Evidence map›Paper›PMID 40800267›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2024

FAT1 weighted MRI: Diffusion meets anatomical imaging and application in thalamic surgery for tremor.

Taco Goedemans, Francisca Ferreira, Thomas Wirth, Lonneke van der Weerd, Flavia V Massey, Marie T Krüger, Vanessa Milanese, Ashkan Pakzad, Thomas Foltynie, Patricia Limousin and 5 more

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Surgical Treatment of Tremor Syndromes: A Guide to the Clinician.Movement disorders clinical practice · 2026
    Review
  3. Article
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

15 authors.

Taco GoedemansUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Francisca FerreiraUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Thomas WirthUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Lonneke van der WeerdUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Flavia V MasseyUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Marie T KrügerUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Vanessa MilaneseDepartment of Neurosurgery, DOMMO Clinic, and Beneficência Portuguesa of São Paulo Hospital, São Paulo, Brazil.
Ashkan PakzadCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Thomas FoltynieUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Patricia LimousinUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Maarten BotDepartment of Neurosurgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Pepijn van den MunckhofDepartment of Neurosurgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Rick SchuurmanDepartment of Neurosurgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Ludvic ZrinzoUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.
Harith AkramUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology & The National Hospital for Neurology and Neurosurgery (UCLH), London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patient-specific targeting of the Ventral intermediate nucleus (Vim) of the thalamus can be achieved with MR connectivity. Nevertheless, there are several drawbacks to using tractography-based targeting methods to visualise distinct thalamic nuclei (e.g., subjective region of interest selection, and thresholding of resulting tracts and clusters). Fractional anisotropy (FA) mapping, another product of diffusion MRI (dMRI), does not rely on tractography, and could thus be clinically more viable for discerning thalamic anatomy for stereotactic surgery. The aim of this study is to develop and present a hybrid, high-resolution, and high-fidelity imaging modality that combines contrast from FA maps as well as anatomical T1 sequences (FAT1 imaging); and to evaluate FAT1 based Vim-target definition. Imaging and outcome data of 35 consecutive refractory tremor patients who had undergone 43 connectivity guided deep brain stimulation (DBS) and/or radiofrequency thermocoagulation (RF-T) between 2013 and 2021 were included. First, the pre-operatively acquired dMRI and MPRAGE sequences were used to create FAT1 maps in retrospect. Then, an FAT1 based Vim-target was planned by an experienced functional neurosurgeon who was blinded for patient outcome. Finally, to investigate FAT1 based targeting, a post-hoc analysis was carried out of the degree of overlap between the newly created FAT1 based Vim-target, and the volume of tissue activation (VTA, in case of DBS) or lesion volume (in case of RF-T). This degree of overlap was compared between favourable and unfavourable outcome groups: outcomes were measured by experts blinded for imaging data at the last follow-up using a Clinical Global Impression-Improvement score (CGI-I), where a CGI-I score of 1-2 (i.e., FTMTRS improvement of ≥50%) was considered favourable. In 36 of the 43 (84%) performed surgeries (24 DBS and 19 RF-T), FAT1 based Vim-targeting was possible. For the group showing favourable outcome (71% of the patients at a median follow-up of 13 months), the mean amount of overlap between the FAT1 based Vim-target and the VTA or lesion was 42% (±13), versus 17% (±15) for patients with an unfavourable outcome (MD 25%, 95% CI 14-35, p < 0.0001). Retrospective use of FAT1 based Vim-targeting as a tool to predict outcome had a sensitivity of 90%, specificity of 80%, positive predictive value of 90%, and negative predictive value of 80%. In conclusion, FAT1 imaging is a new, high-resolution, and high-fidelity modality that combines diffusion and anatomical MRI. It provides a fast and efficacious way of targeting the ventral intermediate nucleus of the thalamus. In this study, FAT1 based targeting was highly accurate in predicting outcomes after deep brain stimulation and radiofrequency thalamotomy.

Indexed as

connectivitydeep brain stimulation (DBS)diffusion weighted imaging (DWI)fractional anisotropy (FA)movement disordersradiofrequency Thalamotomystereotactic neurosurgerytractographyventral intermediate nucleus (Vim)

Identifiers

PMID40800267
PMCPMC12247582

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Registered trials

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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.