Article
Neurosciences
John D. Lewis, Gleb Bezgin, Vladimir S. Fonov, D. Louis Collins, Alan C. Evans
Summary: The study focuses on mapping and parcellating the brain structures, facing challenges due to vast individual differences in morphology. The authors extend a surface-based approach to include both cortical and subcortical structures, resulting in a more uniform functional parcellation. Results show that this extended functional parcellation provides greater homogeneity in functional connectivity patterns, aligns with known cortical and subcortical architecture, and fits better to changes in white/gray contrast data over the lifespan, supporting its use with surface-based metrics.
HUMAN BRAIN MAPPING
(2022)
Article
Neurosciences
Xiaolong Zhang, Urs Braun, Anais Harneit, Zhenxiang Zang, Lena S. Geiger, Richard F. Betzel, Junfang Chen, Janina Schweiger, Kristina Schwarz, Jonathan Rochus Reinwald, Stefan Fritze, Stephanie Witt, Marcella Rietschel, Markus M. Noethen, Franziska Degenhardt, Emanuel Schwarz, Dusan Hirjak, Andreas Meyer-Lindenberg, Danielle S. Bassett, Heike Tost
Summary: The study highlights spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation and altered connectomes in schizophrenia patients and individuals at familial risk for schizophrenia.
Article
Neurosciences
Kristian M. Eschenburg, Thomas J. Grabowski, David R. Haynor
Summary: Deep learning has been applied in various ways in the field of MRI, from accelerating image acquisition and denoising to tissue segmentation and disease diagnosis. Researchers introduced a cortical segmentation method based on resting-state connectivity features, which can generate cortical parcellations for new MRI data.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Biology
Yao Meng, Siqi Yang, Jinming Xiao, Yaxin Lu, Jiao Li, Huafu Chen, Wei Liao
Summary: Mapping the functional topology and relating it to underlying structural principles is crucial for understanding the cerebral cortex. This study reveals a gradient pattern encoded in a functional similarity network and links it to cytoarchitectonic organizing principles.
COMMUNICATIONS BIOLOGY
(2022)
Article
Neurosciences
Stephane Doyen, Peter Nicholas, Anujan Poologaindran, Lewis Crawford, Isabella M. Young, Rafeael Romero-Garcia, Michael E. Sughrue
Summary: By developing a novel connectivity-based parcellation approach, researchers created a personalized neurosurgical application method that can be applied at the single-subject level, overcoming the impact of pathology and resection on brain structure.
HUMAN BRAIN MAPPING
(2022)
Article
Engineering, Industrial
Eduardo M. Coraca, Janito Ferreira, Euripedes G. O. Nobrega
Summary: Vibration-based structural health monitoring requires multiple sensors for reliable monitoring, which necessitates the development of machine learning methods. Recent advancements in deep learning techniques applied to vibration have shown promise in pattern identification from high-dimensional data. However, the lack of expert annotated labels related to damage conditions in real structures has hindered the use of supervised techniques, leading to the development of unsupervised methods. A proposed unsupervised framework combines Variational Autoencoders and a Hidden Markov Model to learn a degradation model and classify the state evolution from measured vibration signals, showing promising results for structural health monitoring.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Neurosciences
Cornelius Weiller, Marco Reisert, Volkmar Glauche, Mariachristina Musso, Michel Rijntjes
Summary: Intelligible communication and covert conscious thought require the integration of external representations and inner abstract concepts. Through studying participants in the Human Connectome Project, researchers have identified two hub regions in the brain that connect different cognitive processes, facilitating recursion and forethought.
Proceedings Paper
Computer Science, Artificial Intelligence
Joaquin Molina, Cristobal Mendoza, Claudio Roman, Josselin Houenou, Cyril Poupon, Jean Francois Mangin, Wael El-Deredy, Cecilia Hernandez, Pamela Guevara
Summary: This paper presents a new cortical parcellation method based on group-wise connectivity and hierarchical clustering. It utilizes fiber clustering and cortical meshes to obtain representative bundles and sub-parcels. Mean connectivity and overlap matrices are computed to hierarchize the information, and morphological operations are used to obtain homogenous parcels. The method allows for the generation of diffusion-based parcellations with different levels of granularity by adjusting parameters.
18TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS
(2023)
Article
Neurosciences
Tong He, Lijun An, Pansheng Chen, Jianzhong Chen, Jiashi Feng, Danilo Bzdok, Avram J. Holmes, Simon B. Eickhoff, B. T. Thomas Yeo
Summary: This paper presents a simple yet powerful approach to translate predictive models from large-scale datasets to small-scale studies, improving the prediction capability. The results demonstrate that meta-matching can greatly enhance predictions of new phenotypes in small independent datasets in various scenarios.
NATURE NEUROSCIENCE
(2022)
Article
Neurosciences
Yanyan Liu, Qiaowen Yu, Luqi Cheng, Jinge Chen, Jie Gao, Yujia Liu, Xiangtao Lin, Ximing Wang, Zhongyu Hou
Summary: This study used resting-state functional magnetic resonance imaging to analyze the subdivision and functional connectivity of the cingulate cortex (CC) in full-term neonates. The findings revealed specific connectivity patterns and functional lateralization in different subregions of the CC. These insights contribute to a better understanding of the functional specialization of the neonatal CC.
Article
Behavioral Sciences
Pinar Demirayak, Kader Karli Oguz, Fatma Seyhun Ustun, Buse Merve Urgen, Yasemin Topac, Irtiza Gilani, Tulay Kansu, Serap Saygi, Tayfun Ozcelik, Huseyin Boyaci, Katja Doerschner
Summary: The homozygous LAMC3 gene mutation is associated with severe bilateral smoothening and thickening of the lateral occipital cortex. Despite significant changes in gray matter structure, the patient exhibited intact perceptual abilities, possibly due to intact white matter structural integrity and functional connectivity in relevant pathways. White matter microstructural integrity results indicated widespread disruptions in connections except for the inferior longitudinal fasciculus, while functional connectivity between major gray matter regions of interest was mostly conserved, explaining intact face, place, and object recognition abilities in the patient.
BRAIN AND BEHAVIOR
(2021)
Article
Multidisciplinary Sciences
Monika Muller, Florian Wuthrich, Andrea Federspiel, Roland Wiest, Niklaus Egloff, Stephan Reichenbach, Aristomenis Exadaktylos, Peter Juni, Michele Curatolo, Sebastian Walther
Summary: This study aimed to evaluate the neural correlates of chronic, stimulus-independent pain in fibromyalgia by integrating four different functional and structural neuroimaging markers. The findings showed no evidence for functional or structural alterations in brain areas involved in acute pain processing, such as thalamus, basal ganglia, insula, etc., in fibromyalgia patients when compared to pain-free controls, even when adjusting for depression and anxiety or limiting the study population to patients not taking centrally acting drugs.
Article
Astronomy & Astrophysics
Vinicius Mikuni, Benjamin Nachman
Summary: Score-based generative models are a new class of algorithms that can generate realistic images in high dimensional spaces, surpassing other models in various applications. This study introduces CaloScore, the first application of a score-based generative model in collider physics, specifically for calorimeter shower generation. It is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.
Article
Computer Science, Interdisciplinary Applications
Berardino Barile, Aldo Marzullo, Claudio Stamile, Francoise Durand-Dubief, Dominique Sappey-Marinier
Summary: This study introduces a framework based on generative adversarial network to create synthetic structural brain networks in Multiple Sclerosis (MS). The quality of generated data is comparable to real data, and augmenting the existing dataset with generated samples leads to an improvement in classification performance.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Neurosciences
Ana Coelho, Ricardo Magalhaes, Pedro S. Moreira, Liliana Amorim, Carlos Portugal-Nunes, Teresa Castanho, Nadine Correia Santos, Nuno Sousa, Henrique M. Fernandes
Summary: This study proposes a new CBP method based on diffusion MRI data and demonstrates its potential to accurately characterize the longitudinal alterations in brain network topology occurring during aging. The method successfully generates highly homogeneous parcels and provides a robust anatomical framework to assess aging-related changes in the brain's structural network.
HUMAN BRAIN MAPPING
(2022)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.