Neural Representation in Visual Word Form Area during Word Reading

Lu, Chengrou; Li, Huiling; Fu, Ruilin; Qu, Jing; Yue, Qingxin; Mei, Leilei

doi:10.1016/j.neuroscience.2020.10.040

Cited by 8 publications

(3 citation statements)

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“…Results showed that the left MFG defined by three different ways, bilateral FFG, left ITG, and left MTG still showed significant correlations between neural DSM and rating‐based phonological DSM (all p s < .05; see Table 3 and Figure S4 ). Second, to examine the contribution of the left MFG to phonological processing of Chinese characters, we calculated partial correlations after controlling for the nDSM in the left IFG, a region that has been repeatedly reported in word reading (Dong et al, 2020 ; Jana et al, 2019 ; Kim et al, 2020 ; Liu et al, 2021 ; Tan et al, 2005 ; Tham et al, 2005; Wu et al, 2012), speech production (Basilakos et al, 2018 ; Flinker et al, 2015 ; Long et al, 2016 ; Mugler et al, 2018 ), and working memory (Basilakos et al, 2018 ; Ekert et al, 2021 ). Results showed that the left MFG defined based on the localizer task ( r = .0155, p = .028), Harvard–Oxford atlas ( r = .0172, p = .0163), and literature ( r = .0210, p = .0053) still showed significant correlations between nDSM and rating‐based phonological DSM.…”

Section: Resultsmentioning

confidence: 99%

“…Phonological processing (i.e., transforming the visual forms of written words into sounds) is one of key components of reading (Coltheart et al, 2001 ; Harm & Seidenberg, 2004 ). Numerous neuroimaging studies have examined the neural basis of phonological processing in reading by using various reading tasks, such as rhyme judgment (Aparicio et al, 2007 ; Booth et al, 2004 ; Dębska et al, 2019 ; Kim et al, 2020 ; Liu et al, 2009 , 2018 ; Mathur et al, 2020 ; Pillay et al, 2014 ; Tan et al, 2003 ; Yen et al, 2019 ), homophone judgment (Chuchu et al, 2018 ; Gitelman et al, 2005 ; Matsuo et al, 2010 ; Siok et al, 2003 ; Tham et al, 2005 ; Yang & Tan, 2019 ), initial consonant judgment (Lee et al, 2012 ; Siok et al, 2003 ), phonological decision (Emmorey et al, 2016 ; Hartwigsen et al, 2010 ; Liebig et al, 2017 ), silent reading (Cheema et al, 2018 ; Cullum et al, 2019 ; Hartwigsen et al, 2010 ; Kumar & Padakannaya, 2019 ; Lu et al, 2021 ; Osipowicz et al, 2011 ; Qu et al, 2019 ; Wan et al, 2017 ), and overt naming tasks (Al et al, 2020 ; Berken et al, 2015 ; Binder et al, 2005 ; Brumberg et al, 2016 ; Dietz et al, 2005 ; Dong et al, 2020 , 2021 ; Graves et al, 2010 ; Mechelli et al, 2007 ). Researchers have observed consistent activation in language‐related brain areas in the left inferior frontal gyrus, left temporoparietal cortex, and left lateral temporal cortex.…”

Section: Introductionmentioning

confidence: 99%

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Neural representation of phonological information during Chinese character reading

Yang

et al. 2022

Human Brain Mapping

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Previous studies have revealed that phonological processing of Chinese characters elicited activation in the left prefrontal cortex, bilateral parietal cortex, and occipitotemporal regions. However, it is controversial what role the left middle frontal gyrus plays in Chinese character reading, and whether the core regions (e.g., the left superior temporal gyrus and supramarginal gyrus) for phonological processing of alphabetic languages are also involved in Chinese character reading. To address these questions, the present study used both univariate and multivariate analysis (i.e., representational similarity analysis, RSA) to explore neural representations of phonological information during Chinese character reading. Participants were scanned while performing a reading aloud task. Univariate activation analysis revealed a widely distributed network for word reading, including the bilateral inferior frontal gyrus, middle frontal gyrus, lateral temporal cortex, and occipitotemporal cortex. More importantly, RSA showed that the left prefrontal (i.e., the left middle frontal gyrus and left inferior frontal gyrus) and bilateral occipitotemporal areas (i.e., the left inferior and middle temporal gyrus and bilateral fusiform gyrus) represented phonological information of Chinese characters. These results confirmed the importance of the left middle frontal gyrus and regions in ventral pathway in representing phonological information of Chinese characters.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural representation of phonological information during Chinese character reading

Yang

et al. 2022

Human Brain Mapping

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“…According to solid neuroimaging literature, orthographic stimuli such as words and letter strings would be preferentially processed by a specialized area of the left infero-temporal cortex named the visual word form area (VWFA) ( McCandliss et al, 2004 ; Lu et al, 2021 ). Its electromagnetic manifestation would be a negative and left-sided component peaking at about 170 ms over the occipitotemporal scalp area.…”

Section: Introductionmentioning

confidence: 99%

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems

Proverbio

Tacchini

Jiang

2022

Front. Behav. Neurosci.

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ObjectiveA majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.MethodsA total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.ResultsClear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.ConclusionThe data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.

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