Gastric Carcinoma in Autoimmune Gastritis: A Histopathologic and Molecular Study.

Patients with autoimmune gastritis (AIG) have a 13-fold risk of developing type-1 neuroendocrine tumors, whereas the risk for gastric adenocarcinoma is still uncertain. Here we describe the clinicopathologic and molecular features of a series of gastric carcinomas (GC) arising in the context of AIG. A total of 26 AIG-associated GC specimens were collected from 4 Italian Institutions. Immunohistochemistry for MUC1, MUC2, MUC5AC, MUC6, CDX2, HER2, PD-L1, CLDN18, mismatch repair (MMR) proteins, and p53 and EBV-encoded RNA (EBER) in situ hybridization were performed. Histologic and immunohistochemical features were jointly reviewed by 5 expert gastrointestinal pathologists. Next-generation sequencing analysis (TrueSight Oncology 500, Illumina) of 523 cancer-related genes was performed on 19 cases. Most tumors were diagnosed as pT1 (52%) and they were located in the corpus/fundus (58%) and associated with operative link for gastritis assessment stage II gastritis (80.8%), absence of parietal cells, complete intestinal metaplasia, and enterochromaffin-like-cell micronodular hyperplasia. Only 4 (15.4%) GCs were diagnosed during follow-up for AIG. The following histotypes were identified: 20 (77%) adenocarcinomas; 3 (11%) mixed neuroendocrine-non-neuroendocrine neoplasms, and 2 (8%) high-grade solid adenocarcinomas with focal neuroendocrine component, 1 (4%) adenocarcinoma with an amphicrine component. Overall, 7 cases (27%) showed MMR deficiency, 3 (12%) were positive (score 3+) for HER2, 6 (23%) were CLDN18 positive, and 11 (42%) had PD-L1 combined positive score ≥ 10. EBER was negative in all cases. Molecular analysis revealed 5/19 (26%) microsatellite instability (MSI) cases and 7 (37%) tumor mutational burden (TMB) high. The most frequently altered genes were TP53 (8/19, 42%), RNF43 (7/19, 37%), ERBB2 (7/19, 37% [2 amplified and 5 mutated cases]), ARID1A (6/19, 32%), and PIK3CA (4/19, 21%). In summary, AIG-associated GCs are often diagnosed at low stage in patients with longstanding misrecognized severe AIG; they often display a neuroendocrine component or differentiation, have relatively higher rates of MMR deficiency, and TMB high.

HER2-low in gastro-oesophageal adenocarcinoma: a real-world pathological perspective.

AIMS: In the DESTINY-Gastric01 trial, a novel HER2-targeted antibody-drug conjugate trastuzumab deruxtecan proved to be effective in HER2-low gastro-oesophageal adenocarcinomas. The aim of our study is to investigate the clinicopathological and molecular features of HER2-low gastric/gastro-oesophageal junction cancers in the real-world setting of a large multi-Institutional series. METHODS: We retrospectively evaluated 1210 formalin-fixed paraffin-embedded samples of gastro-oesophageal adenocarcinomas which were analysed by immunohistochemistry for HER2 protein expression in 8 Italian surgical pathology units from January 2018 to June 2022. We assessed the prevalence of HER2-low (ie, HER2 1+ and HER2 2+ without amplification) and its correlation with clinical and histopathological features, other biomarkers' status, including mismatch repair/microsatellite instability status, Epstein-Barr encoding region (EBER) and PD-L1 Combined Positive Score. RESULTS: HER2 status could be assessed in 1189/1210 cases, including 710 HER2 0 cases, 217 HER2 1+, 120 not amplified HER2 2+, 41 amplified HER2 2+ and 101 HER2 3+. The estimated prevalence of HER2-low was 28.3% (95% CI 25.8% to 31.0%) overall, and was higher in biopsy specimens (34.9%, 95% CI 31.2% to 38.8%) compared with surgical resection specimens (21.0%, 95% CI 17.7% to 24.6%) (p<0.0001). Moreover, HER2-low prevalence ranged from 19.1% to 40.6% among centres (p=0.0005). CONCLUSIONS: This work shows how the expansion of the HER2 spectrum might raise problems in reproducibility, especially in biopsy specimens, decreasing interlaboratory and interobserver concordance. If controlled trials confirm the promising activity of novel anti-HER2 agents in HER2-low gastro-oesophageal cancers, a shift in the interpretation of HER2 status may need to be pursued.

Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions.

Are leaders made or born? Leader-follower roles have been well characterized in social science, but they remain somewhat obscure in sensory-motor coordination. Furthermore, it is unknown how and why leader-follower relationships are acquired, including innate versus acquired controversies. We developed a novel asymmetrical coordination task in which two participants (dyad) need to collaborate in transporting a simulated beam while maintaining its horizontal attitude. This experimental paradigm was implemented by twin robotic manipulanda, simulated beam dynamics, haptic interactions, and a projection screen. Clear leader-follower relationships were learned only when strong haptic feedback was introduced. This phenomenon occurred despite participants not being informed that they were interacting with each other and the large number of equally-valid alternative dyadic coordination strategies. We demonstrate the emergence of consistent leader-follower relationships in sensory-motor coordination, and further show that haptic interaction is essential for dyadic co-adaptation. These results provide insights into neural mechanisms responsible for the formation of leader-follower relationships in our society.

Development of a classifier for gambling disorder based on functional connections between brain regions.

AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training.

Improving human motor performance via physical guidance by an assist robot device is a major field of interest of the society in many different contexts, such as rehabilitation and sports training. In this study, we propose a Bayesian estimation method to predict whether motor performance of a user can be improved or not by the robot guidance from the user's initial skill level. We designed a robot-guided motor training procedure in which subjects were asked to generate a desired circular hand movement. We then evaluated the tracking error between the desired and actual subject's hand movement. Results showed that we were able to predict whether a novel user can reduce the tracking error after the robot-guided training from the user's initial movement performance by checking whether the initial error was larger than a certain threshold, where the threshold was derived by using the proposed Bayesian estimation method. Our proposed approach can potentially help users to decide if they should try a robot-guided training or not without conducting the time-consuming robot-guided movement training.

A multi-site, multi-disorder resting-state magnetic resonance image database.

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants ("traveling subjects") visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Brain network dynamics fingerprints are resilient to data heterogeneity.

CONTEXT: Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. OBJECTIVE: We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models. APPROACH: Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions. MAIN RESULTS: Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data. SIGNIFICANCE: These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.

Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Quantification of anticipation of excitement with a three-axial model of emotion with EEG.

OBJECTIVE: Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one's internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, 'Waku-Waku' (in Japanese), in which people predictively code upcoming emotional events. APPROACH: Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes. MAIN RESULTS: A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations. SIGNIFICANCE: The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.

Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity.

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

EEG Sensorimotor Correlates of Speed During Forearm Passive Movements.

Although passive movement therapy has been widely adopted to recover lost motor functions of impaired body parts, the underlying neural mechanisms are still unclear. In this context, fully understanding how the proprioceptive input modulates the brain activity may provide valuable insights. Specifically, it has not been investigated how the speed of motions, passively guided by a haptic device, affects the sensorimotor rhythms (SMR). On the grounds that faster passive motions elicit larger quantity of afferent input, we hypothesize a proportional relationship between localized SMR features and passive movement speed. To address this hypothesis, we conducted an experiment where healthy subjects received passive forearm oscillations at different speed levels while their electroencephalogram was recorded. The mu and beta event related desynchronization (ERD) and beta rebound of both left and right sensorimotor areas are analyzed by linear mixed-effects models. Results indicate that passive movement speed is correlated with the contralateral beta rebound and ipsilateral mu ERD. The former has been previously linked with the processing of proprioceptive afferent input quantity, while the latter with speed-dependent inhibitory processes. This suggests the existence of functionally-distinct frequency-specific neuronal populations associated with passive movements. In future, our findings may guide the design of novel rehabilitation paradigms.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback.

Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.

Markov Switching Model for Quick Detection of Event Related Desynchronization in EEG.

Quick detection of motor intentions is critical in order to minimize the time required to activate a neuroprosthesis. We propose a Markov Switching Model (MSM) to achieve quick detection of an event related desynchronization (ERD) elicited by motor imagery (MI) and recorded by electroencephalography (EEG). Conventional brain computer interfaces (BCI) rely on sliding window classifiers in order to perform online continuous classification of the rest vs. MI classes. Based on this approach, the detection of abrupt changes in the sensorimotor power suffers from an intrinsic delay caused by the necessity of computing an estimate of variance across several tenths of a second. Here we propose to avoid explicitly computing the EEG signal variance, and estimate the ERD state directly from the voltage information, in order to reduce the detection latency. This is achieved by using a model suitable in situations characterized by abrupt changes of state, the MSM. In our implementation, the model takes the form of a Gaussian observation model whose variance is governed by two latent discrete states with Markovian dynamics. Its objective is to estimate the brain state (i.e., rest vs. ERD) given the EEG voltage, spatially filtered by common spatial pattern (CSP), as observation. The two variances associated with the two latent states are calibrated using the variance of the CSP projection during rest and MI, respectively. The transition matrix of the latent states is optimized by the "quickest detection" strategy that minimizes a cost function of detection latency and false positive rate. Data collected by a dry EEG system from 50 healthy subjects, was used to assess performance and compare the MSM with several logistic regression classifiers of different sliding window lengths. As a result, the MSM achieves a significantly better tradeoff between latency, false positive and true positive rates. The proposed model could be used to achieve a more reactive and stable control of a neuroprosthesis. This is a desirable property in BCI-based neurorehabilitation, where proprioceptive feedback is provided based on the patient's brain signal. Indeed, it is hypothesized that simultaneous contingent association between brain signals and proprioceptive feedback induces superior associative learning.

A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity.

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder with a lifetime prevalence of 2-3%. Recently, brain activity in the resting state is gathering attention for exploring altered functional connectivity in psychiatric disorders. Although previous resting-state functional magnetic resonance imaging studies investigated the neurobiological abnormalities of patients with OCD, there are concerns that should be addressed. One concern is the validity of the hypothesis employed. Most studies used seed-based analysis of the fronto-striatal circuit, despite the potential for abnormalities in other regions. A hypothesis-free study is a promising approach in such a case, while it requires researchers to handle a dataset with large dimensions. Another concern is the reliability of biomarkers derived from a single dataset, which may be influenced by cohort-specific features. Here, our machine learning algorithm identified an OCD biomarker that achieves high accuracy for an internal dataset (AUC = 0.81; N = 108) and demonstrates generalizability to an external dataset (AUC = 0.70; N = 28). Our biomarker was unaffected by medication status, and the functional networks contributing to the biomarker were distributed widely, including the frontoparietal and default mode networks. Our biomarker has the potential to deepen our understanding of OCD and to be applied clinically.

A small number of abnormal brain connections predicts adult autism spectrum disorder.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

EEG Single-Trial Detection of Gait Speed Changes during Treadmill Walk.

In this study, we analyse the electroencephalography (EEG) signal associated with gait speed changes (i.e. acceleration or deceleration). For data acquisition, healthy subjects were asked to perform volitional speed changes between 0, 1, and 2 Km/h, during treadmill walk. Simultaneously, the treadmill controller modified the speed of the belt according to the subject's linear speed. A classifier is trained to distinguish between the EEG signal associated with constant speed gait and with gait speed changes, respectively. Results indicate that the classification performance is fair to good for the majority of the subjects, with accuracies always above chance level, in both batch and pseudo-online approaches. Feature visualisation and equivalent dipole localisation suggest that the information used by the classifier is associated with increased activity in parietal areas, where mu and beta rhythms are suppressed during gait speed changes. Specifically, the parietal cortex may be involved in motor planning and visuomotor transformations throughout the online gait adaptation, which is in agreement with previous research. The findings of this study may help to shed light on the cortical involvement in human gait control, and represent a step towards a BMI for applications in post-stroke gait rehabilitation.

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot.

This paper investigates the influence of the leg afferent input, induced by a leg assistive robot, on the decoding performance of a BMI system. Specifically, it focuses on a decoder based on the event-related (de)synchronization (ERD/ERS) of the sensorimotor area. The EEG experiment, performed with healthy subjects, is structured as a 3 × 2 factorial design, consisting of two factors: "finger tapping task" and "leg condition." The former is divided into three levels (BMI classes), being left hand finger tapping, right hand finger tapping and no movement (Idle); while the latter is composed by two levels: leg perturbed (Pert) and leg not perturbed (NoPert). Specifically, the subjects' leg was periodically perturbed by an assistive robot in 5 out of 10 sessions of the experiment and not moved in the remaining sessions. The aim of this study is to verify that the decoding performance of the finger tapping task is comparable between the two conditions NoPert and Pert. Accordingly, a classifier is trained to output the class of the finger tapping, given as input the features associated with the ERD/ERS. Individually for each subject, the decoding performance is statistically compared between the NoPert and Pert conditions. Results show that the decoding performance is notably above chance, for all the subjects, under both conditions. Moreover, the statistical comparison do not highlight a significant difference between NoPert and Pert in any subject, which is confirmed by feature visualization.