Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads

Description: Despite emerging evidence suggesting a biological basis to our social tiles, our understanding of the neural processes which link two minds is unknown. We implemented a novel approach, which included connectome similarity analysis using resting state intrinsic networks of parent-child dyads as well as daily diaries measured across 14 days. Intrinsic resting-state networks for both parents and their adolescent child were identified using independent component analysis (ICA). Results indicate that parents and children who had more similar RSN connectome also had more similar day-to-day emotional synchrony. Furthermore, dyadic RSN connectome similarity was associated with children's emotional competence, suggesting that being neurally in-tune with their parents confers emotional benefits. We provide the first evidence that dyadic RSN similarity is associated with emotional synchrony in what is often our first and most essential social bond, the parent-child relationship. As part of a larger study, we recruited 76 participants (37 adolescent children and 39 primary caregivers). All participants provided informed consent/assent, and no participants reported any mental health problems (e.g., current clinical diagnose or pharmacological intervention for a mental illness). Among all participants, 31 parent-child dyads (n=62) successfully completed the dyadic resting state scan (parent Mage=43.06 years, range=33 – 57, 12.90% father; child Mage=14.80 years, range=13–17, 48.39% female). All parentchild dyads were biologically related and provided written informed consent/assent. No participants were excluded due to excessive motion (i.e., mean framewise displacement, FD > 0.5 mm) or reported any mental health problems (e.g., current clinical diagnose or pharmacological intervention for a mental illness). All data was ICA-denoised at individual level and rs-maps were estimated at group level using temporal concatenate ICA (TICA).

Related article: http://doi.org/10.1016/j.neuroimage.2017.02.078