2020
DOI: 10.1101/2020.10.08.330886
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Picture naming yields highly consistent cortical activation patterns: test-retest reliability of magnetoencephalography recordings

Abstract: Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a picture naming paradigm, source-localized evoked activity a… Show more

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Cited by 2 publications
(3 citation statements)
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“…To obtain spatial information from MEG data, we used a sophisticated approach that takes into consideration the lesion's effects on the signal conductivity, thus improving the precision of source localisation (for more details, see Piastra et al, 2018, 2022). Importantly, MEG signatures of both picture naming (Ala‐Salomäki et al, 2021; Levelt et al, 1998; Salmelin et al, 1994; Sörös et al, 2003) and context‐driven word production (Piai et al, 2015; Roos & Piai, 2020) are well established in previous literature, showing replicability of LH sources within individuals and across studies, with a well‐characterised time course. During picture naming, evoked activity is consistently observed in visual areas during the first 200 ms after picture presentation, followed by middle and posterior temporal and parietal regions (sometimes bilaterally, but with test–retest reliability only in the LH) from 200 ms onwards.…”
Section: Introductionmentioning
confidence: 61%
See 1 more Smart Citation
“…To obtain spatial information from MEG data, we used a sophisticated approach that takes into consideration the lesion's effects on the signal conductivity, thus improving the precision of source localisation (for more details, see Piastra et al, 2018, 2022). Importantly, MEG signatures of both picture naming (Ala‐Salomäki et al, 2021; Levelt et al, 1998; Salmelin et al, 1994; Sörös et al, 2003) and context‐driven word production (Piai et al, 2015; Roos & Piai, 2020) are well established in previous literature, showing replicability of LH sources within individuals and across studies, with a well‐characterised time course. During picture naming, evoked activity is consistently observed in visual areas during the first 200 ms after picture presentation, followed by middle and posterior temporal and parietal regions (sometimes bilaterally, but with test–retest reliability only in the LH) from 200 ms onwards.…”
Section: Introductionmentioning
confidence: 61%
“…During picture naming, evoked activity is consistently observed in visual areas during the first 200 ms after picture presentation, followed by middle and posterior temporal and parietal regions (sometimes bilaterally, but with test–retest reliability only in the LH) from 200 ms onwards. Around 400 ms onwards, activity is observed in ventral precentral gyrus and IFG (sometimes bilaterally, but with test–retest reliability only in the LH, Ala‐Salomäki et al, 2021; Liljeström et al, 2009; Salmelin et al, 1994; Sörös et al, 2003; Vihla et al, 2006). According to models of word production (e.g., Indefrey & Levelt, 2004), early activity in temporo‐parietal areas reflects conceptual, lexical and phonological retrieval, and later, frontal and sensorimotor activity is associated with phonological and phonetic encoding and articulation.…”
Section: Introductionmentioning
confidence: 99%
“…Since the DICS analysis was conducted in a template brain (FreeSurfer fsaverage-5.1.0 template; Fischl, 2012), the forward model of one participant utilizing a single-compartment realistic boundary element model was used to define the distribution of points across the parcels for all participants. A customized version of the Destrieux anatomical parcellation (Destrieux et al, 2010) with 69 parcels per hemisphere that was constructed using a merge-and-split approach to produce uniform-sized parcels (Ala-Salomäki et al, 2021) was used as the parcellation. The frequency-domain beamformer was implemented using a common weights approach, in which the weights were defined by combining both CSD matrices (i.e., low and high HRV states).…”
Section: Preprocessing and Analysis Of Meg Datamentioning
confidence: 99%