Mild Cognitive Impairment Brain Research Articles

Unravelling Neuroinflammation-Mediated Mitochondrial Dysfunction in Mild Cognitive Impairment: Insights from Targeted Metabolomics

BackgroundThe prevalence of Type 2 Diabetes Mellitus (T2DM) is rising rapidly among the elderly due to age-related metabolic changes. Older adults with T2DM have a 50-65% increased risk of developing cognitive impairment, particularly mild cognitive impairment (MCI), which may progress to neurodegenerative conditions like Alzheimer's disease (AD). Recent studies underscore the significant roles of mitochondrial dysfunction, disrupted glutamate-glutamine cycling, hyperglycemia, and hyperprolinemia in cognitive decline. These interconnected metabolites—glucose, glutamine, glutamate, and proline—are potential targets for understanding the relationship between T2DM and cognitive impairment. Material and MethodThe present targeted NMR based metabolomics study aims to compare the blood plasma/serum metabolic profiles of these four metabolites in age and sex matched MCI (N=27) and T2DM patients (N=38) with respect to normal control (NC, N=23) subjects. The metabolic profiling was performed using 1H NMR spectroscopy. ResultsCompared to NC group, both T2DM and MCI groups exhibited elevated glucose levels. Circulatory glucose and glutamine levels were significantly higher in T2DM subjects than in MCI and NC subjects, while glutamate levels followed a similar trend in both T2DM and MCI groups. However, in MCI patients, circulatory proline and proline-to-glutamine (PQR) and glutamate-to-glutamine was significantly elevated compared to T2DM, while glutamine was significantly reduced. ConclusionThe decreased circulatory levels of glutamine and PQR demonstrated statistically significant correlation with the severity of the cognitive impairment as assessed based on Mini Mental State Examination (MMSE) score suggested augmented utilization of glutamine in MCI patients and accumulation to proline due to active neuro-inflammatory processes and impaired mitochondrial functioning in MCI brain.

Activation of the Neuronal Cell Cycle in Brains in Amnestic Mild Cognitive Impairment: Early Involvement in the Progression of Alzheimer's Disease.

Activation of cell-cycle machinery in Alzheimer's disease (AD) brain was reported by Mark Smith and colleagues and by other researchers. Among other biochemical processes underlying this activation, the notion that AD brain, under the onslaught of oxidative and nitrosative damage leading to neuronal loss, neurons would attempt to replenish their numbers by entering the cell cycle. However, being post-mitotic, neurons entering the cell cycle would become trapped therein, ultimately leading to death of these neurons. Yang and co-workers and the Butterfield laboratory first reported that similar activation of the cell cycle was present in the brains of individuals with amnestic mild cognitive impairment (MCI), arguably the earliest clinical stage of AD, but who demonstrate normal activities of daily living and no dementia. Activation of the cell cycle in MCI brain is consonant with the concept that this process is an early aspect in the progression of AD. This brief review article discusses these findings and recognizes the contribution of Dr. Mark Smith to the investigation of cell-cycle activation in AD brain and other aspects of AD neuropathology.

Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at “very strong evidence” (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area – BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer’s disease clinical spectrum

Abstract Background With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC). Materials and Methods A 18F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (n = 129), and Harvard Aging Brain Study (HABS) NC (n = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and 18F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, 18F-FDG-PET SUVR, and amyloid PET Centiloids. Results DL output remained independently prognostic among other factors in all three datasets (P < .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [−0.005 to 0.044], and HABS-NC sets (0.059 [−0.003 to 0.126]). DL output showed independent (P = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [<0.001-0.036]) over clinical information, 18F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants. Conclusion The 18F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from 18F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.

Protein Oxidation in Aging and Alzheimer's Disease Brain.

Proteins are essential molecules that play crucial roles in maintaining cellular homeostasis and carrying out biological functions such as catalyzing biochemical reactions, structural proteins, immune response, etc. However, proteins also are highly susceptible to damage by reactive oxygen species (ROS) and reactive nitrogen species (RNS). In this review, we summarize the role of protein oxidation in normal aging and Alzheimer's disease (AD). The major emphasis of this review article is on the carbonylation and nitration of proteins in AD and mild cognitive impairment (MCI). The oxidatively modified proteins showed a strong correlation with the reported changes in brain structure, carbohydrate metabolism, synaptic transmission, cellular energetics, etc., of both MCI and AD brains compared to the controls. Some proteins were found to be common targets of oxidation and were observed during the early stages of AD, suggesting that those changes might be critical in the onset of symptoms and/or formation of the pathological hallmarks of AD. Further studies are required to fully elucidate the role of protein oxidation and nitration in the progression and pathogenesis of AD.

Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Altered gut microbiota in older adults with mild cognitive impairment: a case-control study.

The microbiota-gut-brain axis is implicated in Alzheimer's disease. Gut microbiota alterations in mild cognitive impairment (MCI) are inconsistent and remain to be understood. This study aims to investigate the gut microbial composition associated with MCI, cognitive functions, and structural brain differences. A nested case-control study was conducted in a community-based prospective cohort where detailed cognitive functions and structural brain images were collected. Thirty-one individuals with MCI were matched to sixty-five cognitively normal controls by age strata, gender, and urban/rural area. Fecal samples were examined using 16S ribosomal RNA (rRNA) V3-V4 sequencing. Compositional differences between the two groups were identified and correlated with the cognitive functions and volumes/thickness of brain structures. There was no significant difference in alpha and beta diversity between MCIs and cognitively normal older adults. However, the abundance of the genus Ruminococcus, Butyricimonas, and Oxalobacter decreased in MCI patients, while an increased abundance of nine other genera, such as Flavonifractor, were found in MCIs. Altered genera discriminated MCI patients well from controls (AUC = 84.0%) and were associated with attention and executive function. This study provides insights into the role of gut microbiota in the neurodegenerative process.

Multimodal strategy to rescue the brain in mild cognitive impairment: Ketogenic oral nutrition supplementation with B vitamins and aerobic exercise

Mild cognitive impairment (MCI) is characterized by a decline in cognition and is associated with a higher risk of progression to dementia and Alzheimer's disease (AD). Considering the complexity and multifactorial etiology of MCI, dementia, and AD, there is a growing understanding that optimal preventive strategies are likely to require targeting several risk factors and mechanisms simultaneously. Recent evidence shows that interventions such as exercise, in particular aerobic exercise (AE), exogenous sources of ketones (namely ketogenic medium-chain triglyceride [kMCT]), and supplementation with vitamins B12 , B6 and folic acid may positively impact cognitive performance in MCI and AD. Although further studies might be necessary to understand the exact pathways through which this occurs, there are reasons to suspect that, in combination, these interventions could have a synergetic effect. In this paper, we hypothesize that a multicomponent cognitive therapy with ketogenic oral nutritional supplementation with vitamins B12 , B6 , and folic acid, and AE could have a synergistic effect and help delay cognitive decline in patients with MCI. Further studies that include objective measurements of cognitive function are needed to confirm this hypothesis.

Altered central and blood glutathione in Alzheimer\u2019s disease and mild cognitive impairment: a meta-analysis

BackgroundIncreasing evidence implicates oxidative stress (OS) in Alzheimer disease (AD) and mild cognitive impairment (MCI). Depletion of the brain antioxidant glutathione (GSH) may be important in OS-mediated neurodegeneration, though studies of post-mortem brain GSH changes in AD have been inconclusive. Recent in vivo measurements of the brain and blood GSH may shed light on GSH changes earlier in the disease.AimTo quantitatively review in vivo GSH in AD and MCI compared to healthy controls (HC) using meta-analyses.MethodStudies with in vivo brain or blood GSH levels in MCI or AD with a HC group were identified using MEDLINE, PsychInfo, and Embase (1947–June 2020). Standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for outcomes using random effects models. Outcome measures included brain GSH (Meshcher-Garwood Point Resolved Spectroscopy (MEGA-PRESS) versus non-MEGA-PRESS) and blood GSH (intracellular versus extracellular) in AD and MCI. The Q statistic and Egger’s test were used to assess heterogeneity and risk of publication bias, respectively.ResultsFor brain GSH, 4 AD (AD=135, HC=223) and 4 MCI (MCI=213, HC=211) studies were included. For blood GSH, 26 AD (AD=1203, HC=1135) and 7 MCI (MCI=434, HC=408) studies were included. Brain GSH overall did not differ in AD or MCI compared to HC; however, the subgroup of studies using MEGA-PRESS reported lower brain GSH in AD (SMD [95%CI] −1.45 [−1.83, −1.06], p<0.001) and MCI (−1.15 [−1.71, −0.59], z=4.0, p<0.001). AD had lower intracellular and extracellular blood GSH overall (−0.87 [−1. 30, −0.44], z=3.96, p<0.001). In a subgroup analysis, intracellular GSH was lower in MCI (−0.66 [−1.11, −0.21], p=0.025). Heterogeneity was observed throughout (I2 >85%) and not fully accounted by subgroup analysis. Egger’s test indicated risk of publication bias.ConclusionBlood intracellular GSH decrease is seen in MCI, while both intra- and extracellular decreases were seen in AD. Brain GSH is decreased in AD and MCI in subgroup analysis. Potential bias and heterogeneity suggest the need for measurement standardization and additional studies to explore sources of heterogeneity.

Altered central and peripheral glutathione in Alzheimer disease and mild cognitive impairment: A meta‐analysis

AbstractBackgroundIncreasing evidence implicates oxidative stress (OS) in Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Depletion of the brain antioxidant glutathione (GSH) may be important in OS‐mediated neurodegeneration, though post‐mortem brain GSH changes in AD have been inconclusive. Recent in vivo measurements of brain and peripheral GSH may shed light on GSH changes, with implications for its role as a biomarker and therapeutic target. Aim: To quantitatively review in vivo GSH in AD and MCI compared to healthy controls (HC) using meta‐analysis.MethodStudies published before June 2020 containing measurements of in vivo brain or peripheral GSH in MCI or AD with a HC group were identified using Medline, PsychInfo, and Embase. Standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for outcomes using random effects models. Outcome measures included brain GSH (Meshcher‐Garwood Point Resolved Spectroscopy (MEGA‐PRESS versus non) in AD and MCI, and peripheral GSH (intracellular versus extracellular) in AD and MCI. The Q statistic and Egger’s test were used to assess heterogeneity and risk of publication bias respectively.ResultsFor brain GSH, 4 AD (AD=175, HC=272) and 4 MCI (MCI=254, HC=260) studies were included. For peripheral GSH, 26 AD (ADD=981, HCl=993) and 7 MCI (MCI=434, HC=408) studies were included. Brain GSH did not differ in AD or MC compared to HC; however, the subgroup of studies using MEGA‐PRESS reported lower brain GSH in AD (SMD [95%CI] = ‐1.47 [‐1.91, ‐1.02], p=0.02) and MCI (‐1.08 [‐1.53,‐0.64], p=0.01). Peripheral GSH was lower in AD (‐0.94 [‐1.37, ‐0.51], p&lt;0.001). In a subgroup analysis, intracellular GSH was lower in MCI (‐0.66 [‐1.11, ‐0.21], p=0.02). Significant heterogeneity was observed in all analyses and supported the use of random effect models. Egger’s test indicated risk of publication bias in MCI brain GSH literature.Conclusion In vivo measures of GSH in AD and MCI had significant heterogeneity. Peripheral analyses suggested intracellular GSH decreases may be prominent in early disease stages with both intra‐ and extracellular decreases in later stages. Brain GSH may be decreased in AD and MCI but heterogeneity and potential bias indicate the need for measurement standardization and replication.

Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer's Disease.

Graph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer's disease (AD). Specifically, we define different connectedness and 2-connectedness states and evaluate their dynamics in AD and its preclinical stage, mild cognitive impairment (MCI), compared to the normal controls (NC). Results indicate that, compared to MCI and NC, brain networks of AD tend to be more frequently connected at a sparse level. For MCI, we found that their brains are more likely to be 2-connected in the minimal connected state as well indicating increasing redundancy in brain connectivity. Such a redundant design could ensure maintained connectedness of the MCI's brain network in the case that pathological damages break down any link or silenced any node, making it possible to preserve cognitive abilities. Our study suggests that the redundancy in the brain functional chronnectome could be altered in the preclinical stage of AD. The findings can be successfully replicated in a retest study and with an independent MCI dataset. Characterizing redundancy design in the brain chronnectome using connectedness and 2-connectedness analysis provides a unique viewpoint for understanding disease affected brain networks.

Altered Spatiotemporal Dynamics of the Resting Brain in Mild Cognitive Impairment with Lewy Bodies.

Electrophysiological markers of prodromal dementia with Lewy bodies were described in the spectral domain. The sub-second temporal resolution may provide additional information. To evaluate electroencephalography (EEG) microstates in patients with mild cognitive impairment with Lewy bodies and to assess the association between their temporal dynamics and the spectral marker. Temporal parameters of microstates were compared between 21 patients with mild cognitive impairment with Lewy bodies and 21 healthy controls. The dominant alpha frequency was correlated with microstate parameters. Microstates A-D showed higher occurrence in the patient group. Microstate B additionally revealed shorter mean duration and increased time coverage; its occurrence correlated with the dominant alpha frequency in the patient group. Temporal dynamics of all EEG microstates were altered in medication-naïve subjects with prodromal dementia with Lewy bodies. Longitudinal follow-up may reveal how EEG microstates reflect progression of brain function deficits and effects of treatment manipulations. © 2021 International Parkinson and Movement Disorder Society.

Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy.

The mild cognitive impairment (MCI) stage plays an essential role in preventing the progression of older adults to Alzheimer's disease. In this study, neurofeedback training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze the electroencephalogram (EEG) features of patients with MCI. To overcome the information loss problem of traditional multiscale entropy (MSE), WMMSE fully considered the correlation of the sequence and the contribution of each sequence to the total entropy. The experimental group composed of 39 patients with MCI was subjected to NFT for 10 days during two sessions. The control group included 21 patients with MCI without any intervention. The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of the Montreal Cognitive Assessment (MoCA) results shows that the post-NFT score is higher than the pre-NFT in the vast majority of the patients with MCI and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function.

Classification of MCI Brain Network Based on Orthogonal Minimum Spanning Tree

Brain network based on resting-state functional magnetic resonance (rs-fMRI) is the most popular method for brain disease diagnosis, which is expected to provide accurate and effective biomarkers. The original fully connected resting-state networks (RSNs) are too dense and must be filtered to get the real network model. In this study, orthogonal minimum spanning trees (OMSTs) was used to filter the connection matrix of 49 age-matched healthy controls (HC) and 50 patients with mild cognitive impairment (MCI). At the same time, we also used global cost efficiency (GCE) algorithm to filter brain network for comparison with OMSTs. We calculated the topological metrices of brain network. Fisher score was used to select features, and the optimal feature subset was used to construct SVM classifier. The classification accuracy of OMSTs was 87%, while that of GCE algorithm was 81%. The experimental results show that the classification accuracy is greatly improved by using OMSTs, which is an effective brain network filtering method.

The human brain NGF metabolic pathway is impaired in the pre-clinical and clinical continuum of Alzheimers disease.

The NGF metabolic pathway entails the proteins that mature pro-nerve growth factor (proNGF) to NGF and those that degrade NGF. Basal forebrain cholinergic neurons require NGF for maintenance of cholinergic phenotype, are critical for cognition, and degenerate early in Alzheimer's disease (AD). In AD, NGF metabolism is altered, but it is not known whether this is an early phenomenon, nor how it relates to AD pathology and symptomology. We acquired dorsolateral/medial prefrontal cortex samples from individuals with Alzheimer's dementia, Mild Cognitive Impairment (MCI), or no cognitive impairment with high (HA-NCI) and low (LA-NCI) brain Aβ from the Religious Orders Study. Cortical proNGF protein, but not mRNA, was higher in AD, MCI, and HA-NCI, while mature NGF was lower. Plasminogen protein was higher in MCI and AD brain tissue, with plasminogen mRNA notlikewise elevated, suggesting diminished activation of the proNGF convertase, plasmin. The plasminogen activator tPA was lower in HA-NCI while neuroserpin, the CNS tPA inhibitor, was higher in AD and MCI cortical samples. Matrix metalloproteinase 9 (MMP9), which degrades NGF, was overactive in MCI and AD. Transcription of the MMP9 inhibitor TIMP1 was lower in HA-NCI. ProNGF levels correlated with plasminogen, neuroserpin, and VAChT while NGF correlated with MMP9 activity. In NCI, proNGF correlated with cerebral Aβ and tau deposition and to cognitive performance. In summary, proNGF maturation is impaired in preclinical and clinical AD while mature NGF degradation is enhanced. These differences correlate with cognition, pathology, and cholinergic tone, and may suggest novel biomarkers and therapeutic targets.

Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data.

For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach. A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment. RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ2 (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively. Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.

Quantitative 18F-AV1451 Brain Tau PET Imaging in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Patients.

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results: 18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification.

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry.

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer\u2019s disease

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

Exercise-Related Changes of Networks in Aging and Mild Cognitive Impairment Brain.

Aging and mild cognitive impairment (MCI) are accompanied by decline of cognitive functions. Meanwhile, the most common form of dementia is Alzheimer’s disease (AD), which is characterized by loss of memory and other intellectual abilities serious to make difficulties for patients in their daily life. MCI is a transition period between normal aging and dementia, which has been used for early detection of emerging dementia. It converts to dementia with an annual rate of 5–15% as compared to normal aging with 1% rate. Small decreases in the conversion rate of MCI to AD might significantly reduce the prevalence of dementia. Thus, it is important to intervene at the preclinical stage. Since there are still no effective drugs to treat AD, non-drug intervention is crucial for the prevention and treatment of cognitive decline in aging and MCI populations. Previous studies have found some cognitive brain networks disrupted in aging and MCI population, and physical exercise (PE) could effectively remediate the function of these brain networks. Understanding the exercise-related mechanisms is crucial to design efficient and effective PE programs for treatment/intervention of cognitive decline. In this review, we provide an overview of the neuroimaging studies on physical training in normal aging and MCI to identify the potential mechanisms underlying current physical training procedures. Studies of functional magnetic resonance imaging, electroencephalography, magnetoencephalography and positron emission tomography on brain networks were all included. Based on our review, the default mode network, fronto-parietal network and fronto-executive network are probably the three most valuable targets for efficiency evaluation of interventions.