Single-subject network analysis of FDOPA PET in Parkinson’s disease and Schizophrenia
Poster No:
1185
Submission Type:
Abstract Submission
Authors:
Mario Severino1, Julia Schubert2, Giovanna Nordio2, Alessio Giacomel2, Rubaida Easmin2, Nick P. Lao-Kaim3, Pierluigi Selvaggi2,4, Zhilei Xu5, Joana B. Pereira5, Sameer Jahuar6,7, Paola Piccini3, Oliver Howes6,7,8, Federico Turkheimer22, Mattia Veronese1,2
Institutions:
1Department of Information Engineering, University of Padua, Padua, Italy, 2Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London, London, United Kingdom, 3Imperial College London, London, United Kingdom, 4Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy, 5Division of Neuro, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden, 6Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom, 7Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, United Kingdom, 8South London and Maudsley NHS Foundation Trust, London, United Kingdom
First Author:
Co-Author(s):
Julia Schubert
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Giovanna Nordio
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Alessio Giacomel
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Rubaida Easmin
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Pierluigi Selvaggi
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London|Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro
London, United Kingdom|Bari, Italy
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London|Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro
London, United Kingdom|Bari, Italy
Zhilei Xu
Division of Neuro, Department of Clinical Neuroscience, Karolinska Institute
Stockholm, Sweden
Division of Neuro, Department of Clinical Neuroscience, Karolinska Institute
Stockholm, Sweden
Joana B. Pereira
Division of Neuro, Department of Clinical Neuroscience, Karolinska Institute
Stockholm, Sweden
Division of Neuro, Department of Clinical Neuroscience, Karolinska Institute
Stockholm, Sweden
Sameer Jahuar
Institute of Psychiatry, Psychology & Neuroscience, King's College London|Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London
London, United Kingdom|London, United Kingdom
Institute of Psychiatry, Psychology & Neuroscience, King's College London|Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London
London, United Kingdom|London, United Kingdom
Oliver Howes
Institute of Psychiatry, Psychology & Neuroscience, King's College London|Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London|South London and Maudsley NHS Foundation Trust
London, United Kingdom|London, United Kingdom|London, United Kingdom
Institute of Psychiatry, Psychology & Neuroscience, King's College London|Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London|South London and Maudsley NHS Foundation Trust
London, United Kingdom|London, United Kingdom|London, United Kingdom
Federico Turkheimer2
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
London, United Kingdom
Mattia Veronese
Department of Information Engineering, University of Padua|Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
Padua, Italy|London, United Kingdom
Department of Information Engineering, University of Padua|Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience,King’s College London
Padua, Italy|London, United Kingdom
Introduction:
18F-FDOPA Positron Emission Tomography (PET) imaging has been extensively applied to assess the role of the dopamine system in the pathophysiology of various brain conditions, such as Parkinson's Disease (PD) (Darcourt et al., 2014) and schizophrenia (Jauhar et al., 2017). Traditional FDOPA PET analyses focus on absolute tracer uptake in specific regions like the striatum, providing insights into dopamine synthesis. However, this approach may miss inter-regional interactions. Here, we apply a perturbation covariance framework to construct single-subject networks for the assessment of interregional relationships across the whole brain.
Methods:
The method was validated using static FDOPA PET data from diverse patient groups with distinct brain disorders: 71 healthy controls (HC) (Nordio et al., 2023), 33 PD patients (Li et al., 2018), and 105 participants at the different stages of psychosis (i.e., 49 at clinical high risk (CHR) for psychosis (Egerton et al., 2013), 25 with first-episode psychosis (FEP) (Jauhar et al., 2019), 31 with chronic schizophrenia (SCZ) (Egerton et al., 2021)). All data were acquired with a 90-minutes dynamic scan. Standardized uptake value ratio (SUVr) was calculated for 83 regions of the Hammersmith atlas using 60–75 minutes post-injection data as proxy of dopamine synthesis capacity (Veronese et al., 2021).
The HC dataset served as the reference cohort for constructing a group-level molecular network, where nodes represented brain regions and edges represented partial correlation coefficients of SUVr, adjusted for age and sex. A single subject from each patient group was added to the reference group to create a perturbed network. The difference between the perturbed and reference network was normalized, generating an individual-level matrix of z-scores representing connectivity abnormalities (Liu et al., 2016) (Fig. 1). Statistically significant edges (p<0.05) were used to extract a region degree to quantify deviation for each region.
Regional degree was utilized to investigate differences in alterations between PD and SCZ, identifying statistically significant alterations across specific regions. These metrics were further employed as features for a machine learning model to classify the two groups. Subsequently, regional degree was used to assess gradients of alterations across the psychosis spectrum, aiming to capture transitions from individuals at CHR to FEP and SCZ. Additionally, regional degree analysis evaluated differences in deviations between FEP responders (TR) and non-responders (non-TR) to antipsychotic treatment. Finally, single-subject networks were employed for fingerprinting between FEP at baseline and follow-up, using Pearson correlation, where successful identification was defined by matching baseline and follow-up data to the same individual.
The HC dataset served as the reference cohort for constructing a group-level molecular network, where nodes represented brain regions and edges represented partial correlation coefficients of SUVr, adjusted for age and sex. A single subject from each patient group was added to the reference group to create a perturbed network. The difference between the perturbed and reference network was normalized, generating an individual-level matrix of z-scores representing connectivity abnormalities (Liu et al., 2016) (Fig. 1). Statistically significant edges (p<0.05) were used to extract a region degree to quantify deviation for each region.
Regional degree was utilized to investigate differences in alterations between PD and SCZ, identifying statistically significant alterations across specific regions. These metrics were further employed as features for a machine learning model to classify the two groups. Subsequently, regional degree was used to assess gradients of alterations across the psychosis spectrum, aiming to capture transitions from individuals at CHR to FEP and SCZ. Additionally, regional degree analysis evaluated differences in deviations between FEP responders (TR) and non-responders (non-TR) to antipsychotic treatment. Finally, single-subject networks were employed for fingerprinting between FEP at baseline and follow-up, using Pearson correlation, where successful identification was defined by matching baseline and follow-up data to the same individual.
Results:
Statistically significant differences were identified between PD and SCZ, particularly in the substantia nigra and basal ganglia, consistent with disease-specific signatures (Fig. 2A). The classifier demonstrated perfect separation based on regional degree (AUC=1). Significant differences were also observed between individuals at CHR and both FEP and SCZ, though no differences were detected between SCZ and FEP (Fig. 2B). A progressive increase in alterations appeared to align with advancing disease stages.
Regional degree was significantly different between TR and non-TR revealing distinct magnitude of alteration (p<0.001). Finally, fingerprint analysis achieved high accuracy (83.3%) in identifying individuals in both TR and non-TR groups, highlighting the uniqueness of the individual molecular matrices.
Regional degree was significantly different between TR and non-TR revealing distinct magnitude of alteration (p<0.001). Finally, fingerprint analysis achieved high accuracy (83.3%) in identifying individuals in both TR and non-TR groups, highlighting the uniqueness of the individual molecular matrices.
Conclusions:
Perturbation covariance analysis applied to FDOPA PET imaging complements traditional methods by offering new insights into dopamine disruptions. The method can be generalized to any molecular imaging target and holds significant potential for enhancing the understanding of disease mechanisms and evaluating the effects of pharmacological interventions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
PET 2
Keywords:
Data analysis
Degenerative Disease
Dopamine
Positron Emission Tomography (PET)
Psychiatric Disorders
Schizophrenia
Statistical Methods
1|2Indicates the priority used for review
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2. Egerton, A. (2013). Presynaptic Striatal Dopamine Dysfunction in People at Ultra-high Risk for Psychosis: Findings in a Second Cohort. Biological Psychiatry, 74(2), 106–112.
3. Egerton, A. (2021). Dopamine and Glutamate in Antipsychotic-Responsive Compared With Antipsychotic-Nonresponsive Psychosis: A Multicenter Positron Emission Tomography and Magnetic Resonance Spectroscopy Study (STRATA). Schizophrenia Bulletin, 47(2), 505–516.
4. Jauhar, S. (2017). A Test of the Transdiagnostic Dopamine Hypothesis of Psychosis Using Positron Emission Tomographic Imaging in Bipolar Affective Disorder and Schizophrenia. JAMA Psychiatry, 74(12), 1206.
5. Li, W. (2018). 11C‐PE2I and 18F‐Dopa PET for assessing progression rate in Parkinson’s: A longitudinal study. Movement Disorders, 33(1), 117–127.
6. Liu, X. (2016). Personalized characterization of diseases using sample-specific networks. Nucleic Acids Research, 44(22), e164–e164.
7. Nordio, G. (2023). An automatic analysis framework for FDOPA PET neuroimaging. Journal of Cerebral Blood Flow & Metabolism, 43(8), 1285–1300.
8. Veronese, M. (2021). A potential biomarker for treatment stratification in psychosis: evaluation of an [18F] FDOPA PET imaging approach. Neuropsychopharmacology, 46(6), 1122–1132.