Computational reconstruction of evolutionary selection in human brain networks

Comparative analysis of synthetic and conventional magnetic resonance imaging features across various brain regions.

Detection of longitudinal changes in brain structures is a common clinical task when assessing the progress of cerebrovascular and neurodegenerative diseases, which manifest in appearing and disappearing white matter lesions (WMLs). Changes of WMLs are usually quanti ed by their manual outlines and compared across longi- tudinal, serial magnetic resonance (MR) brain images. Since manual outlining in 3D MR images is subjective and inaccurate, several automated methods were proposed so as to enhance the sensitivity, reliability and re- peatability of change detection of WMLs. However, the absence of publicly available synthetic or clinical MR image databases with corresponding ground truth of changes renders the validation and comparison of any new and existing automated methods highly subjective. In this paper, we focus on the validation and comparison of three state-of-the-art intensity based methods for detection of longitudinal changes of WMLs. To objectively assess the three methods we created several synthetic MR image databases using a generative lesion model, which was trained on manually outlined patches of WMLs in a clinical MR image database of 22 patients. Val- idation was also performed on clinical MR image database of MS patients. Performances of the three change detection methods were evaluated by computing the similarity index and sensitivity between the obtained and the ground truth binary change map. The obtained similarity indices were in the range of 0.40-0.77, which should be improved for clinical use, while the comparison of methods revealed that the intensity subtraction method achieved similar performance as the change vector analysis method, which employed two MR sequences for change detection. Third method was based on local steering kernels and exhibited stable performance both on synthetic and clinical MR image databases.

ObjectiveTo generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. MethodsGANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). ResultsThe mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). ConclusionThis was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statistical properties underlying the MR signal and its acquisition, can predict images at any setting from as few as three scans, allowing it to be used in individualized patient- and anatomy-specific contexts. However, the estimation problem in model-based synthetic MR imaging is ill-posed and so regularization, in the form of correlated Gaussian markov random fields, is imposed on the voxel-wise spin-lattice relaxation time, spin-spin relaxation time and the proton density underlying the MR image. We develop theoretically sound but computationally practical matrix-free estimation methods for synthetic MR imaging. Our evaluations demonstrate superior performance of our methods in currently-used clinical settings when compared to existing model-based and deep learning methods. Moreover, unlike deep learning approaches, our fast methodology can synthesize needed images during patient visits, with good estimation and prediction accuracy and consistency. An added strength of our model-based approach, also developed and illustrated here, is the accurate estimation of standard errors of regional contrasts in the synthesized images. A R package symr implements our methodology. Supplementary materials for this article are available online.

ObjectiveOur primary objective was to verify the hypothesis that synthetic magnetic resonance imaging (MRI) is similar to conventional MRI in detecting sacroiliac joint lesions in patients with axial spondyloarthritis (axSpA). A secondary objective was to assess the quantitative value of synthetic mapping in bone marrow edema (BME) and fat metaplasia.MethodsA total of 132 axSpA patients who underwent synthetic and conventional MRI from October 2019 to March 2021 were included in this prospective study. Two independent readers visually evaluated active inflammatory (BME, capsulitis, enthesitis, and inflammation at site of erosion) and structural lesions (erosion, sclerosis, ankylosis, and fat metaplasia) of the sacroiliac joints on conventional and synthetic magnetic resonance (MR) images. In addition, T1, T2, and proton density (PD) values, which were generated by synthetic mapping, were used to further quantitatively evaluate BME and fat metaplasia. A McNemar test was used to compare the differences between the two methods in the detection of sacroiliac joint lesions. Intraclass correlation coefficients (ICCs) were used to assess the inter-reader consistency of quantitative values. Mann–Whitney tests were performed, and receiver operating characteristic (ROC) curves were created for all quantitative analyses.ResultsThere were no statistical difference between synthetic and conventional MRI in the detection of sacroiliac joint lesions (all p-values > 0.05). A total of 103 images of BME and 111 images of fat metaplasia were quantitatively evaluated using T1, T2, and PD values. The consistency of quantitative values among readers was good (ICC 0.903–0.970). T1 and T2 values were consistently higher in BME than in normal marrow (p < 0.001), but PD values were not significantly different (p = 0.830). T2 and PD values were higher in fat metaplasia than in normal marrow, but T1 values were lower (p < 0.001). In the case of BME, T1 values had greater diagnostic efficiency [area under the curve (AUC) 0.99] than T2 values (AUC 0.78). There were no significant differences in the diagnostic efficiency of T1 (AUC 0.88), T2 (AUC 0.88), and PD (AUC 0.88) values in the case of fat metaplasia.ConclusionSynthetic MRI is as effective as conventional MRI in detecting sacroiliac joint lesions in patients with axSpA. Furthermore, synthetic mapping can accurately quantify BME and fat metaplasia.

Synthetic magnetic resonance imaging (SyMRI) allows to obtain different weighted-images using the multiple-dynamic multiple-echo sequence lasting 6min. The aim is to compare quantitatively and qualitatively synthetic- and conventional MRI in patients with multiple sclerosis (MS) and controls assessing the contrast (C), the signal to noise ratio (SNR), and the contrast to noise ratio (CNR). We evaluated the lesion count and lesion-to-white matter contrast ([Formula: see text] in the MS patients. 51 patients underwent synthetic- and conventional MRI. Qualitative analysis was evaluated by assigning scores to all synthetic- and conventional MRI sequences by two neuroradiologists. Lesions were counted in MS patients both in the conventional- and synthetic T2-FLAIR. Regions of interest were placed in the cerebrospinal fluid, in the white- and grey matter. For the sequences were evaluated: C, CNR, and SNR. Synthetic T2-FLAIR images are qualitatively inferior. C and CNR were significantly higher in synthetic T1W and T2W images compared to conventional images, but not for T2-FLAIR. The SNR value was always lower in synthetic images than in conventional ones. SyMRI can be used in clinical practice because it has a similar diagnostic accuracy which reduces the scanning time compared to the conventional one. However, synthetic T2-FLAIR images need to be improved.

Synthetic magnetic resonance imaging (MRI) might replace the conventional MR sequences in brain evaluation to shorten scan time and obtain multiple quantitative parameters. To evaluate the image quality of multiple-delay-multiple-echo (MDME) sequence-derived synthetic brain MR images compared to conventional images by considering a multi-age sample. Image sets of conventional and synthetic MRI of 200 participants were included. On the basis of the presence of intracranial lesions, the participants were divided into a normal group and a pathological group. Two neuroradiologists compared the anonymous and unordered images. Image quality, artifacts, and diagnostic performance were analyzed. In the quantitative analysis, comparing with conventional images, MDME sequence-derived synthetic MRI demonstrated an equal/greater signal-to-noise ratio and contrast-to-noise ratio (CNR) in all age groups. Specifically, for participants aged ≤2 years, synthetic T2-fluid-attenuated inversion recovery imaging showed a significantly higher cerebellum gray/white matter CNR (P < 0.05). In the qualitative and artifact analyses, except for the superior sagittal sinus and cranial nerves, synthetic MRI showed good imaging quality (≥3 points) in all brain structures. On synthetic T1-weighted imaging, high signal intensity within the superior sagittal sinus was found in most of our participants (107/118, 90.7%). No difference was observed between synthetic and conventional MRI in diagnosing the lesions. MDME sequence-derived synthetic MRI showed similar image quality and diagnostic performance with a shorter acquisition time than conventional MRI. However, the high signal intensity within the superior sagittal sinus on synthetic T1-weighted images requires consideration.

Early fossil hominins have often been assigned a chronological age on the basis of modern human data for tooth eruption. Better data and more sophisticated methods are now available to estimate their chronological age from modern human standards for stages of mineralization of individual teeth developing within the jaws. However, while comparisons with modern human dentitions are interesting, they can also be misleading as early hominin teeth and dentitions did not grow like modern human teeth. Chronological age can also be estimated using the microanatomy of tooth enamel and root dentine. Counts of incremental markings in enamel predict much younger ages at death for early fossil hominins than those based on modern human radiographic standards of dental development. Comparative evidence from the skeleton suggests that a greater proportion of adult body mass and stature was achieved earlier in the growth period of fossil hominins than it is in modern humans. The combined skeleto-dental evidence provides the basis for a hypothesis that the earliest hominins grew more like modern great apes, but that Homo erectus had a slightly more prolonged period of growth, and which was still not totally modern human-like in its pattern or timing.

The coexistence of several wireless technologies gives users a wide connectivity choice. However, in this heterogeneous context with the even growth of multimedia data demand and continuous amendments of network conditions, the best network selection represents a crucial issue. In this paper the authors propose a novel Adaptive Real-time Multi-user Access Network Selection (ARMANS) load balancing algorithm, taking into account not only the real-time global traffic load on each network, but also considering the different classes of traffic. The simulation results show that the proposed solution improves both QoS and load balancing in comparison with the case when a classic network selection with no traffic type load balancing is employed.

Synthetic magnetic resonance imaging (SyMRI) enables to reformat various images by adjusting the MR parameters. To investigate whether customization of the repetition time (TR), echo time (TE), and inversion time (TI) in SyMRI could improve the visualization of subthalamic nucleus (STN). We examined five healthy volunteers using both coronal SyMRI and quantitative susceptibility mapping (QSM), seven patients with Parkinson's disease (PD) using coronal SyMRI, and 15 patients with PD using coronal QSM. Two neuroradiologists reformatted SyMRI (optimized SyMRI) by adjusting TR, TE, and TI to achieve maximum tissue contrast between the STN and the adjacent brain parenchyma. The optimized MR parameters in the PD patients varied according to the individual. For regular SyMRI (T2-weighted imaging [T2WI] and STIR), optimized SyMRI, and QSM, qualitative visualization scores of the STN (STN score) were recorded. The contrast-to-noise ratio (CNR) of the STN was also measured. For the STN scores in both groups, the optimized SyMRI were significantly higher than the regular SyMRI (P < 0.05), and there were no significant differences between optimized SyMRI and QSM. For the CNR of differentiation of the STN from the substantia nigra, the optimized SyMRI was higher than the regular SyMRI (volunteer: T2WI P = 0.10 and STIR P = 0.26; PD patient: T2WI P = 0.43 and STIR P = 0.25), but the optimized SyMRI was lower than the QSM (volunteer: P = 0.26; PD patient: P = 0.03). On SyMRI, optimization of MR parameters (TR, TE, and TI) on an individual basis may be useful to increase the conspicuity of the STN.

In this paper, the authors investigate the concept of adaptive path selection in optical burst-switched networks and its potential to reducing the overall burst drop probability. Specifically, the authors assume that each source maintains a (short) list of alternate paths to each destination and uses information regarding the recent congestion status of the network links to rank the paths; it then transmits bursts along the least congested path. The authors present a suite of path selection strategies, each utilizing a different type of information regarding the link congestion status, and evaluate them using simulation. The results demonstrate that, in general, adaptive path selection outperforms shortest path routing, and, depending on the path strategy involved, the network topology, and the traffic pattern, this improvement can be significant. A new framework for the development of hybrid (or meta) path selection strategies, which make routing decisions based on a weighted combination of the decisions taken by several independent path selection strategies, has been presented. This paper presents two instances of such hybrid strategies, i.e., 1) one that assigns static weights and 2) one that dynamically adjusts the weights based on feedback from the network; it has been shown that these strategies can further improve the overall burst drop probability in the network.

Our understanding of human evolution has improved rapidly over recent decades, facilitated by large-scale cataloguing of genomic variability amongst both modern and archaic humans. It seems clear that the evolution of the ancestors of chimpanzees and hominins separated 7-9million years ago with some migration out of Africa by the earlier hominins; Homo sapiens slowly emerged as climate change resulted in drier, less forested African conditions. The African populations expanded and evolved in many different conditions with slow mutation and selection rates in the human genome, but with much more rapid mutation occurring in mitochondrial DNA. We now have evidence stretching back 300000years of humans in their current form, but there are clearly four very different large African language groups that correlate with population DNA differences. Then, about 50000-100000years ago a small subset of modern humans also migrated out of Africa resulting in a persistent signature of more limited genetic diversity amongst non-African populations. Hybridization with archaic hominins occurred around this time such that all non-African modern humans possess some Neanderthal ancestry and Melanesian populations additionally possess some Denisovan ancestry. Human populations both within and outside Africa also adapted to diverse aspects of their local environment including altitude, climate, UV exposure, diet and pathogens, in some cases leaving clear signatures of patterns of genetic variation. Notable examples include haemoglobin changes conferring resistance to malaria, other immune changes and the skin adaptations favouring the synthesis of vitamin D. As humans migrated across Eurasia, further major mitochondrial changes occurred with some interbreeding with ancient hominins and the development of alcohol intolerance. More recently, an ability to retain lactase persistence into adulthood has evolved rapidly under the environmental stimulus of pastoralism with the ability to husband lactating ruminants. Increased amylase copy numbers seem to relate to the availability of starchy foods, whereas the capacity to desaturase and elongate monounsaturated fatty acids in different societies seems to be influenced by whether there is a lack of supply of readily available dietary sources of long-chain polyunsaturated fatty acids. The process of human evolution includes genetic drift and adaptation to local environments, in part through changes in mitochondrial and nuclear DNA. These genetic changes may underlie susceptibilities to some modern human pathologies including folate-responsive neural tube defects, diabetes, other age-related pathologies and mental health disorders.

The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy. The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated. It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method. The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.

Despite almost half a century of research for theory of mind, its evolutionary origin is largely unknown. This paper proposes that the evolutionary origin of theory of mind starts from the beginning of the human evolution to form hominins through bipedalism and the mixed habitat. The feet of the early hominins were still adapted for grasping trees rather than walking for long distances and running fast on the ground. The early hominins lived in the mixed habitat of grassy woodland with patches of denser forest, and freshwater springs. The difficulty of walking in the mixed habitat leads to division of labor for the home specialist group (small children, old people, and mothers with small children, and pregnant women) in the safe forest area and the exploration specialist group (young people without the care of small children) in the dangerous open area. The different tasks, attitudes, and mentalities in different specialist groups produce theory of mind as the ability to attribute different mental states to different specialist groups. (Uniformity of mind instead of theory of mind is for generalists without division of labor). The early Homo species with the open habitat developed theory of mind for hunter specialist group and gatherer specialist group. The middle Homo species with complex stone tools developed theory of mind for the cooperative specialist groups in the large production of complex stone tools. The late Homo species with complex social interaction developed theory of mind for mind reading to enhance cooperation and to detect cheaters in complex social interaction. For religion, the unusually harsh Upper Paleolithic Period developed theory of mind for imaginary specialists in terms of supernatural power, guidance, and comfort. Therefore, the three general types of theory of mind are for specialists in division of labor, mind reading in complex social interaction, and imaginary specialists in imaginary division of labor under harsh conditions. Self-awareness in the mirror self-recognition test is also explained.

BackgroundMagnetic resonance imaging (MRI) can diagnose meniscal lesions anatomically, while quantitative MRI can reflect the changes of meniscal histology and biochemical structure. Our study aims to explore the association between the measurement values obtained from synthetic magnetic resonance imaging (SyMRI) and Stoller grades. Additionally, we aim to assess the diagnostic accuracy of SyMRI in determining the extent of meniscus injury. This potential accuracy could contribute to minimizing unnecessary invasive examinations and providing guidance for clinical treatment.MethodsTotal of 60 (n=60) patients requiring knee arthroscopic surgery and 20 (n=20) healthy subjects were collected from July 2022 to November 2022. All subjects underwent conventional MRI and SyMRI. Manual measurements of the T1, T2 and proton density (PD) values were conducted for both normal menisci and the most severely affected position of injured menisci. These measurements corresponded to the Stoller grade of meniscus injuries observed in the conventional MRI. All patients and healthy subjects were divided into normal group, degeneration group and torn group according to the Stoller grade on conventional MRI. One-way analysis of variance (ANOVA) was employed to compare the T1, T2 and PD values of the meniscus among 3 groups. The accuracy of SyMRI in diagnosing meniscus injury was assessed by comparing the findings with arthroscopic observations. The diagnostic efficiency of meniscus degeneration and tear between conventional MRI and SyMRI were analyzed using McNemar test. Furthermore, a receiver operating characteristic curve (ROC curve) was constructed and the area under the curve (AUC) was utilized for evaluation.ResultsAccording to the measurements of SyMRI, there was no statistical difference of T1 value or PD value measured by SyMRI among the normal group, degeneration group and torn group, while the difference of T2 value was statistically significant among 3 groups (P=0.001). The arthroscopic findings showed that 11 patients were meniscal degeneration and 49 patients were meniscal tears. The arthroscopic findings were used as the gold standard, and the difference of T1 and PD values among the 3 groups was not statistically significant, while the difference of T2 values (32.81±2.51 of normal group, 44.85±3.98 of degeneration group and 54.42±3.82 of torn group) was statistically significant (P=0.001). When the threshold of T2 value was 51.67 (ms), the maximum Yoden index was 0.787 and the AUC value was 0.934.ConclusionsThe measurement values derived from SyMRI could reflect the Stoller grade, illustrating that SyMRI has good consistency with conventional MRI. Moreover, the notable consistency observed between SyMRI and arthroscopy suggests a potential role for SyMRI in guiding clinical diagnoses.