Avalanche dynamics and the human connectome

Transcription

1 Avalanche dynamics and the human connectome J. Matias Palva Neuroscience Center, University of Helsinki

2 Three tiers of systems neuroscience Tier 1 micro Tier 2 macro Tier 3 scaling laws milliseconds seconds neuronal oscillations spike / LFP synchronization evoked / induced responses in EEG/MEG Perception / attention / WM Mental microstates phase-phase interactions Seconds minutes Excitability comodulations Firing rate correlations ISFs in rates, amplitudes, BOLD signals, and psychophysics Alertness / vigilance Mental macrostates amplitude-amplitude interactions Minutes lifetime, (individual, heritable) Tier 1: Neuronal avalanches Tier 2: Neuronal longrange temporal correlations (LRTCs) Behavioral LRTCs Trait, disorder endophenotype / biomarker

3 Human brain imaging for recording neuronal dynamics at all three tiers concurrently: magneto- and electroencephalography (MEG and EEG)

4 HR, % HR, % Visual Experimental paradigms for recording behavioral dynamics at all three tiers concurrently: Continuous performance tasks (CPTs), such as the threshold-stimulus detection task (TSDT) + = SNR Auditory + = SNR

5 HR, % HR, % Visual Experimental paradigms for recording behavioral dynamics at all three tiers concurrently: Continuous performance tasks (CPTs), such as the threshold-stimulus detection task (TSDT) + = SNR HIT MISS Auditory + = SNR 50 stimuli ~200 s HIT MISS What are the neuronal mechanisms and dynamics that underlie or cause these fluctuations in behavioral performance? 50 stimuli

6 Overview of the talk / A glance at Tiers 1-3: Tier 1: Peri-stimulus phase dynamics for detected and undetected stimuli in a threshold-stimulus detection task (TSDT) Tier 2: Infra-slow fluctuations in EEG scalp potentials, behavioral dynamics, amplitudes of fast oscillations, and fmri resting-state networks Tier 3: Neuronal scaling laws of Tier 1 and 2 dynamics Tier 3: Neuronal vs. behavioral scaling laws Tier 3: Neuronal avalanche (Tier 1) propagation pathways vs. Tier 1 and 2 functional connectivity

7 Tier 1 analysis of Hits and Misses in somatosensory TSDT <. > <. >

8 Hits in somatosensory TSDT are associated with a rapid, cascade-like activation of a widespread cortical network Palva, Palva, J Neurosci, 2005

9 Palva,, Palva, J Neurosci, 2005

10 nplf TSDT: neuronal activity cascade for perception v Stim-P-lock, right hand Perceived Unperceived Difference Time (ms) ms ms 30 ms 60 ms P/SMA DLPFC SI PPC 200 ms 300 ms SII Hirvonen,, Palva, in preparation Replication cohort yielded results essentially identical to the 2005 study Source modeling identified DLPFC and PPC to be engaged exclusively by HITs

11 nplf TSDT: neuronal activity cascade for perception v Stim-P-lock, right hand Perceived Unperceived Difference Time (ms) ms ms 30 ms 60 ms P/SMA DLPFC SI PPC 200 ms 300 ms SII Hirvonen,, Palva, in preparation Pre-stimulus phases and multi-scale amplitude levels predict performance => Tier 2 phenomena involved!

12 Tier 2: Multi-second Streaking in behavioral performance Monto, Palva, Voipio, Palva, J Neurosci, 2008 What are the neuronal mechanisms and dynamics that underlie or cause these fluctuations in behavioral performance?

13 Tier 2: Multi-second Streaking in behavioral performance Monto, Palva, Voipio, Palva, J Neurosci, 2008

14 Ongoing brain activity is self-organized into slow covarying neuronal fluctuations in intrinsic connectivity networks Anti-correlated default-mode network (DMN) and dorsal-attention network (DAN) fluctuations could underlie behavioral dynamics in TSDT. Fox, et al., PNAS, 2005 The brain s dark energy Raichle, Science, 2006

15 Functional Network Organization of the Human Brain Graph partitioning (with infomap) of the fmri functional connectome reveals hierarchical modularity in the intrinsic connectivity networks or systems. Power, et al., Neuron, 2011

16 MISS HIT Ongoing brain activity is self-organized into slow covarying neuronal fluctuations in intrinsic connectivity networks Anti-correlated default-mode network (DMN) and dorsal-attention network (DAN) fluctuations could underlie behavioral dynamics in TSDT. Fox, et al., PNAS, 2005 Indeed, fmri BOLD signal is positively correlated with stsdt Hits in DAN and negatively in DMN What are the electrophysiological correlates? Boly, et al., PNAS, 2007

17 Right SM MEG Beta Left SM MEG Beta Parietal MEG Alpha Electrophysiological and BOLD ISFs reveal large-scale correlation structures in cortical and subcortical networks Palva & Palva, NeuroImage, 2012 apfc BOLD dacc BOLD Alertness ICN BOLD EEG Alpha DAN BOLD 50 s 100 s Frequency (Hz)

18 LFP TCN LFP GP Firing Rate (Hz) HC EEG ( V) STN Firing Rate (Hz) STN Firing Rate (Hz) EEG ( V) Parietal MEG Alpha Left SM MEG Beta Right SM MEG Beta Electrophysiological and BOLD ISFs in largescale cortical and subcortical networks apfc BOLD dacc BOLD Alertness ICN BOLD EEG Alpha DAN BOLD s 100 s s 10 s Frequency (Hz) LTha R Tha L BG R BG 40 s 10 s 10 Hz 200 ms Frequency (Hz), NeuroImage, 2012

19 Tier 2, TSDT: Stimulus detection is correlated with slow fluctuations in scalp EEG ISF Hz Monto, Palva, Voipio, Palva, J Neurosci, 2008

20 Tier 2, TSDT: Stimulus detection and scalp ISFs are correlated with fast oscillations ISF Hz = Monto, Palva, Voipio, Palva, J Neurosci, s Amplitudes of fast activity vs. EEG ISFs

21 fbeeg-isfs are correlated with fmri BOLD signals in intrinsic connectivity networks Hiltunen,.., Palva, J. Neurosci., Hz ISFs in full-band EEG are correlated with resting-state networks recorded concurrently with fmri

22 Tier 2, TSDT: Scalp ISFs are correlated with fmri BOLD resting-state networks ISF Hz fmri vs. EEG ISFs Monto, Palva, Voipio, Palva, J Neurosci, 2008 Infra-slow fluctuations are a pervasive characteristic of CNS! Palva & Palva, NeuroImage, 2012 Hiltunen,, Kiviniemi, Palva, J. Neurosci., 2013

23 Brain dynamics in Tier 2 temporal macroscales are governed by infra-slow fluctuations ISF are observed in and mutually correlated among neuronal firing rates, oscillation amplitude dynamics, BOLD signals, EEG potentials, and behavioral performance fluctuations. Do ISFs reflect endogenous fluctuations in macroscopic mental states such as taskorientedness, vigilance, and introspection? What is the dynamical character of these fluctuations?

24 Tier 2 brain dynamics are scale-free and can be characterized with scaling laws (Tier 3!) Self similar or, fractal, brain dynamics suggests that the nervous system operates near a critical regime

25 Tier 2 brain dynamics and Tier 3 scaling laws are highly heritable Neuronal LRTC Low scaling exponent High scaling exponent Linkenkaer-Hansen et al., 2007

26 Tier 2 brain dynamics and Tier 3 scaling laws are highly heritable Neuronal LRTC Low scaling exponent High scaling exponent Behavioral LRTC Linkenkaer-Hansen et al., 2007 Monto et al., 2008 Could genetic variability in neuronal LRTC explain interindividual variability in behavioral TSDT scaling laws?

27 Tier 3: Measuring neuronal activity at Tiers 1 and 2 50 ms 60 s 6 s Tier 2: Infra-slow fluctuations in behavioral performance and amplitudes of fast neuronal oscillations = Long-range temporal correlations (LRTC) 0.2 s Palva, et al., PNAS, 2013

28 Tier 3: Measuring neuronal activity at Tiers 1 and 2 50 ms 60 s 6 s Tier 2: Infra-slow fluctuations in behavioral performance and amplitudes of fast neuronal oscillations = Long-range temporal correlations (LRTC) 0.2 s pocg pocs intps sts mtg itg OTS 0.05 s Tier 1: Millisecond-range neuronal interactions and patterns of propagating activity = Neuronal avalanches see also Shriki, et al., J Neurosci, Latency (ms) 0.2 s 60 s Palva, et al., PNAS, 2013

29 10 5 log 10 P(s) log 10 (<F(τ)>) log 10 (<F(τ)>) Tier 3: Scaling laws quantify Tier 1 and 2 dynamics 3 60 s β ref = 0.5 β V = 0.78 β A = log 10 (τ), s Behavioral LRTC 50 ms 6 s 2 1 β = 0.79 β ref = log 10 (τ), s Neuronal LRTC: β 0.2 s pocg pocs intps sts mtg itg 0 30 Latency (ms) OTS 0.05 s 0.2 s 60 s α lifetime = α size = log 10 (s), [ms, # peaks] Neuronal Avalanches: α PNAS, 2013

30 10 5 log 10 P(s) β log 10 (<F(τ)>) log 10 (<F(τ)>) Tier 3: scaling laws of Tiers 1 and 2 are correlated 3 60 s β ref = 0.5 β V = 0.78 β A = log 10 (τ), s Behavioral LRTC 50 ms 6 s 2 1 β = 0.79 β ref = log 10 (τ), s Neuronal LRTC: β 0.2 s r=-0.90 (p<0.0001) r=-0.89 (p<0.0001) 0.7 pocg pocs intps sts mtg itg α size, α lifetime 0 30 Latency (ms) OTS 0.05 s 0.2 s 60 s α lifetime = α size = log 10 (s), [ms, # peaks] Neuronal Avalanches: α PNAS, 2013

31 Tier 3: Neuronal scaling laws predict inter-individual variability in behavioral scaling laws β behav TASK r = 0.77 (p < 0.002) β behav REST r = 0.76 (p < 0.002) Trait-like resting-state dynamics are preserved during task performance and predict individual behavioral scaling laws β (task) β (rest) Palva, et al., PNAS, 2013

32 Tier 3: Neuronal scaling laws predict inter-individual variability in behavioral scaling laws β behav TASK r = 0.77 (p < 0.002) β behav REST r = 0.76 (p < 0.002) Trait-like resting-state dynamics are preserved during task performance and predict individual behavioral scaling laws β (task) β (rest) Visual Auditory Behavioral scaling laws arise from the endogeneous dynamics of task-specific brain systems Palva, et al., PNAS, 2013

33 Interim Conclusion Neuronal interactions in temporal micro- (T1) and macroscales (T2)...govern neuronal communication both at the levels of neuronal firing and excitability in large-scale systems...may causally underlie micro- and macro-scale mental and behavioral states...can be studied with human brain imaging, but with significant methodological challenges

34 Interim Conclusion Neuronal interactions in temporal micro- (T1) and macroscales (T2)...govern neuronal communication both at the levels of neuronal firing and excitability in large-scale systems...may causally underlie micro- and macro-scale mental and behavioral states...can be studied with human brain imaging, but with significant methodological challenges Neuronal scaling laws (T3)...govern the dynamics of the micro- and macro-scale neuronal and behavioral phenomena...reflect brain-system specific E/I balance? & Linkenkaer-Hansen, J Neurosci, 2012