A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers

Lower GLUT1 and unchanged MCT1 in Alzheimer's disease cerebrovasculature

The brain is a highly demanding organ, utilizing mainly glucose but also ketone bodies as sources of energy. Glucose transporter-1 (GLUT1) and monocarboxylates transporter-1 (MCT1) respectively transport glucose and ketone bodies across the blood-brain barrier. While reduced glucose uptake by the brain is one of the earliest signs of Alzheimer's disease (AD), no change in the uptake of ketone bodies has been evidenced yet. To probe for changes in GLUT1 and MCT1, we performed Western immunoblotting in microvessel extracts from the parietal cortex of 60 participants of the Religious Orders Study. Participants clinically diagnosed with AD had lower cerebrovascular levels of GLUT1, whereas MCT1 remained unchanged. GLUT1 reduction was associated with lower cognitive scores. No such association was found for MCT1. GLUT1 was inversely correlated with neuritic plaques and cerebrovascular β-secretase-derived fragment levels. No other significant associations were found between both transporters, markers of Aβ and tau pathologies, sex, age at death or apolipoprotein-ε4 genotype. These results suggest that, while a deficit of GLUT1 may underlie the reduced transport of glucose to the brain in AD, no such impairment occurs for MCT1. This study thus supports the exploration of ketone bodies as an alternative energy source for the aging brain.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

In Alzheimer-prone brain regions, metabolism and risk-gene expression are strongly correlated

Neuroimaging in the preclinical phase of Alzheimer’s disease provides information crucial to early intervention, particularly in people with a high genetic risk. Metabolic network modularity, recently applied to the study of dementia, is increased in Alzheimer’s disease patients compared with controls, but network modularity in cognitively unimpaired elderly with various risks of developing Alzheimer’s disease needs to be determined. Based on their 5-year cognitive progression, we stratified 117 cognitively normal participants (78.3 ± 4.0 years of age, 52 women) into three age-matched groups, each with a different level of risk for Alzheimer’s disease. From their fluorodeoxyglucose PET we constructed metabolic networks, evaluated their modular structures using the Louvain algorithm, and compared them between risk groups. As the risk for Alzheimer’s disease increased, the metabolic connections among brain regions weakened and became more modular, indicating network fragmentation and functional impairment of the brain. We then set out to determine the correlation between regional brain metabolism, particularly in the modules derived from the previous analysis, and the regional expression of Alzheimer-risk genes in the brain, obtained from the Allen Human Brain Atlas. In all risk groups of this elderly population, the regional brain expression of most Alzheimer-risk genes showed a strong correlation with brain metabolism, particularly in the module that corresponded to regions of the brain that are affected earliest and most severely in Alzheimer’s disease. Among the genes, APOE and CD33 showed the strongest negative correlation and SORL1 showed the strongest positive correlation with brain metabolism. The Pearson correlation coefficients remained significant when contrasted against a null-hypothesis distribution of correlation coefficients across the whole transcriptome of 20 736 genes (SORL1: P = 0.0130; CD33, P = 0.0136; APOE: P = 0.0093). The strong regional correlation between Alzheimer-related gene expression in the brain and brain metabolism in older adults highlights the role of brain metabolism in the genesis of dementia.

Glucose hypometabolism in the Auditory Pathway in Age Related Hearing Loss in the ADNI cohort

PURPOSE

Hearing loss (HL) is one of the most common age-related diseases. Here, we investigate the central auditory correlates of HL in people with normal cognition and mild cognitive impairment (MCI) and test their association with genetic markers with the aim of revealing pathogenic mechanisms.

METHODS

Brain glucose metabolism based on FDG-PET, self-reported HL status, and genetic data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. FDG-PET data was analysed from 742 control subjects (non-HL with normal cognition or MCI) and 162 cases (HL with normal cognition or MCI) with age ranges of 72.2 ± 7.1 and 77.4 ± 6.4, respectively. Voxel-wise statistics of FDG uptake differences between cases and controls were computed using the generalised linear model in SPM12. An additional 1515 FDG-PET scans of 618 participants were analysed using linear mixed effect models to assess longitudinal HL effects. Furthermore, a quantitative trait genome-wide association study (GWAS) was conducted on the glucose uptake within regions of interest (ROIs), which were defined by the voxel-wise comparison, using genotyping data with 5,082,878 variants available for HL cases and HL controls (N = 817).

RESULTS

The HL group exhibited hypometabolism in the bilateral Heschl's gyrus (kleft = 323; kright = 151; Tleft = 4.55; Tright = 4.14; peak Puncorr < 0.001), the inferior colliculus (k = 219;T = 3.53; peak Puncorr < 0.001) and cochlear nucleus (k = 18;T = 3.55; peak Puncorr < 0.001) after age correction and using a cluster forming height threshold P < 0.005 (FWE-uncorrected). Moreover, in an age-matched subset, the cluster comprising the left Heschl's gyrus survived the FWE-correction (kleft = 1903; Tleft = 4.39; cluster PFWE-corr = 0.001). The quantitative trait GWAS identified no genome-wide significant locus in the three HL ROIs. However, various loci were associated at the suggestive threshold (p < 1e-05).

CONCLUSION

Compared to the non-HL group, glucose metabolism in the HL group was lower in the auditory cortex, the inferior colliculus, and the cochlear nucleus although the effect sizes were small. The GWAS identified candidate genes that might influence FDG uptake in these regions. However, the specific biological pathway(s) underlying the role of these genes in FDG-hypometabolism in the auditory pathway requires further investigation.

APOE ε4 and cognitive reserve effects on the functional network in the Alzheimer's disease spectrum

The apolipoprotein E (APOE) ε4 allele is a genetic risk factor for Alzheimer's disease, whereas educational attainments have protective effects against cognitive decline in aging and patients with Alzheimer's disease. We examined the possible effects of years of education and APOE genotype on the topological properties of the functional network in normal aging, mild cognitive impairment and Alzheimer's disease. The years of education showed a significant, negative association with the local efficiency, clustering coefficient and small-worldness of functional networks in APOE ε4 noncarriers but not in ε4 carriers. These associations were mainly observed in normal aging and were reduced in mild cognitive impairment and Alzheimer's disease. Moreover, regions of the inferior frontal gyrus, temporal pole, and cuneus also showed correlations between education and nodal degree. Our findings demonstrated that the protective effects of education persist in APOE ε4 noncarriers but diminish in ε4 carriers. In addition, the protective effects of education were attenuated or reduced in the progression of Alzheimer's disease.

Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment

The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.

Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.

Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder

A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.

APOE alters glucose flux through central carbon pathways in astrocytes

The Apolipoprotein E (APOE) gene is a major genetic risk factor associated with Alzheimer's disease (AD). APOE encodes for three main isoforms in humans (E2, E3, and E4). Homozygous E4 individuals have more than a 10-fold higher risk for developing late-onset AD, while E2 carriers are protected. A hallmark of AD is a reduction in cerebral glucose metabolism, alluding to a strong metabolic component in disease onset and progression. Interestingly, E4 individuals display a similar regional pattern of cerebral glucose hypometabolism decades prior to disease onset. Mapping this metabolic landscape may help elucidate the underlying biological mechanism of APOE-associated risk for AD. Efficient metabolic coupling of neurons and glia is necessary for proper neuronal function, and disruption in glial energy distribution has been proposed to contribute to neuronal cell death and AD pathology. One important function of astrocytes - canonically the primary source of apolipoprotein E in the brain - is to provide metabolic substrates (lactate, lipids, amino acids and neurotransmitters) to neurons. Here we investigate the effects of APOE on astrocyte glucose metabolism in vitro utilizing scintillation proximity assays, stable isotope tracer metabolomics, and gene expression analyses. Glucose uptake is impaired in E4 astrocytes relative to E2 or E3 with specific alterations in central carbon metabolism. Using stable isotope labeled glucose [U-¹³C] allowed analyses of astrocyte-specific deep metabolic networks affected by APOE, and provided insight to the effects downstream of glucose uptake. Enrichment of ¹³C in early steps of glycolysis was lowest in E4 astrocytes (highest in E2), while synthesis of lactate from glucose was highest in E4 astrocytes (lowest in E2). We observed an increase in glucose flux through the pentose phosphate pathway (PPP), with downstream increases in gluconeogenesis, lipid, and de novo nucleotide biosynthesis in E4 astrocytes. There was also a marked increase in ¹³C enrichment in the TCA cycle of E4 astrocytes - whose substrates were also incorporated into biosynthetic pathways at a higher rate. Pyruvate carboxylase (PC) and pyruvate dehydrogenase (PDH) are the two main enzymes controlling pyruvate entry to the TCA cycle. PC gene expression is increased in E4 astrocytes and the activity relative to PDH was also increased, compared to E2 or E3. Decreased enrichment in the TCA cycle of E2 and E3 astrocytes is suggestive of increased oxidation and non-glucose derived anaplerosis, which could be fueling mitochondrial ATP production. Conversely, E4 astrocytes appear to increase carbon flux into the TCA cycle to fuel cataplerosis. Together, these data demonstrate clear APOE isoform-specific effects on glucose utilization in astrocytes, including E4-associated increases in lactate synthesis, PPP flux, and de novo biosynthesis pathways.

A Study on PHF-Tau Network Effected by Apolipoprotein E4

Apolipoprotein E 4 Allele (APOE 4) is an important factors in Mild cognitive impairment (MCI) and Alzheimer's disease(AD). It plays a primary role in abnormal modification of aggregated Tau protein-paired helical filaments Tau (PHF-Tau). In this study, 143 subjects with PHF-Tau PET were divided into 2 groups (APOE 4 carriers and noncarriers). The measurements of the PHF-Tau network properties and resilient were calculated for 2 group networks respectively. APOE 4 carriers group showed significant differences in all the network properties in the results. We also found significant differences of betweenness centrality in some brain regions for APOE 4 carriers. Moreover, the APOE 4 carriers showed less resilient to targeted or random node failure. Our results indicated that the effects of APOE 4 may lead to abnormalities of PHF-Tau protein network. These findings may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

Pain-Evoked Reorganization in Functional Brain Networks

Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) "ventral attention network" regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced "small-worldness," modularity (fewer networks), and global network efficiency and in the emergence of an integrated "pain supersystem" (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized ("hub disruption") so that more hubs were localized in PS, and there was a shift from "connector" hubs linking disparate networks to "provincial" hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

Take the shortcut - direct conversion of somatic cells into induced neural stem cells and their biomedical applications

Second-generation reprogramming of somatic cells directly into the cell type of interest avoids induction of pluripotency and subsequent cumbersome differentiation procedures. Several recent studies have reported direct conversion of human somatic cells into stably proliferating induced neural stem cells (iNSCs). Importantly, iNSCs are easier, faster, and more cost-efficient to generate than induced pluripotent stem cells (iPSCs), and also have a higher level of clinical safety. Stably, self-renewing iNSCs can be derived from different cellular sources, such as skin fibroblasts and peripheral blood mononuclear cells, and readily differentiate into neuronal and glial lineages that are indistinguishable from their iPSC-derived counterparts or from NSCs isolated from primary tissues. This review focuses on the derivation and characterization of iNSCs and their biomedical applications. We first outline different approaches to generate iNSCs and then discuss the underlying molecular mechanisms. Finally, we summarize the preclinical validation of iNSCs to highlight that these cells are promising targets for disease modeling, autologous cell therapy, and precision medicine.

Brain Connectivity Based Prediction of Alzheimer's Disease in Patients With Mild Cognitive Impairment Based on Multi-Modal Images

Structural and metabolic connectivity are advanced features that facilitate the diagnosis of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Connectivity from a single imaging modality, however, did not show evident discriminative value in predicting MCI-to-AD conversion, possibly because the inter-modal information was not considered when quantifying the relationship between brain regions. Here we introduce a novel approach that extracts connectivity based on both structural and metabolic information to improve AD early diagnosis. Principal component analysis was performed on each imaging modality to extract the key discriminative patterns of each brain region in an independent auxiliary domain composed of AD and normal control (NC) subjects, which were then used to project the two subtypes of MCI to the low-dimensional space. The connectivity between each target brain region and all other regions was quantified via a multi-task regression model using the projected data. The prediction performance was evaluated in 75 stable MCI (sMCI) patients and 51 progressive MCI (pMCI) patients who converted to AD within 3 years. We achieved 79.37% accuracy, with 74.51% sensitivity and 82.67% specificity, in predicting MCI-to-AD progression, superior to other existing algorithms using either structural and metabolic connectivities alone or a combination thereof. Our results demonstrate the effectiveness of multi-modal connectivity, serving as robust biomarker for early AD diagnosis.

Altered Cerebro-Cerebellar Limbic Network in AD Spectrum: A Resting-State fMRI Study

Recent evidence suggests that the cerebellum is related to motor and non-motor cognitive functions, and that several coupled cerebro-cerebellar networks exist, including links with the limbic network. Since several limbic structures are affected by Alzheimer pathology, even in the preclinical stages of Alzheimer’s disease (AD), we aimed to investigate the cerebral limbic network activity from the perspective of the cerebellum. Twenty patients with mild cognitive impairment (MCI), 18 patients with AD, and 26 healthy controls (HC) were recruited to acquire Resting-state functional MRI (rs-fMRI). We used seed-based approach to construct the cerebro-cerebellar limbic network. Two-sample t-tests were carried out to explore the differences of the cerebellar limbic network connectivity. The first result, a sub-scale network including the bilateral posterior part of the orbitofrontal cortex (POFC) extending to the anterior insular cortex (AIC) and left inferior parietal lobule (L-IPL), showed greater functional connectivity in MCI than in HC and less functional connectivity in AD than in MCI. The location of this sub-scale network was in accordance with components of the ventral attention network. Second, there was decreased functional connectivity to the right mid-cingulate cortex (MCC) in the AD and MCI patient groups relative to the HC group. As the cerebellum is not compromised by Alzheimer pathology in the prodromal stage of AD, this pattern indicates that the sub-scale ventral attention network may play a pivotal role in functional compensation through the coupled cerebro-cerebellar limbic network in MCI, and the cerebellum may be a key node in the modulation of social cognition.

The Influence of Cerebrospinal Fluid Abnormalities and APOE 4 on PHF-Tau Protein: Evidence From Voxel Analysis and Graph Theory

Mild cognitive impairment (MCI) is a transitional state between the cognitive changes in normal aging and Alzheimer’s disease (AD), which induces abnormalities in specific brain regions. Previous studies showed that paired helical filaments Tau (PHF-Tau) protein is a potential pathogenic protein which may cause abnormal brain function and structure in MCI and AD patients. However, the understanding of the PHF-Tau protein network in MCI patients is limited. In this study, 225 subjects with PHF-Tau Positron Emission Tomography (PET) images were divided into four groups based on whether they carried Apolipoprotein E ε4 (APOE 4) or abnormal cerebrospinal fluid Total-Tau (CSF T-Tau). They are two important pathogenic factors that might cause cognitive function impairment. The four groups were: individuals harboring CSF T-Tau pathology but no APOE 4 (APOE 4−T+); APOE 4 carriers with normal CSF T-Tau (APOE 4+T−); APOE 4 carriers with abnormal CSF T-Tau (APOE 4+T+); and APOE 4 noncarriers with abnormal CSF T-Tau (APOE 4−T−). We explored the topological organization of PHF-Tau networks in these four groups and calculated five kinds of network properties: clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Our findings showed that compared with APOE 4−T− group, the other three groups showed different alterations in the clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Simultaneously, voxel-level analysis was conducted and the results showed that compared with APOE 4−T− group, the other three groups were found increased PHF-Tau distribution in some brain regions. For APOE 4+T+ group, positive correlation was found between the value of PHF-Tau distribution in altered regions and Functional Assessment Questionnaire (FAQ) score. Our results indicated that the effects of APOE 4 and abnormal CSF T-Tau may induce abnormalities of PHF-Tau protein and APOE 4 has a greater impact on PHF-Tau than abnormal CSF T-Tau. Our results may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

APOE genotype affects metabolic and Alzheimer-related outcomes induced by Western diet in female EFAD mice

Development of Alzheimer's disease (AD) is regulated by interactive effects of genetic and environmental risk factors. The most significant genetic risk factor for AD is the ε4 allele of apolipoprotein E ( APOE4), which has been shown to exert greater AD risk in women. An important modifiable AD risk factor is obesity and its associated metabolic dysfunctions. Whether APOE genotype might interact with obesity in females to regulate AD pathogenesis is unclear. To investigate this issue, we studied the effects of Western diet (WD) on female EFAD mice, a transgenic mouse model of AD that includes human APOE alleles ε3 (E3FAD) and ε4 (E4FAD). EFAD mice were fed either control (10% fat, 7% sugar) or WD (45% fat, 17% sugar), and both metabolic and neuropathologic outcomes were determined. Although E4FAD mice generally exhibited poorer metabolic status at baseline, E3FAD mice showed greater diet-induced metabolic impairments. Similarly, E4FAD mice exhibited higher levels of AD-related pathology overall, but only E3FAD showed significant increases on select measures of β-amyloid pathology after exposure to WD. These data demonstrate a gene-environment interaction between APOE and obesogenic diets in females. Understanding how AD-promoting effects of obesity are modulated by genetic factors will foster the identification of at-risk populations and development of preventive interventions.-Christensen, A., Pike, C. J. APOE genotype affects metabolic and Alzheimer-related outcomes induced by Western diet in female EFAD mice.

Genetic interaction is associated with lower metabolic connectivity and memory impairment in clinically mild Alzheimer's disease

Metabolic connectivity as showed by [18F] fluorodeoxyglucose (FDG) positron emission tomography (FDG-PET) reflects neuronal connectivity. The aim of this study was to investigate the genetic impact on metabolic connectivity in default mode subnetworks and its clinical-pathological relationships in patients with Alzheimer's disease (AD). We separately investigated the modulation of 2 default mode subnetworks, as identified with independent component analysis, by comparing APOE-ε4 carriers to noncarriers with AD. We further analyzed the interaction effects of APOE (APOE-ε4 carriers vs noncarriers) with PICALM (rs3851179-GG vs rs3851179-A-allele carriers) on episodic memory (EM) deficits, reduction in cerebral metabolic rate for glucose (CMRgl) and decreased metabolic connectivity in default mode subnetworks. The metabolic connectivity in the ventral default mode network (vDMN) was positively correlated with EM scores (β =0.441, P < .001). The APOE-ε4 carriers had significantly lower metabolic connectivity in the vDMN than the APOE-ε4 carriers (t(96) = -2.233, P = .028). There was an effect of the APOE-PICALM (rs3851179) interactions on reduced CMRgl in regions of vDMN (P < .001), and on memory deficits (F_3,93 =5.568, P = .020). This study identified that PICALM may modulates memory deficits, reduced CMRgl and decreased metabolic connectivity in the vDMN in APOE-ε4 carriers. [18F] FDG-PET-based metabolic connectivity may serve a useful tool to elucidate the neural networks underlying clinical-pathological relationships in AD.

Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study

This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

Metabolic brain networks in aging and preclinical Alzheimer's disease

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69-89) compared to young adults (N = 17, ages 20-30) and patients with Alzheimer's disease (N = 22, ages 69-89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.

Networks of myelin covariance

Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, ). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these "networks of myelin covariance" (Myelin-Nets). The Myelin-Nets were built from quantitative Magnetization Transfer data-an in-vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin-Nets. We therefore selected two age groups: Young-Age (20-31 years old) and Old-Age (60-71 years old) and a pool of participants from 48 to 87 years old for a Myelin-Nets aging trajectory study. We found that the topological organization of the Myelin-Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin-Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

The effects of apolipoprotein ε 4 on aging brain in cognitively normal Chinese elderly: a surface-based morphometry study

BACKGROUND

Default mode network (DMN) has been reported to be susceptible to APOE ε 4 genotype. However, the APOE ε 4-related brain changes in young carriers are different from the ones in elderly carriers. The current study aimed to evaluate the cortical morphometry of DMN subregions in cognitively normal elderly with APOE ε 4.

METHOD

11 cognitively normal senior APOE ε 4 carriers and 27 matched healthy controls (HC) participated the neuropsychological tests, genotyping, and magnetic resonance imaging (MRI) scanning. Voxel-based morphometry (VBM) analysis was used to assess the global volumetric changes. Surface-based morphometry (SBM) analysis was performed to measure regional gray matter volume (GMV) and gray matter thickness (GMT).

RESULTS

Advancing age was associated with decreased GMV of DMN subregions. Compared to HC, APOE ε 4 carriers presented cortical atrophy in right cingulate gyrus (R_CG) (GMV: APOE carriers: 8475.23 ± 1940.73 mm3, HC: 9727.34 ± 1311.57 mm3, t = 2.314, p = 0.026, corrected) and left insular (GMT: APOE ε 4 carriers: 3.83 ± 0.37 mm, HC: 4.05 ± 0.25 mm, t = 2.197, p = 0.033, corrected).

CONCLUSIONS

Our results highlight the difference between different cortical measures and suggest that the cortical reduction of CG and insular maybe a potential neuroimaging marker for APOE 4 ε senior carriers, even in the context of relatively intact cognition.

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients

Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized characteristic path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing functional evidence for the abnormalities of brain networks in DM.