Hierarchical dynamic coding coordinates speech comprehension in the brain.

Speech comprehension requires the human brain to transform an acoustic waveform into meaning. To do so, the brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how these hierarchical features are generated and continuously coordinated. Here, we propose that each linguistic feature is dynamically represented in the brain to simultaneously represent successive events. To test this 'Hierarchical Dynamic Coding' (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and integration of a comprehensive hierarchy of language features spanning acoustic, phonetic, sub-lexical, lexical, syntactic and semantic representations. For this, we recorded 21 participants with magnetoencephalography (MEG), while they listened to two hours of short stories. Our analyses reveal three main findings. First, the brain incrementally represents and simultaneously maintains successive features. Second, the duration of these representations depend on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting the interference between successive features. Overall, HDC reveals how the human brain continuously builds and maintains a language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.

Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval.

The ability to compose successive words into a meaningful phrase is a characteristic feature of human cognition, yet its neural mechanisms remain incompletely understood. Here, we analyze the cortical mechanisms of semantic composition using magnetoencephalography (MEG) while participants read one-word, two-word, and five-word noun phrases and compared them with a subsequent image. Decoding of MEG signals revealed three processing stages. During phrase comprehension, the representation of individual words was sustained for a variable duration depending on phrasal context. During the delay period, the word code was replaced by a working-memory code whose activation increased with semantic complexity. Finally, the speed and accuracy of retrieval depended on semantic complexity and was faster for surface than for deep semantic properties. In conclusion, we propose that the brain initially encodes phrases using factorized dimensions for successive words but later compresses them in working memory and requires a period of decompression to access them.

Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing.

The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.

Effect of Adapted Mindfulness Training in Participants With Elevated Office Blood Pressure: The MB-BP Study: A Randomized Clinical Trial.

Background Hypertension is a leading risk factor for cardiovascular disease. Despite availability of effective lifestyle and medication treatments, blood pressure (BP) is poorly controlled in the United States. Mindfulness training may offer a novel approach to improve BP control. The objective was to evaluate the effects of Mindfulness-Based Blood Pressure Reduction (MB-BP) versus enhanced usual care control on unattended office systolic BP. Methods and Results Methods included a parallel-group phase 2 randomized clinical trial conducted from June 2017 to November 2020. Follow-up time was 6 months. Outcome assessors and data analyst were blinded to group allocation. Participants had elevated unattended office BP (≥120/80 mm Hg). We randomized 201 participants to MB-BP (n=101) or enhanced usual care control (n=100). MB-BP is a mindfulness-based program adapted for elevated BP. Loss-to-follow-up was 17.4%. The primary outcome was change in unattended office systolic BP at 6 months. A total of 201 participants (58.7% women; 81.1% non-Hispanic White race and ethnicity; mean age, 59.5 years) were randomized. Results showed that MB-BP was associated with a 5.9-mm Hg reduction (95% CI, -9.1 to -2.8 mm Hg) in systolic BP from baseline and outperformed the control group by 4.5 mm Hg at 6 months (95% CI, -9.0 to -0.1 mm Hg) in prespecified analyses. Plausible mechanisms with evidence to be impacted by MB-BP versus control were sedentary activity (-350.8 sitting min/wk [95% CI, -636.5 to -65.1] sitting min/wk), Dietary Approaches to Stop Hypertension diet (0.32 score [95% CI, -0.04 to 0.67]), and mindfulness (7.3 score [95% CI, 3.0-11.6]). Conclusions A mindfulness-based program adapted for individuals with elevated BP showed clinically relevant reductions in systolic BP compared with enhanced usual care. Mindfulness training may be a useful approach to improve BP. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03256890 and NCT03859076.

Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.SIGNIFICANCE STATEMENT Starting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multiword sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep neural language models, artificial neural networks trained on text and performing very well on many natural language processing tasks. Then, using a unique combination of MEG and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

Evidence of a predictive coding hierarchy in the human brain listening to speech.

Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.

Endodontic malpractice litigations in the United States from 2000 to 2021.

Background/purpose: Little is known regarding the outcomes and distinguishing characteristics of lawsuits related to endodontic procedures. This study used a verdict-based data from United States of America to analyze the factors associated with endodontic malpractice lawsuits and mitigate the risk of litigation. Materials and methods: The LexisNexis legal database was used to search for endodontic malpractice cases from January 1, 2000 to December 31, 2021 using the terms "medical malpractice" and (I) "endodontist" (II) "endodontics" (III) "root canal" (IV) "dental pulp." Each case was reviewed for reported medical characteristics and litigation outcomes. Results: A total of 650 cases were initially identified, and 97 cases were included in the final analysis. Eighty-four (86.6%) of the 97 defendants were general practitioners; 42 cases favored the plaintiff, 53 (54.6%) favored the defendant, 1 was partial win/loss, and 1 was settled. The annual case mean was 4.41 ± 2.17 (Mean ± SD). The major allegations favored for the patients involving paresthesia, root perforation, rubber dam not use, wrong tooth therapy, and infections. Plaintiffs who claimed with post-procedural reasons had a significantly higher winning rate than non-post-procedural reasons (P < 0.05). Conclusion: In the present study, 54.6% of endodontic litigation favored the dentists in the US. The authors recommend that general practitioners refer complicated cases to endodontists and treat carefully to avoid paresthesia, canal perforation and infections. Clinicians should always diagnose and treat correctly, shared decision making with the patient, use rubber dam routinely, and timely management to prevent malpractice claims.

Neural dynamics of phoneme sequences reveal position-invariant code for content and order.

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as sequences of discrete speech sounds, which are used to recognise discrete words. To examine how the human brain appropriately sequences the speech signal, we recorded two-hour magnetoencephalograms from 21 participants listening to short narratives. Our analyses show that the brain continuously encodes the three most recently heard speech sounds in parallel, and maintains this information long past its dissipation from the sensory input. Each speech sound representation evolves over time, jointly encoding both its phonetic features and the amount of time elapsed since onset. As a result, this dynamic neural pattern encodes both the relative order and phonetic content of the speech sequence. These representations are active earlier when phonemes are more predictable, and are sustained longer when lexical identity is uncertain. Our results show how phonetic sequences in natural speech are represented at the level of populations of neurons, providing insight into what intermediary representations exist between the sensory input and sub-lexical units. The flexibility in the dynamics of these representations paves the way for further understanding of how such sequences may be used to interface with higher order structure such as lexical identity.

Phenytoin induces connective tissue growth factor (CTGF/CCN2) production through NADPH oxidase 4-mediated latent TGFß1 activation in human gingiva fibroblasts: Suppression by curcumin.

OBJECTIVE AND BACKGROUND: Gingival overgrowth (GO) is a common side effect of some drugs such as anticonvulsants, immunosuppressant, and calcium channel blockers. Among them, the antiepileptic agent phenytoin is the most common agent related to this condition due to its high incidence. Transforming growth factor ß (TGFß) importantly contributes to the pathogenesis of GO. Connective tissue growth factor (CTGF or CCN2) is a key mediator of tissue fibrosis and is positively associated with the degree of fibrosis in GO. We previously showed that Src, c-jun N-terminal kinase, and Smad3 mediate TGFß1-induced CCN2 protein expression in human gingival fibroblasts (HGFs). This study investigates whether phenytoin can induce CCN2 synthesis through activated latent TGFß in HGFs and its mechanisms. METHODS: CCN2 synthesis, latent TGFß1 activation, and cellular reactive oxygen species (ROS) generation in HGFs were studied using western blot analysis, a TGFß1 Emax® ImmunoAssay System, and 2',7'-dichlorodihydrofluorescein diacetate (an oxidation-sensitive fluorescent probe), respectively. RESULTS: Phenytoin significantly stimulated CCN2 synthesis, latent TGFß1 activation, and ROS generation in HGFs. Addition of an TGFß-neutralizing antibody, TGFß receptor kinase inhibitor SB431542, and Smad3 inhibitor SIS3 completely inhibited phenytoin-induced CCN2 synthesis. General antioxidant N-acetylcysteine, NADPH oxidase (NOX) inhibitor diphenylene iodonium, and specific NOX4 inhibitor plumbagin almost completely suppressed phenytoin-induced total cellular ROS and latent TGFß1 activation. Curcumin dose-dependently decreased phenytoin-induced TGFß1 activation and CCN2 synthesis in HGFs. CONCLUSIONS: Our findings indicated that NOX4-derived ROS play pivotal roles in phenytoin-induced latent TGFß1 activation. Molecular targeting the phenytoin/NOX4/ROS/TGFß1 pathway may provide promising strategies for the prevention and treatment of GO. Curcumin-inhibited phenytoin-induced CCN2 synthesis is caused by the suppression of latent TGFß1 activation.

Deep language algorithms predict semantic comprehension from brain activity.

Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of automatic translation, summarization and dialogue. However, whether these models encode information that relates to human comprehension still remains controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but they also predict the extent to which subjects understand the corresponding narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear mapping model to predict brain activity from GPT-2's activations. Finally, we show that this mapping reliably correlates ([Formula: see text]) with subjects' comprehension scores as assessed for each story. This effect peaks in the angular, medial temporal and supra-marginal gyri, and is best accounted for by the long-distance dependencies generated in the deep layers of GPT-2. Overall, this study shows how deep language models help clarify the brain computations underlying language comprehension.

Effects of the Mindfulness-Based Blood Pressure Reduction (MB-BP) program on depression and neural structural connectivity.

BACKGROUND: Hypertension-related illnesses are a leading cause of disability and death in the United States, where hypertension prevalence in adults is 46%, with only half of those afflicted having it under control. Due to the significant challenges in long-term efficacy and adverse effects associated with pharmacological interventions, there is an eminent need for complimentary approaches for treating hypertension. Although initial studies of the Mindfulness-Based Blood Pressure Reduction program (MB-BP) indicate that this novel 8-week intervention is effective at inducing lasting decreases in blood pressure, the neural correlates are unknown. METHODS: The objectives of this study were to identify structural neural correlates of MB-BP using diffusion tensor magnetic resonance imaging (DTI) and assess potential correlations with key clinical outcomes. RESULTS: In a subset of participants (14 MB-BP, 22 controls) from a larger stage IIa randomized controlled trial, MB-BP participants exhibited increased interoception and decreased depressive symptoms compared to controls. Analyses of DTI data revealed significant group differences in multiple white matter neural tracts associated with the limbic system and/or blood pressure. Specific changes in neural structural connectivity were significantly associated with measures of interoception and depression. LIMITATIONS: Limitations include small sample size (leading to insufficient power in the analysis of blood pressure) and the study duration (3 months). The main MRI limitation is suboptimal resolution in areas of extensive neural tract crossings. CONCLUSIONS: It is concluded that MB-BP induces alterations in brain structural connectivity which could mediate beneficial changes in depression and interoceptive awareness in individuals with hypertension.

Brains and algorithms partially converge in natural language processing.

Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing.

Severity of periodontitis and salivary interleukin-1ß are associated with psoriasis involvement.

BACKGROUND/PURPOSE: Both psoriasis and periodontal diseases are characterized by an exaggerated immune response to the microbiota residing on epithelial surfaces. This study aimed to explore the associations between the severity of psoriasis and periodontal destruction in patients with psoriasis. METHODS: Thirty-three patients diagnosed with psoriasis were referred from the dermatology clinic of National Taiwan University Hospital. Full-mouth periodontal examination was performed and saliva was collected after patients signed informed consent forms. The Psoriasis Area Severity Index (PASI) as well as clinical periodontal parameters including probing depth (PD), plaque index (PI), gingival index (GI), and clinical attachment level (CAL) were evaluated. Salivary cytokines including interleukin (IL)-1ß, IL-12, IL-17, interferon-γ, and tumor necrosis factor (TNF)-α were tested with the Luminex Bio-Plex system. Anti-inflammatory medication, tobacco use, and underlying comorbidities were included in the analysis. RESULTS: Baseline PASI was significantly associated with PI. PASI at follow-up was positively correlated with CAL ≥ 4 mm (%) and saliva IL-1ß levels. Psoriasis patients who used non-steroidal anti-inflammatory drugs or topical steroids had significantly lower GI, PD ≥ 4 mm (%), and saliva IL-1ß and TNF-α levels. Moreover, a history of tobacco use was associated with higher PD ≥ 4 mm (%). CONCLUSION: PI, CAL, and salivary IL-1ß were associated with PASI. Periodontal severity was associated with psoriasis involvement. Periodontal inflammation in psoriasis may be modified by anti-inflammatory medication and tobacco use. Additional large-scale longitudinal and mechanistic studies are needed.

Depression Predicts Chronic Pain Interference in Racially Diverse, Income-Disadvantaged Patients.

BACKGROUND: Chronic pain is one of the most common reason adults seek medical care in the United States, with prevalence estimates ranging from 11% to 40%. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses, including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. OBJECTIVE AND DESIGN: The objective of the present study was to use a blended analytical approach of machine learning and regression analyses to evaluate the potential relationship between depression and chronic pain in data from a randomized clinical trial of IMGV in diverse, income-disadvantaged patients suffering from chronic pain and depression. METHODS: The analytical approach used machine learning to assess the predictive relationship between depression and pain and identify and select key mediators, which were then assessed with regression analyses. It was hypothesized that depression would predict the pain outcomes of average pain, pain severity, and pain interference. RESULTS: Our analyses identified and characterized a predictive relationship between depression and chronic pain interference. This prediction was mediated by high perceived stress, low pain self-efficacy, and poor sleep quality, potential targets for attenuating the adverse effects of depression on functional outcomes. CONCLUSIONS: In the context of the associated clinical trial and similar interventions, these insights may inform future treatment optimization, targeting, and application efforts in racialized, income-disadvantaged populations, demographics often neglected in studies of chronic pain.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Court decisions in criminal proceedings for dental malpractice in Taiwan.

BACKGROUND/PURPOSE: Malpractice claims place heavy economic and emotional burdens on both dentists and patients. Recently, medical malpractice lawsuits are decreasing in prevalence but increasing in severity. The percentage of dental malpractice payments is also growing among the health profession. The present study aimed to explore criminal convictions in dental malpractice litigation and to analyze the factors affecting the judgment in dental disputes in Taiwan. METHODS: The keywords "dentist," "professional negligence," "medical malpractice," and "professional liability" were used to search Taiwan's Law and Regulations Retrieving System for criminal dental malpractice cases in all district courts from January 1, 2000 to June 30, 2021. The eligible judgments were summarized and analyzed. RESULTS: Overall, 425 cases were identified, with 28 dental disputes included in the final analysis. The dentists lost in 10 cases (35.7%). The average claim time was 36.75 ± 16.34 months. Taipei and Taichung dealt with more lawsuit cases (n = 8). Local clinics were the most common institution of the defendants (75%) and had the highest number of convictions (n = 9). Implant dentistry was the most common specialty involved. Expert testimony of the Medical Review Committee (MRC) had a high K coefficient of agreement with court judgments regarding professional negligence (p < 0.001). CONCLUSION: The overall criminal conviction rate was 35.7%. Implant therapy and local clinics had the highest rate of lawsuits and a considerably higher conviction rate. All guilty dentists were fined or given probation. The court judgments were highly consistent with the expert testimony of the MRC.

Introduction to the special issue on evaluator education.

After years in preparation, we are pleased to introduce this special issue of Evaluation and Program Planning that focuses on evaluator education, a topic that we believe is of considerable importance to the field's future. Before describing the issue's content, let us ground the articles in two ways: by examining the larger context within which evaluator education finds itself, and by briefly explaining who we are and describing how the issue came into being.

Latent Factor Decomposition Model: Applications for Questionnaire Data.

The analysis of clinical questionnaire data comes with many inherent challenges. These challenges include the handling of data with missing fields, as well as the overall interpretation of a dataset with many fields of different scales and forms. While numerous methods have been developed to address these challenges, they are often not robust, statistically sound, or easily interpretable. Here, we propose a latent factor modeling framework that extends the principal component analysis for both categorical and quantitative data with missing elements. The model simultaneously provides the principal components (basis) and each patients' projections on these bases in a latent space. We show an application of our modeling framework through Irritable Bowel Syndrome (IBS) symptoms, where we find correlations between these projections and other standardized patient symptom scales. This latent factor model can be easily applied to different clinical questionnaire datasets for clustering analysis and interpretable inference.