INTRODUCTION

Brain functioning operates through a complex interplay of biochemical and electrical signaling, which can be probed by neuroimaging modalities. Functional MRI (fMRI) is commonly used for inferring neuronal activity in the brain via hemodynamic responses, while PET images spatial distributions and dynamics of molecular (i.e., biochemical) systems. These modalities were initially used to investigate localized effects from functional activation or disease-related brain changes. However, since the brain is organized as a highly connected network, a progression towards network-level analysis methods has occurred in recent decades.

Establishing the notion of “connectivity” is important for any network-level analysis. In the brain, three types of connectivity are naturally defined:

(1) Structural connectivity: white matter microstructure organization, which reveals larger-scale anatomical connections between brain regions. (2) Functional connectivity: statistical dependencies among remote neurophysiological events [1], first described in fMRI by ref. [2] which found nonlocal covarying fluctuations in blood oxygenation level dependent (BOLD) time-series signals and has since been explored in tens of thousands of research studies [3]. Note that correlation-based functional connectivity is distinct from effective connectivity, which attempts to model causal influences between neural units [1]. (3) Molecular connectivity: the covariance of biochemical functions between brain regions, the most studied being metabolic connectivity which identifies regions with coordinated neural activity based on glucose metabolism at synapses [4] quantified by [18F]-fluorodeoxyglucose PET (FDG) [5]. However, relatively recently molecular connectivity has also been studied in dopaminergic, serotonergic, and μ-opioid neurotransmitter systems [6–11], as well as in neurodegeneration-related proteinopathies that spread along anatomical connections [3,12,13].

While the hemodynamic signal provided by fMRI has been the primary tool to investigate brain connectomics, there is a push to increase the use of molecular imaging in connectivity analyses because of the unique, diverse, and complementary information it can provide [14▪▪]. This brief review of molecular connectivity provides a perspective on this growing field of research. We describe common approaches and examples of emerging applications, highlighting the potential of molecular connectivity approaches to significantly improve understanding of brain function and to better understand the information encoded in neuroimaging data. The integration of molecular connectivity with other aspects of brain connectivity, in particular functional connectivity, is also discussed. 

Box 1:

no caption available

ANALYSIS FRAMEWORKS

Broadly, molecular connectivity aims to identify networks of brain regions that exhibit a fixed relationship in expressing some biochemically relevant metric. The choice of the specific relationship, regions, and metrics depends on many factors, including the molecular system being studied, the tracer and acquisition protocol, and the number of available subjects. Molecular connectivity metrics are commonly derived from inter-individual comparison of 3D data: either a static, single-frame acquisition or a parametric image, where the temporal information from dynamically acquired PET data is used to determine biologically meaningful parameters, such as binding potentials or uptake rate constants. However, the more recently introduced functional PET (fPET) directly uses the dynamic 4D images for each individual, allowing more sophisticated intra-individual analysis. Below, we outline common analysis frameworks and examples of their use. Although not novel per se, their application to molecular imaging is relatively recent.

(1) Seed-based connectivity: correlations between a preselected “seed” region and other brain regions/voxels define a covarying spatial network. With a single PET volume, correlations are done across individuals (i.e., with “subject-series” data) while in fPET correlations are done with time-series data, similar to fMRI connectivity analyses. Systems studied include glucose metabolism with FDG [15,16], serotonin transporter with [11C]DASB and [11C]MADAM [6,8], and 5-HT1A with [11C]WAY100635 [11]. (2) Matrix decomposition methods: these approaches search for multivariate associations between brain regions by first constructing a region by subject (or voxel by subject) matrix, and then decomposing the data by optimizing certain metrics, for example, by maximizing variance with principal component analysis (PCA) or maximizing independence with independent component analysis (ICA). PCA has identified neurodegenerative disease-related covariance patterns with FDG [17,18] and [11C]DASB [19]. ICA applied to FDG data identified spatial networks of metabolic activity in Alzheimer's disease [20], and similar networks were compared to functional networks derived from fMRI ICA [21,22]. Other matrix decomposition methods include canonical correlation analysis (CCA) or multiset CCA [9,23] and partial least squares (PLS) [24]. A significant strength of such approaches is the ability to analyze joint or complementary information from several datasets, thus probing more complex brain processes; for example, from multitracer studies performed on the same individuals. (3) Graph theory approaches: graph theory is a natural analysis technique for characterizing the molecular connections between brain regions. PET data are first averaged over regions, then individual series data are correlated for each regional pair to form a region-by-region connectivity matrix. Pairwise correlation suffers from indirect and spurious correlation between regions; instead, computing partial correlations by use of sparse inverse covariance estimation (SICE) [25] overcomes these issues by factoring out the contributions of all other regions. SICE also works well when the number of subjects is smaller than the number of regions, typically the case in PET studies. Connectivity matrices can be thresholded and used to compute various graph theoretic measures, such as network strength, clustering coefficient, and efficiency [26]. In ref. [27], graph theory metrics were shown to be statistically robust in FDG data for glucose metabolism, [18F]FDOPA for dopamine synthesis, and [11C]SB217045 for serotonin 5-HT4 receptor density. Other molecular network topologies investigated include amyloid-β [28] plus 5-HT1A and 5-HTT [29,30]. APPLICATIONS TO NEURODEGENERATION

PET-based molecular connectivity analyses have primarily investigated metabolic and neurotransmitter dysfunction in Alzheimer's and Parkinson's disease. The progressive nature of these conditions leads to disruption in multiple brain systems, either directly or as a consequence of compensatory processes. As such, molecular connectivity analysis provides an excellent framework for capturing complex multilevel, interregional functional changes; the mathematical treatment of the brain as a network of covarying molecular changes is indeed well suited to our current understanding of brain dysfunction.

Alzheimer's disease

Alzheimer's disease, the most common neurodegenerative disorder, is known to be associated with increases in amyloid-β deposition and tau protein aggregation, the topology of which can be imaged by PET with multiple tracers [31]. In particular, a graph theory analysis applied to Florbetapir tracer data in people with predementia Alzheimer's disease revealed widespread restructuring of amyloid-β topology as Alzheimer's disease progresses, reflected by changes in graph theory metrics with increasing amyloid-β deposition [28].

PET has also identified extensive metabolic changes in Alzheimer's disease [32]. PCA applied to FDG data revealed an Alzheimer's disease-related pattern (ADRP) [18], whose expression correlates with clinical cognition metrics and distinguishes people with clinical and prodromal Alzheimer's disease from age-matched controls and non-Alzheimer's disease dementia better than univariate approaches [18,33–35].

While spatial covariance patterns characterize functional disease topography, specifically a “network” of metabolic disruption in diseased populations versus controls, seed-based interregional correlation analysis (IRCA) offers a more targeted study of metabolic connectivity changes when the seed region is chosen selectively. For instance, IRCA applied to FDG data demonstrated a complete loss of metabolic connectivity in early-onset Alzheimer's disease in the default mode network (DMN) [15] – where Alzheimer's disease related amyloid-β deposition is thought to begin [36] – and less extensive metabolic connectivity in prodromal disease compared to controls in both metabolically affected and unaffected regions [37]. This large-scale disruption of metabolic networks in prodromal disease agrees with the hypothesis of neuronal synapse dysfunction (reflected by metabolic disconnection) preceding neuronal death (reflected by the remote metabolism changes in ADRP as disease progresses) [38].

SICE offers a more comprehensive picture of connectivity disruptions by simultaneously quantifying metabolic connections between all brain regions. SICE applied to FDG data identified unique pathological connectivity alterations in Alzheimer's disease versus frontotemporal dementia, which could be used for differential diagnosis of Alzheimer's disease [39]. The regions within a SICE connectivity matrix can also be constrained to well known pathophysiological or neurotransmitter pathways to provide more physiologically interpretable results. For instance, in ref. [40], metabolic connectivity in the mesocorticolimbic dopaminergic pathway was altered in demented Alzheimer's disease patients, but not in Alzheimer's disease patients with mild cognitive impairment, thus demonstrating progressive metabolic disconnection. Relatedly, graph theoretic measures demonstrated a greater degree and extent of metabolic connectivity disruption for early versus late-onset Alzheimer's disease [41], suggesting connectivity reconfigurations vary as a function of both disease progression and subtype.

Parkinson's disease

Parkinson's disease is the second most common yet fastest growing neurodegenerative disease globally [42]. Early clinical motor symptoms occur predominantly due to a progressive loss of dopaminergic function, but alterations to metabolic, serotonergic, glutamatergic, and cholinergic systems also occur and contribute to motor and nonmotor symptoms [43,44]. Since brain metabolism and several neurotransmitter systems are affected, a large focus of molecular connectivity research has been on Parkinson's disease.

Like in Alzheimer's disease, PCA has been applied to FDG data to derive Parkinson's disease-related patterns related to motor disease progression [45–52] and cognitive dysfunction [53], indicating there are separate metabolic network restructurings associated with motor and nonmotor symptomology. Metabolic connectivity has been directly explored in early Parkinson's disease using IRCA and SICE, revealing a widespread loss of long-distance connections, most notably disconnection in frontal and cerebellar regions plus the well known attentional, DMN, motor, and executive resting-state networks [54]. Constraining the connectivity analysis to regions of Braak's alpha-synucleinopathy staging, a loss of metabolic connectivity was observed in regions affected in early disease stages. When constrained to dopaminergic pathways, a loss of metabolic connectivity was observed in the dorsal dopamine pathway with relative sparing in the mesolimbic pathway. However, a similar analysis of molecular connectivity in early disease using [11C]FeCIT (selective to dopamine transporter) found decreased local connectivity in the mesolimbic pathway and widespread loss of connectivity in the nigrostriatal pathway [55].

Molecular Parkinson's disease-related patterns have also been discovered by applying pattern analysis to data obtained from tracers used to image neurotransmitter systems. For instance, widespread disruptions in the serotonergic system have been captured in sporadic and LRRK2 mutation-associated Parkinson's disease by applying PCA to [11C]DASB [19]. Interestingly, unlike univariate analyses, the expression strength of this serotonergic Parkinson's disease pattern correlated with disease duration as well as dopaminergic denervation. The study also revealed a separate but partially overlapping spatial pattern that differentiated nonmanifesting LRRK2 mutation carriers from healthy controls and manifest Parkinson's disease. In a separate study [9], a multitracer pattern analysis drawing on common information from dopamine transporter (DAT) and vesicular monoamine type 2 (VMAT2) imaging identified three orthogonal patterns that reproduced all known aspects of progressive DAT and VMAT2 changes in Parkinson's disease. The orthogonality of the identified patterns suggests that three distinct mechanisms may be involved in presynaptic degeneration, information not otherwise easily achievable with univariate analysis. Additionally, a multiset CCA applied simultaneously to a PET measure of dopamine release, serotonergic innervation, and data from two dopaminergic markers provided evidence that the serotonergic system already contributes to abnormal dopamine release early in the disease and contributes to an increased risk of motor complication, thus possibly informing treatment strategies [23].

The collection of these and similar studies highlights the utility and unique information provided by network-type approaches in general, especially as applied to multitracer studies: they can better capture the multilevel metabolic and neurotransmitter restructurings associated with neurodegeneration. Restructurings that are already observable in early disease also provide context to the neurophysiological changes that may precede clinical symptoms or are instrumental in increasing the risk of Parkinson's disease.

Lewy bodies disorder spectrum

Parkinson's disease is part of a larger collection of alpha-synucleinopathies leading to formation of Lewy bodies. Therefore, it might be reasonable that commonality in molecular connectivity changes exist across the Lewy bodies spectrum. The study reported in [56] investigated metabolic connectivity changes in isolated REM sleep behavior disorder (iRBD), Parkinson's disease, and dementia with Lewy bodies (DLB). Using IRCA, all well known resting-state networks showed restructured connectivity in all cohorts. Overlap between these reconfigurations was mainly seen between iRBD and DLB, with Parkinson's disease having largely different reconfigurations. This is in line with RBD being significantly more common in DLB than in Parkinson's disease [57], thus suggesting metabolic network disruption may be an early indication of conversion from iRBD to DLB. In a separate study, SICE analysis of FDG data in DLB demonstrated altered metabolic connectivity in early-affected regions of Braak staging as well as in the cholinergic and dopaminergic pathways [58]. Comparable dopaminergic changes were observed in Parkinson's disease [54], suggesting this may be a commonality across the Lewy bodies disorder spectrum.

SYNERGY OF MOLECULAR AND FUNCTIONAL CONNECTIVITY

Resting-state fMRI connectivity leverages the BOLD signal, measured with a temporal resolution of nearly 2 s, to test for the temporal coherence of spontaneous neural activity across the brain. However, because the BOLD signal is an indirect measure of neuronal activity, the development of fMRI connectivity-based biomarkers has been limited [59] and fMRI findings are inherently unable to pinpoint the underlying cellular and molecular processes.

Recent studies have therefore focused on overcoming this limitation by leveraging the rich information from molecular imaging to provide neurobiological specificity to the BOLD signal. In its simplest form, “molecular-informed” functional connectivity considers the spatial concordance between the patterns of conventional resting-state fMRI measures and either the distribution of individual-specific PET tracer images [60–63] or population-based neuroreceptor density maps [64,65,66▪▪,67▪]. While high concordance suggests involvement of a particular molecular system in the fMRI results, correlation of brain maps in this fashion suffers from spatial autocorrelation which can artificially inflate significance [68▪▪].

Further approaches have incorporated more complex analysis to determine these spatiotemporal relationships [68▪▪,69,70]. Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT) has become an attractive method for linking molecular information to fMRI functional connectivity networks in an interpretable fashion [70]. Using PET-derived population templates of receptor and transporter spatial distributions, REACT uses a general linear model (GLM) to extract a subject-specific dominant BOLD fluctuation associated with the distribution of a given molecular system. Using this output, a second GLM is then used to estimate the subject-specific, molecular-enriched functional connectivity map. REACT has been used in studies related to 3,4-methylenedioxymethamphetamine (MDMA) [70], methylphenidate [71], multiple sclerosis [72], lysergic acid diethylamide (LSD) [73], chronic pain [74], autism spectrum disorder [75], and anesthesia [76▪]. Interestingly, REACT was recently extended to include five neurotransmitter receptors from postmortem autoradiography with multiple in-vivo neuroimaging modalities (tau, amyloid-β and FDG PET, and structural, functional and arterial spin labeling MRI) to obtain a personalized, generative, whole-brain disease-related alteration model in individuals suffering from Alzheimer's disease [77], which may ultimately lead to personalized treatments.

Instead of using neurotransmitters/receptors, other studies have explored relationships between glucose metabolic connectivity, as measured by FDG, and fMRI functional connectivity, since both relate to neuronal activity. FDG provides a more direct measure of neuronal activity but cannot attain the temporal resolution of fMRI, thus limiting its analysis to slower brain kinetic patterns. Understanding the relation between these two connectivity measures may however provide complementary insights into the brain connectome.

For this comparison, two main approaches for metabolic connectivity have been used: static and functional FDG. FDG data have been traditionally acquired and reconstructed into a single static image of nearly 20-min duration. Thus, instead of testing for the intra-subject temporal coherence of the measured signal (as is the case in fMRI), static PET connectivity (sPET) tests for the inter-individual coherence. This fundamental difference in determining connectivity raises the problem of ergodicity, that is, validity of investigating correlation across subjects in the spatial domain versus identifying networks within-individual data in the spatiotemporal domain, a topic still under investigation. Relatively recent studies – relying on higher sensitivity of modern scanners and advanced data processing methods – have introduced functional PET connectivity (fPET), where the PET data are reconstructed into relatively short temporal frames allowing for the measure of intra-individual temporal coherence, as in fMRI [78].

Thus far, comparisons between FDG versus fMRI connectivity networks have shown mixed results with early sPET studies showing poor-to-moderate concordance, implying that that the connectivity measures from different modalities may not reflect the same underlying network structure [21,79], while more recent sPET studies have found moderate-to-high concordance with spatially similar networks being detected, thus arguing for a common neural substrate of resting-state networks across modalities [22,80,81]. Inconsistent results were attributed to differing imaging protocols.

In principle, fPET may be more informative when comparing metabolic and functional networks [82,83]. The first clinical implementation of fPET [84,85] used PET framing protocols of (12 x 300 s) and (45 x 100 s), respectively, with bolus injection. Mixed results were found, with only ref. [84] finding several similar regions between fPET and fMRI when examining the DMN. Direct comparisons between sPET, fPET with higher temporal resolution (80 x 60 s) and (356 x 16 s), and fMRI, have, on the other hand, shown high concordance between fPET and fMRI networks, confirmed low concordance between sPET and fMRI, and additionally low concordance between fPET and sPET [82,83,86▪]. These apparently inconsistent results are hypothesized to be due to the dimension of time not necessarily being a determinant of connectivity, and that connectivity features vary across temporal scales; thus, the differing PET metrics could represent, to some degree, different processes, a proposition that is currently under further investigation [87,88]. If confirmed, PET data could provide richer and novel information on the dynamics of molecular processes.

CONCLUSION

The concept of molecular connectivity as a complement to functional and structural connectivity is rapidly gaining traction; its investigation is however still in its infancy. The mathematical frameworks required to conduct connectivity analyses are well developed, so future work should include: repeated studies to assess the reliability of the disease-induced molecular connectivity alterations identified thus far; technical studies to discern robustness of networks across different scanners, acquisition and image reconstruction protocols; investigations using different tracers, disease populations, and/or disease subtypes; alterations to connectivity as a function of tasks and interventions; additional studies with multimodal and/or multitracer acquisitions on the same subjects.

Collectively, such studies will help validate molecular connectivity as a valuable tool for extending our understanding of brain function in health and disease, provide new clinically relevant biomarkers, and allows one to optimally utilize and interpret information provided by state-of-the-art imaging methodologies.

Acknowledgements

The authors would like to thank Dr Ju-Chieh (Kevin) Cheng for useful insights and discussions.

Financial support and sponsorship

C.W.J. is supported by a grant from the Pacific Parkinson's Research Institute; J. Hanania and E. Reimers by the Natural Sciences and Engineering Research Council of Canada (NSERC) Grant Number 240670-13.

Conflicts of interest

The authors have no conflict of interest to report.

REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

▪▪ of outstanding interest

REFERENCES 1. Friston KJ. Functional and effective connectivity: a review. Brain Connect 2011; 1:13–36. 2. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34:537–541. 3. Sala A, Perani D. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front Neurosci 2019; 13:457262. 4. Stoessl AJ. Glucose utilization: still in the synapse. Nat Neurosci 2017; 20:382–384. 5. Yakushev I, Chételat G, Fischer FU, et al. Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults. Neuroimage 2013; 79:184–190. 6. Hahn A, Haeusler D, Kraus C, et al. Attenuated serotonin transporter association between dorsal raphe and ventral striatum in major depression. Hum Brain Mapp 2014; 35:3857–3866. 7. Cervenka S, Varrone A, Fransén E, et al. PET studies of D2-receptor binding in striatal and extrastriatal brain regions: biochemical support in vivo for separate dopaminergic systems in humans. Synapse 2010; 64:478–485. 8. Tuominen L, Nummenmaa L, Keltikangas-Järvinen L, et al. Mapping neurotransmitter networks with PET: an example on serotonin and opioid systems. Hum Brain Mapp 2014; 35:1875–1884. 9. Fu JF, Klyuzhin I, McKenzie J, et al. Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations. Neuroimage Clin 2019; 23:101856. 10. Ashok AH, Myers J, Reis Marques T, et al. Reduced mu opioid receptor availability in schizophrenia revealed with [11C]-carfentanil positron emission tomographic imaging. Nat Commun 2019; 10:4493. 11. Pillai RL, Zhang M, Yang J, et al. Molecular connectivity disruptions in males with major depressive disorder. J Cereb Blood Flow Metab 2019; 39:1623–1634. 12. Peng C, Trojanowski JQ, Lee VM. Protein transmission in neurodegenerative disease. Nat Rev Neurol 2020; 16:199–212. 13. Sepulcre J, Grothe MJ, d’Oleire Uquillas F, et al. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med 2018; 24:1910–1918. 14▪▪. Sala A, Lizarraga A, Caminiti SP, et al. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci 2023; 27:353–366. 15. Ballarini T, Iaccarino L, Magnani G, et al. Neuropsychiatric subsyndromes and brain metabolic network dysfunctions in early onset Alzheimer's disease. Hum Brain Mapp 2016; 37:4234–4247. 16. Malpetti M, Sala A, Vanoli EG, et al. Unfavourable gender effect of high body mass index on brain metabolism and connectivity. Sci Rep 2018; 8:12584. 17. Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci 2009; 32:548–557. 18. K. Teune L, Strijkert F, Renken RJ, et al. The Alzheimer's disease-related glucose metabolic brain pattern. Curr Alzheimer Res 2014; 11:725–732. 19. Fu JF, Klyuzhin I, Liu S, et al. Investigation of serotonergic Parkinson's disease-related covariance pattern using [11C]-DASB/PET. Neuroimage Clin 2018; 19:652–660. 20. Pagani M, Giuliani A, Öberg J, et al. Progressive disintegration of brain networking from normal aging to Alzheimer disease: analysis of independent components of 18F-FDG PET data. J Nucl Med 2017; 58:1132–1139. 21. Di X, Biswal BB. Alzheimer's Disease Neuroimaging Initiative. Metabolic brain covariant networks as revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect 2012; 2:275–283. 22. Savio A, Fünger S, Tahmasian M, et al. Resting-state networks as simultaneously measured with functional MRI and PET. J Nucl Med 2017; 58:1314–1317. 23. Fu JF, Matarazzo M, McKenzie J, et al. Serotonergic system impacts levodopa response in early Parkinson's and future risk of dyskinesia. Mov Disord 2021; 36:389–397. 24. Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 2011; 56:455–475. 25. Huang S, Li J, Sun L, et al. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. NeuroImage 2010; 50:935–949. 26. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009; 10:186–198. 27. Veronese M, Moro L, Arcolin M, et al. Covariance statistics and network analysis of brain PET imaging studies. Sci Rep 2019; 9:2496. 28. Pereira JB, Strandberg TO, Palmqvist S, et al. Amyloid network topology characterizes the progression of Alzheimer's disease during the predementia stages. Cereb Cortex 2018; 28:340–349. 29. Bose SK, Mehta MA, Selvaraj S, et al. Presynaptic 5-HT1A is related to 5-HTT receptor density in the human brain. Neuropsychopharmacology 2011; 36:2258–2265. 30. James GM, Baldinger-Melich P, Philippe C, et al. Effects of selective serotonin reuptake inhibitors on interregional relation of serotonin transporter availability in major depression. Front Hum Neurosci 2017; 11:48. 31. Chandra A, Valkimadi PE, Pagano G, et al. Applications of amyloid, tau, and neuroinflammation PET imaging to Alzheimer's disease and mild cognitive impairment. Hum Brain Mapp 2019; 40:5424–5442. 32. Morbelli S, Brugnolo A, Bossert I, et al. Visual versus semi-quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer's Disease Consortium (EADC) project. J Alzheimer Dis 2015; 44:815–826. 33. Habeck C, Foster NL, Perneczky R, et al. Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease. Neuroimage 2008; 40:1503–1515. 34. Meles SK, Pagani M, Arnaldi D, et al. The Alzheimer's disease metabolic brain pattern in mild cognitive impairment. J Cereb Blood Flow Metab 2017; 37:3643–3648. 35. Perovnik M, Rus T, Tomše P, et al. Identification and validation of Alzheimer's disease-related metabolic pattern in patients with pathologically confirmed Alzheimer's disease: neuroimaging/differential diagnosis. Alzheimer Dement 2020; 16:e042629. 36. Drzezga A, Becker JA, Van Dijk KR, et al. Neuronal dysfunction and disconnection of cortical hubs in nondemented subjects with elevated amyloid burden. Brain 2011; 134:1635–1646. 37. Morbelli S, Drzezga A, Perneczky R, et al. Resting metabolic connectivity in prodromal Alzheimer's disease. A European Alzheimer Disease Consortium (EADC) project. Neurobiol Aging 2012; 33:2533–2550. 38. Lacor PN, Buniel MC, Furlow PW, et al. Aβ oligomer-induced aberrations in synapse composition, shape, and density provide a molecular basis for loss of connectivity in Alzheimer's disease. J Neurosci 2007; 27:796–807. 39. Titov D, Diehl-Schmid J, Shi K, et al. Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab 2017; 37:252–262. 40. Iaccarino L, Sala A, Caminiti SP, et al. In vivo MRI structural and PET metabolic connectivity study of dopamine pathways in Alzheimer's disease. J Alzheimer Dis 2020; 75:1003–1016. 41. Chung J, Yoo K, Kim E, et al. Glucose metabolic brain networks in early-onset vs. late-onset Alzheimer's disease. Fronti Aging Neurosci 2016; 8:159. 42. Dorsey E, Sherer T, Okun MS, Bloem BR. The emerging evidence of the Parkinson pandemic. J Parkinsons Dis 2018; 8 (s1):S3–S8. 43. Moustafa AA, Chakravarthy S, Phillips JR, et al. Motor symptoms in Parkinson's disease: a unified framework. Neurosci Biobehav Rev 2016; 68:727–740. 44. Pfeiffer RF. Nonmotor symptoms in Parkinson's disease. Parkinsonism Relat Disord 2016; 22:S119–S122. 45. Eidelberg D, Moeller JR, Dhawan V, et al. The metabolic topography of parkinsonism. J Cereb Blood Flow Metab 1994; 14:783–801. 46. Asanuma K, Tang C, Ma Y, et al. Network modulation in the treatment of Parkinson's disease. Brain 2006; 129:2667–2678. 47. Huang C, Tang C, Feigin A, et al. Changes in network activity with the progression of Parkinson's disease. Brain 2007; 130:1834–1846. 48. Ma Y, Tang C, Spetsieris PG, et al. Abnormal metabolic network activity in Parkinson's disease: test—retest reproducibility. J Cereb Blood Flow Metab 2007; 27:597–605. 49. Wu P, Wang J, Peng S, et al. Metabolic brain network in the Chinese patients with Parkinson's disease based on 18F-FDG PET imaging. Parkinsonism Relat Disord 2013; 19:622–627. 50. Tomše P, Jensterle L, Grmek M, et al. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample. Neuroradiology 2017; 59:507–515. 51. Tomše P, Jensterle L, Rep S, et al. The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease. Phys Med 2017; 41:129–135. 52. Meles SK, Renken RJ, Pagani M, et al. Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts. Eur J Nucl Med Mol Imaging 2020; 47:437–450. 53. Huang C, Mattis P, Tang C, et al. Metabolic brain networks associated with cognitive function in Parkinson's disease. Neuroimage 2007; 34:714–723. 54. Sala A, Caminiti SP, Presotto L, et al. Altered brain metabolic connectivity at multiscale level in early Parkinson's disease. Sci Rep 2017; 7:4256. 55. Caminiti SP, Presotto L, Baroncini D, et al. Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease. Neuroimage Clin 2017; 14:734–740. 56. Boccalini C, Bortolin E, Carli G, et al. Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase. Front Neurosci 2022; 16:930735. 57. Högl B, Stefani A, Videnovic A. Idiopathic REM sleep behaviour disorder and neurodegeneration—an update. Nat Rev Neurol 2018; 14:40–55. 58. Caminiti SP, Tettamanti M, Sala A, et al. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab 2017; 37:1311–1325. 59. Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: is there potential for an imaging biomarker? NeuroImage Clin 2018; 18:849–870. 60. Tomasi D, Wang GJ, Volkow ND. Energetic cost of brain functional connectivity. Proc Natl Acad Sci U S A 2013; 110:13642–13647. 61. Riedl V, Bienkowska K, Strobel C, et al. Local activity determines functional connectivity in the resting human brain: a simultaneous FDG-PET/fMRI study. J Neurosci 2014; 34:6260–6266. 62. Aiello M, Salvatore E, Cachia A, et al. Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. Neuroimage 2015; 113:111–121. 63. Shiyam Sundar LK, Baajour S, Beyer T, et al. Fully integrated PET/MR imaging for the assessment of the relationship between functional connectivity and glucose metabolic rate. Front Neurosci 2020; 14:502366. 64. Preller KH, Burt JB, Ji JL, et al. Changes in global and thalamic brain connectivity in LSD-induced altered states of consciousness are attributable to the 5-HT2A receptor. Elife 2018; 7:e35082. 65. Dukart J, Holiga S, Rullmann M, et al. JuSpace: a tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hoboken, USA: John Wiley & Sons, Inc; 2021. 66▪▪. Hansen JY, Shafiei G, Markello RD, et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci 2022; 25:1569–1581. 67▪. Luppi AI, Hansen JY, Adapa R, et al. In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape. Sci Adv 2023; 9:eadf8332. 68▪▪. Lawn T, Howard MA, Turkheimer F, et al. From neurotransmitters to networks: transcending organisational hierarchies with molecular-informed functional imaging. Neurosci Biobehav Rev 2023; 150:105193. 69. Riedl V, Utz L, Castrillón G, et al. Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proc Natl Acad Sci U S A 2016; 113:428–433. 70. Dipasquale O, Selvaggi P, Veronese M, et al. Receptor-Enriched Analysis of functional connectivity by targets (REACT): a novel, multimodal analytical approach informed by PET to study the pharmacodynamic response of the brain under MDMA. Neuroimage 2019; 195:252–260. 71. Dipasquale O, Martins D, Sethi A, et al. Unravelling the effects of methylphenidate on the dopaminergic and noradrenergic functional circuits. Neuropsychopharmacology 2020; 45:1482–1489. 72. Cercignani M, Dipasquale O, Bogdan I, et al. Cognitive fatigue in multiple sclerosis is associated with alterations in the functional connectivity of monoamine circuits. Brain Commun 2021; 3:fcab023. 73. Lawn T, Dipasquale O, Vamvakas A, et al. Differential contributions of serotonergic and dopaminergic functional connectivity to the phenomenology of LSD. Psychopharmacology 2022; 239:1797–1808. 74. Martins D, Veronese M, Turkheimer FE, et al. A candidate neuroimaging biomarker for detection of neurotransmission-related functional alterations and prediction of pharmacological analgesic response in chronic pain. Brain Commun 2022; 4:fcab302. 75. Wong NM, Dipasquale O, Turkheimer F, et al. Differences in social brain function in autism spectrum disorder are linked to the serotonin transporter: a randomised placebo-controlled single-dose crossover trial. J Psychopharmacol 2022; 36:723–731. 76▪. Lawn T, Martins D, O’Daly O, et al. The effects of propofol anaesthesia on molecular-enriched networks during resting-state and naturalistic listening. Neuroimage 2023; 271:120018. 77. Khan AF, Adewale Q, Baumeister TR, et al. Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease. Brain 2022; 145:1785–1804. 78. Volpi T, Silvestri E, Corbetta M, Bertoldo A. Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18 F] fluorodeoxyglucose Positron Emission Tomography data. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021. 3259–3262. IEEE. 79. Di X, Gohel S, Thielcke A, et al. Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals. Brain Struct Funct 2017; 222:3833–3845. 80. Ripp I, Stadhouders T, Savio A, et al. Integrity of neurocognitive networks in dementing disorders as measured with simultaneous PET/functional MRI. J Nucl Med 2020; 61:1341–1347. 81. Ruppert MC, Greuel A, Freigang J, et al. The default mode network and cognition in Parkinson's disease: a multimodal resting-state network approach. Hum Brain Mapp 2021; 42:2623–2641. 82. Amend M, Ionescu TM, Di X, et al. Functional resting-state brain connectivity is accompanied by dynamic correlations of application-dependent [18F] FDG PET-tracer fluctuations. Neuroimage 2019; 196:161–172. 83. Jamadar SD, Ward PG, Liang EX, et al. Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex 2021; 31:2855–2867. 84. Passow S, Specht K, Adamsen TC, et al. Default-mode network functional connectivity is closely related to metabolic activity. Hum Brain Mapp 2015; 36:2027–2038. 85. Tomasi DG, Shokri-Kojori E, Wiers CE, et al. Dynamic brain glucose metabolism identifies anticorrelated cortical-cerebellar networks at rest. J Cereb Blood Flow Metab 2017; 37:3659–3670. 86▪. Voigt K, Liang EX, Misic B, et al. Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships. Cereb Cortex 2023; 33:1476–1488. 87. Sala A, Lizarraga A, Ripp I, et al. Static versus functional PET: making sense of metabolic connectivity. Cereb Cortex 2022; 32:1125–1129. 88. Jamadar SD, Egan GF. Resting-state FDG-PET connectivity: covariance, ergodicity, and biomarkers. response to commentary by Sala et al.; static versus functional PET: making sense of metabolic connectivity. Cereb Cortex 2022; 32:2054–2055.