Mild Cognitive Impairment Brain Research Articles

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.

The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes.

The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. These fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD). The AD represents a multifarious syndrome characterized by structural, functional, and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI) and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF) to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI) repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI) acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to track the individual progression of the impairments prospecting a proficient usage in a wide range of pathophysiological and physiological studies as well.

Quantitative phosphoproteomic analyses of the inferior parietal lobule from three different pathological stages of Alzheimer's disease.

Alzheimer's disease (AD), the most common age-related neurodegenerative disorder, is clinically characterized by progressive neuronal loss resulting in loss of memory and dementia. AD is histopathologically characterized by the extensive distribution of senile plaques and neurofibrillary tangles, and synapse loss. Amnestic mild cognitive impairment (MCI) is generally accepted to be an early stage of AD. MCI subjects have pathology and symptoms that fall on the scale intermediately between 'normal' cognition with little or no pathology and AD. A rare number of individuals, who exhibit normal cognition on psychometric tests but whose brains show widespread postmortem AD pathology, are classified as 'asymptomatic' or 'preclinical' AD (PCAD). In this study, we evaluated changes in protein phosphorylation states in the inferior parietal lobule of subjects with AD, MCI, PCAD, and control brain using a 2-D PAGE proteomics approach in conjunction with Pro-Q Diamond phosphoprotein staining. Statistically significant changes in phosphorylation levels were found in 19 proteins involved in energy metabolism, neuronal plasticity, signal transduction, and oxidative stress response. Changes in the disease state phosphoproteome may provide insights into underlying mechanisms for the preservation of memory with expansive AD pathology in PCAD and the progressive memory loss in amnestic MCI that escalates to the dementia and the characteristic pathology of AD brain.

Oxidative stress in Alzheimer disease and mild cognitive impairment: evidence from human data provided by redox proteomics.

Alzheimer disease (AD) is a neurodegenerative disease with many known pathological features, yet there is still much debate into the exact cause and mechanisms for progression of this degenerative disorder. The amyloid-beta (Aβ)-induced oxidative stress hypothesis postulates that it is the oligomeric Aβ that inserts into membrane systems to initiate much of the oxidative stress observed in brain during the progression of the disease. In order to study the effects of oxidative stress on tissue from patients with AD and amnestic mild cognitive impairment (MCI), we have developed a method called redox proteomics that identifies specific brain proteins found to be selectively oxidized. Here, we discuss experimental findings of oxidatively modified proteins involved in three key cellular processes implicated in the pathogenesis of AD progression: energy metabolism, cell signaling and neurotransmission, as well as the proteasomal degradation pathways and antioxidant response systems. These proteomics studies conducted by our laboratory and others in the field shed light on the molecular changes imposed on the cells of AD and MCI brain, through the deregulated increase in oxidative/nitrosative stress inflicted by Aβ and mitochondrial dysfunction.

Brain gene expression patterns differentiate mild cognitive impairment from normal aged and Alzheimer's disease

Mild cognitive impairment (MCI) represents a cognitive state intermediate between normal aging and early Alzheimer's disease (AD). To investigate if the molecular signature of MCI parallels the clinical picture, we use microarrays to extensively profile gene expression in 4 cortical brain regions (entorhinal cortex, hippocampus, superior frontal gyrus, post-central gyrus) using the postmortem tissue from cognitively normal aged controls, MCI, and AD cases. Our data reveal that gene expression patterns in MCI are not an extension of aging, and for the most part, are not intermediate between aged controls and AD. Functional enrichment analysis of significant genes revealed prominent upregulation in MCI brains of genes associated with anabolic and biosynthetic pathways (notably transcription, protein biosynthesis, protein trafficking, and turnover) as well as mitochondrial energy generation. In addition, many synaptic genes showed altered expression in MCI, predominantly upregulation, including genes for central components of the vesicle fusion machinery at the synapse, synaptic vesicle trafficking, neurotransmitter receptors, and synaptic structure and stabilization. These data suggest that there is a rebalancing of synaptic transmission in the MCI brain. To investigate if synaptic gene expression levels in MCI were related to cognitive function, Pearson correlation coefficient between the Mini Mental State Examination (MMSE) and region-specific messenger RNA expression were computed for MCI cases. A number of synaptic genes showed strong significant correlations (r > 0.8, p < 0.01) most notably in the entorhinal cortex, with fewer in the hippocampus, and very few in neocortical regions. The synaptic genes with highly significant correlations were predominantly related to synaptic transmission and plasticity, and myelin composition. Unexpectedly, we found that gene expression changes that facilitate synaptic excitability and plasticity were overwhelmingly associated with poorer MMSE, and conversely that gene expression changes that inhibit plasticity were positively associated with MMSE. These data suggest that there are excessive excitability and apparent plasticity in limbic brain regions in MCI, that is associated with impaired synaptic and cognitive function. Such changes would be predicted to contribute to increased excitability, in turn leading to greater metabolic demand and ultimately progressive degeneration and AD, if not controlled.