In Schizophrenia research ; h5-index 61.0

BACKGROUND AND HYPOTHESIS : Mentalizing impairment in schizophrenia has been linked to altered neural responses. This study aimed to replicate previous findings of altered activation of the mentalizing network in schizophrenia and investigate its possible association with impaired domain-general cognition.

STUDY DESIGN : We analyzed imaging data from two large multi-centric German studies including 64 patients, 64 matched controls and a separate cohort of 300 healthy subjects, as well as an independent Australian study including 46 patients and 61 controls. All subjects underwent functional magnetic resonance imaging while performing the same affective mentalizing task and completed a cognitive assessment battery. Group differences in activation of the mentalizing network were assessed by classical as well as Bayesian two-sample t-tests. Multiple regression analysis was performed to investigate effects of neurocognitive measures on activation of the mentalizing network.

STUDY RESULTS : We found no significant group differences in activation of the mentalizing network. Bayes factors indicate that these results provide genuine evidence for the null hypothesis. We found a positive association between verbal intelligence and activation of the medial prefrontal cortex, a key region of the mentalizing network, in three independent samples. Finally, individuals with low verbal intelligence showed altered activation in areas previously implicated in mentalizing dysfunction in schizophrenia.

CONCLUSIONS : Mentalizing activation in patients with schizophrenia might not differ compared to large well-matched groups of healthy controls. Verbal intelligence is an important confounding variable in group comparisons, which should be considered in future studies of the neural correlates of mentalizing dysfunction in schizophrenia.

Tantchik Wladimir, Green Melissa J, Quidé Yann, Erk Susanne, Mohnke Sebastian, Wackerhagen Carolin, Romanczuk-Seiferth Nina, Tost Heike, Schwarz Kristina, Moessnang Carolin, Bzdok Danilo, Meyer-Lindenberg Andreas, Heinz Andreas, Walter Henrik

2023-Mar-13

Mentalizing, Social cognition, fMRI, mPFC

In Forensic science international

Low YuanTing, Tyrrell Eadaoin, Gillespie Eoin, Quigley Cormac

2023-Feb-28

Accelerants, Arson investigation, Chemometrics, Destructive techniques, Fire debris analysis, Ignitable liquids, Non-destructive techniques

In Physics in medicine and biology

OBJECTIVE : Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation.

APPROACH : The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network.

MAIN RESULTS : Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T₂mapping and comparable results to conventional methods were obtained in the human brain.

SIGNIFICANCE : As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Huang Haitao, Yang Qinqin, Wang Jiechao, Zhang Pujie, Cai Shuhui, Cai Congbo

2023-Mar-15

Bloch simulation, deep learning, dynamic convolution, magnetic resonance imaging, synthetic data generation

In Gaceta medica de Mexico

Lanzagorta-Ortega Dioselina, Carrillo-Pérez Diego L, Carrillo-Esper Raúl

2022-Dec-15

Artificial intelligence, Medical education, Medical errors, Patient care, Precision medicine

In Revista brasileira de epidemiologia = Brazilian journal of epidemiology

OBJETIVO : To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil.

METHODS : The study is entitled "Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)" (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year.

RESULTS : In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension.

CONCLUSION : The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.

Delpino Felipe Mendes, Figueiredo Lílian Munhoz, Costa Ândria Krolow, Carreno Ioná, Silva Luan Nascimento da, Flores Alana Duarte, Pinheiro Milena Afonso, Silva Eloisa Porciúncula da, Marques Gabriela Ávila, Saes Mirelle de Oliveira, Duro Suele Manjourany Silva, Facchini Luiz Augusto, Vissoci João Ricardo Nickenig, Flores Thaynã Ramos, Demarco Flávio Fernando, Blumenberg Cauane, Chiavegatto Filho Alexandre Dias Porto, Silva Inácio Crochemore da, Batista Sandro Rodrigues, Arcêncio Ricardo Alexandre, Nunes Bruno Pereira

2023

In Health reports

BACKGROUND : As Canada continues to experience an opioid crisis, it is important to understand the intersection between the demographic, socioeconomic and service use characteristics of those experiencing opioid overdoses to better inform prevention and treatment programs.

DATA AND METHODS : The Statistics Canada British Columbia Opioid Overdose Analytical File (BCOOAF) represents people's opioid overdoses between January 2014 and December 2016 (n = 13,318). The BCOOAF contains administrative health data from British Columbia linked to Statistics Canada data, including on health, employment, social assistance and police contacts. Cluster analysis was conducted using the k-prototypes algorithm.

RESULTS : The results revealed a six-cluster solution, composed of three groups (A, B and C), each with two distinct clusters (1 and 2). Individuals in Group A were predominantly male, used non-opioid prescription medications and had varying levels of employment. Individuals in Cluster A1 were employed, worked mostly in construction, had high incomes and had a high rate of fatal overdoses, while individuals in Cluster A2 were precariously employed and had varying levels of income. Individuals in Group B were predominantly female; were mostly taking prescription opioids, with about one quarter or less receiving opioid agonist treatment (OAT); mostly had precarious to no employment; and had low to no income. People in Cluster B1 were primarily middle-aged (45 to 65 years) and on social assistance, while people in Cluster B2 were older, more frequently used health services and had no social assistance income. Individuals in Group C were primarily younger males aged 24 to 44 years, with higher prevalence of having experienced multiple overdoses, were medium to high users of health care services, were mostly unemployed and were recipients of social assistance. Most had multiple contacts with police. Those in Cluster C1 predominantly had no documented use of prescription opioid medications, and all had no documented OAT, while all individuals in Cluster C2 were on OAT.

INTERPRETATION : The application of machine learning techniques to a multidimensional database enables an intersectional approach to study those experiencing opioid overdoses. The results revealed distinct patient profiles that can be used to better target interventions and treatment.

Chu Kenneth, Carrière Gisèle, Garner Rochelle, Bosa Kevin, Hennessy Deirdre, Sanmartin Claudia

2023-Mar-15

cluster analysis, intersectionality, linked data, opioid overdose