Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease

Dopamine D1-Receptor Organization Contributes to Functional Brain Architecture

Recent work has recognized a gradient-like organization in cortical function, spanning from primary sensory to transmodal cortices. It has been suggested that this axis is aligned with regional differences in neurotransmitter expression. Given the abundance of dopamine D1-receptors (D1DR), and its importance for modulation and neural gain, we tested the hypothesis that D1DR organization is aligned with functional architecture, and that inter-regional relationships in D1DR co-expression modulate functional cross talk. Using the world's largest dopamine D1DR-PET and MRI database (N = 180%, 50% female), we demonstrate that D1DR organization follows a unimodal-transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. We also demonstrate that individual differences in D1DR density between unimodal and transmodal regions are associated with functional differentiation of the apices in the cortical hierarchy. Finally, we show that spatial co-expression of D1DR primarily modulates couplings within, but not between, functional networks. Together, our results show that D1DR co-expression provides a biomolecular layer to the functional organization of the brain.

Connectivity alterations underlying the breakdown of pseudoneglect: New insights from healthy and pathological aging

A right-hemisphere dominance for visuospatial attention has been invoked as the most prominent neural feature of pseudoneglect (i.e., the leftward visuospatial bias exhibited in neurologically healthy individuals) but the neurophysiological underpinnings of such advantage are still controversial. Previous studies investigating visuospatial bias in multiple-objects visual enumeration reported that pseudoneglect is maintained in healthy elderly and amnesic mild cognitive impairment (aMCI), but not in Alzheimer’s disease (AD). In this study, we aimed at investigating the neurophysiological correlates sustaining the rearrangements of the visuospatial bias along the progression from normal to pathological aging. To this aim, we recorded EEG activity during an enumeration task and analyzed intra-hemispheric fronto-parietal and inter-hemispheric effective connectivity adopting indexes from graph theory in patients with mild AD, patients with aMCI, and healthy elderly controls (HC). Results revealed that HC showed the leftward bias and stronger fronto-parietal effective connectivity in the right as compared to the left hemisphere. A breakdown of pseudoneglect in patients with AD was associated with both the loss of the fronto-parietal asymmetry and the reduction of inter-hemispheric parietal interactions. In aMCI, initial alterations of the attentional bias were associated with a reduction of parietal inter-hemispheric communication, but not with modulations of the right fronto-parietal connectivity advantage, which remained intact. These data provide support to the involvement of fronto-parietal and inter-parietal pathways in the leftward spatial bias, extending these notions to the complex neurophysiological alterations characterizing pathological aging.

Visuomotor integration in early Alzheimer's disease: A TMS study

BACKGROUND AND PURPOSE

Cortical visuomotor integration is altered in Alzheimer's disease (AD), even at an early stage of the disease. The aim of this study was to assess the connections between the primary visual (V1) and motor (M1) areas in patients with early AD using a paired-pulse, twin-coil transcranial magnetic stimulation (TMS) technique.

METHODS

Visuomotor connections (VMCs) were assessed in 13 subjects with probable AD and 16 healthy control subjects. A conditioning stimulus over the V1 phosphene hotspot was followed at interstimulus intervals (ISIs) of 18 and 40 ms by a test stimulus over M1, to elicit motor evoked potentials (MEPs) in the contralateral first dorsal interosseous muscle.

RESULTS

Significant effects due to VMCs, consisting of enhanced MEP suppression at ISI of 18 and 40 ms, were observed in the AD patients. Patients with AD showed an excessive inhibitory response of the right M1 to inputs travelling from V1 at given ISIs.

CONCLUSIONS

This study provides neurophysiological evidence of altered functional connectivity between visual and motor areas in AD.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

Integrating additional knowledge into the estimation of graphical models

Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer's disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer's patients compared to other subjects.

The default mode network and cognition in Parkinson's disease: A multimodal resting-state network approach

Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson's disease (PD) has been reported by resting-state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) is largely unknown. We applied multimodal resting-state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG-PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD-MCI (p < .05). Increased metabolic and functional connectivity along fronto-parietal connections was identified in PD-MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z-scores in patients (r = -.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting-state network approaches for identifying prospective biomarkers.

Spatially Distributed Amyloid-β Reduces Glucose Metabolism in Mild Cognitive Impairment

Background:

Several positron emission tomography (PET) studies have explored the relationship between amyloid-β (Aβ), glucose metabolism, and the APOEɛ4 genotype. It has been reported that APOEɛ4, and not aggregated Aβ, contributes to glucose hypometabolism in pre-clinical stages of Alzheimer’s disease (AD) pathology.

Objective:

We hypothesize that typical measurements of Aβ taken either from composite regions-of-interest with relatively high burden actually cover significant patterns of the relationship with glucose metabolism. In contrast, spatially weighted measures of Aβ are more related to glucose metabolism in cognitively normal (CN) aging and mild cognitive impairment (MCI).

Methods:

We have generated a score of amyloid burden based on a joint singular value decomposition (SVD) of the cross-correlation structure between glucose metabolism, as measured by [¹⁸F]2-fluoro-2-deoxyglucose (FDG) PET, and Aβ, as measured by [¹⁸F]florbetapir PET, from the Alzheimer’s Disease Neuroimaging Initiative study. This SVD-based score reveals cortical regions where a reduced glucose metabolism is maximally correlated with distributed patterns of Aβ.

Results:

From an older population of CN and MCI subjects, we found that the SVD-based Aβ score was significantly correlated with glucose metabolism in several cortical regions. Additionally, the corresponding Aβ network has hubs that contribute to distributed glucose hypometabolism, which, in turn, are not necessarily foci of Aβ deposition.

Conclusions:

Our approach uncovered hidden patterns of the glucose metabolism-Aβ relationship. We showed that the SVD-based Aβ score produces a stronger relationship with decreasing glucose metabolism than either APOEɛ4 genotype or global measures of Aβ burden.

Pre- and early postnatal enriched environmental experiences prevent neonatal hypoxia-ischemia late neurodegeneration via metabolic and neuroplastic mechanisms

Prenatal and early postnatal periods are important for brain development and neural function. Neonatal insults such as hypoxia-ischemia (HI) causes prolonged neural and metabolic dysregulation, affecting central nervous system maturation. There is evidence that brain hypometabolism could increase the risk of adult-onset neurodegenerative diseases. However, the impact of non-pharmacologic strategies to attenuate HI-induced brain glucose dysfunction is still underexplored. This study investigated the long-term effects of early environmental enrichment in metabolic, cell, and functional responses after neonatal HI. Thereby, male Wistar rats were divided according to surgical procedure, sham, and HI (performed at postnatal day 3), and the allocation to standard (SC) or enriched condition (EC) during gestation and lactation periods. In-vivo cerebral metabolism was assessed by means of [¹⁸ F]-FDG micro-positron emission tomography, and cognitive, biochemical, and histological analyses were performed in adulthood. Our findings reveal that HI causes a reduction in glucose metabolism and glucose transporter levels as well as hyposynchronicity in metabolic brain networks. However, EC during prenatal or early postnatal period attenuated these metabolic disturbances. A positive correlation was observed between [¹⁸ F]-FDG values and volume ratios in adulthood, indicating that preserved tissue by EC is metabolically active. EC promotes better cognitive scores, as well as down-regulation of amyloid precursor protein in the parietal cortex and hippocampus of HI animals. Furthermore, growth-associated protein 43 was up-regulated in the cortex of EC animals. Altogether, results presented support that EC during gestation and lactation period can reduce HI-induced impairments that may contribute to functional decline and progressive late neurodegeneration.

Electroacupuncture-Related Metabolic Brain Connectivity in Neuropathic Pain due to Brachial Plexus Avulsion Injury in Rats

Objective: The present study aimed to investigate the analgesic effect of electroacupuncture (EA) in neuropathic pain due to brachial plexus avulsion injury (BPAI) and related changes in the metabolic brain connectivity. Methods: Neuropathic pain model due to BPAI was established in adult female Sprague-Dawley rats. EA stimulations (2/15 Hz, 30 min/day, 5-day intervention followed by 2-day rest in each session) were applied to the fifth-seventh cervical "Jiaji" acupoints on the noninjured side from 1st to 12th weeks following BPAI (EA group, n = 8). Three control groups included sham EA (nonelectrical acupuncture applied to 3 mm lateral to the real "Jiaji" acupoints), BPAI-only, and normal rats (no particular intervention; eight rats in each group). Thermal withdrawal latency (TWL) of the noninjured forepaw was regularly tested to evaluate the threshold of thermalgesia. Small animal [fluorine-18]-fluoro-2-deoxy-_D-glucose (¹⁸F-FDG) PET/CT scans of brain were conducted at the end of 4th, 12th, and 16th weeks to explore metabolic alterations of brain. Results: In the EA group, the TWL of the noninjured forepaw significantly decreased following BPAI and then increased following EA stimulation, compared with sham EA (P < 0.001). The metabolic brain connectivity among somatosensory cortex (SC), motor cortex (MC), caudate putamen (Cpu), and dorsolateral thalamus (DLT) in bilateral hemispheres decreased throughout the 16 weeks' observation in the BPAI-only group, compared with the normal rats (P < 0.05). In the EA group, the strength of connectivity among the above regions were found to be increased at the end of 4th week following BPAI modeling, decreased at 12th week, and then increased again at 16th week (P < 0.05). The changes in metabolic connectivity were uncharacteristic and dispersed in the sham EA group. Conclusion: The study revealed long-term and extensive changes of metabolic brain connectivity in EA-treated BPAI-induced neuropathic pain rats. Bilateral sensorimotor and pain-related brain regions were mainly involved in this process. It indicated that modulation of brain metabolic connectivity might be an important mechanism of analgesic effect in EA stimulation for the treatment of neuropathic pain.

Maturation of topological organization of brain networks in male adolescent rats: A longitudinal FDG-PET study

Recent studies have found developmental alterations of the brain during the adolescent period. However, maturation-related changes of the topological properties in brain networks are unexplored so far. We therefore used fluoro-d-glucose positron emission tomography (FDG PET) to explore the maturation-related topological metabolic changes in brain networks from adolescence to adulthood with a longitudinal study in rats (male, n = 6), followed by a graph theoretical analysis. Our results showed reduced normalization characteristic path length and increased small world index of brain networks. Specifically, we found that relative to adulthood, in the adolescent stage rats had significantly increased nodal centrality in right entorhinal cortex, left frontal association cortex, and cerebellum, areas relating to memory, executive function and higher cognitive control and motor control; and significantly reduced nodal centrality in left superior colliculus and left retrosplenial cortex. These findings suggest that moving from adolescence to adulthood, networks of the brain mature accompanied by reassignment of hub regions to increase network efficiency. These results provide an animal model of brain network maturation from adolescence to adulthood which are relevant for understanding of development of psychiatric disorders during adolescence or transition from adolescence to adulthood.

Abnormal Anatomical Rich-Club Organization and Structural-Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease

Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.

Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography

Positron emission tomography (PET) represents a unique molecular tool to get in vivo access to a wide spectrum of biological and neuropathological processes, of crucial relevance for neurodegenerative conditions. Although most PET findings are based on massive univariate approaches, in the last decade the increasing interest in multivariate methods has paved the way to the assessment of unexplored cerebral features, spanning from resting state brain networks to whole-brain connectome properties. Currently, the combination of molecular neuroimaging techniques with multivariate connectivity methods represents one of the most powerful, yet still emerging, approach to achieve novel insights into the pathophysiology of neurodegenerative diseases. In this review, we will summarize the available evidence in the field of PET molecular connectivity, with the aim to provide an overview of how these studies may increase the understanding of the pathogenesis of neurodegenerative diseases, over and above "traditional" structural/functional connectivity studies. Considering the available evidence, a major focus will be represented by molecular connectivity studies using [18F]FDG-PET, today applied in the major neuropathological spectra, from amyloidopathies and tauopathies to synucleinopathies and beyond. Pioneering studies using PET tracers targeting brain neuropathology and neurotransmission systems for connectivity studies will be discussed, their strengths and limitations highlighted with reference to both applied methodology and results interpretation. The most common methods for molecular connectivity assessment will be reviewed, with particular emphasis on the available strategies to investigate molecular connectivity at the single-subject level, of potential relevance for not only research but also diagnostic purposes. Finally, we will highlight possible future perspectives in the field, with reference in particular to newly available PET tracers, which will expand the application of molecular connectivity to new, exciting, unforeseen possibilities.

Predicting Alzheimer's disease severity by means of TMS-EEG coregistration

Clinical manifestations of Alzheimer's disease (AD) are associated with a breakdown in large-scale communication, such that AD may be considered as a "disconnection syndrome." An established method to test effective connectivity is the combination of transcranial magnetic stimulation with electroencephalography (TMS-EEG) because the TMS-induced cortical response propagates to distant anatomically connected regions. To investigate whether prefrontal connectivity alterations may predict disease severity, we explored the relationship of dorsolateral prefrontal cortex connectivity (derived from TMS-EEG) with cognitive decline (measured with Mini Mental State Examination and a face-name association memory task) in 26 patients with AD. The amplitude of TMS-EEG evoked component P30, which was found to be generated in the right superior parietal cortex, predicted Mini Mental State Examination and face-name memory scores: higher P30 amplitudes predicted poorer cognitive and memory performances. The present results indicate that advancing disease severity might be associated with effective connectivity increase involving long-distance frontoparietal connections, which might represent a maladaptive pathogenic mechanism reflecting a damaged excitatory-inhibitory balance between anterior and posterior regions.

Covariance statistics and network analysis of brain PET imaging studies

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer's disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Apolipoprotein E4 Alters Astrocyte Fatty Acid Metabolism and Lipid Droplet Formation

Lipid droplets (LDs) serve as energy rich reservoirs and have been associated with apolipoprotein E (APOE) and neurodegeneration. The E4 allele of APOE (E4) is the strongest genetic risk factor for the development of late onset Alzheimer's disease (AD). Since both E4 carriers and individuals with AD exhibit a state of cerebral lipid dyshomeostasis, we hypothesized that APOE may play a role in regulating LD metabolism. We found that astrocytes expressing E4 accumulate significantly more and smaller LDs compared to E3 astrocytes. Accordingly, expression of perilipin-2, an essential LD protein component, was higher in E4 astrocytes. We then probed fatty acid (FA) metabolism and found E4 astrocytes to exhibit decreased uptake of palmitate, and decreased oxidation of exogenously supplied oleate and palmitate. We then measured oxygen consumption rate, and found E4 astrocytes to consume more oxygen for endogenous FA oxidation and accumulate more LD-derived metabolites due to incomplete oxidation. Lastly, we found that E4 astrocytes are more sensitive to carnitine palmitoyltransferase-1 inhibition than E3 astrocytes. These findings offer the potential for further studies investigating the link between astrocyte lipid storage, utilization, and neurodegenerative disease as a function of APOE genotype.

Metabolic regional and network changes in Alzheimer's disease subtypes

Clinical variants of Alzheimer's disease (AD) include the common amnestic subtype as well as subtypes characterised by leading visual processing impairments or by multimodal neurocognitive deficits. We investigated regional metabolic patterns and networks between AD subtypes. The study comprised 9 age-matched controls and 25 patients with mild to moderate AD. Methods included clinical and neuropsychological assessment, high-resolution FDG PET and T1-weighted 3D MR imaging with PET-MR coregistration, grey matter segmentation, atlas-based regions-of-interest, linear mixed effects and regional correlation analysis. Regional metabolic patterns differed significantly between groups, but significant hypometabolism in the posterior cingulate cortex (PCC) was common to all subtypes. The most distinctive regional abnormality was occipital hypometabolism in the visual subtype. In controls, two large clusters of positive regional metabolic correlations were observed. The most pronounced breakdown of the normal correlation pattern was found in amnestic patients who, in contrast, showed the least regional focal metabolic deficits. The normal positive correlation between PCC and hippocampus was lost in all subtypes. In conclusion, PCC hypometabolism and metabolic correlation breakdown between PCC and hippocampus are the common functional core of all AD subtypes. Network alterations exceed focal regional impairment and are most prominent in the amnestic subtype.

Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer's disease

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using ¹⁸F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using ¹⁸F-FDG PET are the key feature for differentiating disease groups in AD.

Brain network changes and memory decline in aging

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by ¹⁵O-water PET, from 99 participants (mean baseline age 68.6 ± 7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4 year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance.

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.

Interregional correlations of glucose metabolism between the basal ganglia and different cortical areas: an ultra-high resolution PET/MRI fusion study using 18F-FDG

Basal ganglia have complex functional connections with the cerebral cortex and are involved in motor control, executive functions of the forebrain, such as the planning of movement, and cognitive behaviors based on their connections. The aim of this study was to provide detailed functional correlation patterns between the basal ganglia and cerebral cortex by conducting an interregional correlation analysis of the ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) data based on precise structural information. Fifteen participants were scanned with 7-Tesla magnetic resonance imaging (MRI) and high resolution research tomography (HRRT)-PET fusion system using ¹⁸F-FDG. For detailed interregional correlation analysis, 24 subregions of the basal ganglia including pre-commissural dorsal caudate, post-commissural caudate, pre-commissural dorsal putamen, post-commissural putamen, internal globus pallidus, and external globus pallidus and 80 cerebral regions were selected as regions of interest on the MRI image and their glucose metabolism were calculated from the PET images. Pearson's product-moment correlation analysis was conducted for the interregional correlation analysis of the basal ganglia. Functional correlation patterns between the basal ganglia and cerebral cortex were not only consistent with the findings of previous studies, but also showed new functional correlation between the dorsal striatum (i.e., caudate nucleus and putamen) and insula. In this study, we established the detailed basal ganglia subregional functional correlation patterns using ¹⁸F-FDG PET/MRI fusion imaging. Our methods and results could potentially be an important resource for investigating basal ganglia dysfunction as well as for conducting functional studies in the context of movement and psychiatric disorders.

Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.

Modulation of glucose metabolism and metabolic connectivity by β-amyloid

Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar β-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to β-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as β-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of β-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling β-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and β-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of β-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.

Estradiol enhances retention but not organization of hippocampus-dependent memory in intact male mice

Because estrogens have mostly been studied in gonadectomized females, effects of chronic exposure to environmental estrogens in the general population are underestimated. Estrogens can enhance hippocampus-dependent memory through the modulation of information storage. However, declarative memory, the hippocampus-dependent memory of facts and events, demands more than abilities to retain information. Specifically, memory of repetitive events of everyday life such as "where I parked" requires abilities to organize/update memories to prevent proactive interference from similar memories of previous "parking events". Whether such organizational processes are estrogen-sensitive is unknown. We here studied, in intact young and aged adult mice, drinking-water (1μM) estradiol effects on both retention and organizational components of hippocampus-dependent memory, using a radial-maze task of everyday-like memory. Demand on retention vs organization was manipulated by varying the time-interval separating repetitions of similar events. Estradiol increased performance in young and aged mice under minimized organizational demand, but failed to improve the age-associated memory impairment and diminished performance in young mice under high organizational demand. In fact, estradiol prolonged mnemonic retention of successive events without improving organization abilities, hence resulted in more proactive interference from irrelevant memories. c-Fos imaging of testing-induced brain activations showed that the deterioration of young memory was associated with dentate gyrus dysconnectivity, reminiscent of that seen in aged mice. Our findings support the view that estradiol is promnesic but also reveal that such property can paradoxically impair memory. These findings have important outcomes regarding health issues relative to the impact of environmental estrogens in the general population.

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.

Highly adaptive tests for group differences in brain functional connectivity

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that “there is currently no unique solution, but a spectrum of related methods and analytical strategies” to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters.

The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

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Rigorous testing for genuinely altered functional networks between two groups

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The proposed tests are high powered and general across a wide range of scenarios.

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Data-driven penalized network estimation

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Data-driven choice between correlations and partial correlations to describe association

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Some key differences between network estimation and testing are highlighted.

Testing group differences in brain functional connectivity: using correlations or partial correlations?

Resting-state functional magnetic resonance imaging allows one to study brain functional connectivity, partly motivated by evidence that patients with complex disorders, such as Alzheimer's disease, may have altered functional brain connectivity patterns as compared with healthy subjects. A functional connectivity network describes statistical associations of the neural activities among distinct and distant brain regions. Recently, there is a major interest in group-level functional network analysis; however, there is a relative lack of studies on statistical inference, such as significance testing for group comparisons. In particular, it is still debatable which statistic should be used to measure pairwise associations as the connectivity weights. Many functional connectivity studies have used either (full or marginal) correlations or partial correlations for pairwise associations. This article investigates the performance of using either correlations or partial correlations for testing group differences in brain connectivity, and how sparsity levels and topological structures of the connectivity would influence statistical power to detect group differences. Our results suggest that, in general, testing group differences in networks deviates from estimating networks. For example, high regularization in both covariance matrices and precision matrices may lead to higher statistical power; in particular, optimally selected regularization (e.g., by cross-validation or even at the true sparsity level) on the precision matrices with small estimation errors may have low power. Most importantly, and perhaps surprisingly, using either correlations or partial correlations may give very different testing results, depending on which of the covariance matrices and the precision matrices are sparse. Specifically, if the precision matrices are sparse, presumably and arguably a reasonable assumption, then using correlations often yields much higher powered and more stable testing results than using partial correlations; the conclusion is reversed if the covariance matrices, not the precision matrices, are sparse. These results may have useful implications to future studies on testing functional connectivity differences.

Hierarchical multivariate covariance analysis of metabolic connectivity

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

β-Amyloid is associated with aberrant metabolic connectivity in subjects with mild cognitive impairment

Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein E ɛ4 (APOE ɛ4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ɛ4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ɛ4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns.

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification

Emergence of advanced network analysis techniques utilizing resting-state functional magnetic resonance imaging (R-fMRI) has enabled a more comprehensive understanding of neurological disorders at a whole-brain level. However, inferring brain connectivity from R-fMRI is a challenging task, particularly when the ultimate goal is to achieve good control-patient classification performance, owing to perplexing noise effects, curse of dimensionality, and inter-subject variability. Incorporating sparsity into connectivity modeling may be a possible solution to partially remedy this problem since most biological networks are intrinsically sparse. Nevertheless, sparsity constraint, when applied at an individual level, will inevitably cause inter-subject variability and hence degrade classification performance. To this end, we formulate the R-fMRI time series of each region of interest (ROI) as a linear representation of time series of other ROIs to infer sparse connectivity networks that are topologically identical across individuals. This formulation allows simultaneous selection of a common set of ROIs across subjects so that their linear combination is best in estimating the time series of the considered ROI. Specifically, l 1-norm is imposed on each subject to filter out spurious or insignificant connections to produce sparse networks. A group-constraint is hence imposed via multi-task learning using a l 2-norm to encourage consistent non-zero connections across subjects. This group-constraint is crucial since the network topology is identical for all subjects while still preserving individual information via different connectivity values. We validated the proposed modeling in mild cognitive impairment identification and promising results achieved demonstrate its superiority in disease characterization, particularly greater sensitivity to early stage brain pathologies. The inferred group-constrained sparse network is found to be biologically plausible and is highly associated with the disease-associated anatomical anomalies. Furthermore, our proposed approach achieved similar classification performance when finer atlas was used to parcellate the brain space.

Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer's disease and mild cognitive impairment

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network's attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

Partial loss in septo-hippocampal cholinergic neurons alters memory-dependent measures of brain connectivity without overt memory deficits

The functional relevance of septo-hippocampal cholinergic (SHC) degeneration to the degradation of hippocampus-dependent declarative memory (DM) in aging and Alzheimer's disease (AD) remains ill-defined. Specifically, selective SHC lesions often fail to induce overt memory impairments in animal models. In spite of apparent normal performance, however, neuronal activity within relevant brain structures might be altered by SHC disruption. We hypothesized that partial SHC degeneration may contribute to functional alterations within memory circuits occurring in aging before DM decline. In young adult mice, we studied the effects of behaviorally ineffective (saporin-induced) SHC lesions - similar in extent to that seen in aged animals - on activity patterns and functional connectivity between three main neural memory systems: the septo-hippocampal system, the striatum and the amygdala that sustain declarative, procedural and emotional memory, respectively. Animals were trained in a radial maze procedure dissociating the human equivalents of relational/DM and non-R/DM expressions in animals. Test-induced Fos activation pattern revealed that the partial SHC lesion significantly altered the brain's functional activities and connectivity (co-activation pattern) despite the absence of overt behavioral deficit. Specifically, hippocampal CA3 hyperactivity and abnormal septo-hippocampo-amygdalar inter-connectivity resemble those observed in aging and prodromal AD. Hence, SHC neurons critically coordinate hippocampal function in concert with extra-hippocampal structures in accordance with specific mnemonic demand. Although partial SHC degeneration is not sufficient to impact DM performance by itself, the connectivity change might predispose the emergence of subsequent DM loss when, due to additional age-related insults, the brain can no longer compensate the holistic imbalance caused by cholinergic loss.

Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis

Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone--the so-called "k-core"--of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.

Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD.

FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients

The increased risk for the elderly with mild cognitive impairment (MCI) to progress to Alzheimer's disease makes it an appropriate condition for investigation. While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. We sought to investigate the acupuncture effects on the functional connectivity throughout the entire brain in MCI patients compared to healthy controls (HC). The functional magnetic resonance imaging experiment was performed with two different paradigms, namely, deep acupuncture (DA) and superficial acupuncture (SA), at acupoint KI3. We first identified regions showing abnormal functional connectivity in the MCI group compared to HC during the resting state and subsequently tested whether these regions could be modulated by acupuncture. Then, we made the comparison of MCI vs. HC to test whether there were any specific modulatory patterns in the poststimulus resting brain between the two groups. Finally, we made the comparisons of DA vs. SA in each group to test the effect of acupuncture with different needling depths. We found the temporal regions (hippocampus, thalamus, fusiform gyrus) showing abnormal functional connectivity during the resting state. These regions are implicated in memory encoding and retrieving. Furthermore, we found significant changes in functional connectivity related with the abnormal regions in MCI patients following acupuncture. Compared to HC, the correlations related with the temporal regions were enhanced in the poststimulus resting brain in MCI patients. Compared to SA, significantly increased correlations related with the temporal regions were found for the DA condition. The enhanced correlations in the memory-related brain regions following acupuncture may be related to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. The heterogeneous modulatory patterns between DA and SA may suggest that deep muscle insertion of acupuncture is necessary to achieve the appreciable clinical effect.

Resting metabolic connectivity in prodromal Alzheimer's disease. A European Alzheimer Disease Consortium (EADC) project

We explored resting-state metabolic connectivity in prodromal Alzheimer's disease (pAD) patients and in healthy controls (CTR), through a voxel-wise interregional correlation analysis of 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) by means of statistical parametric mapping. Baseline 18F-fluorodeoxyglucose-positron emission tomography of 36 patients with amnestic mild cognitive impairment who converted to Alzheimer's disease (AD) dementia after an average time of 2 years (pAD) and of 105 CTR were processed. The area of hypometabolism in pAD showed less metabolic connectivity in patients than in CTR (autocorrelation and correlation with large temporal and frontal areas, respectively). pAD patients showed limited correlation even in selected nonhypometabolic areas, including the hippocampi and the dorsolateral prefrontal cortex (DLFC). On the contrary, in CTR group correlation was highlighted between hippocampi and precuneus/posterior cingulate and frontal cortex, and between dorsolateral prefrontal cortex and caudate nuclei and parietal cortex. The reduced metabolic connections both in hypometabolic and nonhypometabolic areas in pAD patients suggest that metabolic disconnection (reflecting early diaschisis) may antedate remote hypometabolism (early sign of synaptic degeneration).

Functional biomarkers for neurodegenerative disorders based on the network paradigm

This commentary provides a brief introduction to the various uses that functional neuroimaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders. It then goes on to explain why the emphasis of much recent work has shifted to network-based biomarkers, as opposed to those that examine individual brain regions. A number of examples are referenced that illustrate the points made.

Biomarkers and evolution in Alzheimer disease

Brain regions and their highly neuroplastic long axonal connections that expanded rapidly during hominid evolution are preferentially affected by Alzheimer disease. There is no natural animal model with full disease pathology (neurofibrillary tangles and neuritic amyloid plaques of a severity seen in Alzheimer's disease brains). Biomarkers such as reduced glucose metabolism in association neocortex, defects in long white matter tracts, RNA neurochemical changes, and high CSF levels of total and phosphorylated tau protein, which are helpful to identify MCI and preclinical Alzheimer disease patients, may also provide insights into what brain changes led to this disease being introduced during hominid evolution.

Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease

Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimer's brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.