Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.

While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quantitative trait loci [eQTLs], and epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (https://dice-database.org).

Policies, projections, and the social cost of carbon: Results from the DICE-2023 model.

The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon.

Physics of microscale freeform 3D printing of ice.

Ice is emerging as a promising sacrificial material in the rapidly expanding area of advanced manufacturing for creating precise 3D internal geometries. Freeform 3D printing of ice (3D-ICE) can produce microscale ice structures with smooth walls, hierarchical transitions, and curved and overhang features. However, controlling 3D-ICE is challenging due to an incomplete understanding of its complex physics involving heat transfer, fluid dynamics, and phase changes. This work aims to advance our understanding of 3D-ICE physics by combining numerical modeling and experimentation. We developed a 2D thermo-fluidic model to analyze the transition from layered to continuous printing and a 3D thermo-fluidic model for the oblique deposition, which enables curved and overhang geometries. Experiments are conducted and compared with model simulations. We found that high droplet deposition rates enable the continuous deposition mode with a sustained liquid cap on top of the ice, facilitating smooth geometries. The diameter of ice structures is controlled by the droplet deposition frequency. Oblique deposition causes unidirectional spillover of the liquid cap and asymmetric heat transfer at the freeze front, rotating the freeze front. These results provide valuable insights for reproducible 3D-ICE printing that could be applied across various fields, including tissue engineering, microfluidics, and soft robotics.

Changes in decision-making function in patients with subacute mild traumatic brain injury.

Although awareness regarding patients with mild traumatic brain injury has increased, they have not received sufficient attention in clinics; hence, many patients still experience only partial recovery. Deficits in decision-making function are frequently experienced by these patients. Accurate identification of impairment in the early stages after brain injury is particularly crucial for timely intervention and the prevention of long-term cognitive consequences. Therefore, we investigated the changes in decision-making ability under tasks of ambiguity and risk in patients with mild traumatic brain injury with a rule-based neuropsychological paradigm. In this study, patients (n = 39) and matched healthy controls (n = 38) completed general neuropsychological background tests and decision-making tasks (Iowa Gambling Task and Game of Dice Task). We found that patients had extensive cognitive impairment in general attention, memory and information processing speed in the subacute phase, and confirmed that patients had different degrees of impairment in decision-making abilities under ambiguity and risk. Furthermore, the decline of memory and executive function may be related to decision-making dysfunction.

Deepening the decisional processes under value-based conditions in patients affected by Parkinson's disease: A comparative study.

Patients affected by Parkinson's disease (PD) display a tendency toward making risky choices in value-based conditions. Possible causes may encompass the pathophysiologic characteristics of PD that affect neural structures pivotal for decision making (DM) and the dopaminergic medications that may bias choices. Nevertheless, excluding patients with concurrent impulse control disorders, results are few and mixed. Conversely, other factors, such as individual differences (e.g., emotional state, impulsivity, consideration for future consequences) and cognitive functioning, in particular executive functions (EFs), are involved, even though few studies investigated their possible role. The present study investigated (1) the differences in value-based DM between 33 patients with PD without impulse control disorders and 33 matched healthy controls, and (2) the relationships among decisional performances, EFs, and individual differences in a group of 42 patients with PD who regularly undertake dopaminergic medications. All participants underwent an individual assessment to investigate value-based DM, cognitive abilities, and individual differences associated with DM. Nonparametric analyses showed the presence of riskier decisions in patients compared with healthy controls, depending on the characteristics of the decisional situation. Moreover, parameters of the decisional tasks involving the number of risky choices were significantly related to the posology of dopaminergic medications, EFs, and individual differences. Findings were discussed, highlighting possible clinical implications.

PSAA-nnUNet: An Efficient Method for CT Carotid Artery Image Segmentation.

Carotid artery (CA) stenosis (CAS) constitutes a significant factor to ischaemic cerebrovascular events which exhibiting no overt symptoms in the early stages. Early detection of CAS can prevent ischaemic stroke and improve patient prognosis. In this study, we developed a non-invasive CAS automatic assessment method based on deep learning, intended for the early detection of CAS with CT imaging. The method proposed in this paper consists of three main components. First, we utilised thresholding and the Hessian-based Frangi filter to eliminate irrelevant tissue and enhance vascular structures. Second, we introduced a novel neural network named parameter shared axial attention (PSAA)-nnUNet for the automatic segmentation of CA. Finally, we assessed the degree of CAS with the North American Symptomatic Carotid Endarterectomy Trial (NASCET) formula. The PSAA-nnUNet algorithm proposed in this study achieved a segmentation accuracy of 0.82. The non-invasive CAS automatic assessment method based on PSAA-nnUNet exhibits excellent accuracy and great application potential.

Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.

PURPOSE: To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD). MATERIALS/METHODS: The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors. RESULTS: The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet-LLL first order Maximum, wavelet-LLL glcm MCC, wavelet-LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9 mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC. CONCLUSIONS: This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto-segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto-segmentation methods and enhance trainees' segmentation skills in medical training and education.

The Feasibility and Accuracy of Holographic Navigation with Laser Crosshair Simulator Registration on a Mixed-Reality Display.

Addressing conventional neurosurgical navigation systems' high costs and complexity, this study explores the feasibility and accuracy of a simplified, cost-effective mixed reality navigation (MRN) system based on a laser crosshair simulator (LCS). A new automatic registration method was developed, featuring coplanar laser emitters and a recognizable target pattern. The workflow was integrated into Microsoft's HoloLens-2 for practical application. The study assessed the system's precision by utilizing life-sized 3D-printed head phantoms based on computed tomography (CT) or magnetic resonance imaging (MRI) data from 19 patients (female/male: 7/12, average age: 54.4 ± 18.5 years) with intracranial lesions. Six to seven CT/MRI-visible scalp markers were used as reference points per case. The LCS-MRN's accuracy was evaluated through landmark-based and lesion-based analyses, using metrics such as target registration error (TRE) and Dice similarity coefficient (DSC). The system demonstrated immersive capabilities for observing intracranial structures across all cases. Analysis of 124 landmarks showed a TRE of 3.0 ± 0.5 mm, consistent across various surgical positions. The DSC of 0.83 ± 0.12 correlated significantly with lesion volume (Spearman rho = 0.813, p < 0.001). Therefore, the LCS-MRN system is a viable tool for neurosurgical planning, highlighting its low user dependency, cost-efficiency, and accuracy, with prospects for future clinical application enhancements.

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation.

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model's potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

Test-retest reliability of a finger-tapping fMRI task in a healthy population.

Measuring brain activity during functional MRI (fMRI) tasks is one of the main tools to identify brain biomarkers of disease or neural substrates associated with specific symptoms. However, identifying correct biomarkers relies on reliable measures. Recently, poor reliability was reported for task-based fMRI measures. The present study aimed to demonstrate the reliability of a finger-tapping fMRI task across two sessions in healthy participants. Thirty-one right-handed healthy participants aged 18-60 years took part in two MRI sessions 3 weeks apart during which we acquired finger-tapping task-fMRI. We examined the overlap of activations between sessions using Dice similarity coefficients, assessing their location and extent. Then, we compared amplitudes calculating intraclass correlation coefficients (ICCs) in three sets of regions of interest (ROIs) in the motor network: literature-based ROIs (10-mm-radius spheres centred on peaks of an activation likelihood estimation), anatomical ROIs (regions as defined in an atlas) and ROIs based on conjunction analyses (superthreshold voxels in both sessions). Finger tapping consistently activated expected regions, for example, left primary sensorimotor cortices, premotor area and right cerebellum. We found good-to-excellent overlap of activations for most contrasts (Dice coefficients: .54-.82). Across time, ICCs showed large variability in all ROI sets (.04-.91). However, ICCs in most ROIs indicated fair-to-good reliability (mean = .52). The least specific contrast consistently yielded the best reliability. Overall, the finger-tapping task showed good spatial overlap and fair reliability of amplitudes on group level. Although caution is warranted in interpreting correlations of activations with other variables, identification of activated regions in response to a task and their between-group comparisons are still valid and important modes of analysis in neuroimaging to find population tendencies and differences.

The impact of mild cognitive impairment on decision-making under explicit risk conditions: Evidence from the Personality and Total Health (PATH) Through Life longitudinal study.

OBJECTIVE: Previous research has indicated that cognition and executive function are associated with decision-making, however the impact of mild cognitive impairment (MCI) on decision-making under explicit risk conditions is unclear. This cross-sectional study examined the impact of MCI, and MCI subtypes, on decision-making on the Game of Dice Task (GDT), among a cohort of older adults. METHOD: Data from 245 older adult participants (aged 72-78 years) from the fourth assessment of the Personality and Total Health Through Life study were analyzed. A diagnostic algorithm identified 103 participants with MCI, with subtypes of single-domain amnestic MCI (aMCI-single; n = 38), multi-domain amnestic MCI (aMCI-multi; n = 31), and non-amnestic MCI (n = 33), who were compared with an age-, sex-, education-, and income-matched sample of 142 cognitively unimpaired older adults. Decision-making scores on the GDT (net score, single number choices, and strategy changes) were compared between groups using nonparametric tests. RESULTS: Participants with MCI showed impaired performance on the GDT, with higher frequencies of single number choices and strategy changes. Analyses comparing MCI subtypes indicated that the aMCI-multi subtype showed increased frequency of single number choices compared to cognitively unimpaired participants. Across the sample of participants, decision-making scores were associated with measures of executive function (cognitive flexibility and set shifting). CONCLUSION: MCI is associated with impaired decision-making performance under explicit risk conditions. Participants with impairments in multiple domains of cognition showed the clearest impairments. The GDT may have utility in discriminating between MCI subtypes.

Counting on random number generation: Uncovering mild executive dysfunction in congenital heart disease.

Congenital heart disease (CHD) is associated with various neurocognitive deficits, particularly targeting executive functions (EFs), of which random number generation (RNG) is one indicator. RNG has, however, never been investigated in CHD. We administered the Mental Dice Task (MDT) to 67 young adults with CHD and 55 healthy controls. This 1-minute-task requires the generation of numbers 1 to 6 in a random sequence. RNG performance was correlated with a global EF score. Participants underwent MRI to examine structural-volumetric correlates of RNG. Compared to controls, CHD patients showed increased backward counting, reflecting deficient inhibition of automatized behavior. They also lacked a small-number bias (higher frequency of small relative to large numbers). RNG performance was associated with global EF scores in both groups. In CHD patients, MRI revealed an inverse association of counting bias with most of the volumetric measurements and the amount of small numbers was positively associated with corpus callosum volume, suggesting callosal involvement in the "pseudoneglect in number space". In conclusion, we found an impaired RNG performance in CHD patients, which is associated with brain volumetric measures. RNG, reportedly resistant to learning effects, may be an ideal task for the longitudinal assessment of EFs in patients with CHD.

An intraoperative artificial intelligence system identifying anatomical landmarks for laparoscopic cholecystectomy: a prospective clinical feasibility trial (J-SUMMIT-C-01).

BACKGROUND: We have implemented Smart Endoscopic Surgery (SES), a surgical system that uses artificial intelligence (AI) to detect the anatomical landmarks that expert surgeons base on to perform certain surgical maneuvers. No report has verified the use of AI-based support systems for surgery in clinical practice, and no evaluation method has been established. To evaluate the detection performance of SES, we have developed and established a new evaluation method by conducting a clinical feasibility trial. METHODS: A single-center prospective clinical feasibility trial was conducted on 10 cases of LC performed at Oita University hospital. Subsequently, an external evaluation committee (EEC) evaluated the AI detection accuracy for each landmark using five-grade rubric evaluation and DICE coefficient. We defined LM-CBD as the expert surgeon's "judge" of the cystic bile duct in endoscopic images. RESULTS: The average detection accuracy on the rubric by the EEC was 4.2 ± 0.8 for the LM-CBD. The DICE coefficient between the AI detection area of the LM-CBD and the EEC members' evaluation was similar to the mean value of the DICE coefficient between the EEC members. The DICE coefficient was high score for the case that was highly evaluated by the EEC on a five-grade scale. CONCLUSION: This is the first feasible clinical trial of an AI system designed for intraoperative use and to evaluate the AI system using an EEC. In the future, this concept of evaluation for the AI system would contribute to the development of new AI navigation systems for surgery.

Non-Expert Markings of Active Chronic Graft-Versus-Host Disease Photographs: Optimal Metrics of Training Effects.

Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm2 (29%) pre-training to 14 cm2 (24%) post-training. Total redness error decreased from 122 a*·cm2 (26%) to 95 a*·cm2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture.

[Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function].

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.

Breathing versus feeding in the Pacific hagfish.

Hagfish represent the oldest extant connection to the ancestral vertebrates, but their physiology is not well understood. Using behavioural (video), physiological (respirometry, flow measurements), classical morphological (dissection, silicone injection) and modern imaging approaches (micro-MRI, DICE micro-CT), we examined the interface between feeding and the unique breathing mechanism (nostril opening, high-frequency velum contraction, low-frequency gill pouch contraction and pharyngo-cutaneous duct contraction) in the Pacific hagfish, Eptatretus stoutii. A video tour via micro-MRI is presented through the breathing and feeding passages. We have reconciled an earlier disagreement as to the position of the velum chamber, which powers inhalation through the nostril, placing it downstream of the merging point of the food and water passage, such that the oronasal septum terminates at the anterior end of the velum chamber. When feeding occurs by engulfment of large chunks by the dental plates, food movement through the chamber may transiently interfere with breathing. Swallowing is accelerated by peristaltic body undulation involving the ventral musculature, and is complete within 5 s. After a large meal (anchovy, 20% body mass), hagfish remain motionless, defaecating bones and scales at 1.7 days and an intestinal peritrophic membrane at 5 days. O2 consumption rate approximately doubles within 1 h of feeding, remaining elevated for 12-24 h. This is achieved by combinations of elevated O2 utilization and ventilatory flow, the latter caused by varying increases in velar contraction frequency and stroke volume. Additional imaging casts light on the reasons for the trend for greater O2 utilization by more posterior pouches and the pharyngo-cutaneous duct in fasted hagfish.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Reliability of the fMRI-based assessment of self-evaluation in individuals with internet gaming disorder.

The self-concept-defined as the cognitive representation of beliefs about oneself-determines how individuals view themselves, others, and their actions. A negative self-concept can drive gaming use and internet gaming disorder (IGD). The assessment of the neural correlates of self-evaluation gained popularity to assess the self-concept in individuals with IGD. This attempt, however, seems to critically depend on the reliability of the investigated task-fMRI brain activation. As first study to date, we assessed test-retest reliability of an fMRI self-evaluation task. Test-retest reliability of neural brain activation between two separate fMRI sessions (approximately 12 months apart) was investigated in N = 29 healthy participants and N = 11 individuals with pathological internet gaming. We computed reliability estimates for the different task contrasts (self, a familiar, and an unknown person) and the contrast (self > familiar and unknown person). Data indicated good test-retest reliability of brain activation, captured by the "self", "familiar person", and "unknown person" contrasts, in a large network of brain regions in the whole sample (N = 40) and when considering both experimental groups separately. In contrast to that, only a small set of brain regions showed moderate to good reliability, when investigating the contrasts ("self > familiar and unknown person"). The lower reliability of the contrast can be attributed to the fact that the constituting contrast conditions were highly correlated. Future research on self-evaluation should be cautioned by the findings of substantial local reliability differences across the brain and employ methods to overcome these limitations.

Test-retest reliability of neural alcohol cue-reactivity: Is there light at the end of the magnetic resonance imaging tube?

Over the last decades, the assessment of alcohol cue-reactivity gained popularity in addiction research, and efforts were undertaken to establish neural biomarkers. This attempt however depends on the reliability of cue-induced brain activation. Thus, we assessed test-retest reliability of alcohol cue-reactivity and its implications for imaging studies in addiction. We investigated test-retest reliability of alcohol cue-induced brain activation in 144 alcohol-dependent patients over 2 weeks. We computed established reliability estimates, such as intraclass correlation (ICC), Dice and Jaccard coefficients, for the three contrast conditions of interest: 'alcohol', 'neutral' and the 'alcohol versus neutral' difference contrast. We also investigated how test-retest reliability of the different contrasts affected the capacity to establishing associations with clinical data and determining effect size estimates. Whereas brain activation, indexed by the constituting contrast conditions 'alcohol' and 'neutral' separately, displayed overall moderate (ICC > 0.4) to good (ICC > 0.75) test-retest reliability in areas of the mesocorticolimbic system, the difference contrast 'alcohol versus neutral' showed poor overall reliability (ICC < 0.40), which was related to the intercorrelation between the constituting conditions. Data simulations and analyses of craving data confirmed that the low reliability of the difference contrast substantially limited the capacity to establish associations with clinical data and precisely estimate effect sizes. Future research on alcohol cue-reactivity should be cautioned by the low reliability of the common 'alcohol versus neutral' difference contrast. We propose that this limitation can be overcome by using the constituent task conditions as an individual difference measure, when intending to longitudinally monitor brain responses.

Brain tumour segmentation based on an improved U-Net.

BACKGROUND: Automatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours. METHODS: To solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding-decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics. RESULTS: The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: Our algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.