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

Early alterations of functional connectivity, regional brain volumes and astrocyte markers in the beta-sitosterol beta-d-glucoside (BSSG) rat model of parkinsonism

The β-sitosterol-β-ᴅ-glucoside (BSSG) rat model of experimental parkinsonism develops pathological behaviour and motor changes that progress over time. The purpose of this study was to identify early changes in structure and function of the brain of rats treated with BSSG using both structural and resting-state functional MRI. BSSG and non-BSSG rats were fed five days a week for sixteen weeks, then underwent in vivo MRI scans and an assessment of motor performance 2 and 8 weeks later (18 and week 24 from BSSG). Groups of rats were killed at weeks 19 and 25, then imaged again with MR ex vivo, and immunostained for tyrosine hydroxylase (TH). Since BSSG may interfere with cholesterol metabolism in astrocytes, we also studied potential target engagement and measured levels of astrocyte markers GFAP and S100b. At both studied timepoints, functional connectivity (FC) between brain areas with intermediate connectivity was decreased, but brain volumes increased in the BSSG-treated rats. At week 18/19, fine movements were normal, whereas TH and GFAP were decreased in the striatum, but not in the substantia nigra. At week 24/25, fine movements were impaired, and TH was decreased both in the striatum and the substantia nigra and S100b was increased in the substantia nigra. Astrogliosis may contribute to the increased brain volume found after BSSG exposure. Using the BSSG model of parkinsonism, the results demonstrate early functional and structural alterations in MRI imaging that occur before the appearance of detectable motor symptoms.

Brain Connectivity, Neural Networks, and Resilience in Aging and Neurodegeneration

The importance of complex systems has become increasingly evident in recent years. The nervous system is one such example, with neural networks sitting at the intersection of complex networks and biology. A particularly exciting feature is the resilience of complex systems. For example, the ability of the nervous system to perform even in the face of challenges that include neuronal loss, neuroinflammation, protein accumulation, axonal disruptions, and metabolic stress is an intriguing and exciting line of investigation. In neurodegenerative diseases, neural network resilience is responsible for the time between the earliest disease-linked changes and clinical symptom onset and disease diagnosis. In this way, connectivity resilience of neurons within the complex network of cells that make up the nervous system has significant implications. This review provides an overview of relevant concepts related to complex systems with a focus on the connectivity of the nervous system. It discusses the development of the neural network and how a delicate balance determines how this complex system responds to injury, with examples illustrating maladaptive plasticity. The review then addresses the implications of these concepts, methods to understand brain connectivity and neural networks, and recent research efforts aimed at understanding neurodegeneration from this perspective. This study aims to provide foundational knowledge and an overview of current research directions in this evolving and exciting area of neuroscience.

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.

Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction

Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.

PET, SPECT, and MRI imaging for evaluation of Parkinson's disease

This review assesses the primary neuroimaging techniques used to evaluate Parkinson's disease (PD) - a neurological condition characterized by gradual dopamine-producing nerve cell degeneration. The neuroimaging techniques explored include positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI). These modalities offer varying degrees of insights into PD pathophysiology, diagnostic accuracy, specificity by way of exclusion of other Parkinsonian syndromes, and monitoring of disease progression. Neuroimaging is thus crucial for diagnosing and managing PD, with integrated multimodal approaches and novel techniques further enhancing early detection and treatment evaluation.

Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson's disease

PURPOSE

While fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level.

METHODS

In the current study, this technique was employed to explore Parkinson's disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI.

RESULTS

Our findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni-Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni-Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036).

CONCLUSION

Metabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.

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.

Dopamine in the prefrontal cortex plays multiple roles in the executive function of patients with Parkinson's disease

Parkinson's disease can affect not only motor functions but also cognitive abilities, leading to cognitive impairment. One common issue in Parkinson's disease with cognitive dysfunction is the difficulty in executive functioning. Executive functions help us plan, organize, and control our actions based on our goals. The brain area responsible for executive functions is called the prefrontal cortex. It acts as the command center for the brain, especially when it comes to regulating executive functions. The role of the prefrontal cortex in cognitive processes is influenced by a chemical messenger called dopamine. However, little is known about how dopamine affects the cognitive functions of patients with Parkinson's disease. In this article, the authors review the latest research on this topic. They start by looking at how the dopaminergic system, is altered in Parkinson's disease with executive dysfunction. Then, they explore how these changes in dopamine impact the synaptic structure, electrical activity, and connection components of the prefrontal cortex. The authors also summarize the relationship between Parkinson's disease and dopamine-related cognitive issues. This information may offer valuable insights and directions for further research and improvement in the clinical treatment of cognitive impairment in Parkinson's disease.

Cortical and subcortical functional connectivity and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We focus on canonical cortical networks linked to cognition, including the salience network (SAL), frontoparietal network (FPN), and default mode network (DMN), as well as a subcortical basal ganglia network (BGN). We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. In 82 PD patients, we found that lower MoCA scores were linked with lower intra-network connectivity of the FPN. We also found that lower MoCA scores were linked with lower inter-network connectivity between the SAL and the BGN, the SAL and the DMN, as well as the FPN and the DMN. These data elucidate the relationship of cortical and subcortical functional connectivity with cognitive impairments in PD.

Positive relation between dopamine neuron degeneration and metabolic connectivity disruption in the MPTP plus probenecid mouse model of Parkinson's disease

The clinical manifestation of Parkinson's disease (PD) appears when neurodegeneration is already advanced, compromising the efficacy of disease-modifying treatment approaches. Biomarkers to identify the early stages of PD are therefore of paramount importance for the advancement of the therapy of PD. In the present study, by using a mouse model of PD obtained by subchronic treatment with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the clearance inhibitor probenecid (MPTPp), we identified prodromal markers of PD by combining in vivo positron emission tomography (PET) imaging and ex vivo immunohistochemistry. Longitudinal PET imaging of the dopamine transporter (DAT) by [18F]-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ([18F]-FP-CIT), and brain glucose metabolism by 2-deoxy-2-[18F]-fluoroglucose ([18F]-FDG) were performed before MPTPp treatment and after 1, 3, and 10 MPTPp administrations, in order to assess relation between dopamine neuron integrity and brain connectivity. The results show that in vivo [18F]-FP-CIT in the dorsal striatum was not modified after the first administration of MPTPp, tended to decrease after 3 administrations, and significantly decreased after 10 MPTPp administrations. Post-mortem immunohistochemical analyses of DAT and tyrosine hydroxylase (TH) in the striatum showed a positive correlation with [18F]-FP-CIT, confirming the validity of repeated MPTPp-treated mice as a model that can reproduce the progressive pathological changes in the early phases of PD. Analysis of [18F]-FDG uptake in several brain areas connected to the striatum showed that metabolic connectivity was progressively disrupted, starting from the first MPTPp administration, and that significant connections between cortical and subcortical regions were lost after 10 MPTPp administrations, suggesting an association between dopamine neuron degeneration and connectivity disruption in this PD model. The results of this study provide a relevant model, where new drugs that can alleviate neurodegeneration in PD could be evaluated preclinically.

Distinctive clinical and imaging trajectories in SWEDD and Parkinson's disease patients

A proportion of patients clinically diagnosed with Parkinson's disease (PD) can have a 123I-FP-CIT-SPECT scan without evidence of dopaminergic deficit (SWEDD), generating a debate about the underlying biological mechanisms. This study investigated differences in clinical features, 123I-FP-CIT binding, molecular connectivity, as well as clinical and imaging progression between SWEDD and PD patients. We included 36 SWEDD, 49 de novo idiopathic PD, and 49 healthy controls with 123I-FP-CIT-SPECT from the Parkinson's Progression Markers Initiative. Clinical and imaging 2-year follow-ups were available for 27 SWEDD and 40 PD. Regional-based and voxel-wise analysis assessed dopaminergic integrity in dorsal and ventral striatal, as well as extrastriatal regions, at baseline and follow-up. Molecular connectivity analyses evaluated dopaminergic pathways. Spatial correlation analyses tested whether 123I-FP-CIT-binding alterations would also pertain to the serotoninergic system. SWEDD and PD patients showed comparable symptoms at baseline, except for hyposmia, which was more severe for PD. PD showed significantly lower striatal and extrastriatal 123I-FP-CIT-binding compared to SWEDD and controls. SWEDD exhibited lower binding than controls in striatal regions, insula, and olfactory cortex. Both PD and SWEDD showed extensive altered connectivity of dopaminergic pathways, however, with major impairment in the mesocorticolimbic system for SWEDD. Motor symptoms and dopaminergic deficits worsened after 2 years for PD only. The limited dopaminergic impairment and its stability over time observed for SWEDD, as well as the presence of extrastriatal 123I-FP-CIT binding alterations and prevalent mesocorticolimbic connectivity impairment, suggest other mechanisms contributing to SWEDD pathophysiology.

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.

Computational Neurosurgery in Deep Brain Stimulation

Computational methods and technologies are critical for neurosurgery in general and in deep brain stimulation (DBS) in particular. They increasingly inform every aspect of clinical DBS therapy, from presurgical planning and hardware implantation to postoperative adjustment of stimulation parameters. Computational methods also occupy a prominent position within the DBS research sphere, where they facilitate efforts to better understand DBS' underlying mechanisms and optimize and individualize its delivery. This chapter provides a high-level overview of the various computational tools and methods that have been applied to DBS. First, we discuss the invaluable contribution of computational neuroimaging (primarily magnetic resonance imaging) to DBS, targeting and the role of postoperative methods of image analysis-specifically, electrode localization, volume of activated tissue modeling, and sweet-spot mapping-in precisely localizing DBS' targets in the brain and discerning optimal treatment loci. We then address the growing field of connectomics, which leverages specific magnetic resonance imaging (MRI) sequences and post-acquisition processing algorithms to explore how DBS operates at the level of brain-wide networks. Next, the search for electrophysiological and imaging-based biomarkers of optimal DBS therapy is explored. We lastly touch on the incipient field of spatial characterization analysis and discuss the ongoing development of adaptive, closed-loop DBS systems.

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.

Multiplex core of the human brain using structural, functional and metabolic connectivity derived from hybrid PET-MR imaging

With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.

Brain-wide transcriptome-based metabolic alterations in Parkinson's disease: human inter-region and human-experimental model correlations

Alterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson's disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data comparisons from literature, spanning multiple brain regions. We found high metabolic correlations between the Substantia nigra (SN)- and cerebral cortex-derived tissues. Moreover, three clusters of PD patient cohorts were identified based on perturbed metabolic processes in the SN - each characterised by perturbations in (a) bile acid metabolism (b) omega-3 fatty acid metabolism, and (c) lipoic acid and androgen metabolism - metabolic themes not comprehensively addressed in PD. These perturbations were supported by concurrence between transcriptome and proteome changes in the expression patterns for CBR1, ECI2, BDH2, CYP27A1, ALDH1B1, ALDH9A1, ADH5, ALDH7A1, L1CAM, and PLXNB3 genes, providing a valuable resource for drug targeting and diagnosis. Also, we analysed 58 PD-control transcriptome data comparisons from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and the need to optimize current experimental models to advance research on metabolic aspects of PD.

Sex differences in dementia with Lewy bodies: an imaging study of neurotransmission pathways

PURPOSE

Dementia with Lewy bodies (DLB) is characterized by a wide clinical and biological heterogeneity, with sex differences reported in both clinical and pathologically confirmed DLB cohorts. No research evidence is available on sex differences regarding molecular neurotransmission. This study aimed to assess whether sex can influence neurotransmitter systems in patients with probable DLB (pDLB).

METHODS

We included 123 pDLB patients (male/female: 77/46) and 78 control subjects (male/female: 34/44) for comparison, who underwent 123I-FP-CIT SPECT imaging. We assessed sex differences in the dopaminergic activity of the nigrostriatal and mesolimbic systems using regional-based and voxel-wise analyses of 123I-FP-CIT binding. We tested whether sex-specific binding alterations would also pertain to the serotoninergic and noradrenergic systems by applying spatial correlation analyses. We applied molecular connectivity analyses to assess potential sex differences in the dopaminergic pathways.

RESULTS

We found comparable 123I-FP-CIT binding decreases in the striatum for pDLB males and females compared to controls. However, pDLB females showed lower binding in the extrastriatal projections of the nigrostriatal and mesolimbic dopaminergic systems compared to pDLB males. According to the spatial correlation analysis, sex-specific molecular alterations were also associated with serotonergic and noradrenergic systems. Nigrostriatal and mesolimbic systems' connectivity was impaired in both groups, with males showing local alterations and females presenting long-distance disconnections between subcortical and cortical regions.

CONCLUSIONS

Sex-specific differences in 123I-FP-CIT binding were found in our cohort, namely, a trend for lower 123I-FP-CIT binding in females, significant in the presence of a pDLB diagnosis. pDLB females showed also different patterns of connectivity compared to males, mostly involving extrastriatal regions. The results suggest the presence of a sex-related regional vulnerability to alpha-synuclein pathology, possibly complicated also by the higher prevalence of Alzheimer's disease co-pathology in females, as previously reported in pDLB populations.

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.

Sex differences in brain metabolic connectivity architecture in probable dementia with Lewy bodies

We investigated how sex modulates metabolic connectivity alterations in probable dementia with Lewy bodies (pDLB). We included 131 pDLB patients (males/females: 58/73) and similarly aged healthy controls (HC) (male/female: 59/75) with available (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans. We assessed (1) sex differences in the whole-brain connectivity, identifying pathological hubs, (2) connectivity alterations in functional pathways of the neurotransmitter systems, (3) Resting State Networks (RSNs) integrity. Both pDLB_M (males) and pDLB_F (females) shared dysfunctional hubs in the insula, Rolandic operculum, and inferior parietal lobule, but the pDLB_M group showed more severe and diffuse whole-brain connectivity alterations. Neurotransmitters connectivity analysis revealed common alterations in dopaminergic and noradrenergic pathways. Sex differences emerged particularly in the Ch4-perisylvian division, with pDLB_M showing more severe alterations than pDLB_F. The RSNs analysis showed no sex differences, with decreased connectivity strength in the primary visual, posterior default mode, and attention networks in both groups. Extensive connectivity changes characterize both males and females in the dementia stage, with a major vulnerability of cholinergic neurotransmitter systems in males, possibly contributing to the observed different clinical phenotypes.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

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

Caudate-anchored cognitive connectivity pursuant to orthostatic hypotension in early Parkinson's disease

¹⁸F-Florbetaben is a tracer used to evaluate the metabolic activity of and amyloid accumulation in the brain when measured in early- and late-phase, respectively. The metabolism of neural substrates could be viewed as a network and might be an important factor in cognition. Orthostatic hypotension (OH) might play an indirect moderating role in cognition, and its latent influence could modify the inherent cognitive network. This study aimed to identify changes of cognitive connectivity according to orthostatic stress in patients with early Parkinson's disease (PD). This study included 104 early PD patients who were evaluated with a head-up tilt-test and¹⁸F-Florbetaben positron emission tomography (PET). Cognition was assessed with a comprehensive neuropsychological battery that gauged attention/working memory, language, visuospatial, memory, and executive functions. PET images were analyzed visually for amyloid deposits, and early-phase images were normalized to obtain standardized uptake ratios (SUVRs) of pre-specified subregions relevant to specific cognitive domains. The caudate nucleus was referenced and paired to these pre-specified regions. The correlations between SUVRs of these regions were assessed and stratified according to presence of orthostatic hypotension. Among the patients studied, 22 (21.2%) participants had orthostatic hypotension. Nineteen patients (18.3%) were positive for amyloid-β accumulation upon visual analysis. Moderate correlations between the caudate and pre-specified subregions were observed (Spearman's rho, range [0.331-0.545]). Cognition did not differ, but the patterns of correlation were altered when the disease was stratified by presence of orthostatic stress. In conclusion, cognition in early PD responds to hemodynamic stress by adapting its neural connections between regions relevant to cognitive functions.

Different Features of a Metabolic Connectivity Map and the Granger Causality Method in Revealing Directed Dopamine Pathways: A Study Based on Integrated PET/MR Imaging

BACKGROUND AND PURPOSE

Exploring the directionality of neural information in the brain is important for understanding brain mechanisms and neurodisease development. Granger causality analysis and the metabolic connectivity map can be used to investigate directional transmission of information between brain regions, but their differences in depicting functional effective connectivity are not clear.

MATERIALS AND METHODS

Using the Monash rs-PET/MR imaging data set, we conducted Granger causality and metabolic connectivity map analyses of the dopamine reward circuit in the brain. The dopamine reward circuit is a well-known system consisting primarily of the bilateral orbital frontal cortex, caudate, nucleus accumbens, thalamus, and substantia nigra. We validated these circuit pathways using Granger causality and the metabolic connectivity map for identifying effective connectivities against a priori knowledge by testing the significance of directed pathways (P < .05, false discovery rate-corrected).

RESULTS

We found 3 types of effective connectivities in the dopamine reward circuit: long-range, neighborhood, and symmetric. Granger causality analysis revealed long-range connections in the orbital frontal cortex-caudate and orbital frontal cortex-nucleus accumbens regions. Metabolic connectivity map analysis revealed neighborhood connections in the nucleus accumbens-caudate, substantia nigra-thalamus, and thalamus-caudate regions. Metabolic connectivity map analysis also found symmetric connections in each of the bilateral nucleus accumbens, caudate, thalamus, and orbital frontal cortex-caudate regions. Different patterns in directional networks of the dopamine reward circuit were revealed by Granger causality and metabolic connectivity map analyses.

CONCLUSIONS

Granger causality analysis primarily identified bidirectional cortico-nucleus connections, while the metabolic connectivity map primarily identified direct connections among neighborhood and symmetric regions. The results of this study indicated that investigations of effective connectivities should use an appropriate analysis method depending on the purpose of the study.

Reconfigured metabolism brain network in asymptomatic microtubule-associated protein tau mutation carriers: a graph theoretical analysis

Background

Studies exploring topological properties of the metabolic network during the presymptomatic stage of genetic frontotemporal dementia (FTD) are scarce. However, such knowledge is important for understanding brain function and disease pathogenesis. Therefore, we aimed to explore FTD-specific patterns of metabolism topology reconfiguration in microtubule-associated protein tau (MAPT) mutation carriers before the onset of symptoms.

Methods

Six asymptomatic carriers of the MAPT P301L mutation were compared with 12 non-carriers who all belonged to the same family of FTD. For comparison, we included 32 behavioral variant FTD (bvFTD) patients and 33 unrelated healthy controls. Each participant underwent neuropsychological assessments, genetic testing, and a hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) scan. Voxel-wise gray matter volumes and standardized uptake value ratios were calculated and compared for structural MRI and fluorodeoxyglucose (FDG)-PET, separately. The sparse inverse covariance estimation method (SICE) was applied to topological properties and metabolic connectomes of brain functional networks derived from ¹⁸F-FDG PET/MRI data. Independent component analysis was used to explore the metabolic connectivity of the salience (SN) and default mode networks (DMN).

Results

The asymptomatic MAPT carriers performed normal global parameters of the metabolism network, whereas bvFTD patients did not. However, we revealed lost hubs in the ventromedial prefrontal, orbitofrontal, and anterior cingulate cortices and reconfigured hubs in the anterior insula, precuneus, and posterior cingulate cortex in asymptomatic carriers compared with non-carriers, which overlapped with the comparisons between bvFTD patients and controls. Similarly, significant differences in local parameters of these nodes were present between asymptomatic carriers and non-carriers. The reduction in the connectivity of lost hub regions and the enhancement of connectivity between reconfigured hubs and components of the frontal cortex were marked during the asymptomatic stage. Metabolic connectivity within the SN and DMN was enhanced in asymptomatic carriers compared with non-mutation carriers but reduced in bvFTD patients relative to controls.

Conclusions

Our findings showed that metabolism topology reconfiguration, characterized by the earliest involvement of medial prefrontal areas and active compensation in task-related regions, was present in the presymptomatic phase of genetic FTD with MAPT mutation, which may be used as an imaging biomarker of increased risk of FTD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01000-z.

Differential Alterations to the Metabolic Connectivity of the Cortical and Subcortical Regions in Rat Brain During Ketamine-Induced Unconsciousness

BACKGROUND

Ketamine anesthesia increased glucose metabolism in most brain regions compared to another intravenous anesthetic propofol. However, whether the changes in cerebral metabolic networks induced by ketamine share the same mechanism with propofol remains to be explored. The purpose of the present study was to identify specific features of metabolic network in rat brains during ketamine-induced subanesthesia state and anesthesia state compared to awake state.

METHODS

We acquired fluorodeoxyglucose positron emission tomography (FDG-PET) images in 20 healthy adult Sprague-Dawley rats that were intravenously administrated saline and ketamine to achieve different conscious states: awake (normal saline), subanesthesia (30 mg kg -1 h -1 ), and anesthesia (160 mg kg -1 h -1 ). Based on the FDG-PET data, the alterations in cerebral glucose metabolism and metabolic topography were investigated by graph-theory analysis.

RESULTS

The baseline metabolism in rat brains was found significantly increased during ketamine-induced subanesthesia and anesthesia. The graph-theory analysis manifested a reduction in metabolism connectivity and network global/local efficiency across cortical regions and an increase across subcortical regions during ketamine-induced anesthesia (nonparametric permutation test: global efficiency between awake and anesthesia, cortex: P = .016, subcortex: P = .015; global efficiency between subanesthesia and anesthesia, subcortex: P = .012).

CONCLUSIONS

Ketamine broadly increased brain metabolism alongside decreased metabolic connectivity and network efficiency of cortex network. Modulation of these cortical metabolic networks may be a candidate mechanism underlying general anesthesia-induced loss of consciousness.

Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase

Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.

Altered resting state brain metabolic connectivity in dementia with Lewy bodies

Although dementia with Lewy bodies (DLB) have Parkinsonism in common with Parkinson's disease (PD) or PD dementia (PDD), they have different neuropathologies that underlie Parkinsonism. Altered brain functional connectivity that may correspond to neuropathology has been reported in PD while never been studied in DLB. To identify the characteristic brain connectivity of Parkinsonism in DLB, we compared the resting state metabolic connectivity in striato-thalamo-cortical (STC) circuit, nigrostriatal pathway, and cerebello-thalamo-cortical motor (CTC) circuit in 27 patients with drug-naïve DLB and 27 age- and sex-matched normal controls using 18F-fluoro-2-deoxyglucose PET. We derived 118 regions of interest using the Automated Anatomical Labeling templates and the Wake Forest University Pick-Atlas. We applied the sparse inverse covariance estimation method to construct the metabolic connectivity matrix. Patients with DLB, with or without Parkinsonism, showed lower inter-regional connectivity between the areas included in the STC circuit (motor cortex–striatum, midbrain–striatum, striatum–globus pallidus, and globus pallidus–thalamus) than the controls. DLB patients with Parkinsonism showed less reduced inter-regional connectivity between the midbrain and the striatum than those without Parkinsonism, and higher inter-regional connectivity between the areas included in the CTC circuit (motor cortex–pons, pons–cerebellum, and cerebellum–thalamus) than those without Parkinsonism and the controls. The resting state metabolic connectivity in the STC circuit may be reduced in DLB. In DLB with Parkinsonism, the CTC circuit and the nigrostriatal pathway may be activated to mitigate Parkinsonism. This difference in the brain connectivity may be a candidate biomarker for differentiating DLB from PD or PDD.

The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia

Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.

Brain metabolic connectivity reconfiguration in the semantic variant of primary progressive aphasia

Functional network-level alterations in the semantic variant of Primary Progressive Aphasia (sv-PPA) are relevant to understanding the clinical features and the neural spreading of the pathology. We assessed the effect of neurodegeneration on brain systems reorganization in early sv-PPA, using advanced brain metabolic connectivity approaches. Forty-four subjects with sv-PPA and forty-four age-matched healthy controls (HC) were included. We applied two multivariate approaches to [18F]FDG-PET data - i.e., sparse inverse covariance estimation and seed-based interregional correlation analysis - to assess the integrity of (i) the whole-brain metabolic connectivity and (ii) the connectivity of brain regions relevant for cognitive and behavioral functions. Whole-brain analysis revealed a global-scale connectivity reconfiguration in sv-PPA, with widespread changes in metabolic connections of frontal, temporal, and parietal regions. In comparison to HC, the seed-based analysis revealed a) functional isolation of the left anterior temporal lobe (ATL), b) decreases in temporo-occipital connections and contralateral homologous regions, c) connectivity increases to the dorsal parietal cortex from the spared posterior temporal cortex, d) a disruption of the large-scale limbic brain networks. In sv-PPA, the severe functional derangement of the left ATL may lead to an extensive connectivity reconfiguration, encompassing several brain regions, including those not yet affected by neurodegeneration. These findings support the hypothesis that in sv-PPA the focal vulnerability of the core region (i.e., ATL) can potentially drive the widespread cerebral connectivity changes, already present in the early phase.

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.

Parkinson Disease-Related Brain Metabolic Patterns and Neurodegeneration in Isolated REM Sleep Behavior Disorder

OBJECTIVE

To elucidate the role of Parkinson disease (PD)-related brain metabolic patterns as a biomarker in isolated REM sleep behavior disorder (iRBD) for future disease conversion.

METHODS

This is a prospective cohort study consisting of 30 patients with iRBD, 25 patients with de novo PD with a premorbid history of RBD, 21 patients with longstanding PD on stable treatment, and 24 healthy controls. The iRBD group was longitudinally followed up. All participants underwent ¹⁸F-fluorodeoxyglucose (FDG) PET and were evaluated with olfaction, cognition, and the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) at baseline. From FDG-PET scans, we derived metabolic patterns from the longstanding PD group (PD-RP) and de novo PD group with RBD (dnPDRBD-RP). Subsequently, we calculated the PD-RP and dnPDRBD-RP scores in patients with iRBD. We validated the metabolic patterns in each PD group and separate iRBD cohort (n = 14).

RESULTS

The 2 patterns significantly correlated with each other and were spatially overlapping yet distinct. The MDS-UPDRS motor scores significantly correlated with PD-RP (p = 0.013) but not with dnPDRBD-RP (p = 0.076). In contrast, dnPDRBD-RP correlated with olfaction in butanol threshold test (p = 0.018) in patients with iRBD, but PD-RP did not (p = 0.21). High dnPDRBD-RP in patients with iRBD predicted future phenoconversion with all cutoff ranges from 1.5 to 3 SD of the control value, whereas predictability of PD-RP was only significant in a partial range of cutoff.

CONCLUSION

The dnPDRBD-RP is an efficient neuroimaging biomarker that reflects prodromal features of PD and predicts phenoconversion in iRBD that can be applied individually.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that a de novo PD pattern on FDG-PET predicts future conversion to neurodegenerative disease in patients with iRBD.

In vivo MRI Structural and PET Metabolic Connectivity Study of Dopamine Pathways in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is characterized by an involvement of brain dopamine (DA) circuitry, the presence of which has been associated with emergence of both neuropsychiatric symptoms and cognitive deficits.

OBJECTIVE

In order to investigate whether and how the DA pathways are involved in the pathophysiology of AD, we assessed by in vivo neuroimaging the structural and metabolic alterations of subcortical and cortical DA pathways and targets.

METHODS

We included 54 healthy control participants, 53 amyloid-positive subjects with mild cognitive impairment due to AD (MCI-AD), and 60 amyloid-positive patients with probable dementia due to AD (ADD), all with structural 3T MRI and 18F-FDG-PET scans. We assessed MRI-based gray matter reductions in the MCI-AD and ADD groups within an anatomical a priori-defined Nigrostriatal and Mesocorticolimbic DA pathways, followed by 18F-FDG-PET metabolic connectivity analyses to evaluate network-level metabolic connectivity changes.

RESULTS

We found significant tissue loss in the Mesocorticolimbic over the Nigrostriatal pathway. Atrophy was evident in the ventral striatum, orbitofrontal cortex, and medial temporal lobe structures, and already plateaued in the MCI-AD stage. Degree of atrophy in Mesocorticolimbic regions positively correlated with the severity of depression, anxiety, and apathy in MCI-AD and ADD subgroups. Additionally, we observed significant alterations of metabolic connectivity between the ventral striatum and fronto-cingulate regions in ADD, but not in MCI-AD. There were no metabolic connectivity changes within the Nigrostriatal pathway.

CONCLUSION

Our cross-sectional data support a clinically-meaningful, yet stage-dependent, involvement of the Mesocorticolimbic system in AD. Longitudinal and clinical correlation studies are needed to further establish the relevance of DA system involvement 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.

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.

Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

Gender-Related Vulnerability of Dopaminergic Neural Networks in Parkinson's Disease

Background: In Parkinson's disease (PD), neurodegeneration of dopaminergic systems leads to motor and non-motor abnormalities. Sex might influence the clinical PD phenotypes and progression. Previous molecular imaging data focused only on the nigro-striato-cortical dopamine system that appeared more preserved in women. There is still a lack of evidence on gender/sex differences in the mesolimbic dopaminergic system. We aimed at assessing PD gender differences in both the dopaminergic pathways, by using a brain metabolic connectivity approach. This is based on the evidence of a significant coupling between the neurotransmission and metabolic impairments. Methods: We included 34 idiopathic PD patients (Female/Male: 16/18) and 34 healthy controls for comparison. The molecular architecture of both the dopaminergic networks was estimated throughout partial correlation analyses using brain metabolism data obtained by fluorine-18-fluorodeoxyglucose positron emission tomography (threshold set at p < 0.01, corrected for Bonferroni multiple comparisons). Results: Male patients were characterized by a widespread altered connectivity in the nigro-striato-cortical network and a sparing of the mesolimbic pathway. On the contrary, PD females showed a severe altered connectivity in the mesolimbic network and only a partial reconfiguration of the nigro-striato-cortical network. Discussion: Our findings add remarkable knowledge on the neurobiology of gender differences in PD, with the identification of specific neural vulnerabilities. The gender differences here revealed might be due to the combination of both biological and sociodemographic life factors. Gender differences in PD should be considered also for treatments and the targeting of modifiable risk factors.

Graph theory analysis of the dopamine D2 receptor network in Parkinson's disease patients with cognitive decline

Cognitive decline in Parkinson's disease (PD) is a common sequela of the disorder that has a large impact on patient well-being. Its physiological etiology, however, remains elusive. Our study used graph theory analysis to investigate the large-scale topological patterns of the extrastriatal dopamine D2 receptor network. We used positron emission tomography with [¹¹ C]FLB-457 to measure the binding potential of cortical dopamine D2 receptors in two networks: the meso-cortical dopamine network and the meso-limbic dopamine network. We also investigated the application of partial volume effect correction (PVEC) in conjunction with graph theory analysis. Three groups were investigated in this study divided according to their cognitive status as measured by the Montreal Cognitive Assessment score, with a score ≤25 considered cognitively impaired: (a) healthy controls (n = 13, 11 female), (b) cognitively unimpaired PD patients (PD-CU, n = 13, 5 female), and (c) PD patients with mild cognitive impairment (PD-MCI, n = 17, 4 female). In the meso-cortical network, we observed increased small-worldness, normalized clustering, and local efficiency in the PD-CU group compared to the PD-MCI group, as well as a hub shift in the PD-MCI group. Compensatory reorganization of the meso-cortical dopamine D2 receptor network may be responsible for some of the cognitive preservation observed in PD-CU. These results were found without PVEC applied and PVEC proved detrimental to the graph theory analysis. Overall, our findings demonstrate how graph theory analysis can be used to detect subtle changes in the brain that would otherwise be missed by regional comparisons of receptor density.

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.

Changes in brain glucose metabolism and connectivity in somatoform disorders: an 18F-FDG PET study

Somatoform disorders (SFD) are defined as a syndrome characterized by somatic symptoms which cannot be explained by organic reasons. Chronic or recurrent forms of somatization lead to heavy emotional and financial burden to the patients and their families. However, the underlying etiology of SFD is largely unknown. The purpose of this study is to investigate the changed brain glucose metabolic pattern in SFD. In this study, 18 SFD patients and 21 matched healthy controls were enrolled and underwent an ¹⁸F-FDG PET scan. First, we explored the altered brain glucose metabolism in SFD. Then, we calculated the mean ¹⁸F-FDG uptake values for 90 AAL regions, and detected the changed brain metabolic connectivity between the most significantly changed regions and all other regions. In addition, the Pearson coefficients between the neuropsychological scores and regional brain ¹⁸F-FDG uptake values were computed for SFD patients. We found that SFD patients showed extensive hypometabolism in bilateral superolateral prefrontal cortex, insula, and regions in bilateral temporal gyrus, right angular gyrus, left gyrus rectus, right fusiform gyrus, right rolandic operculum and bilateral occipital gyrus. The metabolic connectivity between right insula and prefrontal areas, as well as within prefrontal areas was enhanced in SFD. And several brain regions were associated with the somatic symptoms, including insula, putamen, middle temporal gyrus, superior parietal gyrus and orbital part of inferior frontal gyrus. Our study revealed widespread alterations of the brain glucose metabolic pattern in SFD patients. Those findings might elucidate the neuronal mechanisms with glucose metabolism and shed light on the pathology of SFD.

Nigrostriatal Degeneration in the Cognitive Part of the Striatum in Parkinson Disease Is Associated With Frontomedial Hypometabolism

PURPOSE

The present study investigated possible associations between cortical dysfunction/degeneration as measured by F-FDG PET and nigrostriatal degeneration according to the specific I-FP-CIT binding ratio (SBR) in striatal subregions defined by striato-cortical anatomical connectivity in Parkinson disease (PD) patients.

MATERIALS AND METHODS

The study included 41 patients (61.4 ± 12.8 years) with PD-typical reduction of striatal FP-CIT SBR and no sign of atypical parkinsonian syndrome on FDG PET. FP-CIT SBR was determined separately in the cognitive (composite of executive and limbic) and sensorimotor part of the striatum according to the Oxford-GSK-Imanova Striatal Connectivity Atlas. Scaled FDG uptake was tested voxelwise for correlation with FP-CIT SBR (familywise error corrected P < 0.05).

RESULTS

A large cluster (17.6 mL) of significant correlation of scaled FDG uptake with FP-CIT SBR in the cognitive part of the striatum, corrected for SBR in the sensorimotor part, was detected in the bilateral medial frontal cortex and the anterior cingulate cortex (partial correlation coefficient R = 0.767); small clusters were detected in ipsilateral caudate and ipsilateral thalamus. There was a small contralateral occipital cluster (3.0 mL) of significant correlation between FDG uptake and sensorimotor SBR corrected for cognitive SBR (R = 0.709).

CONCLUSIONS

The correlation between nigrostriatal degeneration in the cognitive striatum and reduced cerebral glucose metabolism in the medial parts of the frontal cortex including the anterior cingulate suggests that nigrostriatal degeneration is specifically involved in the pathogenesis of cognitive deficits associated with medial frontal dysfunction such as impaired inhibitory control.

Propofol Anesthesia Alters Spatial and Topologic Organization of Rat Brain Metabolism

BACKGROUND

Loss of consciousness during anesthesia reduces local and global rate of cerebral glucose metabolism. Despite this, the influence of gradual anesthetic-induced changes on consciousness across the entire brain metabolic network has barely been studied. The purpose of the present study was to identify specific cerebral metabolic patterns characteristic of different consciousness/anesthesia states induced by intravenous anesthetic propofol.

METHODS

At various times, 20 Sprague-Dawley adult rats were intravenously administered three different dosages of propofol to induce different anesthetic states: mild sedation (20 mg · kg · h), deep sedation (40 mg · kg · h), and deep anesthesia (80 mg · kg · h). Using [F]fluorodeoxyglucose positron emission tomography brain imaging, alterations in the spatial pattern of metabolic distribution and metabolic topography were investigated by applying voxel-based spatial covariance analysis and graph-theory analysis.

RESULTS

Evident reductions were found in baseline metabolism along with altered metabolic spatial distribution during propofol-induced anesthesia. Moreover, graph-theory analysis revealed a disruption in global and local efficiency of the metabolic brain network characterized by decreases in metabolic connectivity and energy efficiency during propofol-induced deep anesthesia (mild sedation global efficiency/local efficiency = 0.6985/0.7190, deep sedation global efficiency/local efficiency = 0.7444/0.7875, deep anesthesia global efficiency/local efficiency = 0.4498/0.6481; mild sedation vs. deep sedation, global efficiency: P = 0.356, local efficiency: P = 0.079; mild sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001; deep sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001). A strong spatial correlation was also found between cerebral metabolism and metabolic connectivity strength, which decreased significantly with deepening anesthesia level (correlation coefficients: mild sedation, r = 0.55, deep sedation, r = 0.47; deep anesthesia, r = 0.23; P < 0.0001 between the sedation and deep anesthesia groups).

CONCLUSIONS

The data revealed anesthesia-related alterations in spatial and topologic organization of metabolic brain network, as well as a close relationship between metabolic connectivity and cerebral metabolism during propofol anesthesia. These findings may provide novel insights into the metabolic mechanism of anesthetic-induced loss of consciousness.

Local and Interregional Neurochemical Associations Measured by Magnetic Resonance Spectroscopy for Studying Brain Functions and Psychiatric Disorders

Magnetic resonance spectroscopy (MRS) studies have found significant correlations among neurometabolites (e.g., between glutamate and GABA) across individual subjects and altered correlations in neuropsychiatric disorders. In this article, we discuss neurochemical associations among several major neurometabolites which underpin these observations by MRS. We also illustrate the role of spectral editing in eliminating unwanted correlations caused by spectral overlapping. Finally, we describe the prospects of mapping macroscopic neurochemical associations across the brain and characterizing excitation-inhibition balance of neural networks using glutamate- and GABA-editing MRS imaging.

Metabolic Network Abnormalities in Drug-Naïve Parkinson's Disease

BACKGROUND

An ideal imaging biomarker for a neurodegenerative disorder should be able to measure abnormalities in the earliest stages of the disease.

OBJECTIVE

We investigated metabolic network changes in two independent cohorts of drug-naïve Parkinson's disease (PD) patients who have not been exposed to dopaminergic medication.

METHODS

We scanned 85 de novo, drug-naïve PD patients and 85 age-matched healthy control subjects from Italy (n = 96) and the United States (n = 74) with [¹⁸ F]-fluorodeoxyglucose PET. All patients had clinical follow-ups to verify the diagnosis of idiopathic PD. Spatial covariance analysis was used to identify and validate de novo PD-related metabolic patterns in the Italian and U.S. cohorts. We compared the de novo PD-related metabolic patterns to the original PD-related pattern that was identified in more advanced patients who had been on chronic dopaminergic treatment.

RESULTS

De novo PD-related metabolic patterns were identified in each of the two independent cohorts of drug-naïve PD patients, and each differentiated PD patients from healthy control subjects. Expression values for these disease patterns were elevated in drug-naïve PD patients relative to healthy controls in the identification as well as in each of the validation subgroups. The two de novo PD-related metabolic patterns were topographically very similar to each other and to the original PD-related pattern.

CONCLUSIONS

Reproducible PD-related patterns are expressed in de novo, drug-naïve PD patients. In PD, disease-related metabolic patterns have stereotyped topographies that develop independently of chronic levodopa treatment. © 2019 International Parkinson and Movement Disorder Society.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

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.