{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:49:28Z","timestamp":1781153368539,"version":"3.54.1"},"reference-count":40,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"Suppl1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:sec><jats:title>OBJECTIVE<\/jats:title><jats:p>Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard stereotactic deep brain stimulation or radiofrequency thalamotomy or, alternatively, minimally invasive techniques, including stereotactic radiosurgery (SRS) and high-intensity focused ultrasound, at the level of the ventral intermediate nucleus (Vim). The aim of the present study was to evaluate potential correlations between pretherapeutic interconnectivity (IC), as depicted on resting-state functional MRI (rs-fMRI), and MR signature volume at 1 year after Vim SRS for tremor, to be able to potentially identify hypo- and hyperresponders based only on pretherapeutic neuroimaging data.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>Seventeen consecutive patients with ET were included, who benefitted from left unilateral SRS thalamotomy (SRS-T) between September 2014 and August 2015. Standard tremor assessment and rs-fMRI were acquired pretherapeutically and 1 year after SRS-T. A healthy control group was also included (n = 12). Group-level independent component analysis (ICA; only n = 17 for pretherapeutic rs-fMRI) was applied. The mean MR signature volume was 0.125 ml (median 0.063 ml, range 0.002\u20130.600 ml). The authors correlated baseline IC with 1-year MR signatures within all networks. A 2-sample t-test at the level of each component was first performed in two groups: group 1 (n = 8, volume &lt; 0.063 ml) and group 2 (n = 9, volume \u2265 0.063 ml). These groups did not statistically differ by age, duration of symptoms, baseline ADL score, ADL point decrease at 1 year, time to tremor arrest, or baseline tremor score on the treated hand (TSTH; p &gt; 0.05). An ANOVA was then performed on each component, using individual subject-level maps and continuous values of 1-year MR signatures, correlated with pretherapeutic IC.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>Using 2-sample t-tests, two networks were found to be statistically significant: network 3, including the brainstem, motor cerebellum, bilateral thalamus, and left supplementary motor area (SMA) (p<jats:sub>FWE<\/jats:sub> = 0.004, cluster size = 94), interconnected with the red nucleus (MNI \u22122, \u221222, \u221232); and network 9, including the brainstem, posterior insula, bilateral thalamus, and left SMA (p<jats:sub>FWE<\/jats:sub> = 0.002, cluster size = 106), interconnected with the left SMA (MNI 24, \u221228, 44). Higher pretherapeutic IC was associated with higher MR volumes, in a network including the anterior default-mode network and bilateral thalamus (ANOVA, p<jats:sub>FWE<\/jats:sub> = 0.004, cluster size = 73), interconnected with cerebellar lobule V (MNI \u221212, \u221270, \u221222). Moreover, in the same network, radiological hyporesponders presented with negative IC values.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSIONS<\/jats:title><jats:p>These findings have clinical implications for predicting MR signature volumes after SRS-T. Here, using pretherapeutic MRI and data processing without prior hypothesis, the authors showed that pretherapeutic network interconnectivity strength predicts 1-year MR signature volumes following SRS-T.<\/jats:p><\/jats:sec>","DOI":"10.3171\/2018.7.gks18752","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T17:47:29Z","timestamp":1551203249000},"page":"63-71","source":"Crossref","is-referenced-by-count":11,"title":["Pretherapeutic resting-state fMRI profiles are associated with MR signature volumes after stereotactic radiosurgical thalamotomy for essential tremor"],"prefix":"10.3171","volume":"129","author":[{"given":"Constantin","family":"Tuleasca","sequence":"first","affiliation":[{"name":"Neurosurgery Service and Gamma Knife Center,"},{"name":"Faculty of Biology and Medicine, University of Lausanne, Switzerland;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean","family":"R\u00e9gis","sequence":"additional","affiliation":[{"name":"Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Najdenovska","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Laboratory (MIAL) and Department of Radiology, Centre d\u2019Imagerie BioM\u00e9dicale (CIBM), and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatiana","family":"Witjas","sequence":"additional","affiliation":[{"name":"Neurology Department, CHU Timone, Marseille, France;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadine","family":"Girard","sequence":"additional","affiliation":[{"name":"AMU, CRMBM UMR CNRS 7339, Facult\u00e9 de M\u00e9decine et APHM, H\u00f4pital Timone, Department of Diagnostic and Interventional Neuroradiology, Marseille, France;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Bolton","sequence":"additional","affiliation":[{"name":"Medical Image Processing Laboratory, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francois","family":"Delaire","sequence":"additional","affiliation":[{"name":"Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marion","family":"Vincent","sequence":"additional","affiliation":[{"name":"Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Faouzi","sequence":"additional","affiliation":[{"name":"Institute of Social and Preventive Medicine, Lausanne, Switzerland; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Philippe","family":"Thiran","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory (LTS 5), Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland;"},{"name":"Faculty of Biology and Medicine, University of Lausanne, Switzerland;"},{"name":"Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meritxell","family":"Bach Cuadra","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Laboratory (MIAL) and Department of Radiology, Centre d\u2019Imagerie BioM\u00e9dicale (CIBM), and"},{"name":"Signal Processing Laboratory (LTS 5), Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Levivier","sequence":"additional","affiliation":[{"name":"Neurosurgery Service and Gamma Knife Center,"},{"name":"Faculty of Biology and Medicine, University of Lausanne, Switzerland;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitri","family":"Van de Ville","sequence":"additional","affiliation":[{"name":"Medical Image Processing Laboratory, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland;"},{"name":"University of Geneva, Faculty of Medicine, Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"Neuroimage","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.neuroimage.2013.10.062","article-title":"Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets","volume":"87","author":"Afshin-Pour B","year":"2014","unstructured":"Afshin-Pour B, Grady C, Strother S: Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets. 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