Abstract

OBJECTIVE This article reviews commonly used imaging modalities in movement disorders, particularly parkinsonism. The review includes the diagnostic utility, role in differential diagnosis, reflection of pathophysiology, and limitations of neuroimaging in the setting of movement disorders. It also introduces promising new imaging modalities and describes the current status of research. LATEST DEVELOPMENTS Iron-sensitive MRI sequences and neuromelanin-sensitive MRI can be used to directly assess the integrity of nigral dopaminergic neurons and thus may reflect disease pathology and progression throughout the full range of severity in Parkinson disease (PD). The striatal uptake of presynaptic radiotracers in their terminal axons as currently assessed using clinically approved positron emission tomography (PET) or single-photon emission computed tomography (SPECT) imaging correlates with nigral pathology and disease severity only in early PD. Cholinergic PET, using radiotracers that target the presynaptic vesicular acetylcholine transporter, constitutes a substantial advance and may provide crucial insights into the pathophysiology of clinical symptoms such as dementia, freezing, and falls. ESSENTIAL POINTS In the absence of valid, direct, objective biomarkers of intracellular misfolded -synuclein, PD remains a clinical diagnosis. The clinical utility of PET- or SPECT-based striatal measures is currently limited given their lack of specificity and inability to reflect nigral pathology in moderate to severe PD. These scans may be more sensitive than clinical examination to detect nigrostriatal deficiency that occurs in multiple parkinsonian syndromes and may still be recommended for clinical use in the future to identify prodromal PD if and when disease-modifying treatments become available. Multimodal imaging to evaluate underlying nigral pathology and its functional consequences may hold the key to future advances.

Original languageEnglish
Pages (from-to)194-218
Number of pages25
JournalCONTINUUM Lifelong Learning in Neurology
Volume29
Issue number1
DOIs
StatePublished - Feb 1 2023

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