Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04
  • 2024-05

    • jnk pathway This association between cortical folding

    2018-11-07

    This association between cortical folding and network functioning may mediate relationships between early sulcal patterns and later functional development (Mangin et al., 2010, for review). For example, we recently showed in a longitudinal study in preschoolers (Borst et al., 2014) that the ACC sulcal pattern, but not the cortical thickness or the surface area of the ACC, at age 5 predicts the efficiency in cognitive control not only at age 5 but also four years later (i.e., at age 9). Of note, the efficiency of cognitive control is only partly explained by these early cerebral constraints given that it can be improved by training and practice during childhood (Diamond, 2013 for a review)–improvements sustained by neuroplasticity mechanisms. An open issue is thus to determine to what extent the receptivity to cognitive training and practice depends on early neurodevelopmental constraints such as the ACC sulcal pattern. The stability assumption of the sulcal pattern was tested in this study from the analysis of ACC morphology from late childhood to adulthood. This cortical region and this developmental period optimize the possibility to detect possible developmental changes in sulcal pattern types. Indeed, ACC presents qualitatively distinct sulcal patterns that can be reliably classified with structural MRI from childhood to adulthood (Cachia et al., 2014; Paus et al., 1996; Yucel et al., 2001), which limits false positive findings (i.e. detecting changes in sulcal pattern because of classification issues instead of actual morphological changes). While our data provide the first direct evidence that ACC folding type is a developmentally stable trait in humans, further longitudinal studies will be required to directly establish that this trait is also stable outside the age-range considered in our analyses. However, available data suggest that cortical shape within the anterior cingulate region is most-likely stable during the first years after birth, as evidenced by recent longitudinal studies reporting that several markers of cortical shape of stable within the ACC region during the first year of postnatal life, including cortical surface curvature (Li et al., 2014), surface area (Li et al., 2013) and local gyrification (Mutlu et al., 2013). Nevertheless, an important goal for future longitudinal studies will be testing if our findings regarding the developmental stability of folding typology generalize to other segments of the life span, and other regions of the cortical sheet. Testing for generalization to the entire cortex will raise the issues of the definition of the qualitative sulcal patterns to be used for the assessment of each cortical area. This will requires the establishment of a dictionary of human jnk pathway folding patterns (Sun et al., 2009), for instance based on the ‘sulcal root’ (Regis et al., 2005), namely indivisible and stable sulcal units corresponding to the first folding locations during antenatal life and that can be recovered after birth from the analysis of the local cortex curvature (Cachia et al., 2003) or depth (Im et al., 2010).
    Conclusion
    Introduction Anxious youth, like anxious adults, display a pattern of vigilant attention toward threat-relevant cues in the laboratory (Bar-Haim et al., 2007). Biased attention toward threat is posited to contribute to the maintenance of anxiety over time (MacLeod et al., 1986), providing persistent opportunities for both physiological reactions and anxiety-promoting beliefs (e.g., the world is full of danger) to be rehearsed. When present in youth, threat vigilance may therefore represent a key mechanism through which life-long trajectories of affective psychopathology (Pine et al., 1998) are initiated and maintained. An excessive focus on threat may promote anxious physiological responding and maladaptive cognitions, but clinical manifestations of anxiety are largely characterized by persistent avoidance of threat. Mogg et al. (2004) proposed a vigilance-avoidance model of cognitive bias in anxiety, suggesting that following early attentional vigilance to threat cues, anxious individuals strategically direct attention away from threat in an attempt to decrease anxiety elicited by aversive stimuli (i.e., avoidance is used as an emotion regulation strategy). Avoidant attentional patterns during late stages of stimulus presentation (i.e., >1500ms post-onset) have been observed in at least some studies of anxious adults (e.g., Bogels and Mansell, 2004)—although avoidance is not strictly confined to late time points in the adult literature, but has also been observed at early time points, particularly when threat severity is strong (Wald et al., 2011; Shechner et al., 2012). In youth, vigilance-avoidance findings are decidedly mixed. While vigilance to threat is relatively well-established in pediatric samples (Bar-Haim et al., 2007)—although there are clear exceptions, e.g., in the fMRI scanner (Monk et al., 2006)—avoidance findings are quite inconsistent, ranging from the hypothesized vigilance-avoidance pattern (In-Albon et al., 2010), to avoidance at early timepoints only (Gamble and Rapee, 2009), to early vigilance with no later group differences (Shechner et al., 2013), to consistent attentional patterns across anxious and non-anxious youth at both early and later timepoints (Price et al., 2013). One possibility is that subsets of anxious youth differ in their attentional patterns (Salum et al., 2012); but the vigilance-avoidance hypothesis implies that the same individuals would be prone to both early vigilance and later avoidance. An additional possibility is that attention measured on the time-course of seconds in the laboratory (even at stimulus presentations ≥2s) represents relatively “automatic” (i.e., involuntary, routinized) forms of avoidance (Najmi et al., 2010; Buetti et al., 2012) that may not be fully developed in youth. If this were the case, we might more readily see avoidance in a less automatic form, i.e., deployed as a strategic (i.e., voluntary, effortful) emotion regulation response to real-world stressors. However, previous attentional bias studies have been limited to laboratory assessments of both vigilance and avoidance, leaving this potential pattern uninvestigated.