Evidence map›Paper›PMID 40661434›Full record

ArticlebioRxiv : the preprint server for biology2025

DeepFace: A High-Precision and Scalable Deep Learning Pipeline for Predicting Large-Scale Brain Activity from Facial Dynamics in Mice.

Kemal Ozdemirli, Tenesha Connor, Kaleb Kim, Macit Emre Lacin, Miguel Maldonado, Thiago Peixoto Leal, Caglar Oksuz, Ignacio Mata, Murat Yildirim

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kemal OzdemirliDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Tenesha ConnorDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Kaleb KimDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Macit Emre LacinDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Miguel MaldonadoDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Thiago Peixoto LealGenomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Caglar OksuzDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Ignacio MataGenomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Murat YildirimDepartment of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.

Funding

Developing next generation multiphoton systems to reveal cortico-thalamic interactions underlying short-term memory in behaving miceR00EB027706 · NIBIB · CLEVELAND CLINIC LERNER COM-CWRU · PI YILDIRIM, MURAT · 2022 to 2024
$747k
NIBIB NIH HHS R00 EB027706
6 · The paper itself

Abstract

We present DeepFace, a next-generation facial analysis pipeline that enhances orofacial tracking and cortical activity prediction in mice. Rather than replacing existing tools, DeepFace builds upon DeepLabCut and Facemap to address scalability bottlenecks and improve behavioral quantification. It offers high precision, keypoint customization, and robust performance across GCaMP6s, GCaMP6f, and jGCaMP8m lines. With scalable batch processing and high-performance computing compatibility, DeepFace enables high-throughput brain-behavior analysis in large-scale preclinical neuroscience.

Identifiers

PMID40661434
PMCPMC12259132

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.