Cortical Effective Connectivity Research Articles

Neural evidence suggests phonological acceptability judgments reflect similarity, not constraint evaluation

Acceptability judgments are a primary source of evidence in formal linguistic research. Within the generative linguistic tradition, these judgments are attributed to evaluation of novel forms based on implicit knowledge of rules or constraints governing well-formedness. In the domain of phonological acceptability judgments, other factors including ease of articulation and similarity to known forms have been hypothesized to influence evaluation. We used data-driven neural techniques to identify the relative contributions of these factors. Granger causality analysis of magnetic resonance imaging (MRI)-constrained magnetoencephalography (MEG) and electroencephalography (EEG) data revealed patterns of interaction between brain regions that support explicit judgments of the phonological acceptability of spoken nonwords. Comparisons of data obtained with nonwords that varied in terms of onset consonant cluster attestation and acceptability revealed different cortical regions and effective connectivity patterns associated with phonological acceptability judgments. Attested forms produced stronger influences of brain regions implicated in lexical representation and sensorimotor simulation on acoustic-phonetic regions, whereas unattested forms produced stronger influence of phonological control mechanisms on acoustic-phonetic processing. Unacceptable forms produced widespread patterns of interaction consistent with attempted search or repair. Together, these results suggest that speakers' phonological acceptability judgments reflect lexical and sensorimotor factors.

Additive and Interactive Effects of Attention-Deficit/Hyperactivity Disorder and Tic Disorder on Brain Connectivity

BackgroundAttention-deficit/hyperactivity disorder (ADHD) and persistent tic disorder (PTD) are two neurodevelopmental disorders that frequently co-occur. Contributions of each disorder to cognitive and behavioral deficits have been reported. In this paper, we tested 3 models of pathophysiology for the two disorders (additive, interactive, and phenotypic) using resting-state connectivity associated with each disorder separately and together. MethodsParticipants were 148 children (55 with ADHD only, 33 with ADHD and PTD, 27 with PTD only, and 33 healthy control subjects) at ages 8 to 12 years. Following diagnostic interviews and behavioral assessment, participants underwent a 128-channel electroencephalography recording. Resting-state, cortical source-level effective connectivity was analyzed across the 4 groups using a 2 × 2 factorial design with factors of ADHD (with/without) and PTD (with/without). ResultsADHD diagnosis was the primary driver of cognitive and behavioral deficits, while deficits associated with PTD were primarily with thought problems and internalizing problems when compared with controls. Subadditive effects were observed in co-occurring ADHD+PTD for parent-rated behavioral problems and cognitive functions. Aberrant effective connectivity was primarily associated with ADHD, more specifically with lower posterior and occipital-frontal connectivity, while children with PTD exhibited greater left postcentral to precuneus connectivity. Weaker ADHD-related connectivity was associated with more severe behavioral problems, including internalizing behaviors, thought problems, and working memory deficits. ConclusionsSimilar to general behavioral deficits, aberrant resting-state neural connectivity in pediatric ADHD and PTD combines additively in co-occurring cases. The findings of this study support ADHD as a focus of treatment in comorbid cases, given the driving role of ADHD in both behavioral and neurophysiological deficits.

Preterm neonates distinguish rhythm violation through a hierarchy of cortical processing

Rhythm is a fundamental component of the auditory world, present even during the prenatal life. While there is evidence that some auditory capacities are already present before birth, whether and how the premature neural networks process auditory rhythm is yet not known. We investigated the neural response of premature neonates at 30–34 weeks gestational age to violations from rhythmic regularities in an auditory sequence using high-resolution electroencephalography and event-related potentials. Unpredicted rhythm violations elicited a fronto-central mismatch response, indicating that the premature neonates detected the rhythmic regularities. Next, we examined the cortical effective connectivity underlying the elicited mismatch response using dynamic causal modeling. We examined the connectivity between cortical sources using a set of 16 generative models that embedded alternate hypotheses about the role of the frontal cortex as well as backward fronto-temporal connection. Our results demonstrated that the processing of rhythm violations was not limited to the primary auditory areas, and as in the case of adults, encompassed a hierarchy of temporo-frontal cortical structures. The result also emphasized the importance of top-down (backward) projections from the frontal cortex in explaining the mismatch response. Our findings demonstrate a sophisticated cortical structure underlying predictive rhythm processing at the onset of the thalamocortical and cortico-cortical circuits, two months before term.

Local brain-state dependency of effective connectivity: a pilot TMS-EEG study [version 2; peer review: 2 approved

BackgroundSpontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs).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 affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 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 seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.

Local brain-state dependency of effective connectivity: a pilot TMS–EEG study

Background Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). Methods We 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. Results Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions TMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed loop stimulation paradigms.

The impact of artifact removal approaches on TMS–EEG signal

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow one to assess cortical excitability and effective connectivity in clinical and basic research. However, obtaining clean TEPs is challenging due to the various TMS-related artifacts that contaminate the electroencephalographic (EEG) signal when the TMS pulse is delivered. Different preprocessing approaches have been employed to remove the artifacts, but the degree of artifact reduction or signal distortion introduced in this phase of analysis is still unknown. Knowing and controlling this potential source of uncertainty will increase the inter-rater reliability of TEPs and improve the comparability between TMS–EEG studies. The goal of this study was to assess the variability in TEP waveforms due to of the use of different preprocessing pipelines. To accomplish this aim, we preprocessed the same TMS–EEG data with four different pipelines and compared the results. The dataset was obtained from 16 subjects in two identical recording sessions, each session consisting of both left dorsolateral prefrontal cortex and left inferior parietal lobule stimulation at 100% of the resting motor threshold. Considerable differences in TEP amplitudes and global mean field power (GMFP) were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated (ρ>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with ρ ranging between 0.2 and 0.9. Moreover, the test–retest reliability of TEPs depended on the preprocessing pipeline. Taken together, these results take us to suggest that the choice of the preprocessing approach has a marked impact on the final TEP, and that further studies are needed to understand advantages and disadvantages of the different approaches.

Characterization of the abnormal cortical effective connectivity in patients with sleep apnea hypopnea syndrome during sleep

Background and objectivesSleep apnea hypopnea syndrome (SAHS) is a prevalent sleep breathing disorder that can lead to brain damage and is also a risk factor for cognitive impairment and some common diseases. Studies on cortical effective connectivity (EC) during sleep may provide more direct and pathological information and shed new light on brain dysfunction due to SAHS. However, the EC is rarely explored in SAHS patients, especially during different sleep stages. MethodsTo this end, six-channel EEG signals of 43 SAHS patients and 41 healthy participants were recorded by whole-night polysomnography (PSG). The symbolic transfer entropy (STE) was applied to measure the EC between cortical regions in different frequency bands. Posterior-anterior ratio (PA) was employed to evaluate the posterior-to-anterior pattern of information flow based on overall cortical EC. The statistical characteristics of the STE and PA and of the intra-individual normalized parameters (STE* and PA*) were served as different feature sets for classifying the severity of SAHS. ResultsAlthough the patterns of STE across electrodes were similar, significant differences were found between the patient and the control groups. The variation trends across stages in the PA were also different in multiple frequency bands between groups. Important features extracted from the STE* and PA* were distributed in multiple rhythms, mainly in δ, α, and γ. The PA* feature set gave the best results, with accuracies of 98.8% and 83.3% for SAHS diagnosis (binary) and severity classification (four-way). ConclusionsThese results suggest that modifications in cortical EC were existed in SAHS patients during sleep, which may help characterize cortical abnormality in patients.

Structural and functional underpinnings of precentral abnormalities in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using various magnetic resonance imaging (MRI) analytical approaches have consistently identified significant precentral abnormalities in ALS, whereas their structural and functional underpinnings remain poorly understood. Using cortical thickness, fractional anisotropy (FA), and effective connectivity, we performed a multimodal MRI study to examine the structural and functional alterations associated with precentral abnormalities in patients with ALS (n=60) compared with healthy controls (n=60). Cortical thickness analysis revealed significant cortical thinning in the right precentral gyrus (PCG), superior frontal gyrus, and superior temporal gyrus in patients with ALS. Tractwise white matter microstructure analyses revealed decreased FA in the tracts connected to the PCG cluster in patients with ALS involving the right corticospinal tract and the middle posterior body of the corpus callosum. Additionally, the cortical thickness of the PCG cluster was found to be positively correlated with FA of the tracts connected to the PCG cluster, suggesting that these two structural features are tightly coupled. Using spectral dynamic causal modelling, effective connectivity analysis among the three regions with cortical thinning revealed decreased self-inhibitory influence in the PCG cluster in patients with ALS, which might be an endophenotypic manifestation of an imbalance in inhibitory and excitatory neurotransmitters in this region. The present data shed new light on the structural and functional underpinnings of precentral abnormalities in ALS.

Probing dynamical cortical gating of attention with concurrent TMS-EEG

Attention facilitates the gating of information from the sending brain area to the receiving areas, with this being achieved by dynamical changes in effective connectivity, which refers to the directional influences between cortical areas. To probe the effective connectivity and cortical excitability modulated by covertly shifted attention, transcranial magnetic stimulation (TMS) was used to directly perturb the right retinotopic visual cortex with respect to attended and unattended locations, and the impact of this was tracked from the stimulated area to other areas by concurrent use of electroencephalography (EEG). TMS to the contralateral visual hemisphere led to a stronger evoked potential than stimulation to the ipsilateral hemisphere. Moreover, stronger beta- and gamma-band effective connectivities assessed as time-delayed phase synchronizations between stimulated areas and other areas were observed when TMS was delivered to the contralateral hemisphere. These effects were more enhanced when they preceded more prominent alpha lateralization, which is known to be associated with attentional gating. Our results indicate that attention-regulated cortical feedforward effective connectivity can be probed by TMS-EEG with direct cortical stimulation, thereby bypassing thalamic gating. These results suggest that cortical gating of the feedforward input is achieved by regulating the effective connectivity in the phase dynamics between cortical areas.

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Functional and effective connectivity based features of EEG signals for object recognition.

Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.

Limb linkage rehabilitation training-related changes in cortical activation and effective connectivity after stroke: A functional near-infrared spectroscopy study

Stroke remains the leading cause of long-term disability worldwide. Rehabilitation training is essential for motor function recovery following stroke. Specifically, limb linkage rehabilitation training can stimulate motor function in the upper and lower limbs simultaneously. This study aimed to investigate limb linkage rehabilitation task-related changes in cortical activation and effective connectivity (EC) within a functional brain network after stroke by using functional near-infrared spectroscopy (fNIRS) imaging. Thirteen stroke patients with either left hemiparesis (L-H group, n = 6) and or right hemiparesis (R-H group, n = 7) and 16 healthy individuals (control group) participated in this study. A multichannel fNIRS system was used to measure changes in cerebral oxygenated hemoglobin (delta HbO2) and deoxygenated hemoglobin (delta HHb) in the bilateral prefrontal cortices (PFCs), motor cortices (MCs), and occipital lobes (OLs) during (1) the resting state and (2) a motor rehabilitation task with upper and lower limb linkage (first 10 min [task_S1], last 10 min [task_S2]). The frequency-specific EC among the brain regions was calculated based on coupling functions and dynamic Bayesian inference in frequency intervals: high-frequency I (0.6–2 Hz) and II (0.145–0.6 Hz), low-frequency III (0.052–0.145 Hz), and very-low-frequency IV (0.021–0.052 Hz). The results showed that the stroke patients exhibited an asymmetric (greater activation in the contralesional versus ipsilesional motor region) cortical activation pattern versus healthy controls. Compared with the healthy controls, the stroke patients showed significantly lower EC (p < 0.025) in intervals I and II in the resting and task states. The EC from the MC and OL to the right PFC in interval IV was significantly higher in the R-H group than in the control group during the resting and task states (p < 0.025). Furthermore, the L-H group showed significantly higher EC from the MC and OL to the left PFC in intervals III and IV during the task states compared with the control group (p < 0.025). The significantly increased influence of the MC and OL on the contralesional PFC in low- and very-low-frequency bands suggested that plastic reorganization of cognitive resources severed to compensate for impairment in stroke patients during the motor rehabilitation task. This study can serve as a basis for understanding task-related reorganization of functional brain networks and developing novel assessment techniques for stroke rehabilitation.

Combined head phantom and neural mass model validation of effective connectivity measures

Objective. Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in a mobile setting. While many connectivity estimators are available, the efficacy of these measures has not been rigorously validated in real-world scenarios. The goal of this study was to quantify the accuracy of independent component analysis and multiple connectivity measures on ground-truth connections while exposed real-world volume conduction and head motion. Approach. We collected high-density EEG from a phantom head with embedded antennae, using neural mass models to generate transiently interconnected signals. The head was mounted upon a motion platform that mimicked recorded human head motion at various walking speeds. We used cross-correlation and signal to noise ratio to determine how well independent component analysis recovered the original antenna signals. For connectivity measures, we computed the average and standard deviation across frequency of each estimated connectivity peak. Main results. Independent component analysis recovered most antenna signals, as evidenced by cross-correlations primarily above 0.8, and maintained consistent signal to noise ratio values near 10 dB across walking speeds compared to scalp channel data, which had decreased signal to noise ratios of ~2 dB at fast walking speeds. The connectivity measures used were generally able to identify the true interconnections, but some measures were susceptible to spurious high-frequency connections inducing large standard deviations of ~10 Hz. Significance. Our results indicate that independent component analysis and some connectivity measures can be effective at recovering underlying connections among brain areas. These results highlight the utility of validating EEG processing techniques with a combination of complex signals, phantom head use, and realistic head motion.

Detecting slow wave sleep and rapid eye movement stage using cortical effective connectivity

In recent neuroimaging research, there has been considerable interest in identifying neuromarkers of sleep. Automatic slow wave sleep (SWS) and rapid eye movement (REM) are two known phases of sleep. However, the level by which those changes contribute to brain interactions has not been well characterized. In recent years, it has been shown that brain connectivity measuring can be helpful in investigation of behavioral states of the brain. By considering the fact that brains have different states in different stages of sleep, the present work employs effective connectivity and machine-learning analysis to quantify and classify SWS and REM stages of sleep. We examine low-density 12-channel EEG data from 8 healthy participants during a full night of sleep. Data were epoched into 30-s windows and SWS and REM stages were labeled by a sleep consultant. Effective connectivity was quantified using a directed metric, generalized partial directed coherence, and measures were used as input features for a machine-learning system. A support vector machine classifier was used to solve 2 binary problems of REM vs. nREM and SWS vs. nSWS. Findings revealed an excellent balanced accuracy of 89.80 % in REM detection and 87.32 % in SWS detection. Overall, our work demonstrates a successful application of effective connectivity analysis and machine learning for sleep neuromarkers in EEG.

Non-REM sleep and the neural correlates of consciousness: more than meets the eye

The scientific study of the neural correlates of consciousness (NCC) has long relied on comparing conditions in which consciousness is normally present with others in which it is impaired. Brain lesions offer a unique opportunity to understand which anatomical networks are needed to sustain consciousness, but provide limited insights on the patterns of neural activity that can support conscious processing. Non-REM sleep, on the other hand, has long epitomized the typical case of a non-conscious yet fully active brain. Consequently, the differences in neural activity existing between wakefulness and non-REM sleep have also been used to define the NCC. Recently, however, several studies have started challenging the traditional understanding of neuronal activity during wakefulness and sleep. First, oscillatory dynamics characteristic of non-REM sleep - such as slow oscillations - have been reported to occur during wakefulness. Second, neural dynamics typical of conscious states have also been observed during non-REM sleep. Finally, the disconnection in cortical effective connectivity that has been indicated as one of the hallmarks of the loss of consciousness that occurs during non-REM sleep has recently been shown to be a less general phenomenon than previously thought. Here I will provide an overview of these recent findings, and discuss their implications for understanding the real nature of the NCC.

Transcranial Alternating Current Stimulation in Patients with Chronic Disorder of Consciousness: A Possible Way to Cut the Diagnostic Gordian Knot?

Unresponsive wakefulness syndrome (UWS) is a chronic disorder of consciousness (DOC) characterized by a lack of awareness and purposeful motor behaviors, owing to an extensive brain connectivity impairment. Nevertheless, some UWS patients may retain residual brain connectivity patterns, which may sustain a covert awareness, namely functional locked-in syndrome (fLIS). We evaluated the possibility of bringing to light such residual neural networks using a non-invasive neurostimulation protocol. To this end, we enrolled 15 healthy individuals and 26 DOC patients (minimally conscious state-MCS- and UWS), who underwent a γ-band transcranial alternating current stimulation (tACS) over the right dorsolateral prefrontal cortex. We measured the effects of tACS on power and partial-directed coherence within local and long-range cortical networks, before and after the protocol application. tACS was able to specifically modulate large-scale cortical effective connectivity and excitability in all the MCS participants and some UWS patients, who could be, therefore, considered as suffering from fLIS. Hence, tACS could be a useful approach in supporting a DOC differential diagnosis, depending on the level of preservation of the cortical large-scale effective connectivity.

Cortical chemoarchitecture shapes macroscale effective functional connectivity patterns in macaque cerebral cortex.

The mammalian cortex is a complex system of-at the microscale level-interconnected neurons and-at the macroscale level-interconnected areas, forming the infrastructure for local and global neural processing and information integration. While the effects of regional chemoarchitecture on local cortical activity are well known, the effect of local neurotransmitter receptor organization on the emergence of large scale region-to-region functional interactions remains poorly understood. Here, we examined reports of effective functional connectivity-as measured by the action of strychnine administration acting on the chemical balance of cortical areas-in relation to underlying regional variation in microscale neurotransmitter receptor density levels in the macaque cortex. Linking cortical variation in microscale receptor density levels to collated information on macroscale functional connectivity of the macaque cortex, we show macroscale patterns of effective corticocortical functional interactions-and in particular, the strength of connectivity of efferent macroscale pathways-to be related to the ratio of excitatory and inhibitory neurotransmitter receptor densities of cortical areas. Our findings provide evidence for the microscale chemoarchitecture of cortical areas to have a direct stimulating influence on the emergence of macroscale functional connectivity patterns in the mammalian brain. Hum Brain Mapp 37:1856-1865, 2016. © 2016 Wiley Periodicals, Inc.

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.

We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) . We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

Opportunities for concurrent transcranial magnetic stimulation and electroencephalography to characterize cortical activity in stroke.

Stroke is the leading cause of disability in the United States. Despite the high incidence and mortality of stroke, sensitive and specific brain-based biomarkers predicting persisting disabilities are lacking. Both neuroimaging techniques like electroencephalography (EEG) and non-invasive brain stimulation (NIBS) techniques such as transcranial magnetic stimulation (TMS) have proven useful in predicting prognosis, recovery trajectories and response to rehabilitation in individuals with stroke. We propose, however, that additional synergetic effects can be achieved by simultaneously combining both approaches. Combined TMS-EEG is able to activate discrete cortical regions and directly assess local cortical reactivity and effective connectivity within the network independent of the integrity of descending fiber pathways and also outside the motor system. Studying cortical reactivity and connectivity in patients with stroke TMS-EEG may identify salient neural mechanisms underlying motor disabilities and lead to novel biomarkers of stroke pathophysiology which can then be used to assess, monitor, and refine rehabilitation approaches for individuals with significant disability to improve outcomes and quality of life after stroke.

Can transcranial direct current stimulation be useful in differentiating unresponsive wakefulness syndrome from minimally conscious state patients?

Disorders of consciousness (DOC) diagnosis relies on the presence or absence of purposeful motor responsiveness, which characterizes the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS), respectively. Functional neuroimaging studies have raised the question of possible residual conscious awareness also in clinically-defined UWS patients. The aim of our study was to identify electrophysiological parameters, by means of a transcranial magnetic stimulation approach, which might potentially express the presence of residual networks sustaining fragmentary behavioral patterns, even when no conscious behavior can be observed. We enrolled 25 severe DOC patients, following post-anoxic or traumatic brain injury and 20 healthy individuals (HC) as control group. Baseline electrophysiological evaluation evidenced, in comparison to HC, a partial preservation of cortical effective connectivity and excitability in clinically defined MCS, whereas these components were absent in clinically defined UWS. Then, we applied an anodal transcranial direct current stimulation (a-tDCS) protocol over the orbitofrontal cortex. a-tDCS was able to boost cortical connectivity and excitability in all HC, MCS, and to unmask such excitability/connectivity in some UWS patients. a-tDCS could be useful in identifying residual connectivity markers in clinically-defined UWS, who may lack of purposeful behavior as a result of a motor-output failure.