Tractostorm: The what,why, and how of tractography dissection reproducibility

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Neutralization of Mitochondrial Superoxide by Superoxide Dismutase 2 Promotes Bacterial Clearance and Regulates Phagocyte Numbers in Zebrafish

Mitochondria are known primarily as the location of the electron transport chain and energy production in cells. More recently, mitochondria have been shown to be signaling centers for apoptosis and inflammation. Reactive oxygen species (ROS) generated as by-products of the electron transport chain within mitochondria significantly impact cellular signaling pathways. Because of the toxic nature of ROS, mitochondria possess an antioxidant enzyme, superoxide dismutase 2 (SOD2), to neutralize ROS. If mitochondrial antioxidant enzymes are overwhelmed during severe infections, mitochondrial dysfunction can occur and lead to multiorgan failure or death. Pseudomonas aeruginosa is an opportunistic pathogen that can infect immunocompromised patients. Infochemicals and exotoxins associated with P. aeruginosa are capable of causing mitochondrial dysfunction. In this work, we describe the roles of SOD2 and mitochondrial ROS regulation in the zebrafish innate immune response to P. aeruginosa infection. sod2 is upregulated in mammalian macrophages and neutrophils in response to lipopolysaccharide in vitro, and sod2 knockdown in zebrafish results in an increased bacterial burden. Further investigation revealed that phagocyte numbers are compromised in Sod2-deficient zebrafish. Addition of the mitochondrion-targeted ROS-scavenging chemical MitoTEMPO rescues neutrophil numbers and reduces the bacterial burden in Sod2-deficient zebrafish. Our work highlights the importance of mitochondrial ROS regulation by SOD2 in the context of innate immunity and supports the use of mitochondrion-targeted ROS scavengers as potential adjuvant therapies during severe infections.     

Molecular profiling of ctDNA in pancreatic cancer: Opportunities and challenges for clinical application

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related mortality within the next decade, with limited effective treatment options and a dismal long-term prognosis for patients. Genomic profiling has not yet manifested clinical benefits for diagnosis, treatment or prognosis in PDAC, due to the lack of available tissues for sequencing and the confounding effects of low tumour cellularity in many biopsy specimens. Increasing focus is now turning to the use of minimally invasive liquid biopsies to enhance the characterisation of actionable PDAC tumour genomes. Circulating tumour DNA (ctDNA) is the most comprehensively studied liquid biopsy analyte in blood and can provide insight into the molecular profile and biological characteristics of individual PDAC tumours, in real-time and in advance of traditional imaging modalities. This can pave the way for identification of new therapeutic targets, novel risk variants and markers of tumour response, to supplement diagnostic screening and provide enhanced scrutiny in treatment stratification. In the roadmap towards the application of precision medicine for clinical management in PDAC, ctDNA analyses may serve a leading role in streamlining candidate biomarkers for clinical integration. In this review, we highlight recent developments in the use of ctDNA-based liquid biopsies for PDAC and provide new insights into the technical, analytical and biological challenges that must be overcome for this potential to be realised.