Metabolic Imaging Techniques in Neurodegenerative Disorders

Metabolic imaging has become an indispensable tool in the study of neurodegenerative disorders, offering non-invasive insights into regional brain function and underlying pathology. Techniques such as fluorodeoxyglucose positron emission tomography (FDG-PET) and dopamine-transporter single-photon emission computed tomography (DAT-SPECT) quantify glucose consumption and dopaminergic integrity, respectively, revealing characteristic patterns of hypometabolism or hypermetabolism linked to diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Beyond traditional univariate analyses, spatial covariance methods identify disease-related metabolic patterns, notably the Parkinson’s disease-related pattern (PDRP), which serves as an objective biomarker of disease activity. Recent advances in network-based approaches reconstruct the metabolic connectome, capturing interregional covariance of glucose uptake and enabling graph-theoretical characterisation of global and local network efficiency. Machine-learning algorithms, including kernel-based classifiers and divergence-based similarity estimators, have further refined diagnostic accuracy and prognostic prediction by integrating complex metabolic features at the individual level. These developments promise earlier detection, stratification of prodromal stages and objective monitoring of therapeutic interventions, thereby bridging the gap between neuroimaging research and clinical practice on a global scale.

Research from Nature Portfolio

Emerging work has demonstrated that acetyl-DL-leucine may stabilise prodromal metabolic signatures in individuals with isolated REM sleep behaviour disorder, a recognised precursor to PD. Over an extended treatment period, patients exhibited improved symptom scores alongside stabilisation of both DAT-SPECT binding ratios and FDG-PET Parkinson’s disease-related pattern z-scores, suggesting potential disease-modifying effects on nigrostriatal integrity and cerebral glucose metabolism.

Another study applied high-order network analysis to early idiopathic PD, using FDG-PET data to reconstruct metabolic connectivity at multiple scales. Sparse-inverse covariance estimation revealed widespread long-distance decreases in frontolateral cortical connectivity, increased basal ganglia interactions and selective impairment in nigrostriatal pathways. These multiscale findings highlight an extensive reconfiguration of metabolic architecture beyond focal striato-cortical derangement, underscoring the extended neural vulnerability in early synucleinopathy.

Metabolic Imaging Techniques in Neurodegenerative Disorders publication trend

The graph below shows the total number of articles in metabolic imaging techniques in neurodegenerative disorders across all publications each year (not limited to Nature Index journals).

Technical terms

Fluorodeoxyglucose positron emission tomography (FDG-PET): A technique measuring regional cerebral glucose uptake to infer neuronal activity.

Dopamine-transporter single-photon emission computed tomography (DAT-SPECT): An imaging modality that quantifies presynaptic dopaminergic terminal integrity.

Parkinson’s disease-related pattern (PDRP): A spatial covariance signature of hyper- and hypometabolism characteristic of PD.

Metabolic connectome: A network representation of interregional metabolic covariance derived from PET data.

Jensen-Shannon Divergence Similarity Estimation (JSSE): A method for calculating individual metabolic connectomes by quantifying distributional similarity between regions.

Kullback-Leibler Divergence Similarity Estimation (KLSE): A divergence-based approach to characterise individual network alterations predictive of disease progression.

References

1. Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis. npj Digital Medicine (2024).
2. Acetyl-DL-leucine in two individuals with REM sleep behavior disorder improves symptoms, reverses loss of striatal dopamine-transporter binding and stabilizes pathological metabolic brain pattern—case reports. Nature Communications (2024).
3. Altered brain metabolic connectivity at multiscale level in early Parkinson’s disease. Scientific Reports (2017).
4. The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification. MedComm (2023).
5. Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia. European Journal of Nuclear Medicine and Molecular Imaging (2020).