Tag: Giovanni Volpe

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis published in Brain Behaviour and Immunity

Illustration of resting state network activity. (Image by the Authors of the manuscript.)
Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis
Paulo Lizano, Chelsea Kiely, Mite Mijalkov, Shashwath A. Meda, Sarah K. Keedy, Dung Hoang, Victor Zeng, Olivia Lutz, Joana B. Pereira, Elena I. Ivleva, Giovanni Volpe, Yanxun Xu, Adam M. Lee, Leah H. Rubin, S Kristian Hill, Brett A. Clementz, Carol A. Tamminga, Godfrey D. Pearlson, John A. Sweeney, Elliot S. Gershon, Matcheri S. Keshavan, Jeffrey R. Bishop
Brain Behavior and Immunity, 114, 3-15 (2023)
doi: 10.1016/j.bbi.2023.07.014

Introduction
High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.

Methods
Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.

Results
Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen’s d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.

Conclusion
These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain’s ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Perspectives on adaptive dynamical systems published in Chaos

Adaptivity across different scientific disciplines (blue) and applications (yellow) as well as its strong inter- linking and interlocking, similar to a system of gears.
(Image by the Authors of the manuscript)
Perspectives on adaptive dynamical systems
Jakub Sawicki, Rico Berner, Sarah A. M. Loos, Mehrnaz Anvari, Rolf Bader, Wolfram Barfuss, Nicola Botta, Nuria Brede, Igor Franović, Daniel J. Gauthier, Sebastian Goldt, Aida Hajizadeh, Philipp Hövel, Omer Karin, Philipp Lorenz-Spreen, Christoph Miehl, Jan Mölter, Simona Olmi, Eckehard Schöll, Alireza Seif, Peter A. Tass, Giovanni Volpe, Serhiy Yanchuk, Jürgen Kurths
Chaos 33, 071501 (2023)
doi: 10.1063/5.0147231
arXiv: 2303.01459

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Invited Talk by G. Volpe at XVII Congress of the Spanish Biophysical Society, Castelldefels, Spain, 30 June 2023

(Image by A. Argun)
Deep Learning for Imaging and Microscopy
Giovanni Volpe
XVII Congress of the Spanish Biophysical Society, Castelldefels, Spain, 30 June 2023
Date: 30 June 2023
Time: 11:00

Invited Talk by G. Volpe at Active Matter at Surfaces and in Complex Environments, Dresden, 20 June 2023

Illustration of anomalous diffusion. (Image by G. Muñoz-Gil)
The anomalous diffusion challenge 2
Giovanni Volpe
Active Matter at Surfaces and in Complex Environments, Dresden, Germany
Date: 20 June 2023
Time: 15:30

Invited Seminar by G. Volpe at LOMA, Bordeaux, 2 May 2023

DeepTrack 2.1 Logo. (Image from DeepTrack 2.1 Project)
Deep Learning for Imaging and Microscopy
Giovanni Volpe
Seminar at LOMA, Bordeaux
2 May 2023, 14:00

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.

Plenary Lecture by G. Volpe at SPIE Optics + Optoelectronics, Prague, 25 April 2023

DeepTrack 2.1 Logo. (Image from DeepTrack 2.1 Project)
AI and deep learning for microscopy
Giovanni Volpe
SPIE Optics + Optoelectronics, Prague, 25 April 2023
Time: 09:45

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.

To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.