Multimodal Imaging in Lewy Body Dementias

Multimodal imaging in Lewy body dementias integrates structural, molecular and functional techniques to capture the complex interplay of α-synuclein pathology, Alzheimer co-pathologies and neuroinflammatory changes. Structural MRI reveals patterns of cortical and subcortical atrophy that may differentiate subtypes and predict progression. Fluorodeoxyglucose PET and arterial spin labelling MRI highlight characteristic occipital and parietal hypometabolism or hypoperfusion, while nigrostriatal dopamine transporter SPECT or PET confirms deficits in dopaminergic pathways. Novel molecular probes for amyloid-β and tau permit in vivo assessment of Alzheimer’s co-pathology, and emerging tracers for α-synuclein hold promise for disease-specific detection. Together with plasma and CSF biomarkers, these imaging modalities enable a precision-medicine approach to diagnosis, prognostication and patient stratification for targeted therapies. Machine-learning methods, including deep convolutional neural networks, have further refined feature extraction and classification, yielding objective markers such as the cingulate island sign. By combining data across modalities, clinicians and researchers aim to resolve phenotypic heterogeneity, monitor disease evolution and evaluate responses to disease-modifying interventions.

Research from Nature Portfolio

Deep-learning approaches have been applied to brain perfusion SPECT to distinguish dementia with Lewy bodies from Alzheimer’s disease and normal ageing with near-clinical accuracy. A convolutional neural network trained on surface perfusion images achieved over 89% discrimination between Lewy body dementia and Alzheimer’s disease, identifying the preserved posterior cingulate relative to medial occipital cortex—known as the cingulate island sign—as the principal differentiating feature. Gradient-weighted class activation mapping visualised how the network progressively focused on this signature during training. Moreover, network output scores correlated with core clinical features of Lewy body dementia, suggesting that deep learning can both enhance differential diagnosis and capture disease-relevant imaging biomarkers objectively.

Multimodal Imaging in Lewy Body Dementias publication trend

The graph below shows the total number of articles in multimodal imaging in lewy body dementias across all publications each year (not limited to Nature Index journals).

Technical terms

Multimodal imaging: The use of complementary imaging techniques (MRI, PET, SPECT) to assess structural, functional and molecular brain changes in a single diagnostic framework.

Cingulate island sign (CIS): Relative preservation of metabolism or perfusion in the posterior cingulate cortex compared to surrounding occipital regions, characteristic of Lewy body dementia.

Convolutional neural network (CNN): A class of deep-learning algorithm designed to process grid-like data such as images, extracting hierarchical features for classification tasks.

Fluorodeoxyglucose PET (18F-FDG PET): A molecular imaging modality that measures regional glucose metabolism to infer neuronal activity patterns in neurodegenerative diseases.

Plasma phosphorylated tau (p-tau181): A blood biomarker reflecting tau phosphorylation status in the brain, used to detect Alzheimer‐type co-pathology within Lewy body dementia.

References

1. MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies. npj Parkinson's Disease (2023).
2. Plasma biomarkers of amyloid, tau, axonal, and neuroinflammation pathologies in dementia with Lewy bodies. Alzheimer's Research & Therapy (2024).
3. Neuroinflammation is associated with Alzheimer’s disease co-pathology in dementia with Lewy bodies. Acta Neuropathologica Communications (2024).
4. Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Scientific Reports (2019).