Back

Localization of Abnormal Brain Regions in Parkinsonian Disorders: An ALE Meta-Analysis

Ellis, E. G.; Joutsa, J.; Morrison-Ham, J.; Caeyenberghs, K.; Corp, D. T.

2022-04-13 neurology
10.1101/2022.04.11.22273755 medRxiv
Show abstract

Parkinsonism is a feature of several neurodegenerative disorders, including Parkinsons disease (PD), progressive supranuclear palsy (PSP), corticobasal degeneration syndrome (CBS) and multiple system atrophy (MSA). Neuroimaging studies have yielded insights into parkinsonism; however it remains unclear whether there is a common neural substrate amongst disorders. The aim of the present meta-analysis was to identify consistent brain alterations in parkinsonian disorders (PD, PSP, CBS, MSA) both individually, and combined, to elucidate the shared substrate of parkinsonism. 33,505 studies were systematically screened following searches of MEDLINE Complete and Embase databases. A series of whole-brain activation likelihood estimation meta-analyses were performed on 126 neuroimaging studies (64 PD; 25 PSP; 18 CBS; 19 MSA) utilizing anatomical MRI, perfusion or metabolism positron emission tomography and single photon emission computed tomography. Abnormality of the caudate, thalamus, middle frontal and temporal gyri was common to all parkinsonian disorders. Localizations of commonly affected brain regions in individual disorders aligned with current diagnostic imaging markers, localizing the midbrain in PSP, putamen in MSA-parkinsonian variant and brainstem in MSA-cerebellar variant. Regions of the basal ganglia and precuneus were most commonly affected in PD, while CBS was characterized by caudate abnormality. To our knowledge, this is the largest meta-analysis of neuroimaging studies in parkinsonian disorders. Findings support the notion that parkinsonism may share a common neural substrate, independent of the underlying disease process, while also highlighting characteristic patterns of brain abnormality in each disorder.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.