Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health

AIM

The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels.

METHODS

We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors.

RESULTS

The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits.

CONCLUSIONS

Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

In vivo tracking of ex vivo generated 89 Zr-oxine labeled plasma cells by PET in a non-human primate model

B cells are an attractive platform for engineering to produce protein-based biologics absent in genetic disorders, and potentially for the treatment of metabolic diseases and cancer. As part of pre-clinical development of B cell medicines, we demonstrate a method to collect, ex vivo expand, differentiate, radioactively label, and track adoptively transferred non-human primate (NHP) B cells. These cells underwent 10- to 15-fold expansion, initiated IgG class switching, and differentiated into antibody secreting cells. Zirconium-89-oxine labeled cells were infused into autologous donors without any preconditioning and tracked by PET/CT imaging. Within 24 hours of infusion, 20% of the initial dose homed to the bone marrow and spleen and distributed stably and equally between the two. Interestingly, approximately half of the dose homed to the liver. Image analysis of the bone marrow demonstrated inhomogeneous distribution of the cells. The subjects experienced no clinically significant side effects or laboratory abnormalities. A second infusion of B cells into one of the subjects resulted in an almost identical distribution of cells, suggesting a non-limiting engraftment niche and feasibility of repeated infusions. This work supports the NHP as a valuable model to assess the potential of B cell medicines as potential treatment for human diseases.

Neurofunctional underpinnings of individual differences in visual episodic memory performance

Episodic memory, the ability to consciously recollect information and its context, varies substantially among individuals. While prior fMRI studies have identified certain brain regions linked to successful memory encoding at a group level, their role in explaining individual memory differences remains largely unexplored. Here, we analyze fMRI data of 1,498 adults participating in a picture encoding task in a single MRI scanner. We find that individual differences in responsivity of the hippocampus, orbitofrontal cortex, and posterior cingulate cortex account for individual variability in episodic memory performance. While these regions also emerge in our group-level analysis, other regions, predominantly within the lateral occipital cortex, are related to successful memory encoding but not to individual memory variation. Furthermore, our network-based approach reveals a link between the responsivity of nine functional connectivity networks and individual memory variability. Our work provides insights into the neurofunctional correlates of individual differences in visual episodic memory performance.

Language network lateralization is reflected throughout the macroscale functional organization of cortex

Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.

Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet

Stereotactic brain tumor segmentation based on 3D neuroimaging data is a challenging task due to the complexity of the brain architecture, extreme heterogeneity of tumor malformations, and the extreme variability of intensity signal and noise distributions. Early tumor diagnosis can help medical professionals to select optimal medical treatment plans that can potentially save lives. Artificial intelligence (AI) has previously been used for automated tumor diagnostics and segmentation models. However, the model development, validation, and reproducibility processes are challenging. Often, cumulative efforts are required to produce a fully automated and reliable computer-aided diagnostic system for tumor segmentation. This study proposes an enhanced deep neural network approach, the 3D-Znet model, based on the variational autoencoder-autodecoder Znet method, for segmenting 3D MR (magnetic resonance) volumes. The 3D-Znet artificial neural network architecture relies on fully dense connections to enable the reuse of features on multiple levels to improve model performance. It consists of four encoders and four decoders along with the initial input and the final output blocks. Encoder-decoder blocks in the network include double convolutional 3D layers, 3D batch normalization, and an activation function. These are followed by size normalization between inputs and outputs and network concatenation across the encoding and decoding branches. The proposed deep convolutional neural network model was trained and validated using a multimodal stereotactic neuroimaging dataset (BraTS2020) that includes multimodal tumor masks. Evaluation of the pretrained model resulted in the following dice coefficient scores: Whole Tumor (WT) = 0.91, Tumor Core (TC) = 0.85, and Enhanced Tumor (ET) = 0.86. The performance of the proposed 3D-Znet method is comparable to other state-of-the-art methods. Our protocol demonstrates the importance of data augmentation to avoid overfitting and enhance model performance.

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis

Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient's PII-including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification.

Spatial Variation in Risk for Highly Pathogenic Avian Influenza Subtype H5N6 Viral Infections in South Korea: Poultry Population-Based Case–Control Study

Given the substantial economic damage caused by the continual circulation of highly pathogenic avian influenza (HPAI) outbreaks since 2003, identifying high-risk locations associated with HPAI infections is essential. In this study, using affected and unaffected poultry farms’ locations during an HPAI H5N6 epidemic in South Korea, we identified places where clusters of HPAI cases were found. Hotspots were defined as regions having clusters of HPAI cases. With the help of the statistical computer program R, a kernel density estimate and a spatial scan statistic were employed for this purpose. A kernel density estimate and detection of significant clusters through a spatial scan statistic both showed that districts in the Chungcheongbuk-do, Jeollabuk-do, and Jeollanam-do provinces are more vulnerable to HPAI outbreaks. Prior to the migration season, high-risk districts should implement particular biosecurity measures. High biosecurity measures, as well as improving the cleanliness of the poultry environment, would undoubtedly aid in the prevention of HPAIV transmission to poultry farms in these high-risk regions of South Korea.

ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R

There is significant interest in adopting surface- and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to perform surface smoothing to avoid issues associated with volumetric smoothing, improved inter-subject alignment, and reduced dimensionality. The CIFTI grayordinate file format introduced by the Human Connectome Project further advances grayordinate-based analysis by combining gray matter data from the left and right cortical hemispheres with gray matter data from the subcortex and cerebellum into a single file. Analyses performed in grayordinate space are well-suited to leverage information shared across the brain and across subjects through both traditional analysis techniques and more advanced statistical methods, including Bayesian methods. The R statistical environment facilitates use of advanced statistical techniques, yet little support for grayordinates analysis has been previously available in R Indeed, few comprehensive programmatic tools for working with CIFTI files have been available in any language Here, we present the ciftiTools R package, which provides a unified environment for reading, writing, visualizing and manipulating CIFTI files and related data formats. We illustrate ciftiTools’ convenient and user-friendly suite of tools for working with grayordinates and surface geometry data in R, and we describe how ciftiTools is being utilized to advance the statistical analysis of grayordinate-based functional MRI data.

Changes in scapular bone density vary by region and are associated with age and sex

BACKGROUND

Decreases in bone density of the scapula due to age and disease can make orthopedic procedures such as arthroplasty and fracture fixation challenging. There is limited information in the literature regarding the effect of age and sex on the patterns of these density changes across the bone. Characterizing these changes could assist the surgeon in planning optimal instrumentation placement.

METHODS

Ninety-seven 3-dimensional models of the scapula were segmented from routine clinical computed tomography scans, and an opportunistic quantitative computed tomography approach was used to obtain detailed calibrated bone density measurements for each bone model. The effects of age and sex on cortical and trabecular bone density were assessed for the entire scapula. Specific regions (eg, scapular spine) where these factors had a significant effect were identified. Three-dimensional models were generated to allow clear visualization of the changes in density patterns.

RESULTS

Cortical bone loss averaged 1.0 mg/cm³ and 0.3 mg/cm³ per year for female and male subjects, respectively, and trabecular bone loss averaged 1.6 mg/cm³ and 1.2 mg/cm³, respectively. However, several regions had loss rates several times greater. Areas that were significantly affected by age included the acromion, scapular spine, base of the coracoid, inferior glenoid neck, and glenoid vault. Areas that were significantly affected by sex were the scapular spine and body.

CONCLUSIONS

These findings provide evidence that the bone density distribution across the scapula changes non-uniformly because of factors including sex and age. Despite overall trends of bone loss, there remains significant variability between individuals, and subject-specific tools for planning surgical procedures in which scapular fixation is required may be beneficial.

The Influence of Hyperoxygenation on Fetal Brain Vascularity Measured Using 3D Power Doppler Ultrasound and the Index "Fractional Moving Blood Volume"

INTRODUCTION

Maternal hyperoxygenation effects on fetal cerebral hemodynamics are largely unknown. This study aimed to determine efficacy and reliability of a validated power Doppler ultrasound (US) index, fractional moving blood volume (FMBV), at measuring fetal cerebral vasculature changes during maternal hyperoxia.

METHODS

The fetal cerebral effects of 10 min of hyperoxygenation at 2 flow rates (52%/60% FiO2) were evaluated in women in their third trimester of pregnancy. 2D-US and 3D-US in a transverse plane were performed before, during, and following maternal hyperoxygenation with FMBV estimation performed offline.

RESULTS

Forty-five cases provided data for analysis. Mean intraobserver ICCs were 0.89 (3D-FMBV) and 0.84 (2D-FMBV). A significant difference in vascularity before and during and before and after 60% hyperoxia was observed (p < 0.05), whereas no significant differences were found at 52% hyperoxia (p > 0.05). Significant differences in vascularity were found between 2D-FMBV and 3D-FMBV (p < 0.01).

CONCLUSION

Measurement of fetal cerebral vascularity by 3D-FMBV and 2D-FMBV was highly reproducible. The differing cerebral vascular changes seen with 60% but not 52% FiO2 suggest a possible "threshold effect" that may have influenced prior studies. Further studies are needed to assess cerebral effects of maternal hyperoxygenation on compromised fetuses.

Persistent homology of tumor CT scans is associated with survival in lung cancer

PURPOSE

Radiomics, the objective study of nonvisual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall topological structure of the data. This niche can be filled by persistent homology, a form of topological data analysis that analyzes high-level structure. We hypothesized that persistent homology features quantified using cubical complexes could be extracted from lung tumor scans and related to survival.

METHODS

We obtained segmented computed tomography (CT) lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. These scans are three-dimensional images whose pixel intensity corresponds to a number of Hounsfield units. Cubical complexes are a topological image analysis method that effectively analyzes the number of topological features in an image as the image is thresholded at different intensities. We calculated a novel output called a feature curve by plotting the number of zero-dimensional (0D) topological features counted from the cubical complex filtration against each Hounsfield value. This curve's first moment of distribution was utilized as a summary statistic to show association with survival in a Cox proportional hazards model. We hypothesized that persistent homology features quantified using cubical complexes could be extracted from lung tumor scans and related to survival.

RESULTS

After controlling for tumor image size, age, and stage, the first moment of the 0D topological feature curve was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). The patients in our study with the lowest first moment scores had significantly better survival (1238 days; 95% CI = 936-1599) compared to the patients with the highest first moment scores (429 days; 95% CI = 326-601; p = 0.0015).

CONCLUSIONS

We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0D topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.

Review of Artificial Intelligence Training Tools and Courses for Radiologists

Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purchase decisions about them, radiologists must understand the underlying principles of AI. Our task force was formed by the Radiology Research Alliance (RRA) of the Association of University Radiologists to identify and summarize a curated list of current educational materials available for radiologists.

Prenatal antidepressant exposure and sex differences in neonatal corpus callosum microstructure

Prenatal exposure to selective serotonin reuptake inhibitor (SSRI) antidepressants may influence white matter (WM) development, as previous studies report widespread microstructural alterations and reduced interhemispheric connectivity in SSRI-exposed infants. In rodents, perinatal SSRIs had sex-specific disruptions in corpus callosum (CC) axon architecture and connectivity; yet it is unknown whether SSRI-related brain outcomes in humans are sex specific. In this study, the neonate CC was selected as a region-of-interest to investigate whether prenatal SSRI exposure has sex-specific effects on early WM microstructure. On postnatal day 7, diffusion tensor imaging was used to assess WM microstructure in SSRI-exposed (n = 24; 12 male) and nonexposed (n = 48; 28 male) term-born neonates. Fractional anisotropy was extracted from CC voxels and a multivariate discriminant analysis was used to identify latent patterns differing between neonates grouped by SSRI-exposure and sex. Analysis revealed localized variations in CC fractional anisotropy that significantly discriminated neonate groups and correctly predicted group membership with an 82% accuracy. Such effects were identified across three dimensions, representing sex differences in SSRI-exposed neonates (genu, splenium), SSRI-related effects independent of sex (genu-to-rostral body), and sex differences in nonexposed neonates (isthmus-splenium, posterior midbody). Our findings suggest that CC microstructure may have a sex-specific, localized, developmental sensitivity to prenatal SSRI exposure.

Novel strategies for the characterization of cancellous bone morphology: Virtual isolation and analysis

OBJECTIVES

The advent of micro-computed tomography (μCT) made cancellous bone more accessible than ever before. Nevertheless, the characterization of cancellous bone is made difficult by its inherent complexity and the difficulties in defining homology across datasets. Here we propose novel virtual methodological approaches to overcome those issues and complement existing methods.

MATERIALS AND METHODS

We present a protocol for the isolation of the whole cancellous region within a μCT scanned bone. This method overcomes the subsampling issues and allows studying cancellous bone as a single unit. We test the protocol on a set of primate bones. In addition, we describe a set of morphological indices calculated on the topological skeleton of the cancellous bone: node density, node connectivity, trabecular angle, trabecular tortuosity, and fractal dimension. The usage of the indices is shown on a small comparative sample of primate femoral heads.

RESULTS

The isolation protocol proves reliable in isolating cancellous structures from several different bones, regardless of their shape. The indices seem to detect some functional differences, although further testing on comparative samples is needed to clarify their potential for the study of cancellous architecture.

CONCLUSIONS

The approaches presented overcome some of the difficulties of trabecular bone studies. The methods presented here represent an alternative or supporting method to the existing tools available to address the biomechanics of cancellous bone.

The effect of age and sex on pelvic bone density measured opportunistically in clinical CT scans

Unstable pelvic ring fractures are severe and complex injuries, and surgical fixation is challenging and can be complicated by early failure due in part to difficulties with securely fixing screws in low-density bone. There is limited information in the literature about how the density distribution across the pelvic bones changes with age and sex. In this study, we used 60 sets of calibrated bone density measurements obtained opportunistically from clinical computed tomography scans of the pelvis. Three-dimensional models of the innominate bone were produced and the effects of age and sex on cortical bone density modeled. Overall trends and regions where these factors had a significant effect were identified, and the results visualized. Across the entire innominate bone, the mean loss of density was found to be 1.6 mg/cc per year, with several specific areas (pubic body, iliac fossa, posterior ilium, and anterior inferior iliac spine for example) showing significant rates of loss up to three times greater than the rest of the bone. Areas significantly affected by sex included the posterior pubic root, anterior aspect of the pubic body, and iliac crest. Despite overall trends of attenuation, there remains significant variability between individuals. This supports the need to further explore subject-specific planning tools for pelvic fracture repair. Statement of clinical significance: Bone density changes across the innominate bone due to age and sex tend to vary between individuals, although consistent effects were seen at specific regions. This information may help in surgical planning of unstable fracture repairs.

RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data

Direct recording of neural activity from the human brain using implanted electrodes (iEEG, intracranial electroencephalography) is a fast-growing technique in human neuroscience. While the ability to record from the human brain with high spatial and temporal resolution has advanced our understanding, it generates staggering amounts of data: a single patient can be implanted with hundreds of electrodes, each sampled thousands of times a second for hours or days. The difficulty of exploring these vast datasets is the rate-limiting step in discovery. To overcome this obstacle, we created RAVE ("R Analysis and Visualization of iEEG"). All components of RAVE, including the underlying "R" language, are free and open source. User interactions occur through a web browser, making it transparent to the user whether the back-end data storage and computation are occurring locally, on a lab server, or in the cloud. Without writing a single line of computer code, users can create custom analyses, apply them to data from hundreds of iEEG electrodes, and instantly visualize the results on cortical surface models. Multiple types of plots are used to display analysis results, each of which can be downloaded as publication-ready graphics with a single click. RAVE consists of nearly 50,000 lines of code designed to prioritize an interactive user experience, reliability and reproducibility.

Acute changes in cerebral blood flow after single-infusion ketamine in major depression: a pilot study

BACKGROUND

Ketamine provides rapid antidepressant response in those struggling with major depressive disorder (MDD). This study measured acute changes in brain activity over 24 hours after a single infusion of ketamine using arterial spin labeled (ASL) functional magnetic resonance imaging (fMRI) in patients with MDD. ASL is a novel technique that provides quantitative values to measure cerebral blood flow (CBF).

METHODS

A single sub-anesthetic dose (0.5 mg/kg) of ketamine was delivered intravenously. Treatment-refractory patients (n=11) were assessed at: Baseline (pre-infusion), and approximately 1hr, 6hrs, and 24hrs post-infusion. Linear mixed-effects models detected changes in CBF with respect to treatment outcome, and results were corrected for false discovery rate (FDR).

RESULTS

After ketamine infusion, increased CBF was observed in the thalamus, while decreased CBF was observed in lateral occipital cortex in all patients. Time-by-response interactions were noted in ventral basal ganglia and medial prefrontal cortex, where CBF change differed according to antidepressant response.

LIMITATIONS

Modest sample size is a limitation of this pilot study; strict statistical correction and visualization of single-subject data attempted to ameliorate this issue.

CONCLUSION

In this pilot study, a sub-anesthetic dose of ketamine was associated with acute neurofunctional changes that may be consistent with altered attention, specifically increased thalamus activity coupled with decreased cortical activity. By contrast, antidepressant response to ketamine was associated with changes in reward-system regions, specifically ventral basal ganglia and medial prefrontal cortex. Further work is needed to determine whether these results generalize to larger samples and/or serial ketamine infusions associated with longer-lasting clinical effects.

Influence of X-ray computed tomography (CT) exposure and reconstruction parameters on positron emission tomography (PET) quantitation

Background

The CT of PET CT provides diagnostic information, anatomic localisation and attenuation correction (AC). When only AC is required, very lose dose CT is desirable. CT iterative reconstruction (IR) improves image quality with lower exposures however there is little data on very low dose IR CT for AC of PET. This work assesses the impact of CT exposure and reconstruction algorithm on PET voxel values.

Method

An anthropomorphic torso phantom was filled with physiologically typical [18]F concentrations in heart, liver and background compartments. A 17-mm-diameter right lung “tumour” filled with [18]F was included (surrounding lung contained no 18[F]). PET was acquired followed by 24 CT acquisitions with varying CT exposures (15–50 mAs, 80–120 kVp, pitch 0.671 or 0.828). Each CT was reconstructed twice using filtered back projection (FBP) or IR and these used for AC of PET. The reference PET reconstruction (RR) used CT acquired at 50 mAs, 120 kVp, pitch 0.828, IR, all others were test PET reconstructions (TR). Regions of interest (ROIs) were drawn in the liver, soft tissue and over “tumour” on each TR and compared with the RR. Voxel values in each TR were compared to the RR using a paired t test and by calculating which and what proportion of voxels in each TR differed by a quantitatively significant difference (QSD) from the RR.

Results

TRs reconstructed using lower dose CTs underestimated mean and maximum ROI activity relative to the RR; greater with IR than FBP. Once CT dose index (CTDI) increased to 1 mGy, differences were less than QSD. On voxel analysis, all TRs were significantly different to the RR (p < 0.0001). TRs reconstructed at the lowest CT exposure with IR had 6% of voxels that differed by greater than QSD. Differences were reduced with increasing CTDI and FBP reconstruction. Voxels which exceeded the QSD were spatially localised to regions of high activity, interfaces between different attenuation and areas of CT beam hardening.

Conclusions

Very low dose CT exposures are feasible for accurate PET AC. Scanner- and reconstruction-specific validation should be employed prior very low dose CT AC for PET.

Exploring Patterns of Dynamic Size Changes of Lesions after Hepatic Microwave Ablation in an In Vivo Porcine Model

Microwave ablation (MWA) is a type of minimally invasive cancer therapy that uses heat to induce necrosis in solid tumours. Inter- and post-ablational size changes can influence the accuracy of control imaging, posing a risk of incomplete ablation. The present study aims to explore post-ablation 3D size dynamics in vivo using computed tomography (CT). Ten MWA datasets obtained in nine healthy pigs were used. Lesions were subdivided along the z-axis with an additional planar subdivision into eight subsections. The volume of the subsections was analysed over different time points, subsequently colour-coded and three-dimensionally visualized. A locally weighted polynomial regression model (LOESS) was applied to describe overall size changes, and Student's t-tests were used to assess statistical significance of size changes. The 3D analysis showed heterogeneous volume changes with multiple small changes at the lesion margins over all time points. The changes were pronounced at the upper and lower lesion edges and characterized by initially eccentric, opposite swelling, followed by shrinkage. In the middle parts of the lesion, we observed less dimensional variations over the different time points. LOESS revealed a hyperbolic pattern for the volumetric changes with an initially significant volume increase of 11.6% (111.6% of the original volume) over the first 32 minutes, followed by a continuous decrease to 96% of the original volume (p < 0.05).

Improved Visualization of the Necrotic Zone after Microwave Ablation Using Computed Tomography Volume Perfusion in an In Vivo Porcine Model

After hepatic microwave ablation, the differentiation between fully necrotic and persistent vital tissue through contrast enhanced CT remains a clinical challenge. Therefore, there is a need to evaluate new imaging modalities, such as CT perfusion (CTP) to improve the visualization of coagulation necrosis. MWA and CTP were prospectively performed in five healthy pigs. After the procedure, the pigs were euthanized, and the livers explanted. Orthogonal histological slices of the ablations were stained with a vital stain, digitalized and the necrotic core was segmented. CTP maps were calculated using a dual-input deconvolution algorithm. The segmented necrotic zones were overlaid on the DICOM images to calculate the accuracy of depiction by CECT/CTP compared to the histological reference standard. A receiver operating characteristic analysis was performed to determine the agreement/true positive rate and disagreement/false discovery rate between CECT/CTP and histology. Standard CECT showed a true positive rate of 81% and a false discovery rate of 52% for display of the coagulation necrosis. Using CTP, delineation of the coagulation necrosis could be improved significantly through the display of hepatic blood volume and hepatic arterial blood flow (p < 0.001). The ratios of true positive rate/false discovery rate were 89%/25% and 90%/50% respectively. Other parameter maps showed an inferior performance compared to CECT.

Recommendations for Processing Head CT Data

Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis.

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Radtools: R utilities for convenient extraction of medical image metadata

The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice.

The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files.

We present radtools, an R package for convenient extraction of medical image metadata. Radtools provides simple functions to explore and return metadata in familiar R data structures. For convenience, radtools also includes wrappers of existing tools for extraction of pixel data and viewing of image slices. The package is freely available under the MIT license at GitHub and is easily installable from the Comprehensive R Archive Network.

Association Between Population Density and Genetic Risk for Schizophrenia

Importance

Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas.

Objective

To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk.

Design, Setting, and Participants

Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and mendelian randomization (MR) were included. The study was conducted between 2016 and 2018.

Exposures

Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models.

Main Outcomes and Measures

Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested.

Results

The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia (r2 = 0.12%; P = 5.69 × 10-5), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P = 3.7 × 10-7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations.

Conclusions and Relevance

The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions.

TCIApathfinder: An R Client for the Cancer Imaging Archive REST API

The Cancer Imaging Archive (TCIA) hosts publicly available deidentified medical images of cancer from over 25 body sites and over 30,000 patients. Over 400 published studies have utilized freely available TCIA images. Images and metadata are available for download through a web interface or a REST API. Here, we present TCIApathfinder, an R client for the TCIA REST API. TCIApathfinder wraps API access in user-friendly R functions that can be called interactively within an R session or easily incorporated into scripts. Functions are provided to explore the contents of the large database and to download image files. TCIApathfinder provides easy access to TCIA resources in the highly popular R programming environment. TCIApathfinder is freely available under the MIT license as a package on CRAN (https://cran.r-project.org/web/packages/TCIApathfinder/index.html) and from https://github.com/pamelarussell/TCIApathfinderSignificance: These findings present a new tool, TCIApathfinder, the first client for The Cancer Imaging Archive (TCIA) for use in the highly popular R computing environment, that will dramatically lower the barrier of access to the valuable tools in TCIA. Cancer Res; 78(15); 4424-6. ©2018 AACR.

freesurfer: Connecting the Freesurfer software with R

We present the package freesurfer, a set of R functions that interface with Freesurfer, a commonly-used open-source software package for processing and analyzing structural neuroimaging data, specifically T1-weighted images. The freesurfer package performs operations on nifti image objects in R using command-line functions from Freesurfer, and returns R objects back to the user.  freesurfer allows users to process neuroanatomical images and provides functionality to convert and read the output of the Freesurfer pipelines more easily, including brain images, brain surfaces, and Freesurfer output tables.

White matter integrity and processing speed in sickle cell anemia

Objective

The purpose of this retrospective cross-sectional study was to investigate whether changes in white matter integrity are related to slower processing speed in sickle cell anemia.

Methods

Thirty-seven patients with silent cerebral infarction, 46 patients with normal MRI, and 32 sibling controls (age range 8–37 years) underwent cognitive assessment using the Wechsler scales and 3-tesla MRI. Tract-based spatial statistics analyses of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) parameters were performed.

Results

Processing speed index (PSI) was lower in patients than controls by 9.34 points (95% confidence interval: 4.635–14.855, p = 0.0003). Full Scale IQ was lower by 4.14 scaled points (95% confidence interval: −1.066 to 9.551, p = 0.1), but this difference was abolished when PSI was included as a covariate (p = 0.18). There were no differences in cognition between patients with and without silent cerebral infarction, and both groups had lower PSI than controls (both p < 0.001). In patients, arterial oxygen content, socioeconomic status, age, and male sex were identified as predictors of PSI, and correlations were found between PSI and DTI scalars (fractional anisotropy r = 0.614, p < 0.00001; r = −0.457, p < 0.00001; mean diffusivity r = −0.341, p = 0.0016; radial diffusivity r = −0.457, p < 0.00001) and NODDI parameters (intracellular volume fraction r = 0.364, p = 0.0007) in widespread regions.

Conclusion

Our results extend previous reports of impairment that is independent of presence of infarction and may worsen with age. We identify processing speed as a vulnerable domain, with deficits potentially mediating difficulties across other domains, and provide evidence that reduced processing speed is related to the integrity of normal-appearing white matter using microstructure parameters from DTI and NODDI.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions.

METHODS

Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking.

RESULTS

In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset.

CONCLUSION

MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study.

Monotonic non-linear transformations as a tool to investigate age-related effects on brain white matter integrity: A Box-Cox investigation

Non-linear effects of age on white matter integrity are ubiquitous in the brain and indicate that these effects are more pronounced in certain brain regions at specific ages. Box-Cox analysis is a technique to increase the log-likelihood of linear relationships between variables by means of monotonic non-linear transformations. Here we employ Box-Cox transformations to flexibly and parsimoniously determine the degree of non-linearity of age-related effects on white matter integrity by means of model comparisons using a voxel-wise approach. Analysis of white matter integrity in a sample of adults between 20 and 89years of age (n=88) revealed that considerable portions of the white matter in the corpus callosum, cerebellum, pallidum, brainstem, superior occipito-frontal fascicle and optic radiation show non-linear effects of age. Global analyses revealed an increase in the average non-linearity from fractional anisotropy to radial diffusivity, axial diffusivity, and mean diffusivity. These results suggest that Box-Cox transformations are a useful and flexible tool to investigate more complex non-linear effects of age on white matter integrity and extend the functionality of the Box-Cox analysis in neuroimaging.

Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions

The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

Big biomedical data as the key resource for discovery science

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's.

Medical image file formats

Image file format is often a confusing aspect for someone wishing to process medical images. This article presents a demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (Dicom). Concepts common to all file formats, such as pixel depth, photometric interpretation, metadata, and pixel data, are first presented. Then, the characteristics and strengths of the various formats are discussed. The review concludes with some predictive considerations about the future trends in medical image file formats.

Differences in paracingulate connectivity associated with epileptiform discharges and uncontrolled seizures in genetic generalized epilepsy

OBJECTIVE

Patients with genetic generalized epilepsy (GGE) frequently continue to have seizures despite appropriate clinical management. GGE is associated with changes in the resting-state networks modulated by clinical factors such as duration of disease and response to treatment. However, the effect of generalized spike and wave discharges (GSWDs) and/or seizures on resting-state functional connectivity (RSFC) is not well understood.

METHODS

We investigated the effects of GSWD frequency (in GGE patients), GGE (patients vs. healthy controls), and seizures (uncontrolled vs. controlled) on RSFC using seed-based voxel correlation in simultaneous electroencephalography (EEG) and resting-state functional magnetic resonance imaging (fMRI) (EEG/fMRI) data from 72 GGE patients (23 with uncontrolled seizures) and 38 healthy controls. We used seeds in paracingulate cortex, thalamus, cerebellum, and posterior cingulate cortex to examine changes in cortical-subcortical resting-state networks and the default mode network (DMN). We excluded from analyses time points surrounding GSWDs to avoid possible contamination of the resting state.

RESULTS

(1) Higher frequency of GSWDs was associated with an increase in seed-based voxel correlation with cortical and subcortical brain regions associated with executive function, attention, and the DMN; (2) RSFC in patients with GGE, when compared to healthy controls, was increased between paracingulate cortex and anterior, but not posterior, thalamus; and (3) GGE patients with uncontrolled seizures exhibited decreased cerebellar RSFC.

SIGNIFICANCE

Our findings in this large sample of patients with GGE (1) demonstrate an effect of interictal GSWDs on resting-state networks, (2) provide evidence that different thalamic nuclei may be affected differently by GGE, and (3) suggest that cerebellum is a modulator of ictogenic circuits.

Reduced default mode network connectivity in treatment-resistant idiopathic generalized epilepsy

PURPOSE

Idiopathic generalized epilepsy (IGE) resistant to treatment is common, but its neuronal correlates are not entirely understood. Therefore, the aim of this study was to examine resting-state default mode network (DMN) functional connectivity in patients with treatment-resistant IGE.

METHODS

Treatment resistance was defined as continuing seizures despite an adequate dose of valproic acid (valproate, VPA). Data from 60 epilepsy patients and 38 healthy controls who underwent simultaneous electroencephalography (EEG) and resting-state functional magnetic resonance imaging (fMRI) were included (EEG/fMRI). Independent component analysis (ICA) and dual regression were used to quantify DMN connectivity. Confirmatory analysis using seed-based voxel correlation was performed.

KEY FINDINGS

There was a significant reduction of DMN connectivity in patients with treatment-resistant epilepsy when compared to patients who were treatment responsive and healthy controls. Connectivity was negatively correlated with duration of epilepsy.

SIGNIFICANCE

Our findings in this large sample of patients with IGE indicate the presence of reduced DMN connectivity in IGE and show that connectivity is further reduced in treatment-resistant epilepsy. DMN connectivity may be useful as a biomarker for treatment resistance.