Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease published in Nature Communications

Comparison of cluster-specific covariance matrixes with node strength. (Image by the Authors.)
Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
Konstantinos Poulakis, Joana B. Pereira, J.-Sebastian Muehlboeck, Lars-Olof Wahlund, Örjan Smedby, Giovanni Volpe, Colin L. Masters, David Ames, Yoshiki Niimi, Takeshi Iwatsubo, Daniel Ferreira, Eric Westman, Japanese Alzheimer’s Disease Neuroimaging Initiative & Australian Imaging, Biomarkers and Lifestyle study
Nature Communications 13, 4566 (2022)
doi: 10.1038/s41467-022-32202-6

Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Presentation by Murat Nurati Yesibolati, 4 August 2022

Measuring translational and rotational dynamics of colloid nanoparticles at the nanoscale with liquid-phase transmission electron microscopy
Murat Nulati Yesibolati, Assistant professor, Technical University of Denmark
4 August 2022, 10:30 CEST

How nanoparticles (NPs) in a liquid suspension grow, transport, and interact with each other and surrounding interfaces are of fundamental interest in the colloidal matter, biomedical applications, microfluidics, and artificial micro/nanoscopic motors. Traditionally, imaging of such liquid processes has been limited to optical microscopy (OM). Bulk-level methods such as conventional OM and light scattering methods such as dynamic light scattering (DLS) cannot deliver nanometer spatial resolution at the single-particle level. Recently, liquid-phase transmission electron microscopy (LPTEM) [1] has revolutionized the access to the nanoscale, label-free imaging of a wide variety of liquid processes. Typically, the liquid cells used for LPTEM consist of electron-transparent silicon nitride (SiNx) windows suspended on two Si chips, which enclose a liquid sample layer with a thickness ranging from a few hundred nanometers to a couple of microns. With LPTEM, NP dynamics, such as nucleation and growth, self-assembly, and interactions, have been studied with sub-nanometer spatial resolution and millisecond temporal resolution.
We demonstrate how LPTEM can be used to measure the motion of individual NPs and agglomerates. Only at low electron flux do we find that individual NPs exhibit Brownian motion consistent with optical control experiments and theoretical predictions for unhindered passive diffusive motion in bulk liquids [2]. For increasing electron flux, we find increasingly faster than passive motion that still appears effectively Brownian. We discuss the possible origins of this beam–sample interaction. This establishes conditions for the use of LPTEM as a reliable tool for imaging nanoscale hydrodynamics at the nanoscale.

Murat N. Yesibolati is an Assistant Professor at Technical University of Denmark (DTU), Denmark. Murat defended his Ph.D. thesis titled “Electron holography and particle dynamics in liquid phase transmission electron microscopy” at DTU in 06.2018 under the supervision of Prof. Kristian Mølhave, DTU. Currently, he is focusing on developing a novel nanochannel liquid cell and exploring mass transport in nanochannels using advanced transmission electron microscopy. His research was supported by the Technical University of Denmark, by the Danish Research Council for Technology, grant no. 12-126194, the Advanced Materials for Energy-Water Systems (AMEWS) Center, Office of Science, Basic Energy Sciences, USA, grant number DE-AC02-06CH11357, and the VILLUM foundation, grant number 00028273.

[1] de Jonge, N. and F.M. Ross, Electron microscopy of specimens in liquid. Nature Nanotechnology, 2011. 6: p. 695.
[2] Yesibolati, M.N., et al., Unhindered Brownian Motion of Individual Nanoparticles in Liquid-Phase Scanning Transmission Electron Microscopy. Nano Letters, 2020. 20(10): p. 7108-7115.

Place: Nexus
Date: 4 August 2022
Time: 10:30 CEST

Unraveling Parkinson’s disease heterogeneity using subtypes based on multimodal data published in Parkinsonism and Related Disorders

Particular of the brain in the group comparison analysis. (Image by the Authors.)
Unraveling Parkinson’s disease heterogeneity using subtypes based on multimodal data
Franziska Albrecht, Konstantinos Poulakis, Malin Freidle, Hanna Johansson, Urban Ekman, Giovanni Volpe, Eric Westman, Joana B. Pereira, Erika Franzén
Parkinsonism and Related Disorders 102, 19-29 (2022)
doi: 10.1016/j.parkreldis.2022.07.014


Parkinson’s disease (PD) is a clinically and neuroanatomically heterogeneous neurodegenerative disease characterized by different subtypes. To this date, no studies have used multimodal data that combines clinical, motor, cognitive and neuroimaging assessments to identify these subtypes, which may provide complementary, clinically relevant information. To address this limitation, we subtyped participants with mild-moderate PD based on a rich, multimodal dataset of clinical, cognitive, motor, and neuroimaging variables.


Cross-sectional data from 95 PD participants from our randomized EXPANd (EXercise in PArkinson’s disease and Neuroplasticity) controlled trial were included. Participants were subtyped using clinical, motor, and cognitive assessments as well as structural and resting-state MRI data. Subtyping was done by random forest clustering. We extracted information about the subtypes by inspecting their neuroimaging profiles and descriptive statistics.


Our multimodal subtyping analysis yielded three PD subtypes: a motor-cognitive subtype characterized by widespread alterations in brain structure and function as well as impairment in motor and cognitive abilities; a cognitive dominant subtype mainly impaired in cognitive function that showed frontoparietal structural and functional changes; and a motor dominant subtype impaired in motor variables without any brain alterations. Motor variables were most important for the subtyping, followed by gray matter volume in the right medial postcentral gyrus.


Three distinct PD subtypes were identified in our multimodal dataset. The most important features to subtype PD participants were motor variables in addition to structural MRI in the sensorimotor region. These findings have the potential to improve our understanding of PD heterogeneity, which in turn can lead to personalized interventions and rehabilitation.

Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning on bioRxiv

Phase-contrast image before virtual staining. (Image by the Authors.)
Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning
Zofia Korczak, Jesús Pineda, Saga Helgadottir, Benjamin Midtvedt, Mattias Goksör, Giovanni Volpe, Caroline B. Adiels

Chemical live/dead assay has a long history of providing information about the viability of cells cultured in vitro. The standard methods rely on imaging chemically-stained cells using fluorescence microscopy and further analysis of the obtained images to retrieve the proportion of living cells in the sample. However, such a technique is not only time-consuming but also invasive. Due to the toxicity of chemical dyes, once a sample is stained, it is discarded, meaning that longitudinal studies are impossible using this approach. Further, information about when cells start programmed cell death (apoptosis) is more relevant for dynamic studies. Here, we present an alternative method where cell images from phase-contrast time-lapse microscopy are virtually-stained using deep learning. In this study, human endothelial cells are stained live or apoptotic and subsequently counted using the self-supervised single-shot deep-learning technique (LodeSTAR). Our approach is less labour-intensive than traditional chemical staining procedures and provides dynamic live/apoptotic cell ratios from a continuous cell population with minimal impact. Further, it can be used to extract data from dense cell samples, where manual counting is unfeasible.

Jesper Boberg and Anders Segerlund will defend their Master thesis on 14 June 2022

Jesper Boberg and Anders Segerlund will defend their Master thesis in MPCAS at the Chalmers University of Technology on 14 June 2022 at 16:00.

Title: Early detection of rare evens: Predicting battery cell deviations.

Despite rigorous quality control in battery cell production, the production process is still subject to quality deviations. These quality deviations; known as “rare events” initially act as inherent passive quality deviations and may not affect the performance of the battery. Yet, a passive quality deviation can transition into an active quality deviation that give rise to behavioral deviations in the battery cell at some point during the battery’s lifetime. An active quality deviation may cause the entire battery to misbehave and eventually fail. This thesis investigates the possibility of predicting these cell deviations in car batteries. Better predictions of these events would avoid expensive and troublesome car failures and instead enable preventive car maintenance to solve the problem.

In this thesis different models have been created and evaluated with the aim of preventing these deviations. The dataset is supplied by Volvo Cars and contains a large amount of data collected from BEV cars where the arguably largest challenge comes from the imbalance of the dataset. In addition to the modelling, the thesis will include a thorough data analysis with the aim of improving both the dataset itself and the data collection process at Volvo Cars.

These deviations occur extremely rarely which also makes a relatively large amount of false positives difficult to avoid. The results show that a quite simple time series model can catch these deviations well but also brings along a large amount of false positives. A recurrent neural network was able to improve this significantly, still being able to catch the deviations while producing a lot fewer false positives.

Name of the master programme: MPALG – Computer Science: Algorithms, Languages, and Logic, MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Herman Johnsson (Volvo Cars)
Opponent: Jonathan Stålberg and Josef Gullholm5

Place: Nexus
Time: 14 June, 2022, 16:00

Deep learning in light–matter interactions published in Nanophotonics

Artificial neurons can be combined in a dense neural network (DNN), where the input layer is connected to the output layer via a set of hidden layers. (Image by the Authors.)
Deep learning in light–matter interactions
Daniel Midtvedt, Vasilii Mylnikov, Alexander Stilgoe, Mikael Käll, Halina Rubinsztein-Dunlop and Giovanni Volpe
Nanophotonics, 11(14), 3189-3214 (2022)
doi: 10.1515/nanoph-2022-0197

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light–matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.

David Rinman will defended his Master thesis on 13 June 2022

David Rinman will defended his Master thesis in MPCAS at the Chalmers University of Technology on 13 June 2022 at 13:00.

Monitoring Monitors; ML-based Anomaly Detection in Loudspeakers

Measuring the input voltage and current passing through a loudspeker and comparing the results to a parametric model is a way to monitor the condition of a loudspeaker in amplifiers. Prior research has shown that this can be done using music as input signal and can therefore work in commercial audio applications during normal operation. However, this solution requires modelling the specific loudspeaker setup which can be impractical in real-world scenarios. The aim of this project is to attempt to overcome these limitations by applying machine learning to the problem of anomaly detection in loudspeakers. Data is collected while playing music through two real functioning speakers where anomalies are simulated by disturbing the movement of their diaphragms. Three models are proposed and evaluated, two of which are based on deep neural networks. The results show that all three models are capable of learning a representation of one of the loudspeakers and detect deviances in these representations, however not for all of the simulated anomalies. Furthermore, the robustness of the models in prescence of nonlinear loudspeaker behavior is examined, and the limitations and benefits of the models are discussed. Finally, suggestions for future research directions are proposed.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Jesper Pedersen (MusicTribe)
Opponent: TBA

Place: van Bahr
Time: 13 June, 2022, 13:00

Kasper Hall and Noell Hall defended their Master thesis on 10 June 2022. Congrats!

Kasper Hall and Noell Hall defended their Master thesis in MPCAS at the Chalmers University of Technology on 10 June 2022 at 14:00. Congrats!

Title: Interference Object Detection using TensorFlow Lite and Transfer Learning for Android Devices

With the rapid evolution of machine learning and artificial intelligence faster and more robust network architectures are developed. This is possible due to the increase in computational power, improved algorithms and the creation of large scale annotated datasets. Re-purposing these state of the art networks using transfer learning allows for customized models to be created and applied to niche problems. In this paper, we create an object detection application able to detect interference points in anechoic testing chambers. The application runs detection on a mobile device using networks created with TensorFlow Lite. Utilizing the detection result the application can give advice on how to improve the installation in the testing chamber and can thus enforce a baseline for how installations are conducted increasing the repeatability of tests.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Giovanni Volpe and Christian Heina, Ericsson
Opponent: Angelo Barona Balda

Place: Origo 5.102
Time: 10 June, 2022, 14:00

Angelo Barona Balda defended his Master Thesis on 10 June 2022. Congrats!

Angelo Barona Balda defended his Master thesis in MPCAS at the Chalmers University of Technology on 10 June 2022 at 13:00. Congrats!

Title: Playful Experiments with Macroscopic Active Matter

Active matter is a substance or system composed of individual agents that consume energy. These agents use the energy to vibrate, self-propel, or apply force to their surroundings.

As it is such a strong theoretical resource, professors use active matter as a gateway to introduce students into non-equilibrium research. However, it is difficult to intuitively explain the behavior of microscopic particles. To better visualize it, academics frequently use simulations , both in classrooms and in research.

In this study, we use the HEXBUGS to replicate active matter simulations performed in previous research papers. We create experiments that are didactic, understandable, and easy to reproduce. Through this, we prove that HEXBUGS behave like active particles.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Giovanni Volpe, Aykut Argun
Opponent: Noel Hall, Kasper Hall

Place: Origo 5.102
Time: 10 June, 2022, 13:00

Yanuar Rizki Pahlevi defended his Master thesis on 9 June 2022. Congrats!

Yanuar Rizki Pahlevi defended his Master thesis in MPCAS at the Chalmers University of Technology on 9 June 2022 at 17:00. Congrats!

Title: Deep Learning for Optical Tweezers. DeepCalib Implementation for Brownian Motion with Delayed Feedback

Brownian motion with delayed feedback theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming variance methods in order to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.

​Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Aykut Argun
Opponent: Ivan Gentile Japiassu

Place: Nexus
Time: 9 June, 2022, 17:00