24Attention selectively routes the most behaviorally relevant information among the vast 25 pool of sensory inputs through cortical regions. Previous studies have shown that visual 26 attention samples the surrounding stimuli periodically. However, the neural mechanism 27 underlying this sampling in the sensory cortex, and whether the brain actively uses these 28 rhythms, has remained elusive. Here, we hypothesize that selective attention controls the 29 phase of oscillatory synaptic activities to efficiently process the relevant information in 30 the brain. We document an attentional modulation of pre-stimulus inter-trial phase 31 coherence (a measure of deviation between instantaneous phases of trials) at low 32 frequencies in macaque visual area MT. Our data reveal that phase coherence increases 33 when attention is deployed towards the receptive field of the recorded neural population. 34 We further show that the attentional enhancement of phase coherence is positively 35 correlated with the attentional modulation of stimulus induced firing rate, and 36 importantly, a higher phase coherence leads to a faster behavioral response. Our results 37 suggest a functional utilization of intrinsic neural oscillatory activities for better 38 processing upcoming environmental stimuli, generating the optimal behavior. 39 attention | local field potentials | phase coherence | reaction time | visual cortex 40 41 72 (Yamagishi et al., 2003). Although there is prominent evidence on attentional modulation of 73 low frequency amplitude, the role of low frequency phase in attentional processing is yet 74 controversial. 75 The phase of low frequency oscillations modulates local neural activities represented by 76 gamma band activity, which presumably enables distant brain regions to interact (Demiralp et 77 al. , 2007). Some studies have shown that the phase of ongoing neural oscillations is responsible 78 for periodic sampling by visual attention (Busch and VanRullen, 2010; VanRullen et al., 2011). 79Furthermore, the phase of low frequency oscillations facilitates information transfer and neural 80 coding in the brain (Voloh and Womelsdorf, 2016). Therefore, low frequency phase can enable 81 the neural system to prepare for processing upcoming sensory stimuli. 82Pre-stimulus neural activity has been shown to be a determinant of retrieving episodic memory, 83 perception of environmental information and attention-related variability in response speed 84 (Addante et al., 2011; Hanslmayr et al., 2013 Hanslmayr et al., , 2007 Shibata et al., 2008). Interestingly, it has 85 been shown that pre-stimulus brain activity causally determines visual perception (Dugué et 86 al., 2011). In addition, it has been shown that the phase of low frequency oscillations is 87 responsible for this causal relationship (Hanslmayr et al., 2013). Furthermore, attention has 88 been reported to determine the phase of low frequency neural oscillations in order to influence 89 neuronal responses and behavioral responses to external events (Lakatos et al.,...

Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain’s maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain’s performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state networks and working memory. However, non-significant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were inconsistent with previous non-joint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.