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Avendaño-Estrada A, Olarte-Casas MÁ, Ávila-Rodríguez MÁ. Vectorial-based analysis of dual-tracer PET imaging: A proof of concept. Comput Biol Med 2024; 168:107705. [PMID: 37979207 DOI: 10.1016/j.compbiomed.2023.107705] [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] [Received: 07/17/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND The diagnosis of neurological diseases is complicated since they often share similar symptoms and occur in different severity levels. Imaging techniques such as PET molecular imaging are helpful for an early and accurate diagnosis and, staging allowing a noninvasive evaluation of the disease. The combination of two radioligands in the same patient could be valuable to achieve these diagnostic goals; nevertheless, the imaging data obtained with two radioligands is commonly interpreted independently. This novel approach to combine the PET data of two radiopharmaceuticals, separately acquired in the same subject, is to obtain new quantitative metrics. PET images of patients with Parkinson's disease (PD) and healthy controls (HC) were analyzed. Voxel-by-voxel uptake is compared by combining the imaging data. Dual-tracer PET imaging analysis was tested with [11C]DTBZ-[11C]Raclopride as proof of concept. RESULTS The new proposed metric based on a resultant vector is capable of efficiently discriminating healthy controls from PD patients (p < 0.0001) allowing the detection of slight changes in patients undergoing therapeutic approaches. Significant differences were found between HC and PD patients for the evaluated radiotracers. CONCLUSIONS The resultant vector appears to deliver useful information that could be helpful to evaluate PD patients under treatment and to improve differential diagnoses.
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Affiliation(s)
- Arturo Avendaño-Estrada
- Unidad Radiofarmacia-Ciclotrón, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico; Centro de Investigación sobre el Envejecimiento, Centro de Investigación y de Estudios Avanzados Sede Sur, Mexico.
| | - Miguel Ángel Olarte-Casas
- Unidad PET/CT, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico
| | - Miguel Ángel Ávila-Rodríguez
- Unidad Radiofarmacia-Ciclotrón, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico; Centro de Investigación sobre el Envejecimiento, Centro de Investigación y de Estudios Avanzados Sede Sur, Mexico.
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Spetsieris PG, Eidelberg D. Parkinson's disease progression: Increasing expression of an invariant common core subnetwork. Neuroimage Clin 2023; 39:103488. [PMID: 37660556 PMCID: PMC10491857 DOI: 10.1016/j.nicl.2023.103488] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States; Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States.
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Caminiti SP, Boccalini C, Nicastro N, Garibotto V, Perani D. Sex differences in brain metabolic connectivity architecture in probable dementia with Lewy bodies. Neurobiol Aging 2023; 126:14-24. [PMID: 36905876 DOI: 10.1016/j.neurobiolaging.2023.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/23/2023] [Accepted: 02/10/2023] [Indexed: 02/19/2023]
Abstract
We investigated how sex modulates metabolic connectivity alterations in probable dementia with Lewy bodies (pDLB). We included 131 pDLB patients (males/females: 58/73) and similarly aged healthy controls (HC) (male/female: 59/75) with available (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans. We assessed (1) sex differences in the whole-brain connectivity, identifying pathological hubs, (2) connectivity alterations in functional pathways of the neurotransmitter systems, (3) Resting State Networks (RSNs) integrity. Both pDLBM (males) and pDLBF (females) shared dysfunctional hubs in the insula, Rolandic operculum, and inferior parietal lobule, but the pDLBM group showed more severe and diffuse whole-brain connectivity alterations. Neurotransmitters connectivity analysis revealed common alterations in dopaminergic and noradrenergic pathways. Sex differences emerged particularly in the Ch4-perisylvian division, with pDLBM showing more severe alterations than pDLBF. The RSNs analysis showed no sex differences, with decreased connectivity strength in the primary visual, posterior default mode, and attention networks in both groups. Extensive connectivity changes characterize both males and females in the dementia stage, with a major vulnerability of cholinergic neurotransmitter systems in males, possibly contributing to the observed different clinical phenotypes.
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Affiliation(s)
- Silvia Paola Caminiti
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Cecilia Boccalini
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Nicastro
- Division of Neurorehabilitation, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Daniela Perani
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Boccalini C, Bortolin E, Carli G, Pilotto A, Galbiati A, Padovani A, Ferini-Strambi L, Perani D. Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase. Front Neurosci 2022; 16:930735. [PMID: 36003959 PMCID: PMC9394228 DOI: 10.3389/fnins.2022.930735] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/19/2022] [Indexed: 12/05/2022] Open
Abstract
Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.
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Affiliation(s)
- Cecilia Boccalini
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Bortolin
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
| | - Giulia Carli
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Parkinson’s Disease Rehabilitation Centre, FERB ONLUS, S. Isidoro Hospital, Trescore Balneario, Italy
| | - Andrea Galbiati
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Parkinson’s Disease Rehabilitation Centre, FERB ONLUS, S. Isidoro Hospital, Trescore Balneario, Italy
| | - Luigi Ferini-Strambi
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Daniela Perani
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy
- *Correspondence: Daniela Perani,
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Perovnik M, Tomše P, Jamšek J, Tang C, Eidelberg D, Trošt M. Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography. Neuroimage Clin 2022; 35:103080. [PMID: 35709556 PMCID: PMC9207351 DOI: 10.1016/j.nicl.2022.103080] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/26/2022] [Accepted: 06/05/2022] [Indexed: 10/28/2022]
Abstract
PURPOSE Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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Neuroimaging of Mouse Models of Alzheimer’s Disease. Biomedicines 2022; 10:biomedicines10020305. [PMID: 35203515 PMCID: PMC8869427 DOI: 10.3390/biomedicines10020305] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/23/2022] Open
Abstract
Magnetic resonance imaging (MRI) and positron emission tomography (PET) have made great strides in the diagnosis and our understanding of Alzheimer’s Disease (AD). Despite the knowledge gained from human studies, mouse models have and continue to play an important role in deciphering the cellular and molecular evolution of AD. MRI and PET are now being increasingly used to investigate neuroimaging features in mouse models and provide the basis for rapid translation to the clinical setting. Here, we provide an overview of the human MRI and PET imaging landscape as a prelude to an in-depth review of preclinical imaging in mice. A broad range of mouse models recapitulate certain aspects of the human AD, but no single model simulates the human disease spectrum. We focused on the two of the most popular mouse models, the 3xTg-AD and the 5xFAD models, and we summarized all known published MRI and PET imaging data, including contrasting findings. The goal of this review is to provide the reader with broad framework to guide future studies in existing and future mouse models of AD. We also highlight aspects of MRI and PET imaging that could be improved to increase rigor and reproducibility in future imaging studies.
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