Article
Neurosciences
Estelle Raffin, Adrien Witon, Roberto F. Salamanca-Giron, Krystel R. Huxlin, Friedhelm C. Hummel
Summary: Discrimination and integration of motion direction involve the interplay of multiple brain areas. The impact of task demand and perceptual decision-making on brain activity occurs at different levels of the visual hierarchy. Lower levels of the visual network support early, feature-based selection, while higher levels integrate sensory information with the subject's internal state, which is predictive of performance.
Review
Neurosciences
Andrew D. Snyder, Liangsuo Ma, Joel L. Steinberg, Kyle Woisard, Frederick G. Moeller
Summary: Dynamic causal modeling (DCM) is a method used for analyzing the directionality of brain connectivity, but there may be a historical trend of underreporting self-connectivity findings in neuropsychiatric fMRI DCM literature. These self-connectivity findings play an important role in regulating neural activity.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Neurosciences
Amirhossein Jafarian, Peter Zeidman, Rob C. Wykes, Matthew Walker, Karl J. Friston
Summary: Adiabatic dynamic causal modelling is a method for inferring slow changes in biophysical parameters controlling fast neuronal fluctuations. It relies on established neural mass models and an adiabatic approximation to summarize fast neuronal states using second order statistics. The method introduces a circular causality involving synaptic parameters and neuronal activity, and is validated through simulations and an illustrative application to seizure activity in an animal model.
Article
Neurosciences
Qunjun Liang, Jinhui Li, Senning Zheng, Jiajun Liao, Ruiwang Huang
Summary: Hierarchical planning (HP) is a strategy that optimizes planning by storing lower-level steps as subgoals. Previous studies have identified the involvement of dmPFC, PMC, and SPL in the computation process of HP, but their interaction and contribution to HP computation remains unclear. Through an fMRI experiment, we confirmed the activity of dmPFC, PMC, and SPL, and used DCM and PEB models to quantify their connectivity and influence on response time.
Article
Engineering, Industrial
Haoyuan Shen, Yizhong Ma, Chenglong Lin, Jian Zhou, Lijun Liu
Summary: This paper proposes a hierarchical Bayesian support vector regression (HBSVR) model for dynamic high-dimensional reliability modeling, which combines the step-size adaptive accelerated Markov Chain Monte Carlo (SAA-MCMC) method with Sequential Minimal Optimization (SMO) for parameter calibration and dynamic update. The HBSVR model is further improved by applying an active learning algorithm to continuously improve model accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Neurosciences
Yu Yao, Klaas E. Stephan
Summary: This article addresses the technical challenges of applying MCMC to hierarchical models for clustering in the space of latent parameters. Specifically focusing on dynamic causal models for fMRI and effective brain connectivity clustering, the study proposes a solution to improve convergence by introducing proposal distributions capturing dependencies between clustering and subject-wise generative model parameters. Validated on synthetic and real-world datasets, the proposed solution shows good convergence performance and superior runtime compared to state-of-the-art Monte Carlo techniques.
HUMAN BRAIN MAPPING
(2021)
Article
Psychiatry
Yi-Bin Xi, Fan Guo, Wen-Ming Liu, Yu-Fei Fu, Jia-Ming Li, Hua-Ning Wang, Fu-Lin Chen, Long-Biao Cui, Yuan-Qiang Zhu, Chen Li, Xiao-Wei Kang, Bao-Juan Li, Hong Yin
Summary: The study showed similarities in network interactions among SN, CEN, and DMN in both schizophrenia patients and healthy controls during rest, but significantly reduced cross-network interactions in schizophrenia patients, with connections correlating positively and negatively with PANSS scores. This suggests that schizophrenia may involve dysregulation among SN, CEN, and DMN in a triplenetwork perspective, with connections between DMN and CEN potentially serving as clinically-relevant neurobiological signatures of schizophrenia symptoms.
SCHIZOPHRENIA RESEARCH
(2021)
Article
Neurosciences
Amirhossein Jafarian, Laura Hughes, Natalie Adams, Juliette Lanskey, Michelle Naessens, Matthew Rouse, Alexander G. Murley, Karl J. Friston, James B. Rowe
Summary: We propose a hierarchical empirical Bayesian framework for testing hypotheses on neurotransmitter concentration using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A two-level model is developed to estimate the synaptic connectivity parameters and infer the influence of neurotransmitter levels on synaptic connections. The method is validated using resting-state MEG and 7T-MRS data from healthy adults, and it shows reliable results for hypothesis testing. This approach has great potential for studying neurological and psychiatric disorders and their response to pharmacological interventions.
Article
Environmental Sciences
Jose-Luis Molina, Jose-Luis Garcia-Arostegui
Summary: This research aims to analyze and model the relationship between binomial rainfall and groundwater levels. It uses Bayesian Causal Reasoning (BCR) based on Bayesian Theorem to capture the inherent causality in the data. The methodology includes classic regression analysis and Bayesian Causal Modelling Translation (BCMT) with iterative steps. This innovative methodology has been successfully applied to aquifer management in the Campo de Cartagena groundwater body in Spain.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Neurosciences
Jinseok Eo, Jiyoung Kang, Tak Youn, Hae-Jeong Park
Summary: The hierarchical characteristics of the brain play a significant role in pharmacological treatment of psychiatric disorders. This study explores long-term changes in neurobiological properties using a microcircuit model and dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The results show that medication affects neurobiological parameters at different hierarchical levels and is associated with symptom improvement.
Article
Neurosciences
Siyi Chen, Ralph Weidner, Hang Zeng, Gereon R. Fink, Hermann J. Mueller, Markus Conci
Summary: The study found that visual performance benefits when an integrated object is task-relevant. Dynamic causal modeling was used to analyze the connections between regions associated with illusory figure completion. The results show that there are feedback connections between different areas of the visual system, playing a key role in processing integrated objects and details.
HUMAN BRAIN MAPPING
(2021)
Article
Physics, Multidisciplinary
Cheng He, Jia Ren, Wenjian Liu
Summary: This study utilizes a dynamic Bayesian network to construct a causal model of air quality in Hong Kong and Macao, and explores the interaction between meteorology and air quality. The findings provide insights into the causal relationships between air pollutants and their determinants, and offer a method for collaborative online forecast of air pollutant concentrations, which is crucial for decision-making and management of atmospheric environments.
Article
Neurosciences
Stefan Frassle, Samuel J. Harrison, Jakob Heinzle, Brett A. Clementz, Carol A. Tamminga, John A. Sweeney, Elliot S. Gershon, Matcheri S. Keshavan, Godfrey D. Pearlson, Albert Powers, Klaas E. Stephan
Summary: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used for studying brain connectivity. Researchers have developed a method called rDCM that extends to rs-fMRI, offering directional estimates and scalability to whole-brain networks. Through simulations and empirical tests, rDCM has shown to be computationally efficient and produce biologically plausible results consistent with established models of effective connectivity.
HUMAN BRAIN MAPPING
(2021)
Article
Meteorology & Atmospheric Sciences
Dylan Harries, Terence J. O'Kane
Summary: The study applies a Bayesian structure learning approach to analyze interactions between global climate modes. Homogeneous dynamic Bayesian network models are constructed for empirical indices time series in NCEP/NCAR and JRA-55 reanalyses, providing measures of confidence. The Bayesian approach is shown to be advantageous in incorporating measures of confidence in structural features compared to point estimates alone.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2021)
Article
Agronomy
Pratishtha Poudel, Nora M. Bello, Romulo P. Lollato, Phillip D. Alderman
Summary: The goal of this study was to use a dynamic ordinary differential equation (ODE) model within a Bayesian framework for stochastic inference on system-level parameters, and compare its predictive performance to more commonly used modeling approaches for repeated measures data. The results showed that none of the modeling approaches clearly outperformed any other in terms of goodness of fit or prediction accuracy.
FIELD CROPS RESEARCH
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Shumei Li, Bin A. Wang, Cheng Li, Ying Feng, Meng Li, Tianyue Wang, Linghui Nie, Changhong Li, Wen Hua, Chulan Lin, Mengchen Liu, Xiaofen Ma, Jin Fang, Guihua Jiang
Summary: The study revealed gray matter volume changes in patients with insomnia disorder at different severity stages, which were related to sleep, mood, and cognitive measures. Increase in gray matter volume was observed in insomnia patients, with slight differences between subthreshold and clinical groups. In the clinical group, mood- and cognitive-related measures were positively correlated with GM volumes, while sleep-related measures showed a negative correlation.
EUROPEAN RADIOLOGY
(2021)
Article
Biophysics
Johannes Forsting, Robert Rehmann, Marlena Rohm, Anne-Katrin Guttsches, Martijn Froeling, Hermien E. Kan, Martin Tegenthoff, Matthias Vorgerd, Lara Schlaffke
Summary: Muscle diffusion tensor imaging-based tractography is a promising tool for detecting subclinical changes in muscle injuries and evaluating pathophysiology in neuromuscular diseases. The study assessed the performance of volume-based tractography in a multicenter setting and found that it can provide comparable results in terms of tract properties and diffusion metrics. Semiautomatic segmentation approaches showed excellent agreement with manual segmentation, indicating the feasibility of pooling data from different centers using these methods.
NMR IN BIOMEDICINE
(2022)
Meeting Abstract
Clinical Neurology
A. Guettsches, R. Rehmann, C. Schneider-Gold, M. Rohm, J. Forsting, M. Froeling, M. Vorgerd, L. Schlaffke
NEUROMUSCULAR DISORDERS
(2022)
Meeting Abstract
Clinical Neurology
A. Guettsches, R. Rehmann, A. Schreiner, M. Rohm, J. Forsting, M. Froeling, M. Tegenthoff, M. Vorgerd, L. Schlaffke
NEUROMUSCULAR DISORDERS
(2022)
Meeting Abstract
Clinical Neurology
A. Guettsches, J. Forsting, M. Rohm, R. Rehmann, M. Froeling, M. Vorgerd, L. Schlaffke
NEUROMUSCULAR DISORDERS
(2022)
Article
Biochemical Research Methods
Ali Hummos, Bin A. Wang, Sabrina Drammis, Michael M. Halassa, Burkhard Pleger
Summary: Interactions across frontal cortex are critical for cognition. Recent research suggests that the mediodorsal thalamus (MD) plays a role in these interactions, but the specific computations and relevance to human decision making remain unclear. In this study, a neural model of an executive frontal-MD network was trained on a human decision-making task, and the results showed that the MD compressed its cortical inputs and efficiently partitioned cortical activity patterns, enhancing task switching. The findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Clinical Neurology
Elena Enax-Krumova, Johannes Forsting, Marlena Rohm, Peter Schwenkreis, Martin Tegenthoff, Christine H. Meyer-Friessem, Lara Schlaffke
Summary: This case-control study aimed to assess muscular alterations in post-COVID-19 condition (PCC) patients using quantitative muscle MRI. The results showed that PCC patients had microstructural abnormalities in muscle diffusion tensor imaging (DTI), potentially due to reversible fiber hypotrophy caused by deconditioning.
EUROPEAN JOURNAL OF NEUROLOGY
(2023)
Article
Multidisciplinary Sciences
Johannes Forsting, Marlena Rohm, Martijn Froeling, Anne-Katrin Guettsches, Nicolina Suedkamp, Andreas Roos, Matthias Vorgerd, Lara Schlaffke, Robert Rehmann
Summary: This study evaluated the differences in qMRI parameters in leg muscles of calpainopathy patients compared to healthy controls and correlated these findings with clinical parameters. The results showed significant differences in FF, FA, and RD between the patient group and control group. Water T2 times were also increased, but only in certain muscles. Clinical assessments were significantly correlated with qMRI values. These findings suggest that qMRI parameters can be used as non-invasive methods to detect early muscle degeneration in calpainopathies.
SCIENTIFIC REPORTS
(2022)
Letter
Clinical Neurology
Elena Enax-Krumova, Johannes Forsting, Marlena Rohm, Peter Schwenkreis, Martin Tegenthoff, Christine H. Meyer-Friessem, Lara Schlaffke
EUROPEAN JOURNAL OF NEUROLOGY
(2023)
Review
Neurosciences
Abhishek Banerjee, Bin A. Wang, Jasper Teutsch, Fritjof Helmchen, Burkhard Pleger
Summary: Evolution has shaped the sensory capacities of different species. Rodents rely heavily on the whisker-based somatosensory system for environmental exploration and navigation, while humans rely more on visual and auditory inputs. Recent research has found surprisingly similar processing rules for detecting tactile stimuli and rule learning across species. This article reviews how the brain utilizes these processing rules during tactile learning and discusses the challenges and relevance of cross-species research.
PROGRESS IN NEUROBIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
R. Rehmann, E. Enax-Krumova, C. H. Meyer-Friessem, L. Schlaffke
Summary: This study used quantitative MRI (qMRI) to analyze myostructural abnormalities in former ICU patients and found that ICU survivors still have long-term sequelae and reduced quality of life even after years of ICU treatment. qMRI provides new insight into the understanding of muscle changes in ICUAW patients and may contribute to the development of personalized rehabilitation programs.
BMC MEDICAL IMAGING
(2023)
Article
Cell Biology
Marlena Rohm, Leon Volke, Lara Schlaffke, Robert Rehmann, Nicolina Suedkamp, Andreas Roos, Anne Schaenzer, Andreas Hentschel, Matthias Vorgerd
Summary: This study investigates early proteomic changes in a mouse model of Pompe disease and identifies potential therapeutic pathways. Proteomic analysis reveals 538 significantly upregulated and 16 significantly downregulated proteins in the quadriceps muscles of Pompe mice compared to wildtype mice. This study highlights the importance of early diagnosis and treatment of Pompe disease and suggests potential therapeutic strategies.
Article
Anatomy & Morphology
Tommaso Gerussi, Jean-Marie Graic, Antonella Peruffo, Mehdi Behroozi, Lara Schlaffke, Stefan Huggenberger, Onur Guentuerkuen, Bruno Cozzi
Summary: This study successfully identified the homologue of the prefrontal cortex (PFC) in bottlenose dolphins using diffusion-weighted imaging. The results showed a similar connectivity pattern between the dolphin PFC and the human PFC. The rotation of the PFC in dolphins might be a result of evolutionary processes.
BRAIN STRUCTURE & FUNCTION
(2023)
Article
Multidisciplinary Sciences
Daniel L. Belavy, Scott D. Tagliaferri, Martin Tegenthoff, Elena Enax-Krumova, Lara Schlaffke, Bjoern Buehring, Tobias L. Schulte, Sein Schmidt, Hans-Joachim Wilke, Maia Angelova, Guy Trudel, Katja Ehrenbrusthoff, Bernadette Fitzgibbon, Jessica Van Oosterwijck, Clint T. Miller, Patrick J. Owen, Steven Bowe, Rebekka Doeding, Svenja Kaczorowski
Summary: In patients with low back pain, clinicians often diagnose non-specific low back pain after ruling out specific causes. However, the current management of non-specific low back pain is generic. To address this, the PREDICT-LBP project aims to develop a classification tool for non-specific low back pain based on pain-related factors collected from a large sample size. This personalized diagnostic approach could lead to better patient outcomes and reduce economic costs.
Article
Anatomy & Morphology
Frederick Benjamin Junker, Lara Schlaffke, Joachim Lange, Tobias Schmidt-Wilcke
Summary: Understanding encoded language involves multiple cognitive processes and their interactions in the human brain. Computational modeling and neuroimaging have been applied to study the neural underpinnings of these processes. In this study, Morse code was used as a model for non-lexical decoding, and the results suggest specific cortical interactions involved in letter-to-phoneme conversion and word comprehension.
BRAIN STRUCTURE & FUNCTION
(2023)
