Conscious experience is dynamic, and its fluidity is particularly marked when attention is not occupied by events in the external world and our minds are free to wander. Our study used measures of neural function, and advanced analyses techniques to examine how unconstrained neural state transitions relate to patterns of ongoing experience. Neural activity was recorded during wakeful rest using functional magnetic resonance imaging and Hidden Markov modelling identified recurrent patterns of brain activity constituting functional dynamic brain states. Individuals making more frequent transitions between states subsequently described experiences highlighting problem solving and lacking unpleasant intrusive features. Frequent switching between states also predicted better health and well-being as assessed by questionnaire. These data provide evidence that the fluidity with which individuals shift through dynamic neural states has an impact on the nature of ongoing thought, and suggest that greater flexibility at rest is an important indicator of a healthy mind.William James (James, 1890) emphasised experience unfolds dynamically over time, 2 using the analogy of a "stream of consciousness". The fluidity of experience is clearly 3 illustrated by the fact that our attention tends to flit from topic to topic, particularly 4 when we are not focused on events in the external world (Smallwood and Schooler, 2006, 5
Gamma activity (γ, >30 Hz) is universally demonstrated across brain regions and species. However, the physiological basis and functional role of γ sub-bands (slow-γ, mid-γ, fast-γ) have been predominantly studied in rodent hippocampus; γ activity in the human neocortex is much less well understood. Here we combined neuroimaging and non-invasive brain stimulation to examine the properties of γ activity sub-bands in the primary motor cortex (M1), and their relationship to both local GABAergic activity and to motor learning. In 33 healthy individuals, we quantified movement-related γ activity in M1 using magnetoencephalography, assessed GABAergic signaling using transcranial magnetic stimulation (TMS), and estimated motor learning via a serial reaction time task. We characterised two distinct γ sub-bands (slow-γ, mid-γ) which show movement-related increase in activity during unilateral index finger movements and are characterised by distinct temporal-spectral-spatial profiles. Bayesian correlation analysis revealed strong evidence for a positive relationship between slow-γ (~30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition). There was also moderate evidence for a relationship between power of the movement-related mid-γ activity (60-90Hz) and motor learning. These relationships were neurochemically- and frequency-specific. These data provide new insights into the neurophysiological basis and functional roles of γ activity in human M1 and allow the development of a new theoretical framework for γ activity in the human neocortex.
Even in response to apparently simple tasks such as hand moving, human brain activity shows remarkable inter-subject variability. Presumably, this variability reflects genuine behavioural or functional variability. Recently, spatial variability of resting-state features in fMRI -specifically connectivity -has been shown to explain (spatial) taskresponse variability. Such a link, however, is still missing for M/EEG data and its spectrally rich structure. At the same time, it has recently been shown that task responses in M/EEG can be well represented using transient spectral events bursting at fast time scales. Here, weshow that individual differences in the spatio-spectral structure of M/EEG task responses, can, to a reasonable degree, be predicted from individual differences in transient spectral events identified at rest. In a MEG dataset of diverse task conditions (including motor responses, working memory and language comprehension tasks) and resting-state sessions for each subject (n = 89), we used Hidden-Markov-Modelling to identify transient spectral events as a feature set to learn the mapping of space-time-frequency content from rest to task. Resulting trial-averaged, subject-specific task-response predictions were then compared with the actual task responses in left-out subjects. All task conditions were predicted significantly above chance. Furthermore, we observed a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. These findings support the idea that subject-specific transient spectral events in resting-state neural activity are linked to, and predictive of, subject-specific trial-averaged task responses in a wide range of experimental conditions.
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