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    • br Effects of early life stress on amygdala

    2018-11-03


    Effects of early life stress on amygdala and striatal development
    Discussion
    Acknowledgements This work was supported by the National Institute of Mental Health (R01MH091864 to N.T.) and the Dana Foundation (20122240). The authors would like to thank Dr. Dylan Gee for helpful comments.
    Introduction Real-time language comprehension is a fast-paced, complex task that includes retrieving and integrating phonological, semantic, syntactic, and pragmatic information with millisecond-level precision. Behavioral and neuroimaging research indicate that the development of adult-like language abilities and the neural structures underlying those abilities is prolonged, continuing through age 12 or later (Atchley et al., 2006; Friedrich and Friederici, 2004; Friederici and Hahne, 2001; Silva-Pereya et al., 2005; Nuñez et al., 2011). Performing well during natural, everyday language tasks but exhibiting subtle processing differences when language capabilities are taxed indicates that children may engage somewhat different skills or strategies than adults during language comprehension (Holland et al., 2007). To better understand the nature of these differences we used event-related potentials (ERPs) and time frequency analysis of EEG to examine the neural oscillations underlying naturally paced sentence comprehension in children and adults. Many theories have noted that the development of effective semantic leptomycin b and syntactic unification may contribute to the prolonged development of language skills (e.g., Brauer and Friederici, 2007; Chou et al., 2006). One must quickly retrieve semantic representations related to each incoming word and then, as each new word in the sentence is encountered, integrate it to form a coherent semantic representation. For example, when hearing the phrase the hairy, it is easier to integrate the word dog with that phrase than table, because a hairy dog refers to a logical semantic representation in a way that a hairy table does not. Syntactic unification is also necessary for successful language comprehension. Continuing our example, in English, adjectives are often followed by nouns; thus, one can integrate the syntactic information in the hairy dog to form a meaningful representation but not the hairy eat. Research using ERPs consistently reports that semantic and syntactic abilities develop through early adolescence to support language comprehension (e.g., Atchley et al., 2006; Friederici and Hahne, 2001). Participants in these studies read or hear sentences containing a semantic error (She buttered her toast with a dress) or a grammatical error (The goose was in the fed). Compared to correct sentences, children and adults exhibit a larger N400 to semantic errors and a larger P600 to grammatical errors. Although study specifics vary, children generally display an N400 that is later, larger and more broadly distributed and a P600 that is larger and later compared to adults (Benau et al., 2011; Friederici and Männel, 2013; Hahne et al., 2004; Friedrich and Friederici, 2004; Friederici, 2006). These developmental differences are thought to reflect higher cognitive demands when children perform the same language task as adults. These findings are informative about the development of early language skills but, due to the process of averaging the EEG signal to produce an ERP, non-phase locked dynamics, providing important information related to semantics and syntax, can be lost. Recent computational advances, such as time frequency analysis, provide different means of analyzing EEG data by decomposing the signal to identify changes in the amplitude, or power, of the response within frequency bands of interest (Davidson and Indefrey, 2007; Cohen, 2014). Given founder effect advantage, time frequency analysis may identify differences in processing that are lost due to the averaging process used in traditional ERP analysis. Changes in the beta frequency band (12–30Hz) have been related to syntactic unification (e.g., Bastiaansen et al., 2010; Davidson and Indefrey, 2007). According to theories of syntactic unification, each incoming word in a sentence activates multiple syntactic possibilities, called lexical frames (Vosse and Kempen, 2000). These lexical frames specify the potential structural environment for each incoming word, and are combined based on various features and constraints to create one stable syntactic structure by which the meaning of the sentence can be decoded. Related to time frequency analysis, beta increases with each word in a visually presented grammatically correct sentence, but decreases at the point of a syntactic error in a sentence, when syntactic unification fails (Bastiaansen et al., 2010; Davidson and Indefrey, 2007). Further, when the words of a sentence are presented in a random order, no increase in beta occurs, presumably due to the lack of syntactic information (Bastiaansen et al., 2010). Although beta responds differently to a syntactic violation than the P600 ERP component, both appear to play an important role in identifying changes in syntactic processing.