Evidence map›Paper›PMID 35859092›Full record

ArticleScientific reports2022

An interactive time series image analysis software for dendritic spines.

Ali Özgür Argunşah, Ertunç Erdil, Muhammad Usman Ghani, Yazmín Ramiro-Cortés, Anna F Hobbiss, Theofanis Karayannis, Müjdat Çetin, Inbal Israely, Devrim Ünay

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
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  3. Altered Structural Plasticity Mediated by mGlu and NMDA Receptors and Impaired Cognition in a Genetic ASD Model (Shank3The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ali Özgür Argunşah *Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, 1400-038, Portugal. argunsah@hifo.uzh.ch.
Ertunç Erdil *ETH Zürich, Computer Vision Laboratory, Zürich, Switzerland.
Muhammad Usman GhaniDepartment of Electrical and Computer Engineering, Boston University, Boston, 02215, MA, USA.
Yazmín Ramiro-CortésChampalimaud Research, Champalimaud Centre for the Unknown, Lisbon, 1400-038, Portugal.
Anna F HobbissChampalimaud Research, Champalimaud Centre for the Unknown, Lisbon, 1400-038, Portugal.
Theofanis KarayannisLaboratory of Neural Circuit Assembly, Brain Research Institute (HiFo), University of Zürich, Zürich, Switzerland.
Müjdat ÇetinDepartment of Electrical and Computer Engineering, Goergen Institute for Data Science, University of Rochester, Rochester, 14627, NY, USA.
Inbal IsraelyChampalimaud Research, Champalimaud Centre for the Unknown, Lisbon, 1400-038, Portugal.
Devrim ÜnayDepartment of Biomedical Engineering, İzmir University of Economics, İzmir, Turkey. unaydevrim@gmail.com.

Funding

Understanding How Activity Drives Diverse Spine Structural InteractionsR01NS112485 · NINDS · UNIVERSITY OF WASHINGTON · PI ISRAELY, INBAL · 2020 to 2024
$1.8M
European Research Council 679175NINDS NIH HHS R01 NS112485
6 · The paper itself

Abstract

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

Indexed as

Dendritic SpinesSoftwareAlgorithmsSynapsesTime Factors

Identifiers

PMID35859092
PMCPMC9300710

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.