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Mind-blanking (MB) is termed as the experience of inability to report mental contents. In contrast to other mental states, such as mind-wandering or sensory perceptions, the neural correlates of MB started getting elucidated only recently. A notable particularity that pertains to MB studies is the way MB is instructed for reports, like by deliberately asking participants to “empty their minds”. Such instructions were shown to induce fMRI activations in frontal brain regions, typically associated with metacognition and self-evaluative processes, suggesting that MB may be a result of intentional mental content suppression. Here, we aim at examining this hypothesis by determining the neural correlates of MB without induction. Using fMRI combined with experience-sampling, univariate analysis of MB reports revealed deactivations in occipital, frontal, somatosensory, and thalamic areas, but no activations in prefrontal regions. In fact, a Bayesian region-of-interest analysis on areas previously shown to be implicated in MB provided evidence for frontal deactivations during MB reports in comparison to other mental states. Further contrast analysis between reports of MB and content-oriented mental states also revealed deactivations in the L angular gyrus. We propose that these effects characterize a cortical profile of MB, where key cortical nodes are unable to communicate and formulate reportable content. Collectively, our results show that study instructions for MB may lead to cortical differences which provide different insights as to the underlying mechanisms leading to the phenomenology of MB.Significance StatementAn emergent research paradigm in the study of mental states has focused on periods of complete thought absence, where people report an inability to report mental content, termed mind blanking. The neural correlates supporting mind blanking are still debated and might vary depending on thought-report instructions. By reanalyzing an fMRI dataset utilizing an unguided, free-thinking paradigm, the present study provides critical insights into the neuronal events that preceded mind-blanking reports. Our findings demonstrate that spontaneous mind-blanking occurrences are associated with large-scale cortical deactivations in frontal, occipital, parietal and thalamical sites, with the frontal evaluative sites and the angular gyrus especially differentiating mind-blanking from other mental states associated with the presence of content.

Spontaneous thinking significantly relies on attention and arousal. As these cognitive faculties change with age, we aimed at providing a comprehensive account to ongoing mental states in seniors, testing how these are influenced by attentional control and arousal. Using experience sampling at rest, 20 senior (65-75yrs) and 20 young participants (20-30yrs) were prompted to report mind-wandering (MW), sensory-related thoughts (S), and the newly introduced state of mind blanking (MB). Attentional control was assessed with the Attentional Style Questionnaire, and arousal with continuous monitoring of pupil diameter. Both age groups showed equally high occurrences of MW compared to MB or S. For young responders, we replicated that MW was more prevalent in easily-distracted participants and that it associated with higher arousal. In seniors, though, MB was more prevalent in easily-distracted participants, and it was associated with higher arousal, reversing the pattern found in young adults and focused seniors. Overall, our results show that attentional control and arousal jointly regulate ongoing mental states in an age-dependent manner and uncover the presence a specific profile of ongoing mental state regulation in healthy aging, being a potentially critical marker of age-associated diseases.

Mind blanking (MB) is a waking state during which we do not report any mental content, challenging the view of a constantly thought-oriented brain. Here, we comprehensively characterize the MB’s neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using fMRI experience-sampling, we show that MB is reported less frequently, faster, and with low transitional dynamics among other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observe higher global signal amplitude during MB reports, indicating a distinct physiological substrate. Using the time-varying functional connectome MB reports get classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of globally positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We observe that this pattern’s rigid signal architecture hinders content reportability due to the brain’s inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioural profile, indicating that non-reportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB’s phenomenology.Significance StatementThe human mind is generally assumed to be thought-oriented. Mind blanking (MB) challenges this stance because it appears as if we are derived of any particular mental content to report. We here show that, during spontaneous thinking, MB is a mental state that happens by default, it has a unique behavioural profile, and it is of a rigid neural architecture that does not permit the formulation of reportable contents. Our work essentially proposes that non-reportable mental events can happen during wakefulness, and challenges the view of the mind as a constant thought-oriented operator.

Background and objectivesPost-concussion syndrome (PCS) consists of neurologic and psychological complaints persisting after a mild traumatic brain injury (mTBI). It affects up to 50% of mTBI patients, causes long-term disability and reduces quality of life. The aim of this scoping review was to examine possible uses of different neuroimaging modalities in PCS.MethodsArticles from Pubmed database were screened to extract studies that investigated the relationship between any neuroimaging features and symptoms of PCS. Descriptive statistics were applied to report results.Results88 out of 939 papers were included in the final review. 12 examined conventional MRI (42% specificity), 27 diffusion weighted imaging (56% specificity), 25 functional MRI (84% specificity), 10 electro(magneto)encephalography (80% specificity), and 14 examined other techniques (71% specificity).ConclusionMRI was the most widely used technique, while functional techniques seem to be the most sensitive tools to evaluate PCS. Common patterns associated with symptoms of PCS were a decreased anticorrelation between the default mode network and the task positive network and reduced brain activity in specific areas (most often prefrontal cortex).SignificanceOur findings highlight the importance to use functional approaches which demonstrated a functional alternation in brain connectivity and activity in most studies assessing PCS.Highlightspost-concussion syndrome causes long term problems for up to 50% of patients after concussion.Among different neuroimaging techniques, fMRI and EEG show to be the most sensitive tools for PCS assessment.Heterogeneity of axonal injury, symptoms, and populations limits having a specific prognostic criteria for the PCS patients.

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness. Keywords: Disorders of consciousness, High-density electroencephalography, Functional brain networks, Unresponsive wakefulness syndrome, Minimally conscious state

Handwritten digit recognition plays a crucial role in various automatic systems and industries nowadays. In steel companies, the code digits written on slab forehead should be recognized with high accuracy because these codes are used for determination of suitable thickness of steel slabs in rolling procedure. Because of special artifacts exist in these kinds of images such as color sagging, faint colors, and color separation, most of the common digit recognition algorithms have low accuracy in recognition of code digits. In this paper, a new two stage algorithm is proposed in order to reach high accuracy in determination of the handwritten codes written on slabs forehead and are corrupted by color sagging, faint colors and color separation. Applying our proposed algorithm on a dataset of images of slabs in Isfahan Steel Company shows that accuracy of the algorithm is 89.12% which is much higher than common and recently proposed algorithms.

Event Related Potentials (ERPs) are generated in ongoing brain electrical activity due to visual, auditory, or sensory stimuli. These signals have very low SNR and are contaminated by background EEG. Extraction of single trial ERPs from background EEG is a challenging task due to overlapping nature of the frequency bands of ERP and EEG signals and much higher power of EEG than ERPs. In this paper we proposed a method based on wavelet transform and adaptive noise cancelers in order to extract single trial ERPs from background EEG in very low SNR conditions. Simulation results show the superiority of the proposed algorithm over the existing methods. In addition, performance of the algorithm is justified under different noise models namely White Gaussian Noise, Auto Regressive, and Real EEG signals.

Detecting the active regions of the brain during cognitive functions is one of the important problems in cognitive neuroscience and disorder diagnosis. One of the promising approaches to solve this problem is to use General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI) data. The main difficulty of the GLM method is to determine a flexible design matrix to model mentioned problem appropriately. In this paper, an approach to the critical construction of a flexible design matrix for precise detection of active regions of the brain, according to response in synthetic fMRI data based on GLM is presented. Should the design matrix is accurate, the next detection algorithm can extract a correct response from a very low signal to noise ratio (SNR); therefore, the presented design matrix is flexible to eschew over fitting and capture unfamiliar slow drifts. Using a sparse Bayesian learning method, some specific regressors are selected for flexible design matrix. Results show clearly prominent performance of suggested algorithm rather than conventional t-test methods and other conventional Bayesian analysis of fMRI data.