Methods Results CO_AutoCorr DN_CompareKSFit FC_LocalSimple FC_Surprise IN_AutoMutualInfoStats NL_BoxCorrDim NL_TSTL PH_Walker SB_TransitionpAlphabet SC_FluctAnal ST_LocalExtrema SY_KPSStest TSTL_localdensity Prediction of time samples Self-similarity Angus Leung, Ahmed Mahmoud, Travis Jeans, Ben Fulcher, Bruno van Swinderen, Naotsugu Tsuchiya angus.leung@monash.edu Background • The neural mechanisms of consciousness remain elusive • Proposed mechanisms are derived from consciousness markers • BUT current consciousness markers have issues: ◦Identified markers have no principled link with consciousness ◦Bias towards time-series features which are visually identifiable ◦Bias towards specific analyis methods from researcher background ◦Bias towards specific data sets • NEW approach: agnostically evaluate as many analysis methods as candidate consciousness markers ◦Made possible by highly comparative time-series analysis [1] ◦hctsa provides a library of >7,000 univariate time-series features from various research fields ◦Evaluate generalisation of candidate markers to multiple, wide-ranging inde- pendent datasets 0.4 0.5 0.6 0.7 discovery accuracy 0.35 0.4 0.45 0.5 0.55 0.6 evaluation accuracy sig. in all flies 0.4 0.5 0.6 0.7 0.8 0.9 discovery consistency 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 ApEn EN_MS_LZcomplexity EN_PermEn EN_SampEn SP_Summaries evaluation consistency 100µm ch1 ch15 x13 discovery flies (D) x49 evaluation flies (E) V 2.25s D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 E14 E15 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 E1 E2 0 0.5 1 norm. value anest wake 0 1 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 -1 1 E1 E2 0 1 W-A wake - anest anest wake e.g. StatAvl250 e.g. MD_rawHRVmeas_SD2 DN_FitKernelSmooth DN_Mean DN_Moments DN_Spread MD_rawHRVmeas ch6 ch6 ch6 ch1 ch1 ch1 ch1 ch1 ch1 ch1 ch3 ch3 ch3 ch3 ch3 ch3 ch3 ch4 ch4 ch4 ch4 ch4 ch4 ch4 ch4 ch4 ch3 ch9 ch9 ch9 ch9 ch9 ch9 ch9 ch8 ch8 ch4 ch3 ch1 ch9 Top 40 features Variability of time samples Variability of kernel-smoothed samples 1000 2000 3000 4000 5000 6000 feature discovery multidose8 multidose4 singledose sleep -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Δ value 1000 2000 3000 4000 5000 6000 feature train W multidose8 W singledose W sleep W train A multidose8 A singledose A sleep S fly 0 0.5 1 scaled value 1.5 • hctsa values for each fly (channel, epoch averaged) • Differences (wake - anaesthesia) in hctsa values for each fly (channel, epoch-pair averaged) • Features related to previously identified markers (coloured x’s; e.g. spectral power, SP) fail to strongly generalise to evaluation flies • Top feature, NL_BoxCorrDim_50_ac_5_minr13, related to fractal geometry • Direction of effect of anaesthesia/sleep is consistent for many features, including those which did not strongly generalise (e.g. SP) • Failure to strongly generalise due to high inter-fly and -dataset variability • Only 47 features strongly generalised across all flies, at centre of fly brain • Features related to self-similarity and -prediction measures • 5280 features across whole fly brain weakly generalise across all flies • Top features related to variance measures strong generalisation weak generalisation 500 1000 1500 2000 t (ms) -2 -1 0 wake unawake discovery multidose8 multidose4 singledose sleep NL_BoxCorrDim_50_ac_5_minr13 log( +12.2) discovery multidose8 multidose4 singledose sleep 0.2 0.4 0.6 0.8 SP_Summaries_fft_area_5_1 Summary • Apply hctsa to neurophysiological recordings obtained during wakefulness and loss of consciousness, using registered report framework • Stage 1 for each feature, train and evaluate a classifier on a discovery dataset: ◦N = 13 flies (D; wake / isoflurane anaesthesia; cross-validation) • Stage 2 for each feature, evaluate generalisation of trained classifier across indi- viduals to independent, previously blinded, evaluation datasets (strong generali- sation) ◦N = 8 + 4 + 18 + 19 = 49 flies (E; wake / isoflurane aneasthesia / sleep) • Stage 2 for each feature, evaluate the co