Metabolic connectivity for differential diagnosis of dementing disorders

Metabolic Brain PET Connectivity

This review examines the role of metabolic connectivity based on fluorodeoxyglucose-PET in understanding brain network organization across neurologic disorders, with a focus on neurodegenerative diseases. The article explores key methodologies for metabolic connectivity study and highlights altered connectivity patterns in Alzheimer's, Parkinson's, frontotemporal dementia, and other conditions. It also discusses emerging applications, including single-subject analyses and brain-organ interactions.

Tumors Affect the Metabolic Connectivity of the Human Brain Measured by 18 F-FDG PET

PURPOSE

18 F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity.

PATIENTS AND METHODS

Forty-three patients with left hemispheric tumors and 18 F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test ( P < 0.05).

RESULTS

Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC ( P < 0.001), except assortativity and betweenness centrality ( P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right ( P < 0.01), with the exception of betweenness centrality ( P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics ( P < 0.001) than temporal tumors, but lower betweenness centrality ( P = 4.5 -7 ). Patients with high KPS showed higher distance local efficiency ( P = 0.01), rich club coefficient ( P = 0.0048), clustering coefficient ( P = 0.00032), betweenness centrality ( P = 0.008), and similarity ( P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses.

CONCLUSIONS

Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage.

METHODS

We performed 18 F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis.

RESULTS

The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model.

DISCUSSION

The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.

Similarity between structural and proxy estimates of brain connectivity

Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

Introduction

Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission.

Methods

We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks.

Results

The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles.

Discussion

These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Brain connectomics: time for a molecular imaging perspective?

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Abnormal metabolic connectivity in default mode network of right temporal lobe epilepsy

Aims

Temporal lobe epilepsy (TLE) is a common neurological disorder associated with the dysfunction of the default mode network (DMN). Metabolic connectivity measured by 18F-fluorodeoxyglucose Positron Emission Computed Tomography (18F-FDG PET) has been widely used to assess cumulative energy consumption and provide valuable insights into the pathophysiology of TLE. However, the metabolic connectivity mechanism of DMN in TLE is far from fully elucidated. The present study investigated the metabolic connectivity mechanism of DMN in TLE using 18F-FDG PET.

Method

Participants included 40 TLE patients and 41 health controls (HC) who were age- and gender-matched. A weighted undirected metabolic network of each group was constructed based on 14 primary volumes of interest (VOIs) in the DMN, in which Pearson's correlation coefficients between each pair-wise of the VOIs were calculated in an inter-subject manner. Graph theoretic analysis was then performed to analyze both global (global efficiency and the characteristic path length) and regional (nodal efficiency and degree centrality) network properties.

Results

Metabolic connectivity in DMN showed that regionally networks changed in the TLE group, including bilateral posterior cingulate gyrus, right inferior parietal gyrus, right angular gyrus, and left precuneus. Besides, significantly decreased (P < 0.05, FDR corrected) metabolic connections of DMN in the TLE group were revealed, containing bilateral hippocampus, bilateral posterior cingulate gyrus, bilateral angular gyrus, right medial of superior frontal gyrus, and left inferior parietal gyrus.

Conclusion

Taken together, the present study demonstrated the abnormal metabolic connectivity in DMN of TLE, which might provide further insights into the understanding the dysfunction mechanism and promote the treatment for TLE patients.

Depletion and activation of microglia impact metabolic connectivity of the mouse brain

AIM

We aimed to investigate the impact of microglial activity and microglial FDG uptake on metabolic connectivity, since microglial activation states determine FDG-PET alterations. Metabolic connectivity refers to a concept of interacting metabolic brain regions and receives growing interest in approaching complex cerebral metabolic networks in neurodegenerative diseases. However, underlying sources of metabolic connectivity remain to be elucidated.

MATERIALS AND METHODS

We analyzed metabolic networks measured by interregional correlation coefficients (ICCs) of FDG-PET scans in WT mice and in mice with mutations in progranulin (Grn) or triggering receptor expressed on myeloid cells 2 (Trem2) knockouts (^-/-) as well as in double mutant Grn^-/-/Trem2^-/- mice. We selected those rodent models as they represent opposite microglial signatures with disease associated microglia in Grn^-/- mice and microglia locked in a homeostatic state in Trem2^-/- mice; however, both resulting in lower glucose uptake of the brain. The direct influence of microglia on metabolic networks was further determined by microglia depletion using a CSF1R inhibitor in WT mice at two different ages. Within maps of global mean scaled regional FDG uptake, 24 pre-established volumes of interest were applied and assigned to either cortical or subcortical networks. ICCs of all region pairs were calculated and z-transformed prior to group comparisons. FDG uptake of neurons, microglia, and astrocytes was determined in Grn^-/- and WT mice via assessment of single cell tracer uptake (scRadiotracing).

RESULTS

Microglia depletion by CSF1R inhibition resulted in a strong decrease of metabolic connectivity defined by decrease of mean cortical ICCs in WT mice at both ages studied (6-7 m; p = 0.0148, 9-10 m; p = 0.0191), when compared to vehicle-treated age-matched WT mice. Grn^-/-, Trem2^-/- and Grn^-/-/Trem2^-/- mice all displayed reduced FDG-PET signals when compared to WT mice. However, when analyzing metabolic networks, a distinct increase of ICCs was observed in Grn^-/- mice when compared to WT mice in cortical (p < 0.0001) and hippocampal (p < 0.0001) networks. In contrast, Trem2^-/- mice did not show significant alterations in metabolic connectivity when compared to WT. Furthermore, the increased metabolic connectivity in Grn^-/- mice was completely suppressed in Grn^-/-/Trem2^-/- mice. Grn^-/- mice exhibited a severe loss of neuronal FDG uptake (- 61%, p < 0.0001) which shifted allocation of cellular brain FDG uptake to microglia (42% in Grn^-/- vs. 22% in WT).

CONCLUSIONS

Presence, absence, and activation of microglia have a strong impact on metabolic connectivity of the mouse brain. Enhanced metabolic connectivity is associated with increased microglial FDG allocation.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Metabolic connectivity is associated with seizure outcome in surgically treated temporal lobe epilepsies: A 18F-FDG PET seed correlation analysis

¹⁸F-FDG PET provides high sensitivity for the pre-surgical assessment of drug-resistant temporal lobe epilepsy (TLE). However, little is known about the metabolic connectivity of epileptogenic networks involved. This study therefore aimed to evaluate the association between metabolic connectivity and seizure outcome in surgically treated TLE.

METHODS

The study included 107 right-handed patients that had undergone a presurgical interictal ¹⁸F-FDG PET assessment followed by an anterior temporal lobectomy and were classified according to seizure outcome 2 years after surgery. Metabolic connectivity was evaluated by seed correlation analysis in left and right epilepsy patients with a Class Engel IA or > IA outcome and compared to age-, sex- and handedness-matched healthy controls.

RESULTS

Increased metabolic connectivity was observed in the >IA compared to the IA group within the operated temporal lobe (respective clusters of 7.5 vs 3.3 cm³ and 2.6 cm³ vs 2.2 cm³ in left and right TLE), and to a lower extent with the contralateral temporal lobe (1.2 vs 0.7 cm³ and 1.7 cm³ vs 0.7 cm³ in left and right TLE). Seed correlations provided added value for the estimated individual performance of seizure outcome over the group comparisons in left TLE (AUC of 0.74 vs 0.67).

CONCLUSION

Metabolic connectivity is associated with outcome in surgically treated TLE with a strengthened epileptogenic connectome in patients with non-free-seizure outcomes. The added value of seed correlation analysis in left TLE underlines the importance of evaluating metabolic connectivity in network related diseases.

Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach

PURPOSE

Sparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDG_cov) as proxy of metabolic brain connectivity. However, this statistical method suffers from the lack of robustness in the connectivity estimation. Patterns of FDG_cov were observed to be spatially similar with patterns of structural connectivity as obtained from DTI imaging. Based on this similarity, we propose to regularize the sparse estimation of FDG_cov using the structural connectivity.

METHODS

We retrospectively analyzed the FDG-PET and DTI data of 26 healthy controls, 41 patients with Alzheimer's disease (AD), and 30 patients with frontotemporal lobar degeneration (FTLD). Structural connectivity matrix derived from DTI data was introduced as a regularization parameter to assign individual penalties to each potential metabolic connectivity. Leave-one-out cross validation experiments were performed to assess the differential diagnosis ability of structure weighted SICE approach. A few approaches of structure weighted were compared with the standard SICE.

RESULTS

Compared to the standard SICE, structural weighting has shown more stable performance in the supervised classification, especially in the differentiation AD vs. FTLD (accuracy of 89-90%, while unweighted SICE only 85%). There was a significant positive relationship between the minimum number of metabolic connection and the robustness of the classification accuracy (r = 0.57, P < 0.001). Shuffling experiments showed significant differences between classification score derived with true structural weighting and those obtained by randomized structure (P < 0.05).

CONCLUSION

The structure-weighted sparse estimation can enhance the robustness of metabolic connectivity, which may consequently improve the differentiation of pathological phenotypes.

BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module. In addition, the accuracy of classification between EMCI and LMCI achieves 79.64% using imbalanced FDG-PET images, which illustrates that the proposed method yields a state-of-the-art result of the classification accuracy between EMCI and LMCI based on PET images.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer's Disease

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer's disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network

Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842.

Static versus Functional PET: Making Sense of Metabolic Connectivity

Recently, Jamadar et al. (2021, Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 31(6), 2855-2867) compared the patterns of brain connectivity or covariance as obtained from 3 neuroimaging measures: 1) functional connectivity estimated from temporal correlations in the functional magnetic resonance imaging blood oxygen level-dependent signal, metabolic connectivity estimated, 2) from temporal correlations in 16-s frames of dynamic [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), which they designate as functional FDG-PET (fPET), and 3) from intersubject correlations in static FDG-PET images (sPET). Here, we discuss a number of fundamental issues raised by the Jamadar study. These include the choice of terminology, the interpretation of cross-modal findings, the issue of group- to single-subject level inferences, and the meaning of metabolic connectivity as a biomarker. We applaud the methodological approach taken by the authors, but wish to present an alternative perspective on their findings. In particular, we argue that sPET and fPET can both provide valuable information about brain connectivity. Certainly, resolving this conundrum calls for further experimental and theoretical efforts to advance the developing framework of PET-based brain connectivity indices.

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment

OBJECTIVES

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis.

METHODS

In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used.

RESULTS

We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50%, 98.39% ± 2.50%, and 99.44% ± 1.11% using the DLG model, while 71.38% ± 0.63%, 63.13% ± 2.87%, and 85.59% ± 6.66% using traditional GWAS. Similar results were obtained from the other two intergroup classifications.

CONCLUSION

The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

Metabolic and amyloid PET network reorganization in Alzheimer's disease: differential patterns and partial volume effects

Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [¹⁸F]FDG- and [¹⁸F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [¹⁸F]FDG- and [¹⁸F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [¹⁸F]FDG- or [¹⁸F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.

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.

Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study

Background

With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome. This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD.

Methods

Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent ¹⁸F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions.

Results

The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson’s Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient.

Conclusions

The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients.

Clinical Trial Registration ChiCTR-DOC-16008645. http://www.chictr.org.cn/showproj.aspx?proj=13865.

Brain Functional and Structural Signatures in Parkinson's Disease

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). ¹⁸F-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia

Purpose

Positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD.

Methods

FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.

Results

The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).

Conclusion

The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users.

Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease

Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on Pearson correlation or linear regression models. However, these models are prone to spurious correlations arising from uninformative shared variance and multicollinearity. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of multicollinear variables derived from such data, being able to utilize the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is conceptually expected to closely reflect the underlying causal relationships between various variables. Hence, we applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N = 972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for each of the 108 cortical and subcortical brain regions. GGMs were estimated using a Bayesian framework for the combined multimodal data and the resulted conditional dependency networks were compared to classical covariance networks based on Pearson correlation. Additionally, graph-theoretical network statistics were calculated to determine network alterations associated with disease status. The resulting conditional dependency matrices were much sparser (≈10% density) than Pearson correlation matrices (≈50% density). Within imaging modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For the associations between different modalities, only few region-specific connections were detected. Network measures such as small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory, i.e., increased small-world coefficient in early mild cognitive impairment (MCI), similar values in late MCI, and decreased values in AD dementia patients compared to cognitively normal controls. In conclusion, GGMs removed commonly shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to correlation networks based on the Pearson coefficient. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.

Disrupted network topology in patients with Lewy bodies dementia compared to Alzheimer's disease, Parkinson disease dementia and Health Control

The clinical manifestation of Lewy body dementia (DLB) is distinct from Alzheimer's disease (AD), but overlap with Parkinson's disease dementia (PDD). However, little is known about different topology properties of abnormal brain networks associated with these neurodegenerative diseases. In order to study the difference of brain networks in various dementia subtypes, we used $^{\mathbf {18}}\text{F}$-Fluorodeoxyglucose positron emission tomography ($^{\mathbf {18}}\text{F}$-FDG PET) images and graph theory methods to investigate altered whole-brain intrinsic glucose metabolic functional networks in three Chinese dementia groups compared to healthy control (HC) group, including 22 AD patients, 18 PDD patients, 22 DLB patients and 22 HC subjects from Huashan Hospital, Shanghai, China. The experimental results disclosed that in the three dementia groups, compared to HC group, the small-world characteristics were lost. Additionally, compared with HC group, the clustering coefficients of three dementia groups were higher; the characteristic path lengths were longer. In terms of local efficiency and global efficiency, it was at the lowest level in DLB group. We also found differences about distributions of hub regions amongst the four groups. This finding could further help physicians to understand pathological mechanisms of different dementia.

Variant-specific vulnerability in metabolic connectivity and resting-state networks in behavioural variant of frontotemporal dementia

Brain connectivity measures represent candidate biomarkers of neuronal dysfunction in neurodegenerative diseases. Previous findings suggest that the behavioural variant of frontotemporal dementia (bvFTD) and its variants (i.e., frontal and temporo-limbic) may be related to the vulnerability of distinct functional connectivity networks. In this study, 82 bvFTD patients were included, and two patient groups were identified as frontal and temporo-limbic bvFTD variants. Two advanced multivariate analytical approaches were applied to FDG-PET data, i.e., sparse inverse covariance estimation (SICE) method and seed-based interregional correlation analysis (IRCA). These advanced methods allowed the assessment of (i) the whole-brain metabolic connectivity, without any a priori assumption, and (ii) the main brain resting-state networks of crucial relevance for cognitive and behavioural functions. In the whole bvFTD group, we found dysfunctional connectivity patterns in frontal and limbic regions and in all major brain resting-state networks as compared to healthy controls (HC N = 82). In the two bvFTD variants, SICE and IRCA analyses identified variant-specific reconfigurations of whole-brain connectivity and resting-state networks. Specifically, the frontal bvFTD variant was characterised by metabolic connectivity alterations in orbitofrontal regions and anterior resting-state networks, while the temporo-limbic bvFTD variant was characterised by connectivity alterations in the limbic and salience networks. These results highlight different neural vulnerabilities in the two bvFTD variants, as shown by the dysfunctional connectivity patterns, with relevance for the different neuropsychological profiles. This new evidence provides further insight in the variability of bvFTD and may contribute to a more accurate classification of these patients.

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.

Network imaging biomarkers: insights and clinical applications in Parkinson's disease

Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.

Metabolic covariance networks combining graph theory measuring aberrant topological patterns in mesial temporal lobe epilepsy

OBJECTIVE

We aimed to study the networks' mechanism of metabolic covariance networks in mesial temporal lobe epilepsy (mTLE), through examining the brain value of fluorine-18-fluorodeoxyglucose positron emission tomography (18 F-FDG-PET).

METHODS

18 F-FDG-PET images from 16 patients with mTLE were analyzed using local and global metabolic covariance network (MCN) approaches, including whole metabolic pattern analysis (WMPA), hippocampus-based (h-) MCN, whole brain (w-) MCN, and edge-based connectivity analysis (EBCA).

RESULTS

WMPA showed a typical ipsilateral hypometabolism and contralateral hypermetabolism pattern to epileptic zones in mTLE. h-MCN revealed decreased hippocampus-based synchronization in contralateral regions. w-MCN exhibited a disrupted metabolic network with globally increased small-world properties and regionally decreased nodal metrics in the ipsilateral hemisphere. Hippocampus (h)-EBCA and whole brain EBCA (w-EBCA) both detected a reduced-connectivity dominated metabolic covariant network. Moreover, the reduced interhemisphere connectivity seemingly played a major role in the aberrant epileptic topological pattern.

CONCLUSION

From a metabolic point of view, we demonstrated the damaging effects with reduced contralateral intranetwork metrics properties and the compensatory effects in contralateral intranetworks with increased network properties. However, the import role of significant reduced interhemisphere connection has rarely been reported in other mTLE studies. Taken together, 18 F-FDG-PET MCN analysis provides new evidence that the mTLE is a system neurological disorder with disrupted networks.

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.

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.

A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Introduction

The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers.

Methods

We studied a total of 148 dementia patients from the Feinstein Institute (Center-A: 25 bvFTD and 10 Alzheimer's disease), Technical University of Munich (Center-B: 44 bvFTD and 29 FTD language variants), and Alzheimer's Disease Neuroimaging Initiative (40 Alzheimer's disease subjects). To identify the covariance pattern of bvFTD (behavioral variant frontotemporal dementia–related pattern [bFDRP]), we applied principal component analysis to combined 18F-fluorodeoxyglucose–positron emission tomography scans from bvFTD and healthy subjects. The phenotypic specificity and clinical correlates of bFDRP expression were assessed in independent testing sets.

Results

The bFDRP was identified in Center-A data (24.1% of subject × voxel variance; P < .001), reproduced in Center-B data (P < .001), and independently validated using combined testing data (receiver operating characteristics–area under the curve = 0.97; P < .0001). The expression of bFDRP was specifically elevated in bvFTD patients (P < .001) and was significantly higher at more advanced disease stages (P = .035:duration; P < .01:severity).

Discussion

The bFDRP can be used as a quantitative imaging marker to gauge the underlying disease process and aid in the differential diagnosis of bvFTD.

Glucose Metabolic Brain Network Differences between Chinese Patients with Lewy Body Dementia and Healthy Control

Dementia with Lewy bodies (DLB) is the second most common degenerative dementia of the central nervous system. The technique ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F FDG PET) was used to investigate brain metabolism patterns in DLB patients. Conventional statistical methods did not consider intern metabolism transforming connections between various brain regions; therefore, most physicians do not understand the underlying neuropathology of DLB patients. In this study, ¹⁸F FDG-PET images and graph-theoretical methods were used to investigate alterations in whole-brain intrinsic functional connectivity in a Chinese DLB group and healthy control (HC) group. This experimental study was performed on 22 DLB patients and 22 HC subjects in Huashan Hospital, Shanghai, China. Experimental results indicate that compared with the HC group, the DLB group has severely impaired small-world network. Compared to those of the HC group, the clustering coefficients of the DLB group were higher and characteristic path lengths were longer, and in terms of global efficiencies, those of the DLB group was also lower. Moreover, four significantly altered regions were observed in the DLB group: Inferior frontal gyrus, opercular part (IFG.R), olfactory cortex (OLF.R), hippocampus (HIP.R), and fusiform gyrus (FFG.L). Amongst them, in the DLB group, betweenness centrality became strong in OLF.R, HIP.R, and FFG.L, whereas betweenness centrality became weaker in IFG.R. Finally, IFGoperc.R was selected as a seed and a voxel-wise correlation analysis was performed. Compared to the HC group, the DLB group showed several regions of strengthened connection with IFGoperc.R; these regions were located in the prefrontal cortex and regions of weakened connection were located in the occipital cortex. The results of this paper may help physicians to better understand and characterize DLB patients.

Altered brain metabolic connectivity at multiscale level in early Parkinson's disease

To explore the effects of PD pathology on brain connectivity, we characterized with an emergent computational approach the brain metabolic connectome using [18F]FDG-PET in early idiopathic PD patients. We applied whole-brain and pathology-based connectivity analyses, using sparse-inverse covariance estimation in thirty-four cognitively normal PD cases and thirty-four age-matched healthy subjects for comparisons. Further, we assessed high-order resting state networks by interregional correlation analysis. Whole-brain analysis revealed altered metabolic connectivity in PD, with local decreases in frontolateral cortex and cerebellum and increases in the basal ganglia. Widespread long-distance decreases were present within the frontolateral cortex as opposed to connectivity increases in posterior cortical regions, all suggestive of a global-scale connectivity reconfiguration. The pathology-based analyses revealed significant connectivity impairment in the nigrostriatal dopaminergic pathway and in the regions early affected by α-synuclein pathology. Notably, significant connectivity changes were present in several resting state networks especially in frontal regions. These findings expand previous imaging evidence of altered connectivity in cognitively stable PD patients by showing pathology-based connectivity changes and disease-specific metabolic architecture reconfiguration at multiple scale levels, from the earliest PD phases. These alterations go well beyond the known striato-cortical connectivity derangement supporting in vivo an extended neural vulnerability in the PD synucleinopathy.

Metabolic connectomics targeting brain pathology in dementia with Lewy bodies

Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak's early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment.

Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease

A progressive loss of dopamine neurons in the substantia nigra (SN) is considered the main feature of idiopathic Parkinson's disease (PD). Recent neuropathological evidence however suggests that the axons of the nigrostriatal dopaminergic system are the earliest target of α-synuclein accumulation in PD, thus the principal site for vulnerability. Whether this applies to in vivo PD, and also to the mesolimbic system has not been investigated yet.

We used [¹¹C]FeCIT PET to measure presynaptic dopamine transporter (DAT) activity in both nigrostriatal and mesolimbic systems, in 36 early PD patients (mean disease duration in months ± SD 21.8 ± 10.7) and 14 healthy controls similar for age. We also performed anatomically-driven partial correlation analysis to evaluate possible changes in the connectivity within both the dopamine networks at an early clinical phase.

In the nigrostriatal system, we found a severe DAT reduction in the afferents to the dorsal putamen (DPU) (η² = 0.84), whereas the SN was the less affected region (η² = 0.31). DAT activity in the ventral tegmental area (VTA) and the ventral striatum (VST) were also reduced in the patient group, but to a lesser degree (VST η² = 0.71 and VTA η² = 0.31). In the PD patients compared to the controls, there was a marked decrease in dopamine network connectivity between SN and DPU nodes, supporting the significant derangement in the nigrostriatal pathway.

These results suggest that neurodegeneration in the dopamine pathways is initially more prominent in the afferent axons and more severe in the nigrostriatal system. Considering PD as a disconnection syndrome starting from the axons, it would justify neuroprotective interventions even if patients have already manifested clinical symptoms.

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In vivo study of mesolimbic and nigrostriatal dopamine systems in early iPD

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Evidence for a severe axonal damage with relative sparing of SN

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Evidence for a moderate damage of the mesolimbic pathway in early iPD

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Significant reduction of molecular connectivity between nigrostriatal nodes

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Justification for neuroprotective interventions in early-iPD phase