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.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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
bioRxiv: https://doi.org/10.1101/2022.07.18.500422

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.

Neural Network Training with Highly Incomplete Datasets published in Machine Learning: Science and Technology

Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Yu-Wei Chang.)
Neural Network Training with Highly Incomplete Datasets
Yu-Wei Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B. Pereira, Giovanni Volpe
Machine Learning: Science and Technology 3, 035001 (2022)
arXiV: 2107.00429
doi: 10.1088/2632-2153/ac7b69

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. By distilling the information available in incomplete datasets without having to reduce their size or to impute missing values, GapNet will permit to extract valuable information from a wide range of datasets, benefiting diverse fields from medicine to engineering.

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.

Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles published in Nature Methods

Kymographs of DNA inside Channel II. (Image by the Authors.)
Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles
Barbora Špačková, Henrik Klein Moberg, Joachim Fritzsche, Johan Tenghamn, Gustaf Sjösten, Hana Šípová-Jungová, David Albinsson, Quentin Lubart, Daniel van Leeuwen, Fredrik Westerlund, Daniel Midtvedt, Elin K. Esbjörner, Mikael Käll, Giovanni Volpe & Christoph Langhammer
Nature Methods 19, 751–758 (2022)
doi: 10.1038/s41592-022-01491-6

Label-free characterization of single biomolecules aims to complement fluorescence microscopy in situations where labeling compromises data interpretation, is technically challenging or even impossible. However, existing methods require the investigated species to bind to a surface to be visible, thereby leaving a large fraction of analytes undetected. Here, we present nanofluidic scattering microscopy (NSM), which overcomes these limitations by enabling label-free, real-time imaging of single biomolecules diffusing inside a nanofluidic channel. NSM facilitates accurate determination of molecular weight from the measured optical contrast and of the hydrodynamic radius from the measured diffusivity, from which information about the conformational state can be inferred. Furthermore, we demonstrate its applicability to the analysis of a complex biofluid, using conditioned cell culture medium containing extracellular vesicles as an example. We foresee the application of NSM to monitor conformational changes, aggregation and interactions of single biomolecules, and to analyze single-cell secretomes.

DeepTrack won the pitching competition at the Startup Camp 2022. Congrats!

DeepTrack team members (left to right) Henrik, Giovanni and Jesus. (Picture by Jonas Sandwall, Chalmers Ventures.)
The DeepTrack team, composed by Henrik Klein Moberg, Jesus Pineda, Benjamin Midtvedt and Giovanni Volpe, won the pitching competition at the Startup Camp 2022 organised by Chalmers Ventures.

In the event, held on Tuesday, 15 March 2022, 16:00-19:00, the ten teams that had gone through the training at the Startup Camp and developed their company ideas, pitched their companies on stage to a panel of entrepreneur experts, the other nine teams, and all business coaches at Chalmers Ventures. DeepTrack obtained the first place among the ten participants. Congrats!

Here a few pictures from the final pitching event of the Startup Camp.

Henrik. (Picture by Jonas Sandwall, Chalmers Ventures.)
DeepTrack team members (left to right) Henrik, Giovanni and Jesus. (Picture by Jonas Sandwall, Chalmers Ventures.)
Panelists. (Picture by Jonas Sandwall, Chalmers Ventures.)

Featured in:
University of Gothenburg – News and Events: AI tool that analyses microscope images won startup competition and AI-verktyg som analyserar mikroskopbilder vann startup-tävling
(Swedish)

Invited Talk by G. Volpe at Complex Lagrangian Problems of Particles in Flows, 15 March 2022

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)
The Anomalous Diffusion Challenge: Objective comparison of methods to decode anomalous diffusion
Giovanni Volpe
Complex Lagrangian Problems of Particles in Flows
Online, 15 March 2022, 10:15 CET

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Links:
Complex Lagrangian Problems of Particles in Flows program

Plenary Talk by G. Volpe at Physics Days 2022 – Future Leaders, 3 March 2022

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)
Deep learning for microscopy, optical tweezers, and active matter
Giovanni Volpe
3 March 2022, 13:15
Plenary talk for Physics Days 2022 – Future Leaders
Online

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in microscopy, optical tweezers, and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy [1,2], to perform virtual staining of [3], to estimate the properties of anomalous diffusion [4,5,6], to characterize microscopic force fields [7], to improve the calculation of optical forces [8], and to characterize nanoparticles [9]. Finally, I will provide an outlook on the future for the application of deep learning in these fields.

References
[1] S. Helgadottir, A. Argun, and G. Volpe. Digital video microscopy enhanced by deep learning. Optica 6, 506 (2019).
[2] B. Midtvedt, S. Helgadottir, A. Argun, J. Pineda, D. Midtvedt, and G. Volpe. Quantitative digital microscopy with deep learning. Appl. Phys. Rev. 8, 011310 (2021).
[3] S. Helgadottir, B. Midtvedt, J. Pineda, et al. Extracting quantitative biological information from bright-field cell images using deep learning. Biophys. Rev. 2, 031401 (2021).
[4] S. Bo, F. Schmidt, R. Eichhorn, and G. Volpe. Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102 (2019).
[5] A. Argun, G. Volpe, and S. Bo. Classification, inference and segmentation of anomalous diffusion with recurrent neural networks. J. Phys. A: Math. Theor. 54, 294003 (2021).
[6] G. Muñoz-Gil, G. Volpe, M. A. Garcia-March, et al. Objective comparison of methods to decode anomalous diffusion. Nat. Commun. 12, 6253 (2021).
[7] A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe. Enhanced force-field calibration via machine learning. Appl. Phys. Rev. 7, 041404 (2020).
[8] I.C.D. Lenton, G. Volpe, A.B. Stilgoe, T.A. Nieminen, and H. Rubinsztein-Dunlop. Machine learning reveals complex behaviours in optically trapped particles. Mach. Learn.: Sci. Technol. 1, 045009 (2020).
[9] B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C.B. Adiels, G. Volpe, and D. Midtvedt. Fast and accurate nanoparticle characterization using deep-learning-enhanced off-axis holography. ACS Nano 15, 2240 (2021).

Link: Physics Days 2022 – Future Leaders
The Physics Days 2022 is organized by the Finnish Physical Society and the Department of Applied Physics at Aalto University.