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Radiomic tractometry: a rich and tract-specific class of imaging biomarkers for neuroscience and medical applications.

ABSTRACT: Tract-specific microstructural analysis of the brain white matter using diffusion MRI is a driver for neuroscientific discovery with a wide range of applications. Current analysis pipelines have conceptual limitations that narrow their applicability and hamper subject-level analysis and predictions. Radiomic tractometry (RadTract) improves upon that, enabling the extraction and analysis of comprehensive and highly informative microstructural feature sets where previous approaches were restricted to bare summary statistics. We demonstrate the added value in a series of neuroscientific applications, including diagnostic tasks as well as the prediction of demographic and clinical measures across various datasets. Being published as an open and easy-to-use Python package, RadTract could spark the establishment of a new generation of tract-specific imaging biomarkers, with direct benefits for a range of applications from basic neuroscience to medical research.

SUBMITTER: Neher P

PROVIDER: S-EPMC10246281 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Radiomic tractometry: a rich and tract-specific class of imaging biomarkers for neuroscience and medical applications.

Neher Peter P Hirjak Dusan D Maier-Hein Klaus K

Research square 20230523

Tract-specific microstructural analysis of the brain white matter using diffusion MRI is a driver for neuroscientific discovery with a wide range of applications. Current analysis pipelines have conceptual limitations that narrow their applicability and hamper subject-level analysis and predictions. Radiomic tractometry (RadTract) improves upon that, enabling the extraction and analysis of comprehensive and highly informative microstructural feature sets where previous approaches were restricted ...[more]

PMID: 37292645

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Project description:BackgroundLately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality.Materials and methodsWe performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups.ResultsAfter applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works.ConclusionsFrom our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.

Project description:Microelectrode arrays are invaluable tools for investigating the electrophysiological behaviour of neuronal networks with high spatiotemporal precision. In recent years, it has become increasingly common to functionalize such electrodes with highly porous platinum to increase their effective surface area, and hence their signal-to-noise ratio. Although such functionalization significantly improves the electrochemical performance of the electrodes, the impact of various electrode morphologies on biocompatibility and electrophysiological performance in cell cultures remains poorly understood. In this study, we introduce reproducible protocols for depositing highly porous platinum with varying morphologies on microelectrodes designed for neural cell cultures. We also evaluate the impact of morphology and electrode size on the signal-to-noise ratio in recordings from rat cortical neurons cultured on these electrodes. Our results indicate that electrodes with a uniform layer of highly nanoporous platinum offer the best trade-off between biocompatibility, electrochemical, and electrophysiological performance. While more microporous electrodes exhibited lower impedance, nanoporous electrodes detected higher extracellular signal amplitudes from neurons, suggesting reduced distance between perisomatic neuronal areas and the electrodes. Additionally, these nanoporous electrodes showed fewer thickness variations at their edges compared to the more porous electrodes. Such edges can be mechanically broken off during cell culturing and contribute to long-term cytotoxic effects, which is highly undesirable. We hope this work will contribute to better standardization in creating and utilizing nanoporous platinum microelectrodes for neuroscience applications. Improving the accessibility and reproducibility of this technology is crucial for enhancing the quality of electrophysiological data and advancing our understanding of neuronal network function and dysfunction.

Dataset Information

Radiomic tractometry: a rich and tract-specific class of imaging biomarkers for neuroscience and medical applications.

Publications

Radiomic tractometry: a rich and tract-specific class of imaging biomarkers for neuroscience and medical applications.

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