A theory-derived index of consciousness, which quantifies the complexity of the brain’s response to a stimulus, measures the level of consciousness in awake, sleeping, anesthetized, and brain-damaged subjects.

The frequency tuning of a system can be directly determined by perturbing it and by observing the rate of the ensuing oscillations, the so called natural frequency. This approach is used, for example, in physics, in geology, and also when one tunes a musical instrument. In the present study, we employ transcranial magnetic stimulation (TMS) to directly perturb a set of selected corticothalamic modules (Brodmann areas 19, 7, and 6) and high-density electroencephalogram to measure their natural frequency. TMS consistently evoked dominant α-band oscillations (8–12 Hz) in the occipital cortex, β-band oscillations (13–20 Hz) in the parietal cortex, and fast β/γ-band oscillations (21–50 Hz) in the frontal cortex. Each cortical area tended to preserve its own natural frequency also when indirectly engaged by TMS through brain connections and when stimulated at different intensities, indicating that the observed oscillations reflect local physiological mechanisms. These findings were reproducible across individuals and represent the first direct characterization of the coarse electrophysiological properties of three associative areas of the human cerebral cortex. Most importantly, they indicate that, in healthy subjects, each corticothalamic module is normally tuned to oscillate at a characteristic rate. The natural frequency can be directly measured in virtually any area of the cerebral cortex and may represent a straightforward and flexible way to probe the state of human thalamocortical circuits at the patient's bedside.

Objective: Validating objective, brain‐based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousness—the Perturbational Complexity Index (PCI)—in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs).Methods: The benchmark population encompassed 150 healthy controls and communicative brain‐injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS]).Results: We found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition.Interpretation: Given its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high‐PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior. Ann Neurol 2016;80:718–729

Patients surviving severe brain injury may regain consciousness without recovering their ability to understand, move and communicate. Recently, electrophysiological and neuroimaging approaches, employing simple sensory stimulations or verbal commands, have proven useful in detecting higher order processing and, in some cases, in establishing some degree of communication in brain-injured subjects with severe impairment of motor function. To complement these approaches, it would be useful to develop methods to detect recovery of consciousness in ways that do not depend on the integrity of sensory pathways or on the subject's ability to comprehend or carry out instructions. As suggested by theoretical and experimental work, a key requirement for consciousness is that multiple, specialized cortical areas can engage in rapid causal interactions (effective connectivity). Here, we employ transcranial magnetic stimulation together with high-density electroencephalography to evaluate effective connectivity at the bedside of severely brain injured, non-communicating subjects. In patients in a vegetative state, who were open-eyed, behaviourally awake but unresponsive, transcranial magnetic stimulation triggered a simple, local response indicating a breakdown of effective connectivity, similar to the one previously observed in unconscious sleeping or anaesthetized subjects. In contrast, in minimally conscious patients, who showed fluctuating signs of non-reflexive behaviour, transcranial magnetic stimulation invariably triggered complex activations that sequentially involved distant cortical areas ipsi- and contralateral to the site of stimulation, similar to activations we recorded in locked-in, conscious patients. Longitudinal measurements performed in patients who gradually recovered consciousness revealed that this clear-cut change in effective connectivity could occur at an early stage, before reliable communication was established with the subject and before the spontaneous electroencephalogram showed significant modifications. Measurements of effective connectivity by means of transcranial magnetic stimulation combined with electroencephalography can be performed at the bedside while by-passing subcortical afferent and efferent pathways, and without requiring active participation of subjects or language comprehension; hence, they offer an effective way to detect and track recovery of consciousness in brain-injured patients who are unable to exchange information with the external environment.

Spindles are non-rapid eye movement (non-REM) sleep EEG rhythms (7-14 Hz) that occur independently or in association with slow oscillations (0.6-0.8 Hz). Despite their proposed function in learning and memory, their role in synaptic plasticity is essentially unknown. We studied the ability of a neuronal firing pattern underlying spindlesin vivoto induce synaptic plasticity in neocortical pyramidal cellsin vitro. A spindle stimulation pattern (SSP) was extracted from a slow oscillation upstate that was recorded in a cat anesthetized with ketamine-xylazine, which is known to induce a sleep-like state. To mimic the recurrence of spindles grouped by the slow oscillation, the SSP was repeated every 1.5 s (0.6 Hz). Whole-cell patch-clamp recordings were obtained from layer V pyramidal cells of rat somatosensory cortex with infrared videomicroscopy, and composite EPSPs were evoked within layers II-III. Trains of EPSPs and action potentials simultaneously triggered by the SSP induced an NMDA receptor-dependent short-term potentiation (STP) and an L-type Ca²⁺channel-dependent long-term potentiation (LTP). The number of spindle sequences affected the amount of STP-LTP. In contrast, spindle trains of EPSPs alone led to long-term depression. LTP was not consistently induced by a regular firing pattern, a mirrored SSP, or a randomized SSP; however, a synthetic spindle pattern consisting of repetitive spike bursts at 10 Hz reliably induced STP-LTP. Our results show that spindle-associated spike discharges are efficient in modifying excitatory neocortical synapses according to a Hebbian rule. This is in support of a role for sleep spindles in memory consolidation.