Pupil dilation reflects covert familiar face recognition under interocular suppression.

In prosopagnosia, brain lesions impair overt face recognition, but not face detection, and may coexist with residual covert recognition of familiar faces. Previous studies that simulated covert recognition in healthy individuals have impaired face detection as well as recognition, thus not fully mirroring the deficits in prosopagnosia. We evaluated a model of covert recognition based on continuous flash suppression (CFS). Familiar and unfamiliar faces and houses were masked while participants performed two discrimination tasks. With increased suppression, face/house discrimination remained largely intact, but face familiarity discrimination deteriorated. Covert recognition was present across all masking levels, evinced by higher pupil dilation to familiar than unfamiliar faces. Pupil dilation was uncorrelated with overt performance across subjects. Thus, CFS can impede overt face recognition without disrupting covert recognition and face detection, mirroring critical features of prosopagnosia. CFS could be used to uncover shared neural mechanisms of covert recognition in prosopagnosic patients and neurotypicals.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Electroencephalographic and morphometric abnormalities in psychopath offenders.

The main goals of the present study were to replicate and extend current knowledge related to paralimbic dysfunctions associated with psychopathy. The research evaluated the quantitative electroencephalography, current density (CD) source and synchronization likelihood analysis during the rest condition and structural magnetic resonance imaging images to compare volumetric and cortical thickness, in inmates recruited from two prisons located in Havana City. The Psychopathy Checklist-Revised (PCL-R) was used as a quantitative measure of psychopathy. This study showed most beta energy and less alpha activity in male psychopath offenders. Low-resolution electromagnetic tomography signified an increase of beta activity in psychopath offender groups within paralimbic regions. The superior temporal gyrus volume was associated with the F1 factor while the fusiform, anterior cingulate and associative occipital areas were primarily associated with the F2 factor of PCL-R scale. Cortical thickness in the left dorsal anterior cingulate cortex and the temporal pole was negatively associated with PCL-R total score.

Objects seen as scenes: Neural circuitry for attending whole or parts.

Depending on our goals, we pay attention to the global shape of an object or to the local shape of its parts, since it's difficult to do both at once. This typically effortless process can be impaired in disease. However, it is not clear which cortical regions carry the information needed to constrain shape processing to a chosen global/local level. Here, novel stimuli were used to dissociate functional MRI responses to global and local shapes. This allowed identification of cortical regions containing information about level (independent from shape). Crucially, these regions overlapped part of the cortical network implicated in scene processing. As expected, shape information (independent of level) was mainly located in category-selective areas specialized for object- and face-processing. Regions with the same informational profile were strongly linked (as measured by functional connectivity), but were weak when the profiles diverged. Specifically, in the ventral-temporal-cortex (VTC) regions favoring level and shape were consistently separated by the mid-fusiform sulcus (MFS). These regions also had limited crosstalk despite their spatial proximity, thus defining two functional pathways within VTC. We hypothesize that object hierarchical level is processed by neural circuitry that also analyses spatial layout in scenes, contributing to the control of the spatial-scale used for shape recognition. Use of level information tolerant to shape changes could guide whole/part attentional selection but facilitate illusory shape/level conjunctions under impoverished vision.

Fast Gaussian Naïve Bayes for searchlight classification analysis.

The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across all searchlights, which entails large computational costs especially when testing the statistical significance of the accuracies with permutation methods. In this article, a new implementation of the Gaussian Naive Bayes classifier is presented (henceforth massive-GNB). This approach allows classification in all searchlights simultaneously, and is faster than previously published searchlight GNB implementations, as well as other more complex classifiers including support vector machines (SVM). To ensure that the gain in speed for GNB would be useful in searchlight analysis, we compared the accuracies of massive-GNB and SVM in detecting the lateral occipital complex (LOC) in an fMRI localizer experiment (26 subjects). Moreover, this region as defined in a meta-analysis of many activation studies was used as a gold standard to compare error rates for both classifiers. In individual searchlights, SVM was somewhat more accurate than massive-GNB and more selective in detecting the meta-analytic LOC. However, with multiple comparison correction at the cluster-level the two classifiers performed equivalently. Thus for cluster-level analysis, massive-GNB produces an accuracy similar to more sophisticated classifiers but with a substantial gain in speed. Massive-GNB (available as a public Matlab toolbox) could facilitate the more widespread use of searchlight analysis.

The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices.

fMRI studies increasingly examine functions and properties of non-primary areas of human auditory cortex. However there is currently no standardized localization procedure to reliably identify specific areas across individuals such as the standard 'localizers' available in the visual domain. Here we present an fMRI 'voice localizer' scan allowing rapid and reliable localization of the voice-sensitive 'temporal voice areas' (TVA) of human auditory cortex. We describe results obtained using this standardized localizer scan in a large cohort of normal adult subjects. Most participants (94%) showed bilateral patches of significantly greater response to vocal than non-vocal sounds along the superior temporal sulcus/gyrus (STS/STG). Individual activation patterns, although reproducible, showed high inter-individual variability in precise anatomical location. Cluster analysis of individual peaks from the large cohort highlighted three bilateral clusters of voice-sensitivity, or "voice patches" along posterior (TVAp), mid (TVAm) and anterior (TVAa) STS/STG, respectively. A series of extra-temporal areas including bilateral inferior prefrontal cortex and amygdalae showed small, but reliable voice-sensitivity as part of a large-scale cerebral voice network. Stimuli for the voice localizer scan and probabilistic maps in MNI space are available for download.

Intra-V1 functional networks and classification of observed stimuli.

Introduction: Previous studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity. Methods: Here, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation. Results: All inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology. Discussion: Our findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

The Cuban Twin Registry: initial findings and perspectives.

The Cuban Twin Registry is a nation-wide, prospective, population-based twin registry comprising all zygosity types and ages. It was initiated in 2004 to study genetic and environmental contributions to complex diseases with high morbidity and mortality in the Cuban population. The database contains extensive information from 55,400 twin pairs enrolled in the period 2004-2006. Additionally, 2,600 new multiple births have been included from 2007 to date. In the past 4 years, more than 130 studies have been carried out using the registry with a classical genetic epidemiological approach in which concordance rates for monozygotic and dizygotic twins and heritability of various disease traits were estimated. This article summarizes the history, registry's methodology, recent research findings, and future directions of work.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG.

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Relationship between gait parameters and cognitive indexes in adult aging.

PURPOSE: This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns. METHODS: The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients' neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject's cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure. RESULTS: The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value. CONCLUSIONS: The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.

The determinants of COVID-induced brain dysfunctions after SARS-CoV-2 infection in hospitalized patients.

The severity of the pandemic and its consequences on health and social care systems were quite diverse and devastating. COVID-19 was associated with an increased risk of neurological and neuropsychiatric disorders after SARS-CoV-2 infection. We did a cross-sectional study of 3 months post-COVID consequences of 178 Cuban subjects. Our study has a unique CUBAN COVID-19 cohort of hospitalized COVID-19 patients and healthy subjects. We constructed a latent variable for pre-health conditions (PHC) through Item Response Theory (IRT) and for post-COVID neuropsychiatric symptoms (Post-COVID-NPS) through Factor Analysis (FA). There seems to be a potential causal relationship between determinants of CIBD and post-COVID-NPS in hospitalized COVID-19 patients. The causal relationships accessed by Structural Equation Modeling (SEM) revealed that PHC (p < 0.001) and pre-COVID cognitive impairments (p < 0.001) affect the severity of COVID-19 patients. The severity of COVID-19 eventually results in enhanced post-COVID-NPS (p < 0.001), even after adjusting for confounders (age, sex, and pre-COVID-NPS). The highest loadings in PHC were for cardiovascular diseases, immunological disorders, high blood pressure, and diabetes. On the other hand, sex (p < 0.001) and pre-COVID-NPS including neuroticism (p < 0.001), psychosis (p = 0.005), cognition (p = 0.036), and addiction (p < 0.001) were significantly associated with post-COVID-NPS. The most common neuropsychiatric symptom with the highest loadings includes pain, fatigue syndrome, autonomic dysfunctionalities, cardiovascular disorders, and neurological symptoms. Compared to healthy people, COVID-19 patients with pre-health comorbidities or pre-neuropsychiatric conditions will have a high risk of getting severe COVID-19 and long-term post-COVID neuropsychiatric consequences. Our study provides substantial evidence to highlight the need for a complete neuropsychiatric follow-up on COVID-19 patients (with severe illness) and survivors (asymptomatic patients who recovered).

Absence of face-specific cortical activity in the complete absence of awareness: converging evidence from functional magnetic resonance imaging and event-related potentials.

In this study, we explored the neural correlates of perceptual awareness during a masked face detection task. To assess awareness more precisely than in previous studies, participants employed a 4-point scale to rate subjective visibility. An event-related fMRI and a high-density ERP study were carried out. Imaging data showed that conscious face detection was linked to activation of fusiform and occipital face areas. Frontal and parietal regions, including the pre-SMA, inferior frontal sulcus, anterior insula/frontal operculum, and intraparietal sulcus, also responded strongly when faces were consciously perceived. In contrast, no brain area showed face-selective activity when participants reported no impression of a face. ERP results showed that conscious face detection was associated with enhanced N170 and also with the presence of a second negativity around 300 msec and a slow positivity around 415 msec. Again, face-related activity was absent when faces were not consciously perceived. We suggest that, under conditions of backward masking, ventral stream and fronto-parietal regions show similar, strong links of face-related activity to conscious perception and stress the importance of a detailed assessment of awareness to examine activity related to unseen stimulus events.

Stable Sparse Classifiers predict cognitive impairment from gait patterns.

Background: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. Purposes: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. Methods: A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified. Results: The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. Conclusions: The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.

Covert face recognition without the fusiform-temporal pathways.

Patients with prosopagnosia are unable to recognize faces consciously, but when tested indirectly they can reveal residual identification abilities. The neural circuitry underlying this covert recognition is still unknown. One candidate for this function is the partial survival of a pathway linking the fusiform face area (FFA) and anterior-inferior temporal (AIT) cortex, which has been shown to be essential for conscious face identification. Here we performed functional magnetic, and diffusion tensor imaging in FE, a patient with severe prosopagnosia, with the goal of identifying the neural substrates of his robust covert face recognition. FE presented massive bilateral lesions in the fusiform gyri that eliminated both FFAs, and also disrupted the fibers within the inferior longitudinal fasciculi that link the visual areas with the AITs and medial temporal lobes. Therefore participation of the fusiform-temporal pathway in his covert recognition was precluded. However, face-selective activations were found bilaterally in his occipital gyri and in his extended face system (posterior cingulate and orbitofrontal areas), the latter with larger responses for previously-known faces than for faces of strangers. In the right hemisphere, these surviving face selective-areas were connected via a partially persevered inferior fronto-occipital fasciculus. This suggests an alternative occipito-frontal pathway, absent from current models of face processing, that could explain the patient's covert recognition while also playing a role in unconscious processing during normal cognition.

A call for international research on COVID-19-induced brain dysfunctions.

COVID-19-induced brain dysfunction (CIBD) will put a strain on world health systems complicated by the heterogeneity of manifestations, which is higher than any other aspect of human biology. Neural, psychological and social causes must be disentangled for effective population-level management of CIBD. International cooperation is required in order to discover neurotechnologies appropriate for health systems.

The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset.

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18-68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64-120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca .

EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel.

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

ERP generator anomalies in presymptomatic carriers of the Alzheimer's disease E280A PS-1 mutation.

Although subtle anatomical anomalies long precede the onset of clinical symptoms in Alzheimer's disease, their impact on the reorganization of brain networks underlying cognitive functions has not been fully explored. A unique window into this reorganization is provided by presymptomatic cases of familial Alzheimer's disease (FAD). Here we studied neural circuitry related to semantic processing in presymptomatic FAD cases by estimating the intracranial sources of the N400 event-related potential (ERP). ERPs were obtained during a semantic-matching task from 24 presymptomatic carriers and 25 symptomatic carriers of the E280A presenilin-1 (PS-1) mutation, as well as 27 noncarriers (from the same families). As expected, the symptomatic-carrier group performed worse in the matching task and had lower N400 amplitudes than both asymptomatic groups, which did not differ from each other on these variables. However, N400 topography differed in mutation carrier groups with respect to the noncarriers. Intracranial source analysis evinced that the presymptomatic-carriers presented a decrease of N400 generator strength in right inferior-temporal and medial cingulate areas and increased generator strength in the left hippocampus and parahippocampus compared to the controls. This represents alterations in neural function without translation into behavioral impairments. Compared to controls, the symptomatic-carriers presented a similar anatomical shift in the distribution of N400 generators to that found in presymptomatic-carriers, albeit with a larger reduction in generator strength. The redistribution of N400 generators in presymptomatic-carriers indicates that early focal degeneration associated with the mutation induces neural reorganization, possibly contributing to a functional compensation that enables normal performance in the semantic task.