Tag: Yu-Wei Chang

Age-related differences in the functional topography of the locus coeruleus and their implications for cognitive and affective functions published on eLife

Average functional gradients of the locus coeruleus in the CamCAN 3T dataset. (Image from the publication.)
Age-related differences in the functional topography of the locus coeruleus and their implications for cognitive and affective functions
Dániel Veréb, Mite Mijalkov, Anna Canal-Garcia, Yu-Wei Chang, Emiliano Gomez-Ruiz, Blanca Zufiria Gerboles, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Giovanni Volpe, Matthew Betts, Heidi IL Jacobs, Joana B Pereira
eLife 12, RP87188 (2023)
doi: 10.7554/eLife.87188.3

The locus coeruleus (LC) is an important noradrenergic nucleus that has recently attracted a lot of attention because of its emerging role in cognitive and psychiatric disorders. Although previous histological studies have shown that the LC has heterogeneous connections and cellular features, no studies have yet assessed its functional topography in vivo, how this heterogeneity changes over aging, and whether it is associated with cognition and mood. Here, we employ a gradient-based approach to characterize the functional heterogeneity in the organization of the LC over aging using 3T resting-state fMRI in a population-based cohort aged from 18 to 88 years of age (Cambridge Centre for Ageing and Neuroscience cohort, n=618). We show that the LC exhibits a rostro-caudal functional gradient along its longitudinal axis, which was replicated in an independent dataset (Human Connectome Project [HCP] 7T dataset, n=184). Although the main rostro-caudal direction of this gradient was consistent across age groups, its spatial features varied with increasing age, emotional memory, and emotion regulation. More specifically, a loss of rostral-like connectivity, more clustered functional topography, and greater asymmetry between right and left LC gradients was associated with higher age and worse behavioral performance. Furthermore, participants with higher-than-normal Hospital Anxiety and Depression Scale (HADS) ratings exhibited alterations in the gradient as well, which manifested in greater asymmetry. These results provide an in vivo account of how the functional topography of the LC changes over aging, and imply that spatial features of this organization are relevant markers of LC-related behavioral measures and psychopathology.

Presentation by Y.-W. Chang at SPIE-ETAI, San Diego, 22 August 2023

The proposed method enables accurate synthesis of longitudinal tau pathology. (Image by Y.-W. Chang.)
Synthesizing tau pathology from structural brain imaging using deep learning
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Date: 22 August 2023
Time: 10:15 AM PDT

In vivo tau-positron emission tomography (PET) is crucial for determining the stage of Alzheimer’s disease (AD). However, this method is expensive, not widely available, and exposes patients to ionizing radiation, which poses a carcinogenic risk. To address this issue, I’ll present our proposed method, a deep-learning synthesis approach for follow-up tau-PET brain images from baseline tau-PET images using a generative adversarial network (GAN). This technique has the potential to provide valuable insights into the progression of AD, the effectiveness of new treatments, and more accurate diagnosis of the disease.

 

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 20-24 August 2023

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 20-24 August 2023, with the presentations listed below.

Giovanni Volpe is also co-author of the presentations:

  • Jiawei Sun (KI): (Poster) Assessment of nonlinear changes in functional brain connectivity during aging using deep learning
    21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
  • Blanca Zufiria Gerbolés (KI): (Poster) Exploring age-related changes in anatomical brain connectivity using deep learning analysis in cognitively healthy individuals
    21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
  • Mite Mijalkov (KI): Uncovering vulnerable connections in the aging brain using reservoir computing
    22 August 2023 • 9:15 AM – 9:30 AM PDT | Conv. Ctr. Room 6C

Presentation by Y.-W. Chang at AI for Scientific Data Analysis, Gothenburg, 31 May 2023

Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Y.-W. Chang.)

Training of neural network with incomplete medical datasets
Yu-Wei Chang

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. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. 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. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

Date: 31 May 2023
Time: 10:15
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference

Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer’s disease published in Alzheimer’s Research & Therapy

Spatial maps depicting the strongest connections from the medial parietal cortex to other cortical and subcortical areas in the PREVENT-AD cohort. (Reproduced from the publication.)
Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer’s disease
Dániel Veréb, Mite Mijalkov, Yu-Wei Chang, Anna Canal-Garcia, Emiliano Gomez-Ruis, Anne Maass, Sylvia Villeneuve, Giovanni Volpe Joana B. Pereira
Alzheimer’s Research & Therapy 15, 82 (2023)
doi: 10.1186/s13195-023-01228-3

Background
The medial parietal cortex is an early site of pathological protein deposition in Alzheimer’s disease (AD). Previous studies have identified different subregions within this area; however, these subregions are often heterogeneous and disregard individual differences or subtle pathological alterations in the underlying functional architecture. To address this limitation, here we measured the continuous connectivity gradients of the medial parietal cortex and assessed their relationship with cerebrospinal fluid (CSF) biomarkers, ApoE ε4 carriership and memory in asymptomatic individuals at risk to develop AD.

Methods
Two hundred sixty-three cognitively normal participants with a family history of sporadic AD who underwent resting-state and task-based functional MRI using encoding and retrieval tasks were included from the PREVENT-AD cohort. A novel method for characterizing spatially continuous patterns of functional connectivity was applied to estimate functional gradients in the medial parietal cortex during the resting-state and task-based conditions. This resulted in a set of nine parameters that described the appearance of the gradient across different spatial directions. We performed correlation analyses to assess whether these parameters were associated with CSF biomarkers of phosphorylated tau181 (p-tau), total tau (t-tau), and amyloid-ß1-42 (Aß). Then, we compared the spatial parameters between ApoE ε4 carriers and noncarriers, and evaluated the relationship between these parameters and memory.

Results
Alterations involving the superior part of the medial parietal cortex, which was connected to regions of the default mode network, were associated with higher p-tau, t-tau levels as well as lower Aß/p-tau levels during the resting-state condition (p < 0.01). Similar alterations were found in ApoE ε4 carriers compared to non-carriers (p < 0.003). In contrast, lower immediate memory scores were associated with changes in the middle part of the medial parietal cortex, which was connected to inferior temporal and posterior parietal regions, during the encoding task (p = 0.001). No results were found when using conventional connectivity measures.

Conclusions
Functional alterations in the medial parietal gradients are associated with CSF AD biomarkers, ApoE e4 carriership, and lower memory in an asymptomatic cohort with a family history of sporadic AD, suggesting that functional gradients are sensitive to subtle changes associated with early AD stages.

Roadmap on Deep Learning for Microscopy on ArXiv

Spatio-temporal spectrum diagram of microscopy techniques and their applications. (Image by the Authors of the manuscript.)
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C.D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A. Ziatdinov, Sergei V. Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K. Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B. Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C. Caicedo, Beth A Cimini, Muyinatu A. Lediju Bell, Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
arXiv: 2303.03793

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Presentation by Y.-W. Chang at SPIE-ETAI, San Diego, 24 August 2022

Deep-learning-detected tau deposition (color in orange) for Alzheimer’s Disease. (Image by Y.-W. Chang.)
Deep-learning analysis in tau PET for Alzheimer’s continuum
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 16:40 (PDT)

Previous studies have suggested that Alzheimer’s disease (AD) is typically characterized by abnormal accumulation of tau proteins in neurofibrillary tangles. This is usually assessed by measuring tau levels in regions of interest (ROIs) defined based on previous post-mortem studies. However, it remains unclear where this approach is suitable for assessing tau accumulation in vivo across the different stages of individuals. This study employed a data-driven deep learning approach to detect tau deposition across different AD stages at the voxel level. Moreover, the classification performance of this approach on distinguishing different AD stages was compared with the one using conventional ROIs.

Presentation by Y.-W. Chang at SPIE-ETAI, San Diego, 24 August 2022

Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Y.-W. Chang.)
Neural network training with highly incomplete medical datasets
Yu-Wei Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B Pereira, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 08:00 (PDT)

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. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. 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. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 21-25 August 2022

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 21-25 August 2022, with the presentations listed below.

Giovanni Volpe is also co-author of the presentations:

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.