild cognitive impairment (aMCI) is often the prodromal stage of Alzheimer's disease (AD). Although previous studies have suggested that computerized cognitive training is an effective non-pharmacological intervention for aMCI, large-sample, randomized controlled studies are warranted to provide a high level of evidence. To identify the efficacy of computerized cognitive training for aMCI. This study will include 260 patients diagnosed with aMCI from 8 centers in China. A computerized multi-domain adaptive training program will be used in this study, and the targeted cognitive domains include memory, attention, language, and executive function. The patients will be randomized into either a cognitive-training group or an active-control group. The intervention is a 12-week internet-based cognitive training performed for 40 minutes per day, 4 days a week. Neuropsychological assessments and structural and functional MRI will be obtained at baseline, at the end of the intervention, and 6 months after randomization. The primary outcome will be the global cognitive function score assessed by Montreal Cognitive Assessment. The secondary outcomes include changes in other neuropsychological assessments and neuroplasticity changes measured by structural and functional MRI. The trial is currently ongoing, and it is anticipated that recruitment will be completed in December 2020. This multi-center, large-sample, randomized controlled trial will investigate the short and long-term effects of computerized cognitive training in patients with aMCI. Furthermore, the combination of functional and structural MRI results will also reveal the underlying mechanisms of the effect of intervention.

The meta-memory ratio: a new cohort-independent way to measure cognitive awareness in asymptomatic individuals at risk for Alzheimer\u2019s disease

BackgroundLack of awareness of cognitive decline (ACD) has been described at the preclinical and prodromal stages of Alzheimer’s disease (AD). In this study, we introduced a meta-memory ratio (MMR) and explored how it is associated with neuroimaging AD biomarkers in asymptomatic individuals at risk for AD.MethodFour hundred forty-eight cognitively healthy participants from two cohorts of subjective memory complainers (INSIGHT-PreAD and ADNI) were included. Regression models were used to assess the impact of AD biomarkers on the MMR.ResultIn both cohorts, there was a significant quadratic effect of cerebral amyloidosis on the MMR value. In particular, participants had a high ACD up to the amyloid positivity threshold, above which a decrease of ACD was eventually observed as the amyloid load increased.ConclusionThis nonlinear evolution of ACD in very early AD must be taken into account in clinical care and for trial enrollment as well.

Volumetric alterations in the hippocampal subfields of subjects at increased risk of dementia

The hippocampus is one of the first regions to demonstrate atrophy during the prodromal stage of Alzheimer's disease. Volumetric analysis of its individual subfields could provide biomarkers with higher sensitivity than whole hippocampal volume during an earlier disease stage. We quantified the hippocampal subfields volume in a large cohort comprising healthy participants (aged 40–59) with dementia family history (FH) and controls (without FH), examined at 2 time points across 2 years. Subfield volumes were quantified using both a T1-weighted and a high-resolution T2 hippocampal magnetic resonance imaging acquisition with Freesurfer. The participants were stratified based on dementia FH, APOE genotype, and CAIDE (Cardiovascular Risk Factors, Aging and Dementia) risk score. Whole hippocampal volume did not differ between the groups. The volume of the molecular layer was lower in participants with an APOE ε4 genotype, but there were no differences between subjects with and without dementia FH or with an increasing CAIDE score. The molecular layer may be the first hippocampal region to demonstrate volumetric alterations in subjects at risk of dementia.

Inhibitory Control Deficits in Individuals with Amnestic Mild Cognitive Impairment: a Meta-Analysis.

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer's disease that is characterized by impairments in episodic memory. Recent evidence has shown that inhibitory control is also impaired in aMCI. The aim of the present meta-analysis was to quantify inhibitory control ability in individuals with aMCI by examining performance across a range of well-defined inhibition paradigms that tapped into one of three inhibitory control subtypes (i) interference control (e.g., Stroop task), (ii) response inhibition (e.g., Go/Nogo task), or (iii) inhibition of cognitive sets (Wisconsin Card Sort Task). Reference databases (PsychINFO, PubMed, and Web of Science) were searched for studies comparing individuals with aMCI to healthy controls on behavioural measures of inhibition. Across 70 effect sizes involving 2184 adults with aMCI and 3049 controls, overall inhibition deficits of moderate magnitude (g = -0.73) were found among individuals with aMCI. Inhibition deficits were moderate in size regardless of inhibitory control subtype: interference control (g = -0.74), response inhibition (g = -0.71), inhibition of cognitive sets (g = -0.76). Subgroup analyses revealed that Stroop outcome measure (reaction time vs. accuracy) and recruitment source (clinical vs. community) moderated interference control deficits. Together these findings support a generalized inhibition deficit in aMCI, and suggest that inhibition tasks should be included routinely in neuropsychological test batteries to provide a more comprehensive overview of executive dysfunction in aMCI.