Assessment of the impact of frailty on adverse surgical outcomes in patients undergoing surgery for intracranial tumors using modified frailty index: A systematic review and meta-analysis.

BACKGROUND: Modified frailty index (MFI) is an emerging quantitative measure of frailty; however, the quantified risk of adverse outcomes in surgeries for intracranial tumors associated with increasing MFI scores has not been thoroughly reviewed in a comprehensive manner. METHODS: MEDLINE (PubMed), Scopus, Web of Science, and Embase were searched to identify observational studies on the association between 5 and 11 item-modified frailty index (MFI) and perioperative outcomes for neurosurgical procedures including complications, mortality, readmission, and reoperation rate. Primary analysis pooled all comparisons with MFI scores greater than or equal to 1 versus non-frail participants using mixed-effects multilevel model for each outcome. RESULTS: In total, 24 studies were included in the review and 19 studies with 114,707 surgical operations were included in the meta-analysis. While increasing MFI scores were associated with worse prognosis for all included outcomes, reoperation rate was only significantly higher in patients with MFI ≥ 3. Among surgical pathologies, glioblastoma was influenced by a greater extent to the impact of frailty on complications and mortality that most other etiologies. In agreement with qualitative evaluation of the included studies, meta-regression did not reveal association between mean age of the comparisons and complications rate. CONCLUSION: The results of this meta-analysis provides quantitative risk assessment of adverse outcomes in neuro-oncological surgeries with increased frailty. The majority of literature suggests that MFI is a superior and independent predictor of adverse outcomes compared to age.

Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy.

Introduction: Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods: Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results: Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion: These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.

The Psychometric Properties of the Persian Versions of the Patient Health Questionnaires 9 and 2 as Screening Tools for Detecting Depression among University Students.

Background: Among the common mental disorders in societies, depression is one of the most common mental disorders that affects all groups and classes of society. Students are among the groups with the highest rates of depression. Therefore, the need for a short and effective tool for screening and early detection of depression is felt. The aim of this research is to determine validity, reliability and the best cut-off point of the patient health questionnaires-9 (PHQ-9) and patient health questionnaires-2 (PHQ-2) in university students. Methods: This cross-sectional study was conducted on 246 students of Kermanshah University of medical science in Kermanshah province of Iran. They completed the PHQ-2, PHQ-9, and the Beck Depression Inventory-II (BDI-II). A structured interview was used to diagnose depression. To analyze the data, Cronbach's alpha for internal consistency, the intra-class correlation (ICC) for test-retest reliability, confirmatory factor analysis for construct validity, Pearson Correlation for Convergent validity, and receiver-operating characteristic (ROC) curve for Criterion validity was used. Results: The mean age of the participants was 20.43 ± 2.29. Cronbach's alpha coefficient for PHQ-9 and PHQ-2 was 0.82 and 0.80, respectively. The test-retest reliability based on intra-class correlation (ICC) for PHQ-9 and PHQ-2 after two weeks was 0.81 and 0.73, respectively (P < 0.001). The correlation coefficient between the PHQ-9 and PHQ-2 with the BDI-II was 0.74 and 0.64, respectively (P < 0.001). Confirmatory factor analysis showed that two-factor model and one factor model had good model fit. The best cut-off point score for the PHQ-9 was 10 with a sensitivity of 0.90 and specificity of 0.93, and the best cut-off point score for the PHQ-2 was 3 with the sensitivity of 0.71 and specificity of 0.92. Conclusions: The PHQ-9 and PHQ-2 are suitable tools to screen depression in the university students in Iran.

A Hybrid Human-Neurorobotics Approach to Primary Intersubjectivity via Active Inference.

Interdisciplinary efforts from developmental psychology, phenomenology, and philosophy of mind, have studied the rudiments of social cognition and conceptualized distinct forms of intersubjective communication and interaction at human early life. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue the study of human/neurorobot interaction consists in a unique opportunity to deepen understanding of underlying mechanisms in social cognition through synthetic modeling, while allowing to examine a second person experiential (2PP) access to intersubjectivity in embodied dyadic interaction. Concretely, we propose the study of primary intersubjectivity as a 2PP experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. From our interpretation of the concept of active inference in free-energy principle theory, we propose an open-source methodology named neural robotics library (NRL) for experimental human/neurorobot interaction, wherein a demonstration program named virtual Cartesian robot (VCBot) provides an opportunity to experience the aforementioned embodied interaction to general audiences. Lastly, through a study case, we discuss some ways human-robot primary intersubjectivity can contribute to cognitive science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation.

A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition.

This study introduces PV-RNN, a novel variational RNN inspired by predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how latent variables can learn meaningful representations and how the inference model can transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation-rather than external inputs during the forward computation-are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. As in other variational Bayes RNNs, our model learns by maximizing a lower bound on the marginal likelihood of the sequential data, which is composed of two terms: the negative of the expectation of prediction errors and the negative of the Kullback-Leibler divergence between the prior and the approximate posterior distributions. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on those two terms. We test the model on two data sets with probabilistic structures and show that with high values of the meta-prior, the network develops deterministic chaos through which the randomness of the data is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows us to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.

How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction.

The current paper examines how a recurrent neural network (RNN) model using a dynamic predictive coding scheme can cope with fluctuations in temporal patterns through generalization in learning. The conjecture driving this present inquiry is that a RNN model with multiple timescales (MTRNN) learns by extracting patterns of change from observed temporal patterns, developing an internal dynamic structure such that variance in initial internal states account for modulations in corresponding observed patterns. We trained a MTRNN with low-dimensional temporal patterns, and assessed performance on an imitation task employing these patterns. Analysis reveals that imitating fluctuated patterns consists in inferring optimal internal states by error regression. The model was then tested through humanoid robotic experiments requiring imitative interaction with human subjects. Results show that spontaneous and lively interaction can be achieved as the model successfully copes with fluctuations naturally occurring in human movement patterns.