Clinical Personal Connectomics Using Hybrid PET/MRI

Altered topological structure of the brain white matter in maltreated children through topological data analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

SimPET: a Preclinical PET Insert for Simultaneous PET/MR Imaging

PURPOSE

SimPET/M7 system is a small-animal dedicated simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) scanner. The SimPET insert has been upgraded from its prototype with a focus on count rate performance and sensitivity. The M7 scanner is a 1-T permanent magnet-based compact MRI system without any cryogens. Here, we present performance evaluation results of SimPET along with the results of mutual interference evaluation and simultaneously acquired PET/MR imaging.

PROCEDURES

Following NEMA NU 4-2008 standard, we evaluated the performance of the SimPET system. The M7 MRI compatibility of SimPET was also assessed by analyzing MRI images of a uniform phantom under different PET conditions and PET count rates with different MRI pulse sequences. Mouse imaging was performed including a whole-body ¹⁸F-NaF PET scan and a simultaneous PET/MRI scan with ⁶⁴Cu-NOTA-ironoxide.

RESULTS

The spatial resolution at center based on 3D OSEM without and with warm background was 0.7 mm and 1.45 mm, respectively. Peak sensitivity was 4.21 % (energy window = 250-750 keV). The peak noise equivalent count rate with the same energy window was 151 kcps at 38.4 MBq. The uniformity was 4.42 %, and the spillover ratios in water- and air-filled chambers were 14.6 % and 12.7 %, respectively. In the hot rod phantom image, 0.75-mm-diameter rods were distinguishable. There were no remarkable differences in the SNR and uniformity of MRI images and PET count rates with different PET conditions and MRI pulse sequences. In the whole-body ¹⁸F-NaF PET images, fine skeletal structures were well resolved. In the simultaneous PET/MRI study with ⁶⁴Cu-NOTA-ironoxide, both PET and MRI signals changed before and after injection of the dual-modal imaging probe, which was evident with the exact spatiotemporal correlation.

CONCLUSIONS

We demonstrated that the SimPET scanner has a high count rate performance and excellent spatial resolution. The combined SimPET/M7 enabled simultaneous PET/MR imaging studies with no remarkable mutual interference between the two imaging modalities.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.