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Article ; Online: Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery With Manifold Learning.

Cohen, Ido / Valsky, Dan / Talmon, Ronen

IEEE transactions on bio-medical engineering

2023 Volume 70, Issue 4, Page(s) 1286–1297

Abstract: During Deep Brain Stimulation (DBS) surgery for treating Parkinson's disease, detecting the Subthalamic Nucleus (STN) and its sub-territory called the Dorsolateral Oscillatory Region (DLOR) is crucial for adequate clinical outcomes. Currently, the ... ...

Abstract	During Deep Brain Stimulation (DBS) surgery for treating Parkinson's disease, detecting the Subthalamic Nucleus (STN) and its sub-territory called the Dorsolateral Oscillatory Region (DLOR) is crucial for adequate clinical outcomes. Currently, the detection is based on human experts, often guided by supervised machine learning detection algorithms. This procedure depends on the knowledge and experience of particular experts and on the amount and quality of the labeled data used for training the machine learning algorithms. In this paper, to circumvent such dependence and the inevitable bias introduced by the training data, we present a data-driven unsupervised algorithm for detecting the STN and the DLOR during DBS surgery based on an agnostic modeling approach. Given measurements, we extract new features and compute a variant of the Mahalanobis distance between these features. We show theoretically that this distance enhances the differences between measurements with different intrinsic characteristics. Incorporating the new features and distance into a manifold learning method, called Diffusion Maps, gives rise to a representation that is consistent with the underlying factors that govern the measurements. Since this representation does not rely on rigid modeling assumptions and is obtained solely from the measurements, it facilitates a broad range of detection tasks; here, we propose a specification for STN and DLOR detection during DBS surgery. We present detection results on 25 sets of measurements recorded from 16 patients during surgery. Compared to a supervised algorithm, our unsupervised method demonstrates similar results in detecting the STN and superior results in detecting the DLOR.
MeSH term(s)	Humans ; Subthalamic Nucleus/surgery ; Deep Brain Stimulation/methods ; Parkinson Disease/surgery ; Parkinson Disease/drug therapy ; Machine Learning ; Supervised Machine Learning
Language	English
Publishing date	2023-03-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2022.3215092
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Article: Immunogenicity and Safety of Modified Vaccinia Ankara (MVA) Vaccine-A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Nave, Lior / Margalit, Ili / Tau, Noam / Cohen, Ido / Yelin, Dana / Lienert, Florian / Yahav, Dafna

Vaccines

2023 Volume 11, Issue 9

Abstract: Prevention of mpox has become an important public health interest. We aimed to evaluate the safety and immunogenicity of the Modified Vaccinia Ankara (MVA) vaccine. We conducted a systematic review and meta-analysis of randomized-controlled trials (RCTs) ...

Abstract	Prevention of mpox has become an important public health interest. We aimed to evaluate the safety and immunogenicity of the Modified Vaccinia Ankara (MVA) vaccine. We conducted a systematic review and meta-analysis of randomized-controlled trials (RCTs) comparing MVA versus no intervention, placebo, or another vaccine. Outcomes included safety and immunogenicity outcomes. We also performed a systematic review of RCTs evaluating various MVA regimens. Fifteen publications were included in the quantitative meta-analysis. All but one (ACAM2000) compared MVA with placebo. We found that cardiovascular adverse events following two MVA doses were significantly more common compared to placebo (relative risk [RR] 4.07, 95% confidence interval [CI] 1.10-15.10), though serious adverse events (SAEs) were not significantly different. Following a single MVA dose, no difference was demonstrated in any adverse event outcomes. Seroconversion rates were significantly higher compared with placebo after a single or two doses. None of the RCTs evaluated clinical effectiveness in preventing mpox. This meta-analysis provides reassuring results concerning the immunogenicity and safety of MVA. Further studies are needed to confirm the immunogenicity of a single dose and its clinical effectiveness. A single vaccine dose may be considered according to vaccine availability, with preference for two doses.
Language	English
Publishing date	2023-08-24
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines11091410
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Book ; Online: BASiS

Streicher, Or / Cohen, Ido / Gilboa, Guy

Batch Aligned Spectral Embedding Space

2022

Abstract: Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra theory. It thus can be extremely instrumental to ... ...

Abstract	Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra theory. It thus can be extremely instrumental to design deep network building blocks with spectral graph characteristics. For instance, such a network allows the design of optimal graphs for certain tasks or obtaining a canonical orthogonal low-dimensional embedding of the data. Recent attempts to solve this problem were based on minimizing Rayleigh-quotient type losses. We propose a different approach of directly learning the eigensapce. A severe problem of the direct approach, applied in batch-learning, is the inconsistent mapping of features to eigenspace coordinates in different batches. We analyze the degrees of freedom of learning this task using batches and propose a stable alignment mechanism that can work both with batch changes and with graph-metric changes. We show that our learnt spectral embedding is better in terms of NMI, ACC, Grassman distance, orthogonality and classification accuracy, compared to SOTA. In addition, the learning is more stable. Comment: 14 pages, 10 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-11-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery with Manifold Learning

Cohen, Ido / Valsky, Dan / Talmon, Ronen

2022

Abstract: During Deep Brain Stimulation(DBS) surgery for treating Parkinson's disease, one vital task is to detect a specific brain area called the Subthalamic Nucleus(STN) and a sub-territory within the STN called the Dorsolateral Oscillatory Region(DLOR). ... ...

Abstract	During Deep Brain Stimulation(DBS) surgery for treating Parkinson's disease, one vital task is to detect a specific brain area called the Subthalamic Nucleus(STN) and a sub-territory within the STN called the Dorsolateral Oscillatory Region(DLOR). Accurate detection of the STN borders is crucial for adequate clinical outcomes. Currently, the detection is based on human experts, guided by supervised machine learning detection algorithms. Consequently, this procedure depends on the knowledge and experience of particular experts and on the amount and quality of the labeled data used for training the machine learning algorithms. In this paper, to circumvent the dependence and bias caused by the training data, we present a data-driven unsupervised method for detecting the STN and the DLOR during DBS surgery. Our method is based on an agnostic modeling approach for general target detection tasks. Given a set of measurements, we extract features and propose a variant of the Mahalanobis distance between these features. We show theoretically that this distance enhances the differences between measurements with different intrinsic characteristics. Then, we incorporate the new features and distances into a manifold learning method, called Diffusion Maps. We show that this method gives rise to a representation that is consistent with the underlying factors that govern the measurements. Since the construction of this representation is carried out without rigid modeling assumptions, it can facilitate a wide range of detection tasks; here, we propose a specification for the STN and DLOR detection tasks. We present detection results on 25 sets of measurements recorded from 16 patients during surgery. Compared to a competing supervised algorithm based on a Hidden Markov Model, our unsupervised method demonstrates similar results in the STN detection task and superior results in the DLOR detection task.
Keywords	Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-08-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: The Underlying Correlated Dynamics in Neural Training

Turjeman, Rotem / Berkov, Tom / Cohen, Ido / Gilboa, Guy

2022

Abstract: Training of neural networks is a computationally intensive task. The significance of understanding and modeling the training dynamics is growing as increasingly larger networks are being trained. We propose in this work a model based on the correlation ... ...

Abstract	Training of neural networks is a computationally intensive task. The significance of understanding and modeling the training dynamics is growing as increasingly larger networks are being trained. We propose in this work a model based on the correlation of the parameters' dynamics, which dramatically reduces the dimensionality. We refer to our algorithm as \emph{correlation mode decomposition} (CMD). It splits the parameter space into groups of parameters (modes) which behave in a highly correlated manner through the epochs. We achieve a remarkable dimensionality reduction with this approach, where networks like ResNet-18, transformers and GANs, containing millions of parameters, can be modeled well using just a few modes. We observe each typical time profile of a mode is spread throughout the network in all layers. Moreover, our model induces regularization which yields better generalization capacity on the test set. This representation enhances the understanding of the underlying training dynamics and can pave the way for designing better acceleration techniques.
Keywords	Computer Science - Machine Learning ; Mathematics - Numerical Analysis
Subject code	006
Publishing date	2022-12-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Do Vedolizumab trough Levels Predict the Outcome of Subsequent Therapy in Inflammatory Bowel Disease?

Levartovsky, Asaf / Cohen, Ido / Abitbol, Chaya Mushka / Yavzori, Miri / Fudim, Ella / Picard, Orit / Kopylov, Uri / Ben-Horin, Shomron / Ungar, Bella

Biomedicines

2023 Volume 11, Issue 6

Abstract: ... ...

Abstract	Background
Language	English
Publishing date	2023-05-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines11061553
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Article ; Online: Umbilical cord blood gases sampling in low-risk vaginal deliveries as a predictor of adverse neonatal outcome.

Gonen, Noa / Cohen, Ido / Gluck, Ohad / Jhucha, Dan / Shmueli, Anat / Barda, Giulia / Weiner, Eran / Barber, Elad

Archives of gynecology and obstetrics

2023 Volume 309, Issue 2, Page(s) 523–531

Abstract: Introduction: There is no clear correlation between abnormal umbilical cord blood gas studies (UCGS) and adverse neonatal outcome in low-risk deliveries. We investigated the need for its routine use in low-risk deliveries.: Methods: We ... ...

Abstract	Introduction: There is no clear correlation between abnormal umbilical cord blood gas studies (UCGS) and adverse neonatal outcome in low-risk deliveries. We investigated the need for its routine use in low-risk deliveries. Methods: We retrospectively compared maternal, neonatal, and obstetrical characteristics among low-risk deliveries (2014-2022) between "normal" and "abnormal" pH groups: A:normal pH ≥ 7.15; abnormal pH < 7.15; B: normal pH ≥ 7.15 and base excess (BE) > - 12 mmol/L; abnormal pH < 7.15 and BE ≤ We retrospectively compared 12 mmol/L; C: normal pH ≥ 7.1; abnormal pH < 7.1; D: normal pH > 7.1 and BE > - 12 mmol/L; abnormal pH < 7.1 and BE ≤ - 12 mmol/L. Results: Of 14,338 deliveries, the rates of UCGS were: A-0.3% (n = 43); B-0.07% (n = 10); C-0.11% (n = 17); D-0.03% (n = 4). The primary outcome, composite adverse neonatal outcome (CANO) occurred in 178 neonates with normal UCGS (1.2%) and in only one case with UCGS (2.6%). The sensitivity and specificity of UCGS as a predictor of CANO were high (99.7-99.9%) and low (0.56-0.59%), respectively. Conclusion: UCGS were an uncommon finding in low-risk deliveries and its association with CANO was not clinically relevant. Consequently, its routine use should be considered.
MeSH term(s)	Pregnancy ; Infant, Newborn ; Female ; Humans ; Retrospective Studies ; Fetal Blood ; Hydrogen-Ion Concentration ; Delivery, Obstetric ; Risk ; Umbilical Cord
Language	English
Publishing date	2023-02-19
Publishing country	Germany
Document type	Journal Article
ZDB-ID	896455-5
ISSN	1432-0711 ; 0932-0067
ISSN (online)	1432-0711
ISSN	0932-0067
DOI	10.1007/s00404-023-06965-2
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Book ; Online: Enhancing Neural Training via a Correlated Dynamics Model

Brokman, Jonathan / Betser, Roy / Turjeman, Rotem / Berkov, Tom / Cohen, Ido / Gilboa, Guy

2023

Abstract: As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present a novel observation: Parameters during training exhibit intrinsic ... ...

Abstract	As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present a novel observation: Parameters during training exhibit intrinsic correlations over time. Capitalizing on this, we introduce Correlation Mode Decomposition (CMD). This algorithm clusters the parameter space into groups, termed modes, that display synchronized behavior across epochs. This enables CMD to efficiently represent the training dynamics of complex networks, like ResNets and Transformers, using only a few modes. Moreover, test set generalization is enhanced. We introduce an efficient CMD variant, designed to run concurrently with training. Our experiments indicate that CMD surpasses the state-of-the-art method for compactly modeled dynamics on image classification. Our modeling can improve training efficiency and lower communication overhead, as shown by our preliminary experiments in the context of federated learning.
Keywords	Computer Science - Machine Learning ; Mathematics - Dynamical Systems
Subject code	006
Publishing date	2023-12-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images.

Cohen, Ido / David, Eli Omid / Netanyahu, Nathan S

Entropy (Basel, Switzerland)

2019 Volume 21, Issue 3

Abstract: In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning ... ...

Abstract	In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of
Language	English
Publishing date	2019-02-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e21030221
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Article ; Online: An impact of lipid profile and lipid lowering drugs on ≥70 year olds of an upper socioeconomic class: a retrospective cohort study.

Eden Friedman, Yehudit / Steinberg, David M / Canetti, Michal / Cohen, Ido / Segev, Shlomo / Salomon, Ophira

Lipids in health and disease

2021 Volume 20, Issue 1, Page(s) 120

Abstract: Background: Life expectancy has greatly increased, generating an improvement in screening programs for disease prevention, lifesaving drugs and medical devices. The impact of lowering low-density lipoprotein cholesterol (LDL-C) in the very elderly is ... ...

Abstract	Background: Life expectancy has greatly increased, generating an improvement in screening programs for disease prevention, lifesaving drugs and medical devices. The impact of lowering low-density lipoprotein cholesterol (LDL-C) in the very elderly is not well-established. Our aim was to explore the association of LDL-C, high density lipoprotein cholesterol (HDL-C) and lipid lowering drugs (LLDs) on cognitive decline, malignancies and overall survival. Methods: This was a retrospective cohort study. Our study comprised 1498 (72.7%) males and 561 (27.3%) females, aged ≥70 who had attended the Institute for Medical Screening (IMS), Sheba Medical Center, Israel at least twice during 2013-2019. Data were obtained from the computerized database of the IMS. A manual quality control to identify potential discrepancies was performed. Results: Overall, 6.3% of the subjects treated with LLDs (95/1421) versus 4.2% not treated (28/638), cognitively declined during the study years. No statistically significant effects of LDL-C, HDL-C and LLDs on cognitive decline were observed after correcting for age, prior stroke and other vascular risk factors. With regard to cancer, after adjusting for confounders and multiple inferences, no definite relationships were found. Conclusions: This analysis of an elderly, high socioeconomic status cohort suggests several relationships between the use of LLDs and health outcomes, some beneficial, especially, with regard to certain types of cancer, but with a higher risk of cognitive decline. Further studies are warranted to clarify the health effects of these medications in the elderly.
MeSH term(s)	Aged ; Cholesterol, LDL/blood ; Cognitive Dysfunction/blood ; Female ; Humans ; Hypercholesterolemia/drug therapy ; Hypercholesterolemia/epidemiology ; Hypolipidemic Agents/therapeutic use ; Lipids/blood ; Male ; Middle Aged ; Proportional Hazards Models ; Retrospective Studies ; Risk Factors ; Social Class
Chemical Substances	Cholesterol, LDL ; Hypolipidemic Agents ; Lipids
Language	English
Publishing date	2021-09-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2091381-3
ISSN	1476-511X ; 1476-511X
ISSN (online)	1476-511X
ISSN	1476-511X
DOI	10.1186/s12944-021-01529-2
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