Altered resting‐state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross‐sectional and machine learning study

Irritable bowel syndrome (IBS) is a disorder involving dysfunctional brain–gut interactions characterized by chronic recurrent abdominal pain, altered bowel habits, and negative emotion. Previous studies have linked the habenula to the pathophysiology of negative emotion and pain. However, no studies to date have investigated habenular function in IBS patients. In this study, we investigated the resting‐state functional connectivity (rsFC) and effective connectivity of the habenula in 34 subjects with IBS and 34 healthy controls and assessed the feasibility of differentiating IBS patients from healthy controls using a machine learning method. Our results showed significantly enhanced rsFC of the habenula‐left dorsolateral prefrontal cortex (dlPFC) and habenula‐periaqueductal grey (PAG, dorsomedial part), as well as decreased rsFC of the habenula‐right thalamus (dorsolateral part), in the IBS patients compared with the healthy controls. Habenula‐thalamus rsFC was positively correlated with pain intensity (r = .467, p = .005). Dynamic causal modeling (DCM) revealed significantly decreased effective connectivity from the right habenula to the right thalamus in the IBS patients compared to the healthy controls that was negatively correlated with disease duration (r = ?.407, p = .017). In addition, IBS was classified with an accuracy of 71.5% based on the rsFC of the habenula‐dlPFC, habenula‐thalamus, and habenula‐PAG in a support vector machine (SVM), which was further validated in an independent cohort of subjects (N = 44, accuracy = 65.2%, p = .026). Taken together, these findings establish altered habenular rsFC and effective connectivity in IBS, which extends our mechanistic understanding of the habenula's role in IBS.     

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

70kVp联合深度学习算法改进儿童腹部双低CTA图像质量的研究

目的 评价70 kVp结合深度学习图像重组(DLIR)算法在低辐射低对比剂用量情况下,是否可以提升0.625 mm薄层小儿腹部CTA图像质量.方法 观察组选取连续的37例[7个月～14岁,平均(6.87±3.11)岁]腹部增强CTA检查,检查使用低辐射剂量,低对比剂检查方案,扫描采用70 kVp,对比剂用量0.8～1....     

From the Cover: Unlocking adults’ implicit statistical learning by cognitive depletion

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

Human learning is thought to be supported by the interactions between two basic memory systems of the brain, namely declarative and nondeclarative memory (1). Declarative memory is characterized by voluntary, explicit, attention-based processes, such as recall and recognition of facts/events, and is mediated by medial-temporal lobe and prefrontal cortex structures (2). Nondeclarative memory, also referred to as procedural memory, on the other hand is part of implicit memory and includes the acquisition of a heterogeneity of skills, habits, and procedures. It is mediated by basal ganglia, cerebellar, and neocortical structures, as well as parts of the prefrontal cortex [e.g., Broca''s area (3–5)].Accumulating evidence supports a competitive relationship between these two memory systems during human skill learning. Suppression of the declarative memory system by interventions like repetitive transcranial magnetic stimulation (TMS), distraction tasks, alcohol consumption, hypnosis, intake of benzodiazepines, or cognitive fatigue, can actually enhance performance in implicit, perceptual-motor learning tasks such as the serial-reaction time task (6–11) or intuitive reasoning tasks (12). These findings suggest that higher-level cognitive functions associated with declarative memory and supported by the prefrontal cortex can interfere with behavior that is naturally driven by implicit learning processes (13). However, it remains unresolved whether competing memory systems also affect implicit statistical learning abilities that are critical for the early, rapid acquisition of language in infants (14). This is an important question, as it could explain why infants and children pick up languages with less effort than adults (cf “What don’t we know?”) (15).Language acquisition involves many different memory and learning processes that are dependent on both procedural and declarative memory (2, 16). The first step for infants acquiring language is to gain knowledge about the phonological structure in one’s spoken language system, the probabilistic constraints on how speech sounds combine (i.e., phonotactic learning), and the segments of continuous speech (i.e., word forms) (17). Word form learning takes place already in the first 12 months of life and is an important precursor to vocabulary acquisition (i.e., mapping form to meaning) and more complex language acquisition (e.g., grammar) later in development (18). In the present study, we focus on statistical learning mechanisms that contribute to word segmentation and thus novel word form learning in the early stages of language acquisition.Statistical learning is generally known as the ability to pick up on patterns in the environment through extraction of frequent regularities and distributional properties. The term was first introduced in the field of cognitive psychology by the work of Saffran, Aslin, and Newport (1996) (19), who demonstrated that infants of only 8-mo-old can extract word boundaries and segment novel word forms from a continuous stream of speech sounds with no other cue than the transitional probabilities between syllables. Later, this learning was also demonstrated in older children and adults (20, 21) and across different domains (e.g., music and grammar) or modalities (e.g., auditory, visual, and motor) (22, 23), indicating that statistical learning is a largely continuous and domain-general learning mechanism for skill acquisition across the human life span.In a typical statistical learning experiment, participants are repeatedly exposed to patterned stimuli such as consonant strings from an artificial grammar, or recurrent syllable triplets. Learning is then typically assessed postexposure by using a two-alternative forced-choice recognition task in which triplets from the exposure stream are pitted against foils. Participants have to indicate which of the two triplets sounded most familiar, and above-chance accuracy is taken as indication of learning. Since statistical learning occurs without any instruction or intention to learn, it is often assumed to result in implicit memory representations (24). This view is also supported by the evidence that statistical learning occurs in infants and even in sleeping neonates (25). However, in recent work, Batterink and colleagues demonstrated that even without intention to learn, adults acquire mainly explicit knowledge of the novel word forms during statistical learning (26–29). This can be derived from the observation that participants’ performance was above chance when they were confident remembering the triplet but at chance when they were unconfident. Knowledge is implicit when participants lack awareness of what they have learned. This means that if participants perform also above chance when they are unconfident, knowledge is inferred to be implicit (30). In contrast, if they perform at chance level when confidence is low, no implicit knowledge is gained. Although statistical learning may produce additional implicit knowledge that cannot be assessed by the recognition and memory judgement tasks (e.g., ref. 28), Batterink’s earlier findings show that adults store the acquired word knowledge mainly in the explicit memory system.We and others have proposed that cognitive development and maturation of the prefrontal areas negatively affect language acquisition, such as word form or grammar learning (31–35). For instance, we showed that children outperform adults on the Hebb repetition learning paradigm (32, 33), a memory paradigm in which participants are asked to immediately recall syllable sequences that consist of hidden repeated word forms. Interestingly, in a follow-up study, we found that cognitive depletion by TMS to the left dorsolateral prefrontal cortex (DLPFC), an area closely related to declarative memory and cognitive control, enhanced Hebb performance in adult participants (34). This suggests that late-developing prefrontal cognitive mechanisms can induce changes in efficiently acquiring sequential language information from the environment, a finding that is largely in line with previously reported evidence in skill learning (13). Recently, we corroborated this idea further by showing enhanced phonotactic constraint learning in adults under cognitive fatigue (35). Based on these findings, we hypothesize that the higher cognitive control system could reduce access to implicit memory processes in adults, thereby making them less efficient in language acquisition relative to infants and children. This idea is in line with the well-known less-is-more hypothesis that attributes developmental changes in language acquisition, such as phonology and grammar, to maturational changes in attention and memory capacities (36–38). In our previous work, participants were explicitly asked to memorize (34) or produce (35) syllable sequences and thus exposure to the novel language was not passive, or “infant like.” Moreover, we did not separate implicit and explicit memory representations. Thus it remains unresolved how higher-order cognitive functions affect acquisition of implicit linguistic knowledge during passive listening to continuous speech using statistical learning mechanisms that support infant language acquisition (23, 39).The aim of the current study was to directly address this question using the auditory statistical learning paradigm. In particular, we aimed to determine whether a temporary depletion of the higher cognitive control system, using two different interventions, can unlock adults’ implicit statistical learning processes that serve infant word segmentation. To investigate this, we exposed young adults to continuous streams of syllables with, unknown to them, repeating three-syllable pseudowords, while watching a silent film. In the first experiment, inhibitory continuous theta-burst stimulation was used to induce a long-lasting disruption in left DLPFC or a control site prior to exposure, similar to the method used in Smalle et al., 2017 (34). In the second experiment, participants first performed an effortful dual working-memory task under high– or low–cognitive-load( HCL and LCL, respectively) conditions, which induces cognitive fatigue that hampers subsequent cognitive performance (7, 35, 40), or did not perform a cognitive load task prior to the language exposure (control or no-load condition). Our primary measure of statistical learning was the offline recognition of the hidden words, which was assessed 15 min after exposure. This was combined with a memory judgement procedure, which measured how confident the participants were that they remembered the hidden words. This task dissociates explicit versus implicit memory representations (e.g., refs. 27–29, 41). In both experiments, electroencephalography (EEG) was also measured during the 20-min language exposure in order to investigate an online perceptual component as second independent measure of statistical learning. Research has shown that the steady-state response of the brain shows a decrease at the frequency of individual syllables and an increase at the rhythm of three-syllable words while listening to continuous sound streams that consist of repeating three-syllable structures. This shift in neural entrainment indicates online statistical learning of novel words as a function of auditory exposure (29). Overall, we predicted that TMS-induced disruption of the DLPFC (in experiment 1) and cognitive fatigue (in experiment 2) would enhance statistical language learning and especially strengthen implicit memory representations for the hidden novel words.     

铅对小鼠学习记忆及自主活动的影响

目的研究铅对小鼠学习记忆及自主活动的影响.方法将经过行为学筛选的48只2月龄昆明小鼠随机分成4组,醋酸铅组(简称成年组)、生理盐水组、假手术组及无处理组,每组12只,雌雄各半;将12只2周龄昆明小鼠设为幼年组.成年组及幼年组脑室内注射1μl 1mol/L醋酸铅,生理盐水组注射生理盐水1μ1,假手术组不注射,无处理组不进行任何干预.于脑室内注射后3 d、5 d、1周、2周及3周运用程控自主活动箱及LashleyⅢ水迷宫检测小鼠活动度和学习记忆能力的变化.结果成年组水迷宫潜伏期明显延长,成年组及幼年组3 d～3周水迷宫潜伏期分别与生理盐水组、假手术组及无处理组比较,差异均有统计学意义(均P＜0.01).成年组5 d及1周自主活动次数增加,与生理盐水及假手术组比较,差异均有统计学意义(均P＜0.05),幼年组5 d、1周及2周自主活动次数增加,与生理盐水及假手术组比较,差异均有统计学意义(P＜0.05或P＜0.01).结论脑室内注射醋酸铅能明显降低小鼠的学习记忆功能,增加自主活动次数.     

我院聘用护士自主学习中存在的问题与干预对策

为了解医院聘用护士自主学习现状,分析存在的问题,制定有效的干预措施,提高护士自主学习的能力。采用文献计量学方法,对全院聘用护士2005年度业务学习及读书应用项目的资料来源、内容和记录方法进行统计。结果显示,学习笔记及读书应用项目,来源于各类护理专业统计源期刊比例偏低,学习笔记记录方法欠科学,不便查阅。读书应用项目总体质量较低。针对护士在自主学习中,利用信息资源和主动选择利用有效信息的能力较差,缺乏科学、有效的读书方法和积累整理资料的能力等问题,提出引导护士掌握科学、有效的学习方法和笔记记录的方法;指导护士理论联系实际,积极开展读书应用活动;充分发挥医院电子网络的作用,进行广泛阅读,提高聘用护士获取信息的能力。     

Neural Representations of Food-Related Attributes in the Human Orbitofrontal Cortex during Choice Deliberation in Anorexia Nervosa

Decisions about what to eat recruit the orbitofrontal cortex (OFC) and involve the evaluation of food-related attributes such as taste and health. These attributes are used differently by healthy individuals and patients with disordered eating behavior, but it is unclear whether these attributes are decodable from activity in the OFC in both groups and whether neural representations of these attributes are differentially related to decisions about food. We used fMRI combined with behavioral tasks to investigate the representation of taste and health attributes in the human OFC and the role of these representations in food choices in healthy women and women with anorexia nervosa (AN). We found that subjective ratings of tastiness and healthiness could be decoded from patterns of activity in the OFC in both groups. However, health-related patterns of activity in the OFC were more related to the magnitude of choice preferences among patients with AN than healthy individuals. These findings suggest that maladaptive decision-making in AN is associated with more consideration of health information represented by the OFC during deliberation about what to eat.SIGNIFICANCE STATEMENT An open question about the OFC is whether it supports the evaluation of food-related attributes during deliberation about what to eat. We found that healthiness and tastiness information was decodable from patterns of neural activity in the OFC in both patients with AN and healthy controls. Critically, neural representations of health were more strongly related to choices in patients with AN, suggesting that maladaptive overconsideration of healthiness during deliberation about what to eat is related to activity in the OFC. More broadly, these results show that activity in the human OFC is associated with the evaluation of relevant attributes during value-based decision-making. These findings may also guide future research into the development of treatments for AN.     

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020—March 2021) Using a Statistical Learning Strategy

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.     

Automated Classification of 6-n-Propylthiouracil Taster Status with Machine Learning

Several studies have used taste sensitivity to 6-n-propylthiouracil (PROP) to evaluate interindividual taste variability and its impact on food preferences, nutrition, and health. We used a supervised learning (SL) approach for the automatic identification of the PROP taster categories (super taster (ST); medium taster (MT); and non-taster (NT)) of 84 subjects (aged 18–40 years). Biological features determined from subjects were included for the training system. Results showed that SL enables the automatic identification of objective PROP taster status, with high precision (97%). The biological features were classified in order of importance in facilitating learning and as prediction factors. The ratings of perceived taste intensity for PROP paper disks (50 mM) and PROP solution (3.2 mM), along with fungiform papilla density, were the most important features, and high estimated values pushed toward ST prediction, while low values leaned toward NT prediction. Furthermore, TAS2R38 genotypes were significant features (AVI/AVI, PAV/PAV, and PAV/AVI to classify NTs, STs, and MTs, respectively). These results, in showing that the SL approach enables an automatic, immediate, scalable, and high-precision classification of PROP taster status, suggest that it may represent an objective and reliable tool in taste physiology studies, with applications ranging from basic science and medicine to food sciences.