Mindcontrol: A web application for brain segmentation quality control

Removing facial features from structural MRI images biases visual quality assessment

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N = 185). By comparing two linear mixed-effects models, we determined that four trained human raters' perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N = 581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC's quality metrics were mostly insensitive to defacing.

Defacing biases visual quality assessments of structural MRI

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters' perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC's quality metrics were mostly insensitive to defacing.

Separating neurocognitive mechanisms of maintenance and compensation to support financial ability in middle-aged and older adults: The role of language and the inferior frontal gyrus

This study investigated the role of brain regions involved in arithmetic processing in explaining individual differences in financial ability in 67 50-74-year-old cognitively normal adults. Structural integrity and resting-state functional connectivity measures were collected in the MRI scanner. Outside the scanner, participants performed financial ability and other cognitive tasks, and answered questionnaires to determine dementia risk, and financial risk and protective factors. Regions of interest involved in arithmetic processing were defined, focusing on language- and quantity-processing areas in temporo-frontal and parieto-frontal cortices, respectively. Our results showed that structural integrity and functional connectivity in brain regions associated with arithmetic retrieval were positively associated with financial ability, with language skill mediating left IFG structural integrity and financial ability. Connectivity patterns suggested that reliance on quantity mechanisms (i.e. calculation) was associated with poorer financial ability. Analyses revealed that reliance on these brain mechanisms did not depend on participants' age or risk of dementia and that protective factors such as household income or financial literacy supported the maintenance of connectivity related to financial abilities.

Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.

Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

PURPOSE

Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks.

METHODS

We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures.

CONCLUSION

Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.

Quality control in functional MRI studies with MRIQC and fMRIPrep

The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and "MRI schools", ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.

Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.

Pet ownership is associated with greater cognitive and brain health in a cross-sectional sample across the adult lifespan

Human-animal interactions that stem from pet ownership have a wide range of benefits for social, emotional, and physical health. These factors also tend to improve cognition. Following this logic, owning a pet could indirectly enhance cognitive and brain health through mechanisms like improvements in well-being, socialization, and decreased stress. In the present study, cross-sectional data were drawn from the Alabama Brain Study on Risk for Dementia in which 95 participants aged 20–74 were recruited. Specifically, 56 adults were pet-owners and 39 adults were not pet-owners. Multivariate analyses revealed that pet ownership was related to higher levels of cognition and larger brain structures, and these effects were largest in dog owners. The most consistent cognitive relationships were found with better processing speed, attentional orienting, and episodic memory for stories, and with dorsal attention, limbic, and default mode networks. Moreover, we show that owning a pet can reduce one’s brain age by up to 15 years. Pet ownership was not related to indirect factors including social, emotional, and physical health. We found also that older adults’ brain health benefited from owning more than one pet versus owning one or fewer pets. These findings indicate that pet ownership, especially dog ownership, may play a role in enhancing cognitive performance across the adult lifespan, which could in turn influence protection against age-related cognitive decline.

Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics"

In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

Intra-individual cognitive variability in neuropsychological assessment: a sign of neural network dysfunction

Intra-Individual Cognitive Variability (IICV) predicts progression in neurocognitive disorders . Given important clinical applications, we investigated the association between IICV and multiple brain metrics across 17 networks to better understand the brain mechanisms underlying this performance measure. Sixty-three middle-aged and older adults without dementia underwent a neuropsychological battery, resting-state fMRI, and structural MRI scans. In a linear mixed effect model, higher IICV was associated with lower functional connectivity in control C network relative to medial occipital network (the reference). A multivariate partial least squares analysis revealed that lower mean and higher variability were both associated with lower connectivity in sensorimotor and default mode networks, while higher mean and higher variability were associated with lower volume in default mode and limbic networks. This study suggests that IICV signals widespread network dysfunction across multiple brain networks. These brain abnormalities offer new insights into mechanisms of early cognitive dysfunction. Clinical implications are discussed.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study

Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents consensus in the appropriate quality control strategy to use. This study aimed to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies on the reduction of the measurement error. Structural brain MRI from 259 participants of The Maastricht Study were used. Morphological estimates were automatically extracted using FreeSurfer 6.0. Segmentations with inaccuracies were manually edited, and morphological estimates were compared before and after editing. In parallel, 12 quality control strategies were applied to the full sample. Those included: two manual strategies, in which images were visually inspected and either excluded or manually edited; five automated strategies, where outliers were excluded based on the tools "MRIQC" and "Qoala-T", and the metrics "morphological global measures", "Euler numbers" and "Contrast-to-Noise ratio"; and five semi-automated strategies, where the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. In order to quantify the effects of each quality control strategy, the proportion of unexplained variance relative to the total variance was extracted after the application of each strategy, and the resulting differences compared. Manually editing brain surfaces produced particularly large changes in subcortical brain volumes and moderate changes in cortical surface area, thickness and hippocampal volumes. The performance of the quality control strategies depended on the morphological measure of interest. Overall, manual quality control strategies yielded the largest reduction in relative unexplained variance. The best performing automated alternatives were those based on Euler numbers and MRIQC scores. The exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance. Manual quality control strategies are the most reliable solution for quality control of brain segmentation and parcellation. However, measures must be taken to prevent the subjectivity associated with these strategies. The detection of inaccurate segmentations based on Euler numbers or MRIQC provides a time efficient and reproducible alternative. The exclusion of outliers based on global morphological estimates must be avoided.

Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center

Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer’s disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets.

REGION SPECIFIC AUTOMATIC QUALITY ASSURANCE FOR MRI-DERIVED CORTICAL SEGMENTATIONS

Quality control (QC) is a vital step for all scientific data analyses and is critically important in the biomedical sciences. Image segmentation is a common task in medical image analysis, and automated tools to segment many regions from human brain MRIs are now well established. However, these methods do not always give anatomically correct labels. Traditional methods for QC tend to reject statistical outliers, which may not necessarily be inaccurate. Here, we make use of a large database of over 12,000 brain images that contain 68 parcellations of the human cortex, each of which was assessed for anatomical accuracy by a human rater. We trained three machine learning models to determine if a region was anatomically accurate (as 'pass', or 'fail') and tested the performance on an independent dataset. We found good performance for the majority of labeled regions. This work will facilitate more anatomically accurate large-scale multi-site research.

Technical Note: MRQy - An open-source tool for quality control of MR imaging data

PURPOSE

There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development.

METHODS

Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures.

RESULTS

MRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts.

CONCLUSIONS

MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open-source at http://github.com/ccipd/MRQy for community use and feedback.

Structural complexity is negatively associated with brain activity: a novel multimodal test of compensation theories of aging

Fractal dimensionality (FD) measures the complexity within the folds and ridges of cortical and subcortical structures. We tested the degree that FD might provide a new perspective on the atrophy-compensation hypothesis: age or disease-related atrophy causes a compensatory neural response in the form of increased brain activity in the prefrontal cortex to maintain cognition. Brain structural and functional data were collected from 63 middle-aged and older adults and 18 young-adult controls. Two distinct patterns of FD were found that separated cortical from subcortical structures. Subcortical FD was more strongly negatively correlated with age than cortical FD, and cortical FD was negatively associated with brain activity during memory retrieval in medial and lateral parietal cortices uniquely in middle-aged and older adults. Multivariate analyses revealed that the lower FD/higher brain activity pattern was associated with poorer cognition-patterns not present in young adults, consistent with compensation. Bayesian analyses provide further evidence against the modal interpretation of the atrophy-compensation hypothesis in the prefrontal cortex-a key principle found in some neurocognitive theories of aging.

Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model

Monitoring the quality of image segmentation is key to many clinical applications. This quality assessment can be carried out by a human expert when the number of cases is limited. However, it becomes onerous when dealing with large image databases, so partial automation of this process is preferable. Previous works have proposed both supervised and unsupervised methods for the automated control of image segmentations. The former assume the availability of a subset of trusted segmented images on which supervised learning is performed, while the latter does not. In this paper, we introduce a novel unsupervised approach for quality assessment of segmented images based on a generic probabilistic model. Quality estimates are produced by comparing each segmentation with the output of a probabilistic segmentation model that relies on intensity and smoothness assumptions. Ranking cases with respect to these two assumptions allows the most challenging cases in a dataset to be detected. Furthermore, unlike prior work, our approach enables possible segmentation errors to be localized within an image. The proposed generic probabilistic segmentation method combines intensity mixture distributions with spatial regularization prior models whose parameters are estimated with variational Bayesian techniques. We introduce a novel smoothness prior based on the penalization of the derivatives of label maps which allows an automatic estimation of its hyperparameter in a fully data-driven way. Extensive evaluation of quality control on medical and COCO datasets is conducted, showing the ability to isolate atypical segmentations automatically and to predict, in some cases, the performance of segmentation algorithms.

Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline

BACKGROUND AND PURPOSE

Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.

METHODS

We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage.

RESULTS

The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure.

CONCLUSIONS

Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease

Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.

MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan

While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age-related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1-weighted/T2-weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18-83. In accordance with previous cross-sectional studies of adults, we observed age-related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age-related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U-shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis

Existing brain region segmentation algorithms based on deep convolutional neural networks (CNN) are inefficient for object boundary segmentation. In order to enhance the segmentation accuracy of brain tissue, this paper proposed an object region segmentation algorithm that combines pixel-level information and semantic information. Firstly, we extract semantic information by CNN with the attention module and get the coarse segmentation results through a specific pixel-level classifier. Then, we exploit conditional random fields to model the relationship between the underlying pixels so as to get local features. Finally, the semantic information and the local pixel-level information are respectively used as the unary potential and the binary potential of the Gibbs distribution, and the combination of both can obtain the fine region segmentation algorithm based on the fusion of pixel-level information and the semantic information. A large number of qualitative and quantitative test results show that our proposed algorithm has higher precision than the existing state-of-the-art deep feature models, which can better solve the problem of rough edge segmentation and produce good 3D visualization effect.

Shape-related characteristics of age-related differences in subcortical structures

OBJECTIVES

With an increasing aging population, it is important to understand biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences. Fractal dimensionality is more sensitive to age-related differences, but is borne out of mathematical principles, rather than neurobiological relevance. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture.

METHODS

Structural MRIs from a combined sample of over 600 healthy adults were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each, volume and fractal dimensionality were calculated, as well as four distinct shape measures. These measures were examined for their utility in explaining age-related variability in brain structure.

RESULTS

The four shape measures were able to account for 80%-90% of the variance in fractal dimensionality. Of the distinct shape measures, surface-to-volume ratio was the most sensitive biomarker.

CONCLUSION

Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements. Our results indicate that shape characteristics are useful biological markers of aging.

Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging

Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.

Cree BAC, Hollenbach JA, Bove R, Kirkish G, Sacco S, Caverzasi E, Bischof A, Gundel T, Zhu AH, Papinutto N, Stern WA, Bevan C, Romeo A, Goodin DS, Gelfand JM, Graves J, Green AJ, Wilson MR, Zamvil SS, Zhao C, Gomez R, Ragan NR, Rush GQ, Barba P, Santaniello A, Baranzini SE, Oksenberg JR, Henry RG, Hauser SL. Silent progression in disease activity-free relapsing multiple sclerosis

Objective

Rates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow‐up. Notably, “no evidence of disease activity” at 2 years did not predict long‐term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long‐term disability accumulation.

Methods

Disability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0–5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA‐DRB1*15:01 as covariates.

Results

Relapses were associated with a transient increase in disability over 1‐year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05).

Interpretation

Long‐term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing–remitting MS. Ann Neurol 2019;85:653–666

From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research

Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best medium to facilitate novel insights by way of improved collaboration and communication. Here, we review the landscape of web technologies used in neuroimaging research, and discuss future applications, areas for improvement, and the limitations of using web technology in research. Fully incorporating web technology in our research lifecycle requires not only technical skill, but a widespread culture change; a shift from the small, focused "wet lab" to a multidisciplinary and largely collaborative "web lab."

Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data

Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies.

Hack weeks as a model for data science education and collaboration

As scientific disciplines grapple with more datasets of rapidly increasing complexity and size, new approaches are urgently required to introduce new statistical and computational tools into research communities and improve the cross-disciplinary exchange of ideas. In this paper, we introduce a type of scientific workshop, called a hack week, which allows for fast dissemination of new methodologies into scientific communities and fosters exchange and collaboration within and between disciplines. We present implementations of this concept in astronomy, neuroscience, and geoscience and show that hack weeks produce positive learning outcomes, foster lasting collaborations, yield scientific results, and promote positive attitudes toward open science.

Across many scientific disciplines, methods for recording, storing, and analyzing data are rapidly increasing in complexity. Skillfully using data science tools that manage this complexity requires training in new programming languages and frameworks as well as immersion in new modes of interaction that foster data sharing, collaborative software development, and exchange across disciplines. Learning these skills from traditional university curricula can be challenging because most courses are not designed to evolve on time scales that can keep pace with rapidly shifting data science methods. Here, we present the concept of a hack week as an effective model offering opportunities for networking and community building, education in state-of-the-art data science methods, and immersion in collaborative project work. We find that hack weeks are successful at cultivating collaboration and facilitating the exchange of knowledge. Participants self-report that these events help them in both their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration, and cultivate best practices.