Marianna Inglese

RESEARCH OUTPUT

Selected publications

1. Clustering Algorithm Reveals Dopamine-Motor Mismatch in Cognitively Preserved Parkinson's Disease.Annals of clinical and translational neurology · 2026
   OBJECTIVE: To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. METHODS: n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort. Clustering analysis stratified dopaminergic denervation, measured with 123I-FP-CIT-SPECT, and motor impairment into mild [D and M] and severe [D+ and M+]. Differences in terms of biomarkers and clinical progression were assessed across subgroups. Causal mediation analysis evaluated the effect of co-pathology on the relationship between subgroups and cognitive decline. RESULTS: Four subgroups were identified. Two subgroups showed concordant profiles: the severe dopaminergic and motor impairment subgroup [D+/M+] exhibited poorer memory performance, pathological Aβ1-42, as well as higher longitudinal Levodopa equivalent daily dose (LEDD) values and faster progression of motor disability; the mild dopaminergic and motor deficits [D/M] subgroup displayed a benign clinical profile and stable disease progression. Two subgroups exhibited dopaminergic and motor severity mismatch: the mild dopaminergic but severe motor impairment [D/M+] subgroup showed severe and rapidly progressive rigidity. CSF Aβ1-42 levels mediated the association between D+/M+ and cognitive decline in patients who were cognitively preserved at onset, accounting for 13% of the total effect. The external cohort supported the malignancy of D+/M+ and the presence of rigidity in D/M+. INTERPRETATION: Concordant severe impairment reflects a malignant profile linked to Aβ-related cognitive decline, while mild concordant cases show stable progression. Mismatch subgroups display distinct clinical patterns, underscoring the value of integrating imaging and motor features for early disease stratification.

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2. From Radiomics to Generative Models: Evaluating Early Radiation Effects in Metastatic Brain Lesions.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
   Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding tissues. Despite its efficacy, radiation-induced effects such as early radiation effects (ERE) and adverse radiation effects (ARE) complicate diagnosis and management, with ARE occurring in up to 30% of patients, often presenting as ring-enhancing T2/FLAIR hyperintensities. To address these challenges, we aimed to compare standard radiomics-based machine learning approaches with pretrained generative models for assessing ERE in BM lesions. A cohort of 21 patients for a total of 35 lesions (17 treatment-naïve and 18 post-SRS +/- combination therapy) who underwent multiparametric 18F-FPIA PET/MRI was analyzed. The study investigated: 1) Multiparametric analysis of PET and MRI diffusion/perfusion parameters (ADC, Ktrans, CBF, K1, vt); 2) MRI-based radiomics; 3) static PET radiomics; 4) Dynomics; 5) a combination of PET and MRI radiomics; and 6) low-level embeddings from a pretrained generative diffusion model applied to full T1, static PET, and their combination. Using manually contoured lesion masks for analyses 1-5 and lesion-free embeddings for analysis 6, multiple classifiers (SVM, XGBoost, Linear regressor) were applied after feature standardization and principal component analysis (retaining 90% variance). Fivefold cross-validation demonstrated comparable performances across radiomic approaches (Accuracy: 71.95±0.05%, AUC: 0.72±0.05%), while the pretrained generative model achieved significantly higher performance (Accuracy: 83.82±0.01%, AUC: 0.83±0.01%) without requiring lesion segmentation in assessing ERE in BM lesions.Clinical Relevance-This study shows the potential of generative models to streamline and enhance the assessment of early radiation effects in parenchymal metastatic lesions without need of lesion segmentation.

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3. Advancing Generalisable Neural Network-Based PET Quantification: A Multicenter [11C]PBR28 study.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
   Quantifying the volume of distribution (VT) in Positron Emission Tomography (PET) is widely considered the gold standard for assessing tracer binding. However, this process requires an accurate estimation of the tracer's input function (IF) obtained through arterial sampling and metabolite correction-procedures that are both invasive and technically demanding. To overcome these limitations, we introduce a neural network-based framework for estimating the IF directly from [11C]PBR28 dynamic PET data, exploring generalisability across datasets and scanners. The framework employs a patched variational autoencoder (pVAE) for dimensionality reduction, generating IFs with uncertainty (NNIF-dPET), and computes VT from the mean output signal. Additionally, we evaluate two alternative methods: NNIF-IDIF, which derives IFs from image-derived input functions, and NNIF-unBlood, which uses uncorrected blood signals as input. NNIF-dPET achieves accuracy comparable to true arterial IFs while outperforming IDIF-based methods, suggesting that latent space representations can effectively approximate whole-blood activity for parent plasma input function estimation, rather than relying on pre-selected voxels.Clinical Relevance-This approach highlights the potential for scalable, non-invasive PET quantification across diverse clinical settings.

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4. Multimodal Generative Modeling for DaT Scan Reconstruction in Parkinson's Disease.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
   The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered by tight regulations and ethical considerations. In this study, we introduce a multimodal deep learning model designed to reconstruct (and thereby enable future synthesis of) 123I-FPCIT SPECT (DaT) scans by leveraging both the target DaT and co-registered T1-weighted MRI scans. Through extensive experimentation on a large Parkinson's Progression Markers Initiative (PPMI) dataset, comprising healthy controls, Parkinson's disease (PD) patients, and individuals without imaging evidence of dopaminergic deficits (SWEDD), we demonstrate that the proposed framework yields DaT images that strongly preserve clinical signal distributions. Our findings show minimal intensity discrepancies, unbiased contrast-to-noise ratios, and robust region-based analyses across pathological and demographic subgroups, highlighting the feasibility of this approach for large-scale data augmentation in neurodegenerative research.Clinical Relevance-By enabling the reconstruction of DaT scans from multi-contrast inputs, this framework has the potential to enhance future generation of synthetic data that are applicable to early PD detection, disease progression studies, and model training in clinical scenarios where data are often limited, and privacy constraints are stringent.

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5. Generation of synthetic TSPO PET maps from structural MRI images.Frontiers in neuroinformatics · 2025
   INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning model to generate TSPO PET images from structural MRI data collected in human subjects. METHODS: A total of 204 scans, from participants with knee osteoarthritis (n = 15 scanned once, 15 scanned twice, 14 scanned three times), back pain (n = 40 scanned twice, 3 scanned three times), and healthy controls (n = 28, scanned once), underwent simultaneous 3 T MRI and [11C]PBR28 TSPO PET scans. A 3D U-Net model was trained on 80% of these PET-MRI pairs and validated using 5-fold cross-validation. The model's accuracy in reconstructed PET from MRI only was assessed using various intensity and noise metrics. RESULTS: The model achieved a low voxel-wise mean squared error (0.0033 ± 0.0010) across all folds and a median contrast-to-noise ratio of 0.0640 ± 0.2500 when comparing true to reconstructed PET images. The synthesized PET images accurately replicated the spatial patterns observed in the original PET data. Additionally, the reconstruction accuracy was maintained even after spatial normalization. DISCUSSION: This study demonstrates that deep learning can accurately synthesize TSPO PET images from conventional, T1-weighted MRI. This approach could enable low-cost, noninvasive neuroinflammation imaging, expanding the clinical applicability of this imaging method.

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