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Hanania JU, Reimers E, Bevington CWJ, Sossi V. PET-based brain molecular connectivity in neurodegenerative disease. Curr Opin Neurol 2024; 37:353-360. [PMID: 38813843 DOI: 10.1097/wco.0000000000001283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
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.
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Affiliation(s)
| | - Erik Reimers
- Department of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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2
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Volpi T, Silvestri E, Aiello M, Lee JJ, Vlassenko AG, Goyal MS, Corbetta M, Bertoldo A. The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity? J Cereb Blood Flow Metab 2024; 44:1433-1449. [PMID: 38443762 DOI: 10.1177/0271678x241237974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.
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Affiliation(s)
- Tommaso Volpi
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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Ruppert-Junck MC, Heinecke V, Librizzi D, Steidel K, Beckersjürgen M, Verburg FA, Schurrat T, Luster M, Müller HH, Timmermann L, Eggers C, Pedrosa D. Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson's disease. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06796-6. [PMID: 38884774 DOI: 10.1007/s00259-024-06796-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 06/07/2024] [Indexed: 06/18/2024]
Abstract
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.
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Affiliation(s)
- Marina C Ruppert-Junck
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany.
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany.
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany.
| | - Vanessa Heinecke
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
| | - Damiano Librizzi
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Kenan Steidel
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
| | - Maya Beckersjürgen
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
| | - Frederik A Verburg
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tino Schurrat
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Markus Luster
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Hans-Helge Müller
- Institute for Medical Bioinformatics and Biostatistics, Philipps-University Marburg, Marburg, Germany
| | - Lars Timmermann
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Carsten Eggers
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Knappschaftskrankenhaus Bottrop GmbH, Bottrop, Germany
| | - David Pedrosa
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany
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Hobbs NZ, Papoutsi M, Delva A, Kinnunen KM, Nakajima M, Van Laere K, Vandenberghe W, Herath P, Scahill RI. Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group. J Huntingtons Dis 2024; 13:163-199. [PMID: 38788082 PMCID: PMC11307036 DOI: 10.3233/jhd-240016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.
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Affiliation(s)
- Nicola Z. Hobbs
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Marina Papoutsi
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
- IXICO plc, London, UK
| | - Aline Delva
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
| | | | | | - Koen Van Laere
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
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Cruz-Cortes Á, Avendaño-Estrada A, Alcauter S, Núñez-Enríquez JC, Rivera-Bravo B, Olarte-Casas MÁ, Ávila-Rodríguez MÁ. Semiquantitative analysis of cerebral [ 18F]FDG-PET uptake in pediatric patients. Pediatr Radiol 2023; 53:2574-2585. [PMID: 37910188 PMCID: PMC10698097 DOI: 10.1007/s00247-023-05794-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Glycolytic metabolism in the brain of pediatric patients, imaged with [18F] fluorodeoxyglucose-positron emission tomography (FDG-PET) is incompletely characterized. OBJECTIVE The purpose of the current study was to characterize [18F]FDG-PET brain uptake in a large sample of pediatric patients with non-central nervous system diseases as an alternative to healthy subjects to evaluate changes at different pediatric ages. MATERIALS AND METHODS Seven hundred ninety-five [18F]FDG-PET examinations from children < 18 years of age without central nervous system diseases were included. Each brain image was spatially normalized, and the standardized uptake value (SUV) was obtained. The SUV and the SUV relative to different pseudo-references were explored as a function of age. RESULTS At all evaluated ages, the occipital lobe showed the highest [18F]FDG uptake (0.27 ± 0.04 SUV/year), while the parietal lobe and brainstem had the lowest uptake (0.17 ± 0.02 SUV/year, for both regions). An increase [18F]FDG uptake was found for all brain regions until 12 years old, while no significant uptake differences were found between ages 13 (SUV = 5.39) to 17 years old (SUV = 5.52) (P < 0.0001 for the whole brain). A sex dependence was found in the SUVmean for the whole brain during adolescence (SUV 5.04-5.25 for males, 5.68-5.74 for females, P = 0.0264). Asymmetries in [18F]FDG uptake were found in the temporal and central regions during infancy. CONCLUSIONS Brain glycolytic metabolism of [18F]FDG, measured through the SUVmean, increased with age until early adolescence (< 13 years old), showing differences across brain regions. Age, sex, and brain region influence [18F]FDG uptake, with significant hemispheric asymmetries for temporal and central regions.
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Affiliation(s)
- Álvaro Cruz-Cortes
- Unidad de Radiofarmacia-Ciclotrón, División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
| | - Arturo Avendaño-Estrada
- Unidad de Radiofarmacia-Ciclotrón, División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico.
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro City, Mexico
| | - Juan Carlos Núñez-Enríquez
- Unidad de Investigación Médica en Epidemiología Clínica, UMAE Hospital de Pediatría, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de Mexico, Mexico
| | - Belen Rivera-Bravo
- División de Investigación Facultad de Medicina Universidad Nacional Autónoma de México, Unidad PET/CT, Ciudad de Mexico, Mexico
| | - Miguel Ángel Olarte-Casas
- División de Investigación Facultad de Medicina Universidad Nacional Autónoma de México, Unidad PET/CT, Ciudad de Mexico, Mexico
| | - Miguel Ángel Ávila-Rodríguez
- Unidad de Radiofarmacia-Ciclotrón, División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
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Volpi T, Vallini G, Silvestri E, Francisci MD, Durbin T, Corbetta M, Lee JJ, Vlassenko AG, Goyal MS, Bertoldo A. A new framework for metabolic connectivity mapping using bolus [ 18F]FDG PET and kinetic modeling. J Cereb Blood Flow Metab 2023; 43:1905-1918. [PMID: 37377103 PMCID: PMC10676136 DOI: 10.1177/0271678x231184365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/11/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023]
Abstract
Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.
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Affiliation(s)
- Tommaso Volpi
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Giulia Vallini
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Tony Durbin
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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PET imaging of animal models with depressive-like phenotypes. Eur J Nucl Med Mol Imaging 2023; 50:1564-1584. [PMID: 36642759 PMCID: PMC10119194 DOI: 10.1007/s00259-022-06073-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 12/03/2022] [Indexed: 01/17/2023]
Abstract
Major depressive disorder is a growing and poorly understood pathology. Due to technical and ethical limitations, a significant proportion of the research on depressive disorders cannot be performed on patients, but needs to be investigated in animal paradigms. Over the years, animal studies have provided new insight in the mechanisms underlying depression. Several of these studies have used PET imaging for the non-invasive and longitudinal investigation of the brain physiology. This review summarises the findings of preclinical PET imaging in different experimental paradigms of depression and compares these findings with observations from human studies. Preclinical PET studies in animal models of depression can be divided into three main different approaches: (a) investigation of glucose metabolism as a biomarker for regional and network involvement, (b) evaluation of the availability of different neuroreceptor populations associated with depressive phenotypes, and (c) monitoring of the inflammatory response in phenotypes of depression. This review also assesses the relevance of the use of PET imaging techniques in animal paradigms for the understanding of specific aspects of the depressive-like phenotypes, in particular whether it might contribute to achieve a more detailed characterisation of the clinical depressive phenotypes for the development of new therapies for depression.
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8
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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Doyen M, Hossu G, Heyer S, Zaragori T, Imbert L, Verger A. Identification of resting-state networks using dynamic brain perfusion SPECT imaging: A fSPECT case report. Front Hum Neurosci 2023; 17:1125765. [PMID: 37151905 PMCID: PMC10157397 DOI: 10.3389/fnhum.2023.1125765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/21/2023] [Indexed: 05/09/2023] Open
Abstract
Connectivity studies with nuclear medicine systems are scarce in literature. They mainly employ PET imaging and group level analyses due to the low temporal resolution of PET and especially SPECT imaging. Our current study analyses connectivity at an individual level using dynamic SPECT imaging, which has been enabled by the improved temporal resolution performances provided by the 360°CZT cameras. We present the case of an 80-year-old man referred for brain perfusion SPECT imaging for cognitive disorders for whom a dynamic SPECT acquisition was performed utilizing a 360°CZT camera (temporal sampling of 15 frames × 3 s, 10 frames × 15 s, 14 frames × 30 s), followed by a conventional static acquisition of 15 m. Functional SPECT connectivity (fSPECT) was assessed through a seed correlation analysis and 5 well-known resting-state networks were identified: the executive, the default mode, the sensory motor, the salience, and the visual networks. This case report supports the feasibility of fSPECT imaging to identify well known resting-state networks, thanks to the novel properties of a 360°CZT camera, and opens the way to the development of more dedicated functional connectivity studies using brain perfusion SPECT imaging.
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Affiliation(s)
- Matthieu Doyen
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Nancy, France
- IADI, INSERM U1254, Université de Lorraine, Nancy, France
- *Correspondence: Matthieu Doyen,
| | - Gabriela Hossu
- IADI, INSERM U1254, Université de Lorraine, Nancy, France
- CHRU-Nancy, INSERM, CIC, Innovation Technologique, Université de Lorraine, Nancy, France
| | - Sébastien Heyer
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Nancy, France
| | | | - Laetitia Imbert
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Nancy, France
- IADI, INSERM U1254, Université de Lorraine, Nancy, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Nancy, France
- IADI, INSERM U1254, Université de Lorraine, Nancy, France
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10
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Combining CRISPR-Cas9 and brain imaging to study the link from genes to molecules to networks. Proc Natl Acad Sci U S A 2022; 119:e2122552119. [PMID: 36161926 DOI: 10.1073/pnas.2122552119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Receptors, transporters, and ion channels are important targets for therapy development in neurological diseases, but their mechanistic role in pathogenesis is often poorly understood. Gene editing and in vivo imaging approaches will help to identify the molecular and functional role of these targets and the consequence of their regional dysfunction on the whole-brain level. We combine CRISPR-Cas9 gene editing with in vivo positron emission tomography (PET) and functional MRI (fMRI) to investigate the direct link between genes, molecules, and the brain connectome. The extensive knowledge of the Slc18a2 gene encoding the vesicular monoamine transporter (VMAT2), involved in the storage and release of dopamine, makes it an excellent target for studying the gene network relationships while structurally preserving neuronal integrity and function. We edited the Slc18a2 in the substantia nigra pars compacta of adult rats and used in vivo molecular imaging besides behavioral, histological, and biochemical assessments to characterize the CRISPR-Cas9-mediated VMAT2 knockdown. Simultaneous PET/fMRI was performed to investigate molecular and functional brain alterations. We found that stage-specific adaptations of brain functional connectivity follow the selective impairment of presynaptic dopamine storage and release. Our study reveals that recruiting different brain networks is an early response to the dopaminergic dysfunction preceding neuronal cell loss. Our combinatorial approach is a tool to investigate the impact of specific genes on brain molecular and functional dynamics, which will help to develop tailored therapies for normalizing brain function.
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11
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van Lanen RHGJ, Wiggins CJ, Colon AJ, Backes WH, Jansen JFA, Uher D, Drenthen GS, Roebroeck A, Ivanov D, Poser BA, Hoeberigs MC, van Kuijk SMJ, Hoogland G, Rijkers K, Wagner GL, Beckervordersandforth J, Delev D, Clusmann H, Wolking S, Klinkenberg S, Rouhl RPW, Hofman PAM, Schijns OEMG. Value of ultra-high field MRI in patients with suspected focal epilepsy and negative 3 T MRI (EpiUltraStudy): protocol for a prospective, longitudinal therapeutic study. Neuroradiology 2022; 64:753-764. [PMID: 34984522 PMCID: PMC8907090 DOI: 10.1007/s00234-021-02884-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/09/2021] [Indexed: 10/30/2022]
Abstract
PURPOSE Resective epilepsy surgery is a well-established, evidence-based treatment option in patients with drug-resistant focal epilepsy. A major predictive factor of good surgical outcome is visualization and delineation of a potential epileptogenic lesion by MRI. However, frequently, these lesions are subtle and may escape detection by conventional MRI (≤ 3 T). METHODS We present the EpiUltraStudy protocol to address the hypothesis that application of ultra-high field (UHF) MRI increases the rate of detection of structural lesions and functional brain aberrances in patients with drug-resistant focal epilepsy who are candidates for resective epilepsy surgery. Additionally, therapeutic gain will be addressed, testing whether increased lesion detection and tailored resections result in higher rates of seizure freedom 1 year after epilepsy surgery. Sixty patients enroll the study according to the following inclusion criteria: aged ≥ 12 years, diagnosed with drug-resistant focal epilepsy with a suspected epileptogenic focus, negative conventional 3 T MRI during pre-surgical work-up. RESULTS All patients will be evaluated by 7 T MRI; ten patients will undergo an additional 9.4 T MRI exam. Images will be evaluated independently by two neuroradiologists and a neurologist or neurosurgeon. Clinical and UHF MRI will be discussed in the multidisciplinary epilepsy surgery conference. Demographic and epilepsy characteristics, along with postoperative seizure outcome and histopathological evaluation, will be recorded. CONCLUSION This protocol was reviewed and approved by the local Institutional Review Board and complies with the Declaration of Helsinki and principles of Good Clinical Practice. Results will be submitted to international peer-reviewed journals and presented at international conferences. TRIAL REGISTRATION NUMBER www.trialregister.nl : NTR7536.
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Affiliation(s)
- R H G J van Lanen
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands. .,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.
| | - C J Wiggins
- Scannexus, Ultra-High Field MRI Research Center, Maastricht, the Netherlands
| | - A J Colon
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - W H Backes
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - J F A Jansen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - D Uher
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - G S Drenthen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - D Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - M C Hoeberigs
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - S M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, the Netherlands
| | - G Hoogland
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - K Rijkers
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - G L Wagner
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | | | - D Delev
- Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany
| | - H Clusmann
- Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany
| | - S Wolking
- Department of Epileptology and Neurology, RWTH Aachen University Hospital, Aachen, Germany
| | - S Klinkenberg
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - R P W Rouhl
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - P A M Hofman
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - O E M G Schijns
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
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12
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Volpi T, Silvestri E, Corbetta M, Bertoldo A. Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [ 18F]fluorodeoxyglucose Positron Emission Tomography data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3259-3262. [PMID: 34891936 DOI: 10.1109/embc46164.2021.9630441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [18F]fluorodeoxyglucose ([18F]FDG) PET data in four healthy subjects.
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13
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Yakushev I, Ripp I, Wang M, Savio A, Schutte M, Lizarraga A, Bogdanovic B, Diehl-Schmid J, Hedderich DM, Grimmer T, Shi K. Mapping covariance in brain FDG uptake to structural connectivity. Eur J Nucl Med Mol Imaging 2021; 49:1288-1297. [PMID: 34677627 PMCID: PMC8921091 DOI: 10.1007/s00259-021-05590-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov 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 FDGcov. 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 FDGcov 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 FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.
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Affiliation(s)
- Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany.
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.
| | - Isabelle Ripp
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai, China
| | - Alex Savio
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
| | - Michael Schutte
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Department Biology II, Ludwig Maximilian University of Munich, Munich, Germany
| | - Aldana Lizarraga
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Borjana Bogdanovic
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dennis M Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Kuangyu Shi
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
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14
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Krämer SD, Schuhmann MK, Schadt F, Israel I, Samnick S, Volkmann J, Fluri F. Changes of cerebral network activity after invasive stimulation of the mesencephalic locomotor region in a rat stroke model. Exp Neurol 2021; 347:113884. [PMID: 34624326 DOI: 10.1016/j.expneurol.2021.113884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/02/2021] [Accepted: 10/02/2021] [Indexed: 11/29/2022]
Abstract
Motor deficits after stroke reflect both, focal lesion and network alterations in brain regions distant from infarction. This remote network dysfunction may be caused by aberrant signals from cortical motor regions travelling via mesencephalic locomotor region (MLR) to other locomotor circuits. A method for modulating disturbed network activity is deep brain stimulation. Recently, we have shown that high frequency stimulation (HFS) of the MLR in rats has restored gait impairment after photothrombotic stroke (PTS). However, it remains elusive which cerebral regions are involved by MLR-stimulation and contribute to the improvement of locomotion. Seventeen male Wistar rats underwent photothrombotic stroke of the right sensorimotor cortex and implantation of a microelectrode into the right MLR. 2-[18F]Fluoro-2-deoxyglucose ([18F]FDG)-positron emission tomography (PET) was conducted before stroke and thereafter, on day 2 and 3 after stroke, without and with MLR-HFS, respectively. [18F]FDG-PET imaging analyses yielded a reduced glucose metabolism in the right cortico-striatal thalamic loop after PTS compared to the state before intervention. When MLR-HFS was applied after PTS, animals exhibited a significantly higher uptake of [18F]FDG in the right but not in the left cortico-striatal thalamic loop. Furthermore, MLR-HFS resulted in an elevated glucose metabolism of right-sided association cortices related to the ipsilateral sensorimotor cortex. These data support the concept of diaschisis i.e., of dysfunctional brain areas distant to a focal lesion and suggests that MLR-HFS can reverse remote network effects following PTS in rats which otherwise may result in chronic motor symptoms.
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Affiliation(s)
- Stefanie D Krämer
- Radiopharmaceutical Sciences/Biopharmacy, Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | | | - Fabian Schadt
- Department of Nuclear Medicine, Interdisciplinary PET center, University Hospital Würzburg, Würzburg, Germany
| | - Ina Israel
- Department of Nuclear Medicine, Interdisciplinary PET center, University Hospital Würzburg, Würzburg, Germany
| | - Samuel Samnick
- Department of Nuclear Medicine, Interdisciplinary PET center, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Felix Fluri
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany.
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15
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Ionescu TM, Amend M, Hafiz R, Biswal BB, Maurer A, Pichler BJ, Wehrl HF, Herfert K. Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity. Neuroimage 2021; 243:118501. [PMID: 34428573 DOI: 10.1016/j.neuroimage.2021.118501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/23/2021] [Accepted: 08/20/2021] [Indexed: 11/28/2022] Open
Abstract
Although brain research has taken important strides in recent decades, the interaction and coupling of its different physiological levels is still not elucidated. Specifically, the molecular substrates of resting-state functional connectivity (rs-FC) remain poorly understood. The aim of this study was elucidating interactions between dopamine D2 receptors (D2R) and serotonin transporter (SERT) availabilities in the striatum (CPu) and medial prefrontal cortex (mPFC), two of the main dopaminergic and serotonergic projection areas, and the default-mode network. Additionally, we delineated its interaction with two other prominent resting-state networks (RSNs), the salience network (SN) and the sensorimotor network (SMN). To this extent, we performed simultaneous PET/fMRI scans in a total of 59 healthy rats using [11C]raclopride and [11C]DASB, two tracers used to image quantify D2R and SERT respectively. Edge, node and network-level rs-FC metrics were calculated for each subject and potential correlations with binding potentials (BPND) in the CPu and mPFC were evaluated. We found widespread negative associations between CPu D2R availability and all the RSNs investigated, consistent with the postulated role of the indirect basal ganglia pathway. Correlations between D2Rs in the mPFC were weaker and largely restricted to DMN connectivity. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between and within the SN, SMN and the posterior DMN, underlining the complex role of serotonergic neurotransmission in this region. Here we show direct relationships between rs-FC and molecular properties of the brain as assessed by simultaneous PET/fMRI in healthy rodents. The findings in the present study contribute to the basic understanding of rs-FC by revealing associations between inter-subject variances of rs-FC and receptor and transporter availabilities. Additionally, since current therapeutic strategies typically target neurotransmitter systems with the aim of normalizing brain function, delineating associations between molecular and network-level brain properties is essential and may enhance the understanding of neuropathologies and support future drug development.
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Affiliation(s)
- Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Mario Amend
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Rakibul Hafiz
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, USA
| | - Andreas Maurer
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Hans F Wehrl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany.
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16
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Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. 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. [PMID: 33529320 DOI: 10.1093/cercor/bhaa393] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/26/2020] [Accepted: 11/30/2020] [Indexed: 12/12/2022] Open
Abstract
Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Phillip G D Ward
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Emma X Liang
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia
| | - Edwina R Orchard
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Vic, 3800 Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
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17
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Ionescu TM, Amend M, Hafiz R, Biswal BB, Wehrl HF, Herfert K, Pichler BJ. Elucidating the complementarity of resting-state networks derived from dynamic [ 18F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI. Neuroimage 2021; 236:118045. [PMID: 33848625 PMCID: PMC8339191 DOI: 10.1016/j.neuroimage.2021.118045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/15/2021] [Accepted: 04/04/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanisms of the blood oxygen level-dependant (BOLD) signal are not fully resolved due to its inherent complexity. In contrast, [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) has been shown to provide a more direct measure of local synaptic activity and may have additional value for the readout and interpretation of brain connectivity. We performed an RSN analysis from simultaneously acquired PET/fMRI data on a single-subject level to directly compare fMRI and [18F]FDG-PET-derived networks during the resting state. Simultaneous [18F]FDG-PET/fMRI scans were performed in 30 rats. Pairwise correlation analysis, as well as independent component analysis (ICA), were used to compare the readouts of both methods. We identified three RSNs with a high degree of similarity between PET and fMRI-derived readouts: the default-mode-like network (DMN), the basal ganglia network and the cerebellar-midbrain network. Overall, [18F]FDG connectivity indicated increased integration between different, often distant, brain areas compared to the results indicated by the more segregated fMRI-derived FC. Additionally, several networks exclusive to either modality were observed using ICA. These networks included mainly bilateral cortical networks of a limited spatial extent for fMRI and more spatially widespread networks for [18F]FDG-PET, often involving several subcortical areas. This is the first study using simultaneous PET/fMRI to report RSNs subject-wise from dynamic [18F]FDG tracer delivery and BOLD fluctuations with both independent component analysis (ICA) and pairwise correlation analysis in small animals. Our findings support previous studies, which show a close link between local synaptic glucose consumption and BOLD-fMRI-derived FC. However, several brain regions were exclusively attributed to either [18F]FDG or BOLD-derived networks underlining the complementarity of this hybrid imaging approach, which may contribute to the understanding of brain functional organization and could be of interest for future clinical applications.
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Affiliation(s)
- Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Mario Amend
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Rakibul Hafiz
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, United States
| | - Hans F Wehrl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany.
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18
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Ruppert MC, Greuel A, Freigang J, Tahmasian M, Maier F, Hammes J, van Eimeren T, Timmermann L, Tittgemeyer M, Drzezga A, Eggers C. The default mode network and cognition in Parkinson's disease: A multimodal resting-state network approach. Hum Brain Mapp 2021; 42:2623-2641. [PMID: 33638213 PMCID: PMC8090788 DOI: 10.1002/hbm.25393] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/12/2021] [Accepted: 02/17/2021] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Marina C Ruppert
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
| | - Andrea Greuel
- Department of Neurology, University Hospital of Marburg, Marburg, Germany
| | - Julia Freigang
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Franziska Maier
- Medical Faculty, Department of Psychiatry, University Hospital Cologne, Cologne, Germany
| | - Jochen Hammes
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
| | - Thilo van Eimeren
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany.,Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany.,Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Alexander Drzezga
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-2), Jülich, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
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19
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Li S, Jamadar SD, Ward PG, Premaratne M, Egan GF, Chen Z. Analysis of continuous infusion functional PET (fPET) in the human brain. Neuroimage 2020; 213:116720. [DOI: 10.1016/j.neuroimage.2020.116720] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/03/2020] [Accepted: 03/06/2020] [Indexed: 12/16/2022] Open
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