Brain Molecular Connectivity in Neurodegenerative Conditions

Abstract

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer’s disease topography

“…Another resting state network, which loss is commonly involved in cognitive impairment is the default mode network (DMN) ( Carli et al, 2021 ). DMN is, however, intact in DLB, as suggested by a recent meta -analysis ( Ma et al, 2022 ).…”

Neuroimaging of Mouse Models of Alzheimer’s Disease

“…A broad review highlights the strengths and limitations of current PET ligands as they relate to human pathology [ 36 ]. Exciting new advances in analytic methods for PET data have led to molecular connectivity studies, which may bolster the ability to non-invasively monitor brain connectivity as AD progresses [ 37 ].…”

Parkinson’s disease progression: Increasing expression of an invariant common core subnetwork

“…Machine learning-based biomarkers have been introduced with some success but the results are not necessarily understandable in terms of disease mechanisms ( Katako et al, 2018 , Peña-Nogales et al, 2019 ). Increasing interest has also arisen in multivariate approaches to map changes in metabolic connectivity in disease states ( Carli et al, 2021 , Hahn et al, 2018 , Sala et al, 2017 , Yakushev et al, 2017 ). In Parkinson’s disease (PD) ( Obeso et al, 2017 , Politis, 2014 ), principal component analysis (PCA) ( Jollife and Cadima, 2016 ) has been applied to positron emission tomography (PET) metabolic group image data both regionally ( Alexander and Moeller, 1994 , Eidelberg et al, 1994 , Moeller and Strother, 1991 ) and in voxel-based analysis ( Eidelberg, 2009 , Spetsieris and Eidelberg, 2011 ) to identify orthogonal overlapping PC partition layers of the data that reflect specific spatial covariance patterns associated with the disease.…”