Evidence map›Paper›PMID 42681302›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Computational Pipeline in Clinical Connectomics.

Valentina Pacella, Sara Bertagnoli

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. 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

2 authors.

Valentina PacellaGroupe d'Imagerie Neurofonctionnelle (GIN), Institut des Maladies Neurodégénératives-UMR 5293, University of Bordeaux, Bordeaux, France. valentina.pacella@u-bordeaux.fr.
Sara BertagnoliSocial and Cognitive Neuroscience Laboratory, Department of Psychology, University La Sapienza, Roma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the neural basis of cognitive deficits following brain injury requires methods that can move beyond focal lesion analysis to capture the distributed nature of brain networks. This chapter presents a practical, step-by-step protocol for indirect disconnection mapping, a method that estimates white matter tract disruption from structural imaging alone, without requiring diffusion-weighted scans. By leveraging normative tractography data, this approach provides a means to infer the network-level consequences of lesions in both MRI and CT datasets. The procedure integrates widely used neuroimaging tools (MRIcron, BCBtoolkit, FSL, and SPM12) and supports both group-level and single-case analyses, including voxelwise statistical testing and Bayesian inference. Illustrated with the CT and MRI imaging of a patient, the workflow enables researchers and clinicians to map cognitive symptoms to patterns of structural disconnection, offering a reproducible and anatomically grounded framework for investigating and predicting brain-behavior relationships in clinical populations.

Indexed as

BrainConnectomeImage Processing, Computer-AssistedBayes TheoremDiffusion Tensor ImagingHumansMagnetic Resonance ImagingNeuroimagingSoftwareTomography, X-Ray ComputedWhite MatterLesion analysisNeuroimagingStrokeWhite matter disconnection

Identifiers

PMID42681302

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.