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Rosanova, M.

Publications and source records attributed to Rosanova, M..

At least 19 recordsLinked to original sources

Lack of vision shifts occipital dynamics toward a frontal-like regime and enhances top-down connectivity

The human occipital cortex is robustly repurposed for non-visual cognition after blindness, yet it remains unknown whether this functional reassignment is accompanied by a fundamental retuning of its intrinsic neural dynamics. Here, we recorded the electroencephalographic (EEG) responses to transcranial magnetic stimulation (TMS) of frontal and occipital areas to causally probe local cortical reactivity and large-scale effective connectivity in 16 blind individuals and 16 sighted controls. Direct perturbation of the occipital cortex revealed a profound shift in its intrinsic operating regime: compared with sighted participants, blind individuals exhibited faster, lower-amplitude TMS-evoked potentials that closely resembled the electrophysiological signature of the frontal cortex, which turned out to be comparable in the two populations. At the network level, source analyses further showed a specific enhancement of frontal-to-occipital effective connectivity, whereas occipital-to-frontal propagation remained unchanged. Importantly, among blind participants, faster occipital dynamics were associated with stronger frontal-driven occipital recruitment, directly linking local temporal retuning to enhanced top-down network influence. Together, these findings indicate that blindness reshapes both the intrinsic dynamics and large-scale integration of the occipital cortex. We propose that successful functional reassignment of the occipital cortex requires not only changes in cortical connectivity but also a tuning of the temporal operating regime toward a faster dynamics, enabling its embedding into distributed cognitive networks and providing a mechanistic framework for the functional reassignment of deafferented cortex.

neuroscience↗

Temporal fingerprints of TMS-evoked potentials across thalamocortical circuits

BackgroundElectroencephalographic (EEG) potentials evoked by transcranial magnetic stimulation (TMS) offer a direct window into cortical dynamics. Yet, a systematic exploration of their morphological features, analogous to sensory-evoked potentials, is lacking, especially for stimulation outside the motor cortex. AimTo obtain region-specific properties of frontal, parietal and occipital networks from the time course of TMS-evoked potentials (TEPs). Materials and MethodsWe implemented and applied an automatic procedure to compute peak-to-peak amplitude, peak latency, and inter-peak interval of TEPs recorded from 40 neurotypical subjects stimulated over left occipital (n=25), parietal (n=25), and frontal (n=25) cortices. ResultsOccipital TEPs showed the largest peak-to-peak amplitude and longest latency of the first waveform component, independently of stimulation intensity and consistent with the recruitment of a large patch of densely interconnected neurons. Concerning later components, both latency and inter-peak interval systematically decreased along the posterior-to-anterior axis, reflecting progressively faster recurrent dynamics from the alpha-dominated occipital circuitry to the tightly coupled loops between frontal cortex and subcortical structures. Parietal TEPs showed intermediate amplitude and latency measures, consistent with the heterogeneous cytoarchitectonic and connectional organization of the superior parietal cortex. ConclusionsOur findings suggest that TEP morphology is shaped by the distinct properties of the stimulated networks, with early amplitude reflecting the extent of local recruitment and later temporal features tracking the rhythm of recurrent activity. This work offers a mechanistically grounded and practically accessible approach, also released as a Python-based tool, that allows to characterize cortical reactivity across different brain-states and populations.

neuroscience↗

Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses

Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical care. The perturbational complexity index (PCI) provides a reliable assessment of consciousness capacity, but its reliance on transcranial magnetic stimulation and EEG (TMS-EEG) limits bedside scalability. PCI and brain criticality capture complementary dimensions of brain dynamics: PCI quantifies the complexity of the brains evoked response to perturbation, whereas criticality characterizes the intrinsic organization of spontaneous activity. Here, we tested whether resting-state EEG signatures of criticality predict PCImax in disorders of consciousness, extending prior findings from anesthesia to severe brain injury. In 26 patients with vascular, traumatic, or anoxic brain injury, multivariate criticality related features did not generalize PCImax prediction across the full heterogeneous cohort. However, criticality features predicted PCImax when analyses were restricted to non-anoxic patients and when restricting analyses to patients with non-zero PCImax values. These findings suggest that spontaneous criticality measures index the brains intrinsic dynamical regime that supports complex perturbational responses, while their correspondence with PCImax depends on whether the injured brain retains sufficient capacity to sustain large-scale evoked responses. Together, our results extend the relationship between resting-state criticality and evoked perturbational complexity to disorders of consciousness and support the development of stratified EEG measures in severe brain injury.

neuroscience↗

Distinct cortical excitability and connectivity profiles within the human SMA complex

Introduction and aimUnderstanding brain functions increasingly relies on a network-based perspective, emphasizing interactions across distributed regions. High-order cognitive functions, like language and executive processes, engage such networks rather than isolated cortical areas, yet mapping their dynamics in humans remains challenging. The supplementary motor area (SMA) complex serves as a crucial hub, functioning as an interface between cognitive and sensorimotor processing. Navigated transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables causal, time-resolved assessment of cortical excitability and connectivity with high temporal and spatial precision. Extending prior proof-of-concept work, this study aims to provide normative neurophysiological signatures of the SMA complex (pre-SMA and SMA). MethodsTwenty-one healthy subjects underwent a TMS-EEG session where six stimulation targets over the SMA complex (three in pre-SMA, one in the border between pre-SMA and SMA, two in SMA) were recorded. Global cortical excitability via global mean field power (GMFP) was computed over three time-windows (10-50, 50-100, and 100-200 ms) and compared across targets. Normalized power in different frequency bands (, {beta}1, {beta}2, {gamma}) and natural frequency (NF) were also computed and compared across stimulation sites. Then, we calculated tractography-derived connectivity metrics to examine corticothalamic projections from each stimulation site. Apparent fiber density (AFD)-derived connection strength quantified intra-axonal fibre volume along pathways to the whole thalamus, whereas fibre bundle capacity (FBC) assessed the aggregate intra-axonal cross-sectional area at pathway endpoints, providing an estimate of information transmission capacity to each of the 23 thalamic nuclei. ResultsCortical excitability and oscillatory properties varied significantly across TMS targets. A gradient in cortical excitability was observed, where anterior and middle portions of pre-SMA exhibited significantly lower GMFP in the early post-stimulus phase (i.e., 10-50 ms) than SMA portions; interestingly, these differences were clearly dampened in a later time-window (50-100 ms) and disappeared after this period. Furthermore, higher and lower {beta}2 power were observed in anterior pre-SMA than posterior pre-SMA portions. No differences were observed in NF. Crucially, we also observed significant differences in whole- and single-nuclei thalamic structural connectivity. DiscussionThese findings provide direct human in vivo evidence for distinct neurophysiological and structural connectivity profiles of the pre-SMA and SMA, possibly reflecting their different cytoarchitectonic, myeloarchitectonic and connectivity properties. Variations in cortical excitability, oscillatory dynamics, and FBC along the rostro-caudal axis may mirror functional specialization, with pre-SMA supporting cognitive control processes and SMA engaging motor-related thalamocortical networks. Together, the results demonstrate the sensitivity of TMS-EEG and diffusion-weighted connectomics to measure fine-grained neurophysiological differences of corticothalamic projections, which can lead to individualized neuromodulation targeting.

neuroscience↗

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

BackgroundTranscranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows assessing the neurophysiological properties of cortical neurons. However, TMS-evoked EEG potentials (TEPs) can be affected by components unrelated to TMS direct neuronal activation. Accurate, automatic tools are therefore needed to establish the quality of TEPs. ObjectiveTo assess the discriminability of EEG responses to TMS vs. EEG responses to sham stimulations using sequence-to-sequence machine learning (ML). MethodsTwo indipendent TMS-EEG datasets including TMS and several sham stimulation conditions were obtained from the left motor area of healthy volunteers (N=33 across datasets). A Bi-directional Long Short-Term Memory (BiLSTM) ML network was used to label each time point of the EEG signals as pertaining to TMS or sham conditions. Main outcome measures included accuracy at single-trial level and after averaging five to twenty trials. ResultsFor TMS conditions, post-stimulus vs. baseline/pre-stimulus EEG comparisons yielded moderate (60%-75%) single-trial accuracy and high-accuracy (>75%) for 20 trials across datasets, while for sham conditions post- vs. baseline/pre-stimulus EEG comparisons yielded lower accuracy rates than for TMS conditions, except for unmasked auditory stimulation. Furthermore, baseline/pre-stimulus TMS vs. baseline/pre-stimulus sham EEG comparisons showed chance-level accuracy, whereas post-stimulus TMS vs. post-stimulus sham EEG comparisons had moderate (single trial) to high (20 trial) accuracy, except for TMS with and without the click noise masking. Single-subject findings were comparable to group-level results across datasets. ConclusionsTEPs after active TMS are discernible from various sham stimulations even after a handful of trials and at the single-subject level using a BiLSTM ML approach.

neuroscience↗

The specific spatiotemporal evolution of TMS-evoked potentials reflects the engagement of cortical circuits

Transcranial Magnetic Stimulation (TMS) evokes electroencephalographic (EEG) responses that can persist for hundreds of milliseconds. While the first 80 ms after the pulse are widely accepted to reflect genuine cortical responses to TMS, later components have mainly been attributed to the effects of sensory co-stimulations. Here we reappraise this view by investigating the target-specificity of the spatiotemporal evolution of TMS-evoked potentials (TEPs). To this end, we compared TEPs elicited by targeting the premotor and primary motor cortices in 16 healthy subjects, under conditions designed to optimize TMS effectiveness on the cortex while minimizing peripheral confounds. As a counterfactual, we conducted the same comparison on the EEG responses evoked by realistic sham TMS and high-intensity somatosensory scalp stimulation. We found that EEG responses to motor and premotor TMS can exhibit distinct spatiotemporal evolutions, lasting up to 300 ms, both at the group and single-subject levels. These differences were absent or marginally detectable in both realistic sham TMS and high-intensity somatosensory scalp stimulation. Our findings suggest that, when effectiveness is optimized and peripheral confounds are controlled, TMS elicits specific long-lasting genuine EEG responses that reflect the initial engagement of specific cortical targets. These results challenge previous assumptions and highlight how TMS-EEG can be reliably used to assess large-scale properties within corticothalamic networks.

physiology↗

Extracting Reproducible Components from Electroencephalographic Responses to Transcranial Magnetic Stimulation with Group Task-Related Component Analysis

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a powerful technique for investigating human cortical circuits. However, characterizing TMS-evoked potentials (TEPs) at the group level typically relies on grand averaging across stimulus repetitions (trials) and subjects - an approach that assumes a level of spatial and temporal consistency that is often lacking in TEPs. Here, we introduce an adaptation of Group Task-Related Component Analysis (gTRCA), a novel multivariate signal decomposition method, to automatically extract TEP components that are maximally reproducible across both trials and subjects. Following the validation of a new permutation-based statistical test for gTRCA using simulated data, the method was applied to two independent TMS-EEG datasets, in which stimulation was targeted to the primary motor cortex (M1) in cohorts of 16 and 22 healthy participants. We found that gTRCA reliably identified TEP components that were reproducible at the group level. Notably, the main gTRCA component captured the key spatial, temporal, and spectral features of motor TEPs, remained robust despite reduced number of stimuli and participants, and was consistent across different recordings. These findings demonstrate that gTRCA affords a more reliable characterization of TEPs at the group level, thereby facilitating the translation of TMS-EEG research into clinical practice.

neuroscience↗

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subjects behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subjects behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/578243v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@cae203org.highwire.dtl.DTLVardef@1bc5223org.highwire.dtl.DTLVardef@b48e43org.highwire.dtl.DTLVardef@1e7fe34_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia

1Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness. 2 Significance StatementComplexity has long been of interest in consciousness science and had a fundamental impact on many of todays theories of consciousness. The perturbational complexity index (PCI) uses the complexity of the brains response to cortical perturbations to quantify the presence of consciousness. We propose criticality as a unifying framework underlying maximal complexity and sensitivity to perturbation in the conscious brain. We demonstrate that criticality measures derived from resting-state electroencephalography can distinguish conscious from unconscious states, using propofol, xenon and ketamine anesthesia, and from these measures we were able to predict the PCI with a mean error below 7%. Our results support the hypothesis that critical brain dynamics are implicated in the emergence of consciousness and may provide new directions for the assessment of consciousness.

neuroscience↗

Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: Insights into interregional and interpersonal differences in pain processing

Pain-related depression of motor cortico-spinal excitability has been explored using transcranial magnetic stimulation (TMS)-based motor evoked potentials. Recently, TMS combined with concomitant high-density electroencephalography (TMS-EEG) enabled cortical excitability (CE) assessments in non-motor areas, offering novel insights into CE changes during pain states. Here, pain-related CE changes were explored in the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC). CE was recorded in 24 healthy participants before (Baseline), during painful heat (Acute Pain), and non-painful warm (Non-noxious warm) stimulation for eight minutes at the right forearm in a randomized sequence, followed by a pain-free stimulation measurement. Local CE was measured as peak-to-peak amplitude of the early latencies of the TMS-evoked potential (<120 ms) on each target. Furthermore, global-mean field power (GMFP) was used to measure global excitability. Relative to the Baseline, Acute Pain induced a decrease of -9.9{+/-}8.8% in the peak-to-peak amplitude in M1 and -10.2{+/-}7.4% in DFPFC, while no significant differences were found for Non-noxious warm (+0.6{+/-}8.0% in M1 and +3.4{+/-}7.2% in DLPFC; both P<0.05). A reduced GMFP of - 9.1{+/-}9.0% was only found in M1 during Acute Pain compared with Non-noxious warm (P=0.003). Participants with the largest reduction in local CE under Acute Pain showed a negative correlation between DLPFC and M1 local CE (r=-0.769; P=0.006). Acute experimental pain drove differential pain-related effects on local and global CE changes in motor and non-motor areas at a group level while also revealing different interindividual patterns of CE changes, which can be explored when designing personalized treatment plans. SUMMARYCortical motor and prefrontal areas present reduced excitability during acute pain, but they occur in different patterns across individuals and present distinct impacts on global connectivity.

neuroscience↗

Local brain-state dependency of effective connectivity: evidence from TMS-EEG

BackgroundSpontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). If not controlled for, they might increase variability in responses and mask meaningful changes in the signals of interest when studying the brain with TMS combined with electroencephalography (TMS-EEG). To address this challenge in future closed-loop stimulation paradigms, we need to understand how spontaneous oscillations affect TMS-evoked responses. ObjectiveTo describe the effect of the pre-stimulus phase of cortical mu (8-13 Hz) and beta (13-30 Hz) oscillations on TMS-induced effective connectivity patterns. MethodsWe applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. ResultsPhase had significant effects on the GMFA in 11 out of 12 datasets (3 subjects x 2 stimulation sites x 2 frequency bands). Seven of the datasets showed significant differences in the time range 15-50 ms, nine in 50-150 ms, and eight after 150 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. ConclusionsTMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This may be crucial for efficient design of future brain-state-dependent and closed-loop stimulation paradigms.

neuroscience↗

The rt-TEP tool: real-time visualization of TMS-evoked potentials to maximize cortical activation and minimize artifacts

BackgroundThe impact of transcranial magnetic stimulation (TMS) on cortical neurons is currently hard to predict based on a priori biophysical and anatomical knowledge alone. This problem can hamper the reliability and reproducibility of protocols aimed at measuring electroencephalographic (EEG) responses to TMS. New MethodWe introduce and release a novel software tool to facilitate and standardize the acquisition of TMS-evoked potentials (TEPs). The tool, rt-TEP (real-time TEP), interfaces with different EEG amplifiers and offers a series of informative visualization modes to assess in real time the immediate impact of TMS on the underlying neuronal circuits. ResultsWe show that rt-TEP can be used to abolish or minimize magnetic and muscle artifacts contaminating the post-stimulus period thus affording a clear visualization and quantification of the amplitude of the early (<50 ms) EEG response after averaging a limited number of trials. This real-time readout can then be used to adjust TMS parameters (e.g. site, orientation, intensity) and experimental settings (e.g. loudness and/or spectral features of the noise masking) to ultimately maximize direct cortical effects over the undesired sensory effects of the coils discharge. Comparison with Existing MethodsThe ensemble of real-time visualization modes of rt-TEP are not implemented in any current commercial software and provide a key readout to titrate TMS parameters beyond the a priori information provided by anatomical models. ConclusionsReal-time optimization of stimulation parameters with rt-TEP can facilitate the acquisition of reliable TEPs with a high signal-to-noise ratio and improve the standardization and reproducibility of data collection across laboratories. HighlightsO_LIrt-TEP provides an immediate EEG readout to assess the quality of TEPs in real time C_LIO_LIrt-TEP interfaces with most commercial EEG systems C_LIO_LIInformative, real time visualization allows maximising the cortical impact of TMS while minimizing artifacts C_LIO_LIrt-TEP facilitates the acquisition of reliable TEPs with a high signal-to-noise ratio C_LI

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TAAC - TMS Adaptable Auditory Control: a universal tool to mask TMS click

BackgroundCoupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows recording the EEG response to a direct, non-invasive cortical perturbation. However, obtaining a genuine TMS-evoked EEG potential requires controlling for several confounds, among which a main source is represented by the auditory evoked potentials (AEPs) associated to the TMS discharge noise (TMS click). This contaminating factor can be in principle prevented by playing a masking noise through earphones. New methodHere we release TMS Adaptable Auditory Control (TAAC), a highly flexible, open-source, Matlab(R)-based interface that generates in real-time customized masking noises. TAAC creates noises starting from the stimulator-specific TMS click and tailors them to fit the individual, subject-specific click perception by mixing and manipulating the standard noises in both time and frequency domains. ResultsWe showed that TAAC allows us to provide standard as well as customized noises able to effectively and safely mask the TMS click. Comparison with existing methodsHere, we showcased two customized noises by comparing them to two standard noises previously used in the TMS literature (i.e., a white noise and a noise generated from the stimulator-specific TMS click only). For each, we quantified the Sound Pressure Level (SPL; measured by a Head and Torso Simulator - HATS) required to mask the TMS click in a population of 20 healthy subjects. Both customized noises were effective at safe (according to OSHA and NIOSH safety guidelines), lower SPLs with respect to standard noises. ConclusionsAt odds with previous methods, TAAC allows creating effective and safe masking noises specifically tailored on each TMS device and subject. The combination of TAAC with tools for the real-time visualization of TEPs can help control the influence of auditory confounds also in non-compliant patients. Finally, TAAC is a highly flexible and open-source tool, so it can be further extended to meet different experimental requirements.

neuroscience↗

EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery.

BackgroundQuantitative EEG (qEEG) can capture changes in brain activity that follow a stroke. Accordingly, EEG metrics could be used to monitor patients state and recovery. Although qEEG metrics traditionally focus on oscillatory activity, recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. ObjectiveTo assess neurophysiological impairment and recovery from mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD). To relate neurophysiological recovery with patients functional outcome. MethodsEighteen patients (n=18) with Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n=16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients. ResultsAt T0, stroke patients showed significantly more negative SE values than HC (p=0.003), reflecting broad-band EEG slowing. Moreover, SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1 in our patient sample. This SE renormalization significantly correlated with NIHSS improvement (R= 0.63, p=0.005). ConclusionsSE is a reliable readout of the electric changes occurring in the brain after an ischaemic cortical lesion. Moreover, SE holds the promise to be a robust method to assess stroke impairment as well as to monitor and predict functional outcome.

neuroscience↗

Local sleep-like cortical reactivity in the awake brain after focal injury

The functional consequences of brain injury are known to depend on neuronal alterations extending beyond the area of structural damage. Although a lateralized EEG slowing over the injured hemisphere was known since the early days of clinical neurophysiology, its electrophysiological mechanisms were not systematically investigated. In parallel, basic sleep research has thoroughly characterized the neuronal events underlying EEG slow waves in physiological conditions. These EEG events reflect brief interruptions of neuronal firing (OFF-periods) that can occur locally and have prominent consequences on network and behavioral functions. Notably, the EEG slow waves observed following focal brain injury have been never explicitly connected to local sleep-like neuronal events. In previous works, probing cortical circuits with transcranial magnetic stimulation coupled with EEG (TMS/EEG) proved as an effective way to reveal the tendency of cortical circuits to transiently plunge into silent OFF-periods. Here, using this approach, we show that the intact cortex surrounding focal brain injuries engages locally in pathological sleep-like dynamics. Specifically, we employed TMS/EEG in a cohort of thirty conscious awake patients with chronic focal and multifocal brain injuries of various etiologies. TMS systematically evoked prominent slow waves associated with sleep-like OFF-periods in the area surrounding focal cortico-subcortical lesions. These events were associated with a local disruption of signal complexity and were absent when stimulating the contralateral hemisphere. Perilesional sleep-like OFF-periods may represent a valid read-out of the electrophysiological state of discrete cortical circuits following brain injury as well as a potential target of interventions aimed at fostering functional recovery. One Sentence SummaryFocal cortical injuries are associated with local intrusion of sleep-like dynamics over the perilesional areas which disrupt local signal complexity and coexist with typical wakefulness cortical reactivity patterns within the same brain.

neuroscience↗

A fast and general method to empirically estimate the complexity of distributed causal interactions in the brain

BackgroundThe Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain injured patients, PCI depends on elaborate experimental setups and offline processing and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings. ObjectiveWe aim to address these limitations by introducing PCIST, a fast method for estimating perturbational complexity of any given brain response signal. MethodsPCIST is based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intra-cerebral single-pulse electrical stimulation (SPES). ResultsWhen calculated on TMS/hd-EEG potentials, PCIST performed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler set-up. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracerebral complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG. ConclusionsPCIST represents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.

neuroscience↗

TMS orientation and pulse waveform manipulation activates different neural populations: direct evidence from TMS-EEG

Monophasic and biphasic TMS pulses and coil orientations produce different responses in terms of motor output and sensory perception. Those differences have been attributed to the activation of specific neural populations. However, up to date, direct evidence supporting this hypothesis is still missing since studies were mostly based on indirect measures of cortical activation, i.e., motor evoked potentials or phosphenes. Here, we investigated for the first time the impact of different coil orientations and waveforms on a non-primary cortical area, namely the premotor cortex, by measuring TMS evoked EEG potentials (TEPs). We aimed at determining whether TEPs produced by differently oriented biphasic and monophasic TMS pulses diverge and whether these differences are underpinned by the activation of specific neural populations. To do so, we applied TMS over the right premotor cortex with monophasic or biphasic waveforms oriented perpendicularly (in the anterior-posterior direction and vice-versa) or parallel (latero-medial or medio-laterally) to the target gyrus. EEG was concurrently recorded from 60 electrodes. We analyzed TEPs at the level of EEG sensors and cortical sources both in time and time-frequency domain. Biphasic pulses evoked larger early TEP components, which reflect cortical excitability properties of the underlying cortex, in both parallel directions when compared to the perpendicular conditions. Conversely, monophasic pulses, when oriented perpendicularly to the stimulated gyrus, elicited a greater N100, which is a reliable TEP component linked to GABAb-mediated inhibitory processes, than when parallel to the gyrus. Our results provide direct evidence supporting the hypothesis that TMS pulse waveform and TMS coil orientations affect which neural population is engaged.

neuroscience↗

Sleep-like bistability, loss of causality and complexity in the brain of Unresponsive Wakefulness Syndrome patients

Unresponsiveness Wakefulness Syndrome (UWS) patients may retain intact portions of the thalamocortical system that are spontaneously active and responsive to sensory stimuli. In these patients, Transcranial Magnetic Stimulation combined with electroencephalography (TMS/EEG) also reveals preserved cortical reactivity, but in most cases, the residual thalamocortical circuits fail to engage complex causal interactions, as assessed by the perturbational complexity index (PCI). Another condition during which thalamocortical circuits are intact, active and reactive, yet unable to generate complex responses, is physiological non-rapid eye movement (NREM) sleep. The underlying mechanism is bistability: the tendency of cortical neurons to fall into a silent period (OFF-period) upon receiving an input. Here we tested whether a pathological form of bistability may be responsible for loss of brain complexity in UWS patients. Time-frequency decomposition analysis of TMS/EEG responses in UWS patients revealed the occurrence of OFF-periods (detected as a transient suppression of high-frequency oscillations in the EEG) similar to the ones evoked by TMS in the cortex of healthy sleeping subjects. Pathological OFF-periods were detected in any cortical area, significantly impaired local causal interactions (as measured by PLF) and prevented the build-up of global complexity (as measured by PCI) in the brain of UWS patients. Our results draw a first link between neuronal events (OFF-periods) and global brain dynamics (complexity) in UWS patients. To the extent that sleep-like bistability represents the common functional endpoint of loss of complexity, detecting its presence and tracking its evolution over time, may offer a valuable read-out to devise, guide and titrate therapeutic strategies aimed at restoring consciousness.

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