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Behavioral, Brain Imaging, and Genomic Measures To Predict Functional Outcomes After Bed Rest And Spaceflight Mulavara A, Peters B, De Dios Y, Gadd N, Caldwell E, Watson C, Oddsson L, Kreutzberg G, Zanello S, Clark T, Waddington G, Oman C, Cohen H, Wood S, Seidler R, Reschke M, Bloomberg J. Astronauts show variability in adaptive responses 100 30 s 80 p e 25 t S tc 60 )c e e r s r ( 20 o e C m 40 t i n T e c 15 r e 20 P 0 10 L-60 L-30 R+1 R+6 R+30 L-60 L-30 R+1 R+6 R+30 Preflight Postflight Preflight Postflight After long duration space flight Seat Egress and Walk Test Tandem Walk Test Sensorimotor Adaptability Training Response to novel sensory environment State 1 State 2 e c n a m With SA training r100% o f r e P f o le Adaptive v e Response L • Training that provides multiple sensory challenges enhances ability to adapt to novel sensory environments • Promotes “learning to learn” Study Objectives 1. Develop a set of predictive measures capable of identifying individual differences in sensorimotor adaptability. 2. Use this information to design sensorimotor adaptability training countermeasures that are customized for each crewmember’s individual sensory bias and adaptive capacity. Sensorimotor Predictors Retrospective Study Prospective Data Retrospective Data (1 session ~ 3 Hours) Treatments Short/long duration Behavioral Metrics space flight - Sensory utilization - Motor Learning Brain Structural and Functional 22 ISS (13 FTT, 3 FT, 6 USOS PFT) Metrics 7 Shuttle 70 day, 6° Head down tilt bed rest Genomic Metrics 10 Controls Retrospective FTT/FT data from completed missions Ground Based Study (N = 16 normal subjects): Do these metrics predict adaptive locomotor performance in a sensory discordant environment? Locomotor Adaptive Predictors Challenge Behavioral Metrics - Visual Dependency - Proprioceptive Function - Balance Control - Motor Learning Brain Structural and Functional Metrics Genomic Metrics Sensory Bias: Proprioceptive sensitivity measured by balance control on gravity bed correlates with the ability to respond to a locomotor balance challenge 0.25 R² = 0.5829 h 0.20 c) os p , e e n ti sl e ris 0.15 f a eb m m i t o er df i e 0.10 r tc sn ee gr e af rf eid 0.05 v ( A 0.00 0 2 4 6 8 Mediolateral shear force (Fy, N) B. Peters, et al. 2014 Best adapter Motor Learning: Adaptation rate on the adaptive functional mobility test correlated with the ability to respond to a locomotor balance challenge 0.25 h c) os 0.2 p, e e n ti sl e r is f a 0.15 eb m m i t o er df i e 0.1 r tc sn ee gr e areffid 0.05 R² = 0.3004 v ( A Best adapter 0 0 0.2 0.4 0.6 0.8 1 B. Peters, et al. 2014 Rate of adaptation Diffusion Tensor Imaging: Fractional Anisotropy (FA) of white matter tracts distributed widely over the brain correlated significantly with the ability to respond to a locomotor balance challenge – e.g. Larger FA measured in the body of corpus callosum corresponds with better adaptability to a locomotor balance challenge. 1-p 1.0 0.5 0.0 Fractional Anisotropy h c) os p, e e n ti sl e r is f a eb m m i t o edrf Best ie rtc adapter sn ee gr e af rf eid B. Peters, et al. v ( A 2014 Fractional Anisotropy (FA) Peak Voxel Body of Corpus Callosum Presence of the alleles - Met in COMT (rs4680) and Val in BDNF (rs6265) – differentiates visuo-motor adaptation during locomotion Adaptive Functional Mobility Test: Negotiate a complex obstacle while wearing up-down vision displacing goggles. Repeated 10 trials. Measure time to complete course (TCC, seconds).17101 DRD2 COMT BDNF Dral rs1076560 rs4680 rs6265 rs553668 6/15 (40%) 7/15 (47%) 8/15 (53%) 3/15 (20%) 300 Met-Met/ GG/GT Val-Val 6.7/6.3 kb Top Met-Val )s 250 Half ( Top Half - Ranked n 1/15 (7%) 2/15 (13%) 0/15 (0%) 5/15 (33%) o i 200 t e TT Val-Val Val-Met 6.7/6.7 kb lp Bottom Half - m 150 Ranked 3/15 (20%) 2/15 o 6/15 (40%) 1/15 (6.7%) C Met-Met/ (13.3%) o 100 Bottom GG/GT Met-Val Val-Val 6.7/6.3 kb t e m Half 50 i 1/15 (7%) 4/15 (27%) 5/15 (33%) 6/15 (40%) T 0 TT Val-Val Val-Met 6.7/6.7 kb 0 5 10 15 Trial Number