Evidence map›Paper›PMID 39998695›Full record

ArticleNeuroinformatics2025

Overcoming Neuroanatomical Mapping and Computational Barriers in Human Brain Synaptic Architecture.

Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Ram Jagadeesan

Abstract readLetter
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In one paragraph

Article in Neuroinformatics, 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

6 authors.

Rahul KumarDepartment of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1011 NW 15th Street, Gautier Building, MC R629, Miami, Florida, 33136-1019, USA. rahulca13@gmail.com.
Ethan WaisbergDepartment of Clinical Neuroscience, University of Cambridge, Cambridge, UK.
Joshua OngDepartment of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan, USA.
Phani PaladuguSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Dylan AmiriDepartment of Biology, University of Miami, Coral Gables, Florida, USA.
Ram JagadeesanDepartment of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this Matters Arising, we critically examine the data processing and computational challenges highlighted under the high-resolution, three-dimensional reconstruction of human cortical tissue by Shapson-Coe et al. While the study represents a technical milestone in connectomics, involving a 1.4-petabyte dataset derived from mapping a cubic millimeter of temporal cortex, the findings also reveal the substantial obstacles inherent in scaling such approaches to the entire human brain. Beyond the application of artificial intelligence (AI) for segmentation and synapse detection, the study underscores the immense complexity of data acquisition, cleaning, alignment, and visualization at this scale. This article contextualizes these challenges by comparing the computational and infrastructural requirements of the Shapson-Coe work to other large-scale neuroscience initiatives, such as the fruit fly brain atlas, and explores emerging technologies like quantum computing and neuromorphic hardware as potential solutions. Additionally, we discuss the ethical and logistical implications of managing zettabyte-scale datasets and emphasize the necessity of international collaboration to achieve the ambitious goal of mapping the human connectome. By critically addressing these challenges and potential solutions, this article aims to guide future advancements in the field of connectomics and their transformative applications in neuroscience, artificial intelligence, and medicine.

Indexed as

BrainBrain MappingConnectomeSynapsesAnimalsArtificial IntelligenceHumansImaging, Three-DimensionalArtificial intelligenceBrain networksMachine learningNeuroimagingNeuroscience

Identifiers

What OpenQuestion holds

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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.