Human Imaging Data Research Articles

The Rostral Zona Incerta: A Subcortical Integrative Hub and Potential Deep Brain Stimulation Target for Obsessive-Compulsive Disorder

BackgroundThe zona incerta (ZI) is involved in mediating survival behaviors and is connected to a wide range of cortical and subcortical structures, including key basal ganglia nuclei. Based on these connections and their links to behavioral modulation, we propose that the ZI is a connectional hub for mediating between top-down and bottom-up control and a possible target for deep brain stimulation for obsessive-compulsive disorder. MethodsWe analyzed the trajectory of cortical fibers to the ZI in nonhuman and human primates based on tracer injections in monkeys and high-resolution diffusion magnetic resonance imaging in humans. The organization of cortical and subcortical connections within the ZI were identified in the nonhuman primate studies. ResultsMonkey anatomical data and human diffusion magnetic resonance imaging data showed a similar trajectory of fibers/streamlines to the ZI. Prefrontal cortex/anterior cingulate cortex terminals all converged within the rostral ZI, with dorsal and lateral areas being most prominent. Motor areas terminated caudally. Dense subcortical reciprocal connections included the thalamus, medial hypothalamus, substantia nigra/ventral tegmental area, reticular formation, and pedunculopontine nucleus and a dense nonreciprocal projection to the lateral habenula. Additional connections included the amygdala, dorsal raphe nucleus, and periaqueductal gray. ConclusionsDense connections with dorsal and lateral prefrontal cortex/anterior cingulate cortex cognitive control areas and the lateral habenula and the substantia nigra/ventral tegmental area, coupled with inputs from the amygdala, hypothalamus, and brainstem, suggest that the rostral ZI is a subcortical hub positioned to modulate between top-down and bottom-up control. A deep brain stimulation electrode placed in the rostral ZI would not only involve connections common to other deep brain stimulation sites but also capture several critically distinctive connections.

A stabilized linear finite element method for anisotropic poroelastodynamics with application to cardiac perfusion

We propose a variational multiscale method stabilization of a linear finite element method for nonlinear poroelasticity. Our approach is suitable for the implicit time integration of poroelastic formulations in which the solid skeleton is anisotropic and incompressible. A detailed numerical methodology is presented for a monolithic formulation that includes both structural dynamics and Darcy flow. Our implementation of this methodology is verified using several hyperelastic and poroelastic benchmark cases, and excellent agreement is obtained with the literature. Grid convergence studies for both anisotropic hyperelastodynamics and poroelastodynamics demonstrate that the method is second-order accurate. The capabilities of our approach are demonstrated using a model of the left ventricle (LV) of the heart derived from human imaging data. Simulations using this model indicate that the anisotropicity of the myocardium has a substantial influence on the pore pressure. Furthermore, the temporal variations of the various components of the pore pressure (hydrostatic pressure and pressure resulting from changes in the volume of the pore fluid) are correlated with the variation of the added mass and dynamics of the LV, with maximum pore pressure being obtained at peak systole. The order of magnitude and the temporal variation of the pore pressure are in good agreement with the literature.

Diffusion MRI alterations coincide with memory deficits in a novel hAβKI mouse model of Alzheimer’s Disease

AbstractBackgroundThe Model Organism Development and Evaluation for Late‐Onset Alzheimer’s Disease (MODEL‐AD) Consortium is developing the next generation of Alzheimer’s disease (AD) models based on human genomic and imaging data. Recently, MODEL‐AD has generated a new human Aβ Knock‐In (hAβKI) mouse. These mice exhibit age‐dependent cognitive and synaptic impairments. Preclinical neuroimaging was performed, specifically diffusion MRI (dMRI) to investigate brain‐wide structural alterations in conjunction with connectomic analyses across the hAβKI mouse lifespan.MethodhAβKI and WT mice were compared at 4, 12, and 18 months (mo) of age. Two cohorts were generated for: 1) behavior and long‐term potentiation (LTP), and 2) high resolution diffusion tensor imaging at 9.4T (30 directions b=3000 mm2/sec) to assess regional white and gray matter changes. Regional tissue features based on the AMBMC atlas were extracted from fractional anisotropy (FA), radial (RD), axial (AxD) and mean diffusivity (MD) parametric maps.ResultSignificant impairments in hippocampal LTP were observed in male hAβKI mice at 4mo of age relative to WT but not at 12 or 18mo. In contrast, in female hAβKI mice, LTP impairments were observed only at 12 and 18mo relative to WT. dMRI heatmaps of MD highlighted regional differences between sexes across time and genotype, with MD progressively decreasing with time. Male hAβKI mice had progressive changes within the brain with advancing age whereas the largest changes in females was at 12mo of age. AxD in 18mo male hAβKI mice was increased in 4 of 7 white matter regions but not in female hAβKI mice compared to WT.ConclusionIn summary, using the newly developed hAβKI mouse model of AD, we identified progressive altered regional dMRI tissue metrics. The hippocampal CA1 region appeared to be particularly vulnerable, findings consistent with observed impairments in LTP and behavior. MRI of new mouse models is a powerful tool to investigate tissue level modifications to brain structure and function to inform human disease.

Motion opponency at the middle temporal cortex: Preserved motion information and the effect of perceptual learning.

Motion opponency, first observed within the primate middle temporal cortex (MT), refers to the suppressing effect of opposite motion directions on neuronal activity. Namely, when opposing motion directional signals stimulate an MT neuron's receptive field, this neuron's response is comparable with that induced by flicker noise. Under such suppression, it is unknown whether any directional information is still represented at MT. In this study, we applied support vector machine (SVM) learning to human functional magnetic resonance imaging data to investigate if any motion defined orientation information was still available from suppressed MT. We found that, at least at the level of ±45° discrimination, such orientation information was still available. Interestingly, after behavioural perceptual learning that improved human discrimination of fine orientation discrimination (e.g. 42° vs. 48°) using the MT-suppressive motion stimuli, the SVM discrimination of ±45° worsened when functional magnetic resonance imaging (fMRI) signals at post-learning MT were used. This result is consistent with findings in Thompson et al. (2013) that, post-perceptual learning, MT suppression was not released, suggesting that motion opponency was perhaps functionally too important for perceptual learning to overcome.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

First-in-Humans Evaluation of Safety and Dosimetry of 64Cu-LLP2A for PET Imaging.

There remains an unmet need for molecularly targeted imaging agents for multiple myeloma (MM). The integrin very late antigen 4 (VLA4), is differentially expressed in malignant MM cells and in pathogenic inflammatory microenvironmental cells. [64Cu]Cu-CB-TE1A1P-LLP2A (64Cu-LLP2A) is a VLA4-targeted, high-affinity radiopharmaceutical with promising utility for managing patients diagnosed with MM. Here, we evaluated the safety and human radiation dosimetry of 64Cu-LLP2A for potential use in MM patients. Methods: A single-dose [natCu]Cu-LLP2A (Cu-LLP2A) tolerability and toxicity study was performed on CD-1 (Hsd:ICR) male and female mice. 64Cu-LLP2A was synthesized in accordance with good-manufacturing-practice-compliant procedures. Three MM patients and six healthy participants underwent 64Cu-LLP2A-PET/CT or PET/MRI at up to 3 time points to help determine tracer biodistribution, pharmacokinetics, and radiation dosimetry. Time-activity curves were plotted for each participant. Mean organ-absorbed doses and effective doses were calculated using the OLINDA software. Tracer bioactivity was evaluated via cell-binding assays, and metabolites from human blood samples were analyzed with analytic radio-high-performance liquid chromatography. When feasible, VLA4 expression was evaluated in the biopsy tissues using 14-color flow cytometry. Results: A 150-fold mass excess of the desired imaging dose was tolerated well in male and female CD-1 mice (no observed adverse effect level). Time-activity curves from human imaging data showed rapid tracer clearance from blood via the kidneys and bladder. The effective dose of 64Cu-LLP2A in humans was 0.036 ± 0.006 mSv/MBq, and the spleen had the highest organ uptake, 0.142 ± 0.034 mSv/MBq. Among all tissues, the red marrow demonstrated the highest residence time. Image quality analysis supports an early imaging time (4-5 h after injection of the radiotracer) as optimal. Cell studies showed statistically significant blocking for the tracer produced for all human studies (82.42% ± 13.47%). Blood metabolism studies confirmed a stable product peak (>90%) up to 1 h after injection of the radiopharmaceutical. No clinical or laboratory adverse events related to 64Cu-LLP2A were observed in the human participants. Conclusion: 64Cu-LLP2A exhibited a favorable dosimetry and safety profile for use in humans.

Development of 3-dimensional printed simulation surgical training models for endoscopic endonasal and transorbital surgery.

BackgroundEndoscopic skull base surgery (ESBS) is complex, requiring methodical and unremitting surgical training. Herein, we describe the development and evaluation of a novel three-dimensional (3D) printed simulation model for ESBS. We further validate the efficacy of this model as educational support in neurosurgical training.MethodsA patient-specific 3D printed simulation model using living human imaging data was established and evaluated in a task-based hands-on dissection program. Endoscopic endonasal and transorbital procedures were simulated on the model by neurosurgeons and otorhinolaryngology surgeons of varying experience. All procedures were recorded using a high-definition camera coupled with digital video recorder system. The participants were asked to complete a post-procedure questionnaire to validate the efficacy of the model.ResultsFourteen experts and 22 trainees participated in simulations, and the 32 participants completed the post-procedure survey. The anatomical realism was scored as 4.0/5.0. The participants rated the model as helpful in hand-eye coordination training (4.7/5.0) and improving surgical skills (4.6/5.0) for ESBS. All participants believed that the model was useful as educational support for trainees (4.7 [ ± 0.5]). However, the color (3.6/5.0) and soft tissue feedback parameters (2.8/5) scored low.ConclusionThis study shows that high-resolution 3D printed skull base models for ESBS can be generated with high anatomical accuracy and acceptable haptic feedback. The simulation program of ESBS using this model may be supplemental or provide an alternative training platform to cadaveric dissection.

Effector-selective modulation of the effective connectivity within frontoparietal circuits during visuomotor tasks.

Despite extensive research, the functional architecture of the subregions of the dorsal posterior parietal cortex (PPC) involved in sensorimotor processing is far from clear. Here, we draw a thorough picture of the large-scale functional organization of the PPC to disentangle the fronto-parietal networks mediating visuomotor functions. To this aim, we reanalyzed available human functional magnetic resonance imaging data collected during the execution of saccades, hand, and foot pointing, and we combined individual surface-based activation, resting-state functional connectivity, and effective connectivity analyses. We described a functional distinction between a more lateral region in the posterior intraparietal sulcus (lpIPS), preferring saccades over pointing and coupled with the frontal eye fields (FEF) at rest, and a more medial portion (mpIPS) intrinsically correlated to the dorsal premotor cortex (PMd). Dynamic causal modeling revealed feedforward-feedback loops linking lpIPS with FEF during saccades and mpIPS with PMd during pointing, with substantial differences between hand and foot. Despite an intrinsic specialization of the action-specific fronto-parietal networks, our study reveals that their functioning is finely regulated according to the effector to be used, being the dynamic interactions within those networks differently modulated when carrying out a similar movement (i.e. pointing) but with distinct effectors (i.e. hand and foot).

A semi-supervised classification RBM with an improved fMRI representation algorithm

Background and objectiveTraining an effective and robust supervised learning classifier is not easy due to the limitations of acquiring and labeling considerable human functional magnetic resonance imaging (fMRI) data. Semi-supervised learning uses unlabeled data for feature learning and combines them into labeled data to build better classification models. MethodsSince no premises or assumptions are required, a restricted Boltzmann machine (RBM) is suitable for learning data representation of neuroimages. In our study, an improved fMRI representation algorithm with a hybrid L1/L2 regularization method (HRBM) was proposed to optimize the original model for sparsity. Different from common semi-supervised classification models that treat feature learning and classification as two separate training steps, we then constructed a new semi-supervised classification RBM based on a joint training algorithm with HRBM, named Semi-HRBM. This joint training algorithm jointly trains the objective function of feature learning and classification process, so that the learned features can effectively represent the original fMRI data and adapt to the classification tasks. ResultsThis study uses fMRI data to identify categories of visual stimuli. In the fMRI data classification task under four visual stimuli (house, face, car, and cat), our HRBM has satisfactory feature representation capabilities and better performance for multiple classification tasks. Taking the supervised RBM (sup-RBM) as an example, our Semi-HRBM classification model improves the average accuracy of the four-classification task by 7.68%, and improves the average F1 score of each visual stimulus task by 8.90%. In addition, the generalization ability of the model was also improved. ConclusionThis research might contribute to enrich solutions for insufficiently labeled neuroimaging samples, which could help to identify complex brain states under different stimuli or tasks.

Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts.

IntroductionThe lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.MethodsMulti-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.ResultsWe were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).ConclusionWe developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

Avalanche criticality in individuals, fluid intelligence, and working memory.

The critical brain hypothesis suggests that efficient neural computation can be achieved through critical brain dynamics. However, the relationship between human cognitive performance and scale‐free brain dynamics remains unclear. In this study, we investigated the whole‐brain avalanche activity and its individual variability in the human resting‐state functional magnetic resonance imaging (fMRI) data. We showed that though the group‐level analysis was inaccurate because of individual variability, the subject wise scale‐free avalanche activity was significantly associated with maximal synchronization entropy of their brain activity. Meanwhile, the complexity of functional connectivity, as well as structure–function coupling, is maximized in subjects with maximal synchronization entropy. We also observed order–disorder phase transitions in resting‐state brain dynamics and found that there were longer times spent in the subcritical regime. These results imply that large‐scale brain dynamics favor the slightly subcritical regime of phase transition. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance and found that these regions were located in the prefrontal cortex and inferior parietal cortex, which were believed to be important nodes of brain networks underlying human intelligence. Our results reveal the possible role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, ranging from subcriticality to supercriticality.

Deep learning in resting-state fMRI.

Modeling the rich, dynamic spatiotemporal variations captured by human brain functional magnetic resonance imaging (fMRI) data is a complicated task. Analysis at the brain's regional and connection levels provides more straightforward biological interpretation for fMRI data and has been instrumental in characterizing the brain thus far. Here we hypothesize that spatiotemporal learning directly in the four-dimensional (4D) fMRI voxel-time space could result in enhanced discriminative brain representations compared to widely used, pre-engineered fMRI temporal transformations, and brain regional and connection-level fMRI features. Motivated by this, we extend our recently reported structural MRI (sMRI) deep learning (DL) pipeline to additionally capture temporal variations, training the proposed 4D DL model end-to-end on preprocessed fMRI data. Results validate that the complex non-linear functions of the used deep spatiotemporal approach generate discriminative encodings for the studied learning task, outperforming both standard machine learning (SML) and DL methods on the widely used fMRI voxel/region/connection features, except the relatively simplistic measure of central tendency - the temporal mean of the fMRI data. Additionally, we identify the fMRI features for which DL significantly outperformed SML methods for voxel-level fMRI features. Overall, our results support the efficiency and potential of DL models trainable at the voxel level fMRI data and highlight the importance of developing auxiliary tools to facilitate interpretation of such flexible models.

Optical breast atlas as a testbed for image reconstruction in optical mammography

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Local neuronal relational structures underlying the contents of human conscious experience.

While most theories of consciousness posit some kind of dependence on global network activities, I consider here an alternative, localist perspective—in which localized cortical regions each underlie the emergence of a unique category of conscious experience. Under this perspective, the large-scale activation often found in the cortex is a consequence of the complexity of typical conscious experiences rather than an obligatory condition for the emergence of conscious awareness—which can flexibly shift, depending on the richness of its contents, from local to more global activation patterns. This perspective fits a massive body of human imaging, recordings, lesions and stimulation data but opens a fundamental problem: how can the information, defining each content, be derived locally in each cortical region. Here, I will discuss a solution echoing pioneering structuralist ideas in which the content of a conscious experience is defined by its relationship to all other contents within an experiential category. In neuronal terms, this relationship structure between contents is embodied by the local geometry of similarity distances between cortical activation patterns generated during each conscious experience, likely mediated via networks of local neuronal connections. Thus, in order for any conscious experience to appear in an individual’s mind, two central conditions must be met. First, a specific configural pattern (“bar-code”) of neuronal activity must appear within a local relational geometry, i.e. a cortical area. Second, the individual neurons underlying the activated pattern must be bound into a unified functional ensemble through a burst of recurrent neuronal firing: local “ignitions”.

Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats.

Escape from threats has paramount importance for survival. However, it is unknown if a single circuit controls escape vigor from innate and conditioned threats. Cholecystokinin (cck)-expressing cells in the hypothalamic dorsal premammillary nucleus (PMd) are necessary for initiating escape from innate threats via a projection to the dorsolateral periaqueductal gray (dlPAG). We now show that in mice PMd-cck cells are activated during escape, but not other defensive behaviors. PMd-cck ensemble activity can also predict future escape. Furthermore, PMd inhibition decreases escape speed from both innate and conditioned threats. Inhibition of the PMd-cck projection to the dlPAG also decreased escape speed. Intriguingly, PMd-cck and dlPAG activity in mice showed higher mutual information during exposure to innate and conditioned threats. In parallel, human functional magnetic resonance imaging data show that a posterior hypothalamic-to-dlPAG pathway increased activity during exposure to aversive images, indicating that a similar pathway may possibly have a related role in humans. Our data identify the PMd-dlPAG circuit as a central node, controlling escape vigor elicited by both innate and conditioned threats.

Comparing the diagnostic performance of radiotracers in recurrent prostate cancer: a systematic review and network meta-analysis

PurposeMany radiotracers are currently available for the detection of recurrent prostate cancer (rPC), yet many have not been compared head-to-head in comparative imaging studies. There is therefore an unmet need for evidence synthesis to guide evidence-based decisions in the selection of radiotracers. The objective of this study was therefore to assess the detection rate of various radiotracers for the rPC.MethodsThe PUBMED, EMBASE, and the EU and NIH trials databases were searched without date or language restriction for comparative imaging tracers for 13 radiotracers of principal interest. Key search terms included 18F-PSMA-1007, 18F-DCPFyl, 68Ga-PSMA-11, 18F-PSMA-11, 68Ga-PSMA-I&T, 68Ga-THP-PSMA, 64Cu-PSMA-617, 18F-JK-PSMA-7, 18F-Fluciclovine, 18F-FABC, 18F-Choline, 11C-Choline, and 68Ga-RM2. Studies reporting comparative imaging data in humans in rPC were selected. Single armed studies and matched pair analyses were excluded. Twelve studies with eight radiotracers were eligible for inclusion. Two independent reviewers screened all studies (using the PRISMA-NMA statement) for inclusion criteria, extracted data, and assessed risk of bias (using the QUADAS-2 tool). A network meta-analysis was performed using Markov-Chain Monte Carlo Bayesian analysis to obtain estimated detection rate odds ratios for each tracer combination.ResultsA majority of studies were judged to be at risk of publication bias. With the exception of 18F-PSMA-1007, little difference in terms of detection rate was revealed between the three most commonly used PSMA-radiotracers (68Ga-PSMA-11, 18F-PSMA-1007, 18F-DCFPyl), which in turn showed clear superiority to choline and fluciclovine using the derived network.ConclusionDifferences in patient-level detection rates were observed between PSMA- and choline-radiotracers. However, there is currently insufficient evidence to favour one of the four routinely used PSMA-radioligands (PSMA-11, PSMA-1007, PSMA-I&T, and DCFPyl) over another owing to the limited evidence base and risk of publication bias revealed by our systematic review. A further limitation was lack of reporting on diagnostic accuracy, which might favour radiotracers with low specificity in an analysis restricted only to detection rate. The NMA derived can be used to inform the design of future clinical trials and highlight areas where current evidence is weak.

Mapping the Human Brain: What Is the Next Frontier?

Abstract The structure and function of the human brain have long been the topic of exploration by scientists. To understand how the brain works, researchers have created various brain navigation maps, i.e. brain atlases, which are still being developed and improved. To clarify the trend of human brain mapping and to achieve better applications in brain science and diseases, this commentary briefly traces the history of human brain mapping and describes insights into the next stage of building brain atlases.

Detection of transient neurotransmitter response using personalized neural networks

Measurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic positron emission tomography (PET) images typically suffers from limited detection sensitivity and high false positive (FP) rates. Measurement of TNR of a voxel-level can be particularly problematic due to high image noise. In this work, we perform voxel-level TNR detection using artificial neural networks (ANN) and compare their performance to previously used standard statistical tests. Different ANN architectures were trained and tested using simulated and real human PET imaging data, obtained with the tracer [11C]raclopride (a D2 receptor antagonist). A distinguishing feature of our approach is the use of ‘personalized’ ANNs that are designed to operate on the image from a specific subject and scan. Training of personalized ANNs was performed using simulated images that have been matched with the acquired image in terms of the signal, resolution, and noise.In our tests of TNR detection performance, the F-test of the linear parametric neurotransmitter PET model fit residuals was used as the reference method. For a moderate TNR magnitude, the areas under the receiver operating characteristic curves in simulated tests were 0.64 for the F-test and 0.77–0.79 for the best ANNs. At a fixed FP rate of 0.01, the true positive rates were 0.6 for the F-test and 0.8–0.9 for the ANNs. The F-test detected on average 28% of a 8.4 mm cluster with a strong TNR, while the best ANN detected 47%. When applied to a real image, no significant abnormalities in the ANN outputs were observed. These results demonstrate that personalized ANNs may offer a greater detection sensitivity of dopamine release and other types of TNR compared to previously used method based on the F-test.

The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging.

Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies.

Aortic adventitial thickness as a marker of aortic atherosclerosis, vascular stiffness, and vessel remodeling in systemic lupus erythematosus.

There is limited human imaging data on the association of adventitial thickness (AT) with arterial disease. Systemic lupus erythematosus (SLE) is a prototypical disease model for studying markers of premature arterial disease. To determine if increased aortic AT is associated with aortic atherosclerosis [increased intima media thickness (IMT) or plaques], stiffness [increased pressure-strain elastic modulus (PSEM)], and vessel remodeling. In total, 70 SLE patients and 26 age- and sex-matched controls underwent transesophageal echocardiography (TEE). Two-dimensional guided M-mode images were obtained to assess AT, IMT, and plaques, and PSEM at the proximal, mid, and distal thoracic aorta. Images were interpreted by 3 observers unaware of the subjects' clinical data and each other's measurements. Abnormal aortic AT, IMT, and PSEM were defined as > 2SD above the overall mean values in controls and corresponded to > 1 mm, > 1 mm, and > 10.6 Pascal units, respectively. Plaques were defined as focal-protruding IMT > 50% of the surrounding vessel wall. Abnormal aortic AT, atherosclerosis, and abnormal stiffness were more frequent in SLE patients than in controls (all p ≤ 0.02). In SLE patients, abnormal AT combined with atherosclerosis was associated with larger aortic end-diastolic diameters than in controls (p ≤ 0.05). In SLE patients, aortic AT was greater in patients with atherosclerosis and in those with abnormal stiffness than in patients without these abnormalities (all p ≤ 0.02). In patients with abnormal AT, the degree of aortic stiffness was similar to those with atherosclerosis (p = 0.22). In patients with SLE, increased aortic AT is associated with aortic atherosclerosis, abnormal stiffness, and eccentric vessel remodeling. Key Points • In patients with SLE, abnormal aortic adventitial thickness is associated with aortic atherosclerosis, abnormal stiffness, and eccentric vessel remodeling. • In patients with SLE, aortic adventitial thickening may contribute to the extent of aortic atherosclerosis, abnormal aortic stiffness, and vessel remodeling. • To our knowledge, this is the first human imaging study to characterize the aortic adventitial layer and delineate its association with aortic disease.