Dataset Information

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study.

ABSTRACT:

Background

We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters.

Methods

Sixty-two patients (n = 6 E46K-SNCA, n = 8 dementia with Lewy bodies (DLB) and n = 48 idiopathic Parkinson's disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cognitive assessment. Hierarchical cluster analysis (HCA) was performed on patients' samples based on nonmotor variables. T1, diffusion-weighted, and resting-state functional MRI data were acquired. Whole-brain comparisons were performed.

Results

HCA revealed two subtypes, the mild subtype (n = 29) and the severe subtype (n = 33). The mild subtype patients were slightly impaired in some nonmotor domains (fatigue, depression, olfaction, and orthostatic hypotension) with no detectable cognitive impairment; the severe subtype patients (PD patients, all DLB, and the symptomatic E46K-SNCA carriers) were severely impaired in motor and nonmotor domains with marked cognitive, visual and bradykinesia alterations. Multimodal MRI analyses suggested that the severe subtype exhibits widespread brain alterations in both structure and function, whereas the mild subtype shows relatively mild disruptions in occipital brain structure and function.

Conclusions

These findings support the potential value of incorporating an extensive nonmotor evaluation to characterize specific clinical patterns and brain degeneration patterns of synucleinopathies.

SUBMITTER: Lucas-Jimenez O

PROVIDER: S-EPMC9953265 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study.

Lucas-Jiménez Olaia O Ibarretxe-Bilbao Naroa N Diez Ibai I Peña Javier J Tijero Beatriz B Galdós Marta M Murueta-Goyena Ane A Del Pino Rocío R Acera Marian M Gómez-Esteban Juan Carlos JC Gabilondo Iñigo I Ojeda Natalia N

Biomedicines 20230215 2

<h4>Background</h4>We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters.<h4>Methods</h4>Sixty-two patients (<i>n</i> = 6 E46K-SNCA, <i>n</i> = 8 dementia with Lewy bodies (DLB) and <i>n</i> = 48 idiopathic Parkinson's disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cogniti ...[more]

PMID: 36831109

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Project description:Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19-73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results. It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion. While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.

Dataset Information

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study.

Background

Methods

Results

Conclusions

Publications

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study.

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