Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep , an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data 24Preprocessing of fMRI in a nutshell, for a summary). Extracting a signal that is most faithful to the 25 underlying neural activity is crucial to ensure the validity of inference and interpretability of results 6 .

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Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

Like many cancers, an early diagnosis of melanoma is fundamental to ensure a good prognosis, although an important proportion of stage I–II patients may still develop metastasis during follow‐up. The aim of this work was to discover serum biomarkers in patients diagnosed with primary melanoma that identify those at a high risk of developing metastasis during the follow‐up period. Proteomic and mass spectrophotometry analysis was performed on serum obtained from patients who developed metastasis during the first years after surgery for primary tumors and compared with that from patients who remained disease‐free for more than 10 years after surgery. Five proteins were selected for validation as prognostic factors in 348 melanoma patients and 100 controls by ELISA: serum amyloid A and clusterin; immune system proteins; the cell adhesion molecules plakoglobin and vitronectin and the antimicrobial protein dermcidin. Compared to healthy controls, melanoma patients have high serum levels of these proteins at the moment of melanoma diagnosis, although the specific values were not related to the histopathological stage of the tumors. However, an analysis based on classification together with multivariate statistics showed that tumor stage, vitronectin and dermcidin levels were associated with the metastatic progression of patients with early‐stage melanoma. Although melanoma patients have increased serum dermcidin levels, the REPTree classifier showed that levels of dermcidin <2.98 μg/ml predict metastasis in AJCC stage II patients. These data suggest that vitronectin and dermcidin are potent biomarkers of prognosis, which may help to improve the personalized medical care of melanoma patients and their survival.