Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

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The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive ablative treatment for essential tremor. The size and location of therapeutic lesions producing the optimal clinical benefits while minimizing adverse effects are not known. We examined these relationships in patients with essential tremor undergoing MRgFUS. We studied 66 patients with essential tremor who underwent MRgFUS between 2012 and 2017. We assessed the Clinical Rating Scale for Tremor (CRST) scores at 3 months after the procedure and tracked the adverse effects (sensory, motor, speech, gait, and dysmetria) 1 day (acute) and 3 months after the procedure. Clinical data associated with the postoperative Day 1 lesions were used to correlate the size and location of lesions with tremor benefit and acute adverse effects. Diffusion-weighted imaging was used to assess whether acute adverse effects were related to lesions encroaching on nearby major white matter tracts (medial lemniscus, pyramidal, and dentato-rubro-thalamic). The area of optimal tremor response at 3 months after the procedure was identified at the posterior portion of the ventral intermediate nucleus. Lesions extending beyond the posterior region of the ventral intermediate nucleus and lateral to the lateral thalamic border were associated with increased risk of acute adverse sensory and motor effects, respectively. Acute adverse effects on gait and dysmetria occurred with lesions inferolateral to the thalamus. Lesions inferolateral to the thalamus or medial to the ventral intermediate nucleus were also associated with acute adverse speech effects. Diffusion-weighted imaging revealed that lesions associated with adverse sensory and gait/dysmetria effects compromised the medial lemniscus and dentato-rubro-thalamic tracts, respectively. Lesions associated with adverse motor and speech effects encroached on the pyramidal tract. Lesions larger than 170 mm3 were associated with an increased risk of acute adverse effects. Tremor improvement and acute adverse effects of MRgFUS for essential tremor are highly dependent on the location and size of lesions. These novel findings could refine current MRgFUS treatment planning and targeting, thereby improving clinical outcomes in patients.

Purpose: To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features. Methods: The FID appliance (FID-A) is an open-source, MAT-LAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data. Results: All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set.

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

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∀These authors contributed equally to this work. AbstractBackground. There is growing recognition that connectome architecture shapes cortical and subcortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown. Methods. Here we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 ± 12.9 years) and N = 113 controls (64 female, 23.5 ± 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI. Results. We find that regional deformation is correlated with the deformation of structurally-and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex. Conclusions. Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation. Keywords. connectome | schizophrenia | intrinsic networks | disease epicenter | anterior cingulate | ventral attention network Running title. Network-driven tissue volume loss in schizophrenia