Ulcerated CRTC1::TRIM11 cutaneous tumor with metastases.

CRTC1::TRIM11 cutaneous tumor (CTCT) is a rare skin tumor of uncertain differentiation. In the 49 reported cases, only four cases showed regional or distant metastasis, but follow-up remains limited. Herein, we present a case of metastatic CTCT with ulceration, a histological feature that has not been previously described. A 75-year-old male with a 2-month history of toe ulceration underwent a shave biopsy, which showed a dermal nodular neoplasm that was immunoreactive for SOX10 and S100, negative for Melan-A, and was initially diagnosed as melanoma. Upon pathology review at our institution, the tumor was composed of intersecting fascicles and nests of epithelioid and spindle cells. Additional immunohistochemistry revealed immunoreactivity of the tumor for MiTF and NTRK and negativity for HMB-45 and PRAME. Next-generation sequencing identified CRTC1::TRIM11 fusion, leading to a revised diagnosis of CTCT. The patient proceeded to a toe amputation and sentinel lymph node (SLN) biopsy 5 months after the shave biopsy. The amputation showed residual CTCT and a focus on lymphovascular invasion. The SLN revealed multifocal subcapsular metastases. The patient was started on adjuvant nivolumab and showed biopsy-proven recurrence in the right inguinal lymph nodes and imaging findings suspicious for pulmonary metastases 8 months after the excision. In summary, we present a case of CTCT with ulceration and lymphovascular invasion. We also provide additional evidence that a subset of CTCT behaves aggressively. The optimal surgical and medical treatments are unknown.

Multiscale Modes of Functional Brain Connectivity.

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a paradigm shift in functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

Automating the Human Connectome Project's Temporal ICA Pipeline.

Functional magnetic resonance imaging (fMRI) data are dominated by noise and artifacts, with only a small fraction of the variance relating to neural activity. Temporal independent component analysis (tICA) is a recently developed method that enables selective denoising of fMRI artifacts related to physiology such as respiration. However, an automated and easy to use pipeline for tICA has not previously been available; instead, two manual steps have been necessary: 1) setting the group spatial ICA dimensionality after MELODIC's Incremental Group-PCA (MIGP) and 2) labeling tICA components as artifacts versus signals. Moreover, guidance has been lacking as to how many subjects and timepoints are needed to adequately re-estimate the temporal ICA decomposition and what alternatives are available for smaller groups or even individual subjects. Here, we introduce a nine-step fully automated tICA pipeline which removes global artifacts from fMRI dense timeseries after sICA+FIX cleaning and MSMAll alignment driven by functionally relevant areal features. Additionally, we have developed an automated "reclean" Pipeline for improved spatial ICA (sICA) artifact removal. Two major automated components of the pipeline are 1) an automatic group spatial ICA (sICA) dimensionality selection for MIGP data enabled by fitting multiple Wishart distributions; 2) a hierarchical classifier to distinguish group tICA signal components from artifactual components, equipped with a combination of handcrafted features from domain expert knowledge and latent features obtained via self-supervised learning on spatial maps. We demonstrate that the dimensionality estimated for the MIGP data from HCP Young Adult 3T and 7T datasets is comparable to previous manual tICA estimates, and that the group sICA decomposition is highly reproducible. We also show that the tICA classifier achieved over 0.98 Precision-Recall Area Under Curve (PR-AUC) and that the correctly classified components account for over 95% of the tICA-represented variance on multiple held-out evaluation datasets including the HCP-Young Adult, HCP-Aging and HCP-Development datasets under various settings. Our automated tICA pipeline is now available as part of the HCP pipelines, providing a powerful and user-friendly tool for the neuroimaging community.

An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank.

Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).