Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique.

Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG. Even though these methods exhibit good performance, most of them have inherent inaccuracies that originate from their operating principles that constrain the solution or require a complex calculation process. The back-projection CPI (BP-CPI) method is relatively new and has the advantage of being constraint-free and computation inexpensive. The method has shown relatively good accuracy, which is necessary to become a clinical tool. However, better performance must be achieved. In the present study, two improvements are proposed. Both are embedded as adjacent stages to the BP-CPI and are based on the multi-resolution optimization approach (MR-CPI). A series of Monte-Carlo simulations were performed to examine the characteristics of the proposed improvements. Additional tests were done, including different EEG noise levels and variation in electrode-numbers. The results showed highly accurate cortical potential estimations, with a reduction in estimation error by a factor of 3.75 relative to the simple BP-CPI estimation error. We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. The MR-CPI methods were shown to be reliable for estimating cortical potentials, enabling researchers to obtain fast and robust high-resolution EEGs.

Do conscious perception and unconscious processing rely on independent mechanisms? A meta-contrast study.

There is currently no consensus regarding what measures are most valid to demonstrate perceptual processing without awareness. Likewise, whether conscious perception and unconscious processing rely on independent mechanisms or lie on a continuum remains a matter of debate. Here, we addressed these issues by comparing the time courses of subjective reports, objective discrimination performance and response priming during meta-contrast masking, under similar attentional demands. We found these to be strikingly similar, suggesting that conscious perception and unconscious processing cannot be dissociated by their time course. Our results also demonstrate that unconscious processing, indexed by response priming, occurs, and that objective discrimination performance indexes the same conscious processes as subjective visibility reports. Finally, our results underscore the role of attention by showing that how much attention the stimulus receives relative to the mask, rather than whether processing is measured by conscious discrimination or by priming, determines the time course of meta-contrast masking.

Visual consciousness and intertrial feature priming.

Intertrial repetition priming plays a striking role in visual search. For instance, when searching for a target with a unique color, performance is substantially better when the specific color of the target repeats on successive trials (Maljkovic & Nakayama, 1994). Recent research has relied on objective measures of performance to show that priming improves the perceptual quality of the repeated target. Here, we examined the relation between priming and conscious perception of the target by adding a subjective measure of perception. We used backward masking to create liminal perception, that is, different levels of subjectively conscious perception of the target using exactly the same stimulus conditions. The displays in either probe trials (in which priming benefits are measured, experiment 1) or in prime trials (in which memory traces are laid down, experiment 2) were masked. The results showed that intertrial priming improves full access to awareness of the repeated target but only for targets that already achieved partial access to awareness. In addition, they show that full awareness of the target is necessary in both the prime and probe trials for intertrial priming effects to emerge. Implications for the role of implicit short-term memory in visual search are discussed.

MIJ821 (onfasprodil) in healthy volunteers: First-in-human, randomized, placebo-controlled study (single ascending dose and repeated intravenous dose).

This single-center study administered MIJ821 (onfasprodil) as an intravenous infusion to healthy volunteers and included two parts: a single ascending dose study (Part 1) and a repeated intravenous dose study (Part 2). Primary objective was to evaluate the safety and tolerability of single ascending intravenous doses infused over a 40-min period and of two repeated doses (1 week apart) of MIJ821 in healthy volunteers. Secondary objectives were to assess the pharmacokinetics of MIJ821 after intravenous infusion in Part 1 and Part 2 of the study. Overall, 43 subjects in Part 1 and 12 subjects in Part 2 were randomized in the study. Median age in Part 1 and Part 2 was 45.0 and 43.5 years, respectively, with the majority being Caucasian (Part 1: 84%; Part 2: 92%). 19 subjects (44.2%) in Part 1 and 8 subjects (66.7%) in Part 2 experienced at least one adverse event (AE). Following single dose in Part 1 and Part 2, the AUCinf values of MIJ821 increased in a dose-proportional manner across the dose range 0.016-0.48 mg/kg and the Cmax values in a slight overproportional manner across the dose range 0.048-0.48 mg/kg. At the highest dose of 0.48 mg/kg, the geometric mean AUCinf was 708 h ng/mL and the geometric mean Cmax was 462 ng/mL. Inspection of 1-h post-dose resting electroencephalography activity across cohorts showed a relationship to administered dose, providing exploratory evidence of distal target engagement. In conclusion, MIJ821 showed a good safety and tolerability profile in healthy volunteers. Dissociative AEs were mild, transient, and dose-dependent.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

OBJECTIVE: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. BACKGROUND: Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. METHODS: Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. RESULTS: The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). CONCLUSIONS: This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Brain Network Analysis of EEG Recordings Can Be Used to Assess Cognitive Function in Teenagers With 15q13.3 Microdeletion Syndrome.

15q13.3 microdeletion syndrome causes a spectrum of cognitive disorders, including intellectual disability and autism. We assessed the ability of the EEG analysis algorithm Brain Network Analysis (BNA) to measure cognitive function in 15q13.3 deletion patients, and to differentiate between patient and control groups. EEG data was collected from 10 individuals with 15q13.3 microdeletion syndrome (14-18 years of age), as well as 30 age-matched healthy controls, as the subjects responded to Auditory Oddball (AOB) and Go/NoGo cognitive tasks. It was determined that BNA can be used to evaluate cognitive function in 15q13.3 microdeletion patients. This analysis also significantly differentiates between patient and control groups using 5 scores, all of which are produced from ERP peaks related to late cortical components that represent higher cognitive functions of attention allocation and response inhibition (P < 0.05).

Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers.

Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization. Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features. Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR). Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression.

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

Prior conscious experience enhances conscious perception but does not affect response priming☆.

Recent research shows that prior experience and expectations strongly enhance a visual stimulus' access to conscious awareness. However, whether such advance knowledge also influences this stimulus' indirect impact on behavior is poorly understood. The resolution of this question has the potential of providing strong tests between current models of conscious perception because these diverge on whether a factor that affects conscious access by a stimulus necessarily also affects the strength of this stimulus' representation and hence, its indirect impact on behavior. In five experiments we show that three different manipulations of prior experience with a stimulus boosted conscious perception of a similar stimulus (measured using both subjective reports and objective performance) but did not affect its indirect impact on motor action (measured by response priming). In particular, we observed a robust "awareness priming" effect: how clearly a stimulus was subjectively perceived on a recent trial irrespective of its physical strength, strongly affected conscious perception of a similar stimulus on the current trial but did not increase response priming. We discuss the implications of these findings for current models of conscious vision as well as for the study of unconscious processing.

The unitary zROC slope in amnesics does not reflect the absence of recollection: critical simulations in healthy participants of the zROC slope.

The functional deficit underlying amnesia has been argued to be in recollective processing. This idea is based on the DPSD model, wherein recognition comprises a mixture of recollection and familiarity signals, with familiarity conforming to an equal-variance signal-detection mechanism while recollection is binary. This model interprets the greater variance for targets than for lures revealed in sub-unit zROC slopes, to be a consequence of the mixture of two signals. Importantly, equal variance between targets and lures is found in amnesic, and is consequently interpreted by DPSD to reflect impairment to recollection alongside the sparing of familiarity. Here, we pointed to a logical fallacy in this interpretation. We then asked participants, in two experiments, to make remember-know (RK) and confidence judgments. Simulating equal variance in healthy participants, we either excluded from the analysis 'remember' responses, reflecting recollection, or the most accurate memories, reflecting strength. We found that only the exclusion of the strongest responses led to equal-variance distributions. In addition, we found that accuracy was associated with an interlaced ordering of RK response groups nested under confidence, a pattern hard to reconcile with classic recognition models (DPSD, UVSD). This pattern can, however, be accommodated by the Continuous Dual Process (CDP) model (Wixted and Mickes, 2010), wherein both familiarity and recollection are continuous signals. Amnesia may thus be characterized as the inability to form strong memories, recollection as well as familiarity.

Clinically Effective Treatment of Fibromyalgia Pain With High-Definition Transcranial Direct Current Stimulation: Phase II Open-Label Dose Optimization.

Despite promising preliminary results in treating fibromyalgia (FM) pain, no neuromodulation technique has been adopted in clinical practice because of limited efficacy, low response rate, or poor tolerability. This phase II open-label trial aims to define a methodology for a clinically effective treatment of pain in FM by establishing treatment protocols and screening procedures to maximize efficacy and response rate. High-definition transcranial direct current stimulation (HD-tDCS) provides targeted subthreshold brain stimulation, combining tolerability with specificity. We aimed to establish the number of HD-tDCS sessions required to achieve a 50% FM pain reduction, and to characterize the biometrics of the response, including brain network activation pain scores of contact heat-evoked potentials. We report a clinically significant benefit of a 50% pain reduction in half (n = 7) of the patients (N = 14), with responders and nonresponders alike benefiting from a cumulative effect of treatment, reflected in significant pain reduction (P = .035) as well as improved quality of life (P = .001) over time. We also report an aggregate 6-week response rate of 50% of patients and estimate 15 as the median number of HD-tDCS sessions to reach clinically meaningful outcomes. The methodology for a pivotal FM neuromodulation clinical trial with individualized treatment is thus supported. ONLINE REGISTRATION: Registered in Clinicaltrials.gov under registry number NCT01842009. PERSPECTIVE: In this article, an optimized protocol for the treatment of fibromyalgia pain with targeted subthreshold brain stimulation using high-definition transcranial direct current stimulation is outlined.

Comparing unconscious processing during continuous flash suppression and meta-contrast masking just under the limen of consciousness.

Stimuli can be rendered invisible using a variety of methods and the method selected to demonstrate unconscious processing in a given study often appears to be arbitrary. Here, we compared unconscious processing under continuous flash suppression (CFS) and meta-contrast masking, using similar stimuli, tasks and measures. Participants were presented with a prime arrow followed by a target arrow. They made a speeded response to the target arrow direction and then reported on the prime's visibility. Perception of the prime was made liminal using either meta-contrast masking (Experiment 1) or CFS (Experiments 2 and 3). Conscious perception of the prime was assessed using a sensitive visibility scale ranging from 0 to 3 and unconscious processing was measured as the priming effect on target discrimination performance of prime-target direction congruency when prime visibility was null. Crucially, in order to ensure that the critical stimuli were equally distant from the limen of consciousness, we sought stimulus and temporal parameters for which the proportion of 0-visibility trials was comparable for the two methods. We found that the method used to prevent conscious perception matters: unconscious processing was substantial with meta-contrast masking but absent with CFS. These findings suggest that CFS allows very little perceptual processing, if at all, and that previous reports of high-level and complex unconscious processing during CFS may result from partial awareness.