News

Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data published in Cerebral Cortex

Brain nodes. (Image taken from the article.)
Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Anna Canal-Garcia, Emiliano Gómez-Ruiz, Mite Mijalkov, Yu-Wei Chang, Giovanni Volpe, Joana B Pereira, Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex, bhab429 (2022)
doi: 10.1093/cercor/bhab429

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Yanuar Rizki Pahlevi joins the Soft Matter Lab

(Photo by A. Argun.)
Yanuar Rizki Pahlevi joined the Soft Matter Lab on 18 January 2022.

Yanuar is a master student in Complex Adaptive Systems at Chalmers University of Technology.

During his time at the Soft Matter Lab, he will focus on implementing deep learning techniques to predict delay in Brownian motion with delayed feedback.

Lukas Niese defended his Master thesis on 17 January 2022. Congrats!

Lukas Niese defended his Master thesis in Physics at the Technische Universität Dresden on 17 January 2022. Congrats!

(Image from Lukas Niese’s Master Thesis)
Title: Application of Deep Learning for Investigation of Chemotactic Behaviour in Marine Microorganisms

Deep learning has recently become a powerful instrument, enhancing research in many fields and profiting from abundant availability of manifold data sets. In active matter research, medicine and biology there is huge demand of robust and accurate methods to track and analyse micro scale particles and cells in microscopy images. The Pyhton based software Deeptrack 2.0 offers a basic toolkit to build customized deep learning methods for particle localization, classification and tracking. In this project Deeptrack 2.0 was used to track marine microorganisms and investigate their motion in response to chemical stimulants, known as chemotaxis. In addition, the accuracy of particle localization and classification was measured by three different benchmark tests, which imitated shapes and movement of real microorganisms. The results were compared with the performance ofthe algorithmic standard method Trackmate by Fiji ImageJ. Deeptrack 2.0 has shown a significantly better performance for particles with complex shapes and with time varying appearance were to be tacked. However Trackmate is slightly more accurate in locating small particles appearing in Gaussian intensity distribution. In the experimental part two test assays have been developed and proven a facile and robust way to study chemoattraction in the autotrophic green alga Dunaliella tertiolecta. Deeptrack was successfully applied create and analyze the cell trajectories according to velocity and spatial distribution in individuals. Based on the developed combination of experiment and computational analysis, further investigations can be carried out to elucidate the chemical and ecological nature of chemotaxis in Dunaliella tertiolecta.

​Adviser: Prof. Giovanni Volpe
Examiner: Prof. Alexander Eychmüller (TU Dresden)
Date: 17 January 2022
Time: 17:00
Place: TU Dresden and Online via Zoom

Book “Simulation of Complex Systems” published at IOP

Book cover. (From the IOP website.)
The book Simulation of Complex Systems, authored by Aykut Argun, Agnese Callegari and Giovanni Volpe, has been published by IOP in December 2021.

The book is available for the students of Gothenburg University and Chalmers University of Technology through the library service of each institution.
The example codes presented in the book can be found on GitHub.

Links
@ IOP Publishing

@ Amazon.com

Citation 
Aykut Argun, Agnese Callegari & Giovanni Volpe. Simulation of Complex Systems. IOP Publishing, 2022.
ISBN: 9780750338417 (Hardback) 9780750338431 (Ebook).

Hari Prakash Thanabalan joins the Soft Matter Lab

Hari Prakash Thanabalan (Photo by A. Argun.)
Hari Prakash Thanabalan starts his PhD at the Physics Department of the University of Gothenburg on 10th January 2022.

Hari Prakash has a Master degree in Advanced Robotics from Queen Mary University of London, United Kingdom.

In his PhD, he will focus on the development of soft robots to study collective emergent behaviours in soft active matter systems.

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT published in Frontiers of Computational Neuroscience

CT is split into smaller patches. (Image by the Authors.)
Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT
Meera Srikrishna, Rolf A. Heckemann, Joana B. Pereira, Giovanni Volpe, Anna Zettergren, Silke Kern, Eric Westman, Ingmar Skoog and Michael Schöll
Frontiers of Computational Neuroscience 15, 785244 (2022)
doi: 10.3389/fncom.2021.785244

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.

Mathias Samuelsson joins the Soft Matter Lab

(Photo by A. Argun.)
Mathias Samuelsson joined the Soft Matter Lab on 10 January 2022.

Mathias is a master student in Physics at the University of Gothenburg.

During his time at the Soft Matter Lab, he will focus on the simulation of an intracavity optical tweezers with the help of neural networks.

Angelo Barona Balda joins the Soft Matter Lab

(Photo by A. Argun.)
Angelo Barona Balda joined the Soft Matter Lab on 10 January 2022.

Angelo is a master student in Complex Adaptive Systems at Chalmers University of Technology.

During his time at the Soft Matter Lab, he will investigate the behaviour of active matter via experiments with toy robots (HEXBUG nano®).