A sensory signature of unaffected biological parents predicts the risk of autism in their offspring.

AIM: Despite the emphasis on sensory dysfunction phenotypes in the revised diagnostic criteria for autism spectrum disorder (ASD), there has been limited research, particularly in the field of neurobiology, investigating the concordance in sensory features between individuals with ASD and their genetic relatives. Therefore, our objective was to examine whether neurobehavioral sensory patterns could serve as endophenotypic markers for ASD. METHODS: We combined questionnaire- and lab-based sensory evaluations with sensory fMRI measures to examine the patterns of sensory responsivity in 30 clinically diagnosed with ASD, 26 matched controls (CON), and 48 biological parents for both groups (27 parents of individuals with ASD [P-ASD] and 21 for individuals with CON [P-CON]). RESULTS: The ASD and P-ASD groups had higher sensory responsivity and rated sensory stimuli as more unpleasant than the CON and P-CON groups, respectively. They also exhibited greater hemodynamic responses within the sensory cortices. Overlapping activations were observed within these sensory cortices in the ASD and P-ASD groups. Using a machine learning approach with robust prediction models across cohorts, we demonstrated that the sensory profile of biological parents accurately predicted the likelihood of their offspring having ASD, achieving a prediction accuracy of 71.4%. CONCLUSIONS: These findings provide support for the hereditary basis of sensory alterations in ASD and suggest a potential avenue to improve ASD diagnosis by utilizing the sensory signature of biological parents, especially in families with a high risk of ASD. This approach holds promising prospects for early detection, even before the birth of the offspring.

A Multicomponent Cognitive Intervention May Improve Self-Reported Daily Function of Adults With Subjective Cognitive Decline.

IMPORTANCE: Limited evidence exists to support cognitive intervention improving the daily function of adults with subjective cognitive decline (SCD). OBJECTIVE: To examine the preliminary efficacy of a group-based multicomponent cognitive intervention that integrates Lifestyle Redesign® (LR) techniques. DESIGN: Single-arm two-period crossover trial; 16-wk waiting period, 16-wk intervention, and 16-wk follow-up. SETTING: Memory clinic in a medical center, Taiwan. PARTICIPANTS: Purposive sample of adults ages >55 yr with SCD. INTERVENTION: Sixteen 1.5-hr weekly multicomponent sessions of cognitive training, cognitive rehabilitation, psychological intervention, and lifestyle intervention. OUTCOMES AND MEASURES: Primary outcomes were (1) self-reported daily function, measured with the Activities of Daily Living Questionnaire (ADLQ) and Cognitive Failure Questionnaire; (2) performance-based daily function, measured with the Brief University of California San Diego Performance-Based Skills Assessment-Traditional Chinese Version; and (3) functional cognition, measured with the Contextual Memory Test (CMT) and Miami Prospective Memory Test. Secondary outcomes included cognitive functions, anxiety, and depression. RESULTS: Seventeen participants completed the intervention; 4 missed the follow-up. The generalized estimating equations model showed significant changes from baseline to pretest (control) and pretest to posttest (intervention) on the ADLQ (p = .014) and CMT-delayed (p = .003). Effects remained at the 16-wk follow-up. After adjusting for the effects of covariates, the self-reported daily function of participants ages ≤ 63 yr improved more than that of other participants (p = .003). CONCLUSIONS AND RELEVANCE: Multicomponent cognitive interventions integrating LR techniques may improve self-reported daily function and context-dependent memory function of adults with SCD, with efficacy sustained at follow-up. What This Article Adds: A group-based multicomponent cognitive intervention consisting of cognitive training, cognitive rehabilitation, psychoeducation, and lifestyle intervention may provide benefits for the daily function and cognitive function of adults with SCD.

Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder.

BACKGROUND: A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear. METHODS: We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals. RESULTS: We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination. LIMITATIONS: The sample size was small. CONCLUSION: Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.

Topological Phase Transitions of Dirac Magnons in Honeycomb Ferromagnets.

The study of the magnonic thermal Hall effect in magnets with Dzyaloshinskii-Moriya interaction (DMI) has recently drawn attention because of the underlying topology. Topological phase transitions may arise when there exist two or more distinct topological phases, and they are often revealed by a gap-closing phenomenon. In this work, we consider the magnons in honeycomb ferromagnets described by a Heisenberg Hamiltonian containing both an out-of-plane DMI and a Zeeman interaction. We demonstrate that the magnonic system exhibits temperature (or magnetic field) driven topological phase transitions due to magnon-magnon interactions. Specifically, when the temperature increases, the magnonic energy gap at Dirac points closes and reopens at a critical temperature, T_{c}. By showing that the Chern numbers of the magnonic bands are distinct above and below T_{c}, we confirm that the gap-closing phenomenon is indeed a signature for the topological phase transitions. Furthermore, our analysis indicates that the thermal Hall conductivity in the magnonic system exhibits a sign reversal at T_{c}, which can serve as an experimental probe of its topological nature. Our theory predicts that in CrI_{3} such a phenomenon exists and is experimentally accessible.

Spatial Attention Disregard in Children With Hemiplegic Cerebral Palsy.

IMPORTANCE: Children with hemiplegic cerebral palsy (CP) demonstrate spatial attention disregard, but the rehabilitation approach to CP is traditionally motor oriented. OBJECTIVE: To explore spatial attention disregard in children with hemiplegic CP and its relationship to their motor performance in daily activities. DESIGN: Cross-sectional study. SETTING: Community. PARTICIPANTS: Twenty-five children with hemiplegic CP and 25 age-matched typically developing children. OUTCOMES AND MEASURES: For spatial attention performance, the Random Visual Stimuli Detection Task; for developmental disregard, the Observatory Test of Capacity, Performance, and Developmental Disregard; and for motor performance, the Melbourne Assessment 2. RESULTS: Children with hemiplegic CP evidenced spatial attention disregard on their more affected sides, and this phenomenon was correlated with developmental disregard. CONCLUSIONS AND RELEVANCE: Children with hemiplegic CP demonstrate developmental disregard in both the motor and the visual-spatial attention domains. Including evaluation of and intervention for visual-spatial attention for children with hemiplegic CP in the traditionally motor-oriented rehabilitation approach is recommended. WHAT THIS ARTICLE ADDS: This research provides evidence that children with hemiplegic CP demonstrate disregard in the domain of visual-spatial attention. The findings suggest that evaluation of and intervention for visual-spatial attention should be included in CP rehabilitation in addition to the traditionally motor-oriented approach.

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

At 120 msec you can spot the animal but you don't yet know it's a dog.

Earlier studies suggested that the visual system processes information at the basic level (e.g., dog) faster than at the subordinate (e.g., Dalmatian) or superordinate (e.g., animals) levels. However, the advantage of the basic category over the superordinate category in object recognition has been challenged recently, and the hierarchical nature of visual categorization is now a matter of debate. To address this issue, we used a forced-choice saccadic task in which a target and a distractor image were displayed simultaneously on each trial and participants had to saccade as fast as possible toward the image containing animal targets based on different categorization levels. This protocol enables us to investigate the first 100-120 msec, a previously unexplored temporal window, of visual object categorization. The first result is a surprising stability of the saccade latency (median RT ∼ 155 msec) regardless of the animal target category and the dissimilarity of target and distractor image sets. Accuracy was high (around 80% correct) for categorization tasks that can be solved at the superordinate level but dropped to almost chance levels for basic level categorization. At the basic level, the highest accuracy (62%) was obtained when distractors were restricted to another dissimilar basic category. Computational simulations based on the saliency map model showed that the results could not be predicted by pure bottom-up saliency differences between images. Our results support a model of visual recognition in which the visual system can rapidly access relatively coarse visual representations that provide information at the superordinate level of an object, but where additional visual analysis is required to allow more detailed categorization at the basic level.

Quasicondensation in Two-Dimensional Fermi Gases.

In this paper we follow the analysis and protocols of recent experiments, combined with simple theory, to arrive at a physical understanding of quasi-condensation in two dimensional Fermi gases. A key signature of quasi-condensation, which contains aspects of Berezinskii-Kosterlitz-Thouless behavior, is a strong zero momentum peak in the pair momentum distribution. Importantly, this peak emerges at a reasonably well defined onset temperature. The resulting phase diagram, pair momentum distribution, and algebraic power law decay are compatible with recent experiments throughout the continuum from BEC to BCS.

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities.

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants from the expected patterns of sensory input. Studies show that MMN diminishes when sequence patterns become more predictable over a longer timescale. This implies that MMN is composed of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms, with one peaking early in the central-frontal area and the other late in a more frontal area. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multitiered neural marker applicable in clinical settings.

Three-dimensional ultrafast charge-density-wave dynamics in CuTe.

Charge density waves (CDWs) involved with electronic and phononic subsystems simultaneously are a common quantum state in solid-state physics, especially in low-dimensional materials. However, CDW phase dynamics in various dimensions are yet to be studied, and their phase transition mechanism is currently moot. Here we show that using the distinct temperature evolution of orientation-dependent ultrafast electron and phonon dynamics, different dimensional CDW phases are verified in CuTe. When the temperature decreases, the shrinking of c-axis length accompanied with the appearance of interchain and interlayer interactions causes the quantum fluctuations (QF) of the CDW phase until 220 K. At T < 220 K, the CDWs on the different ab-planes are finally locked with each other in anti-phase to form a CDW phase along the c-axis. This study shows the dimension evolution of CDW phases in one CDW system and their stabilized mechanisms in different temperature regimes.

PLI-Based Connectivity in Resting-EEG is a Robust and Generalizable Feature for Detecting MCI and AD: A Validation on a Diverse Multisite Clinical Dataset.

The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.

Enhanced Superconductivity and Rashba Effect in a Buckled Plumbene-Au Kagome Superstructure.

Plumbene, with a structure similar to graphene, is expected to possess a strong spin-orbit coupling and thus enhances its superconducting critical temperature (Tc ). In this work, a buckled plumbene-Au Kagome superstructure grown by depositing Au on Pb(111) is investigated. The superconducting gap monitored by temperature-dependent scanning tunneling microscopy/spectroscopy shows that the buckled plumbene-Au Kagome superstructure not only has an enhanced Tc with respect to that of a monolayer Pb but also possesses a higher value than what owned by a bulk Pb substrate. By combining angle-resolved photoemission spectroscopy with density functional theory, the monolayer Au-intercalated low-buckled plumbene sandwiched between the top Au Kagome layer and the bottom Pb(111) substrate is confirmed and the electron-phonon coupling-enhanced superconductivity is revealed. This work demonstrates that a buckled plumbene-Au Kagome superstructure can enhance superconducting Tc and Rashba effect, effectively triggering the novel properties of a plumbene.

Reentrant superconducting phase in conical-ferromagnet-superconductor nanostructures.

We study a bilayer consisting of an ordinary superconductor and a magnet with a spiral magnetic structure of the Ho type. We use a self-consistent solution of the Bogolioubov-de Gennes equations to evaluate the pair amplitude, the transition temperature, and the thermodynamic functions, namely, the free energy and entropy. We find that for a range of thicknesses of the magnetic layer the superconductivity is reentrant with temperature T: as one lowers T the system turns superconducting, and when T is further lowered it turns normal again. This behavior is reflected in the condensation free energy and the pair potential, which vanish both above the upper transition and below the lower one. The transition is strictly reentrant: the low and high temperature phases are the same. The entropy further reveals a range of temperatures where the superconducting state is less ordered than the normal one.

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain.

The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

Person-identifying brainprints are stably embedded in EEG mindprints.

Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.

Higher Rumination Tendency Is Associated with Reduced Positive Effects of Daily Activity Participation in People with Depressive Disorder.

Objectives: Rumination, a response style characterized by self-reflection loops of negative thoughts, tends to exacerbate depressive symptoms and may impair daily functional behaviors of individuals with depression. However, the specific impacts of rumination on activity participation remain unclear. The current study was aimed at examining the differences in daily activity participation profiles between clinically depressed people with higher versus lower rumination tendencies, with the hope to provide insightful suggestions for improving the quality of life of ruminative individuals with major depression. Methods: We recruited 143 participants with a depression-related diagnosis from psychiatric daycare centers or clinics and analyzed the differences in activity participation profiles between individuals with higher versus lower rumination tendencies. Results: Although compared to those with lower rumination tendencies, participants with higher rumination tendencies spent a longer time in activity participation; they experienced lower participation quality during these activities. Furthermore, their activity participation was primarily motivated by meeting others' expectations rather than self-interest. They also misattributed participation restriction to "lack of family support," indicating that the unhealthy rumination pattern might be the cause of their lack of positive feelings from engaging in meaningful daily activities. Conclusions: The current results suggest that the unhealthy motivation behind activity participation seems to be an important factor that decreases the quality of participation in individuals with higher rumination tendency. Establishing a healthy motivation for activity participation is therefore critical for improving their quality of participation. As an initial step, OT interventions could put a focus on helping them clarify and escape from the source of negative rumination cycles that impede their positive feeling of activity participation.

Proximity-Effect-Induced Anisotropic Superconductivity in a Monolayer Ni-Pb Binary Alloy.

A proximity effect facilitates the penetration of Cooper pairs that permits superconductivity in a normal metal, offering a promising approach to turn heterogeneous materials into superconductors and develop exceptional quantum phenomena. Here, we have systematically investigated proximity-induced anisotropic superconductivity in a monolayer Ni-Pb binary alloy by combining scanning tunneling microscopy/spectroscopy (STM/STS) with theoretical calculations. By means of high-temperature growth, the (33×33)R30o Ni-Pb surface alloy has been fabricated on Pb(111) and the appearance of a domain boundary as well as a structural phase transition can be deduced from a half-unit-cell lattice displacement. Given the high spatial and energy resolution, tunneling conductance (dI/dU) spectra have resolved the reduced but anisotropic superconducting gap ΔNiPb ≈ 1.0 meV, in stark contrast to the isotropic ΔPb ≈ 1.3 meV. In addition, the higher density of states at the Fermi energy (D(EF)) of the Ni-Pb surface alloy results in an enhancement of coherence peak height. According to the same Tc ≈ 7.1 K with Pb(111) from the temperature-dependent ΔNiPb and the short decay length Ld ≈ 3.55 nm from the spatially monotonic decrease of ΔNiPb, both results are supportive of a proximity-induced superconductivity. Despite a lack of a bulk counterpart, the atomically thick Ni-Pb bimetallic compound opens a pathway to engineer superconducting properties down to the two-dimensional limit, giving rise to the emergence of anisotropic superconductivity via a proximity effect.

The temporal dynamics of object processing in visual cortex during the transition from distributed to focused spatial attention.

Several major cognitive neuroscience models have posited that focal spatial attention is required to integrate different features of an object to form a coherent perception of it within a complex visual scene. Although many behavioral studies have supported this view, some have suggested that complex perceptual discrimination can be performed even with substantially reduced focal spatial attention, calling into question the complexity of object representation that can be achieved without focused spatial attention. In the present study, we took a cognitive neuroscience approach to this problem by recording cognition-related brain activity both to help resolve the questions about the role of focal spatial attention in object categorization processes and to investigate the underlying neural mechanisms, focusing particularly on the temporal cascade of these attentional and perceptual processes in visual cortex. More specifically, we recorded electrical brain activity in humans engaged in a specially designed cued visual search paradigm to probe the object-related visual processing before and during the transition from distributed to focal spatial attention. The onset times of the color popout cueing information, indicating where within an object array the subject was to shift attention, was parametrically varied relative to the presentation of the array (i.e., either occurring simultaneously or being delayed by 50 or 100 msec). The electrophysiological results demonstrate that some levels of object-specific representation can be formed in parallel for multiple items across the visual field under spatially distributed attention, before focal spatial attention is allocated to any of them. The object discrimination process appears to be subsequently amplified as soon as focal spatial attention is directed to a specific location and object. This set of novel neurophysiological findings thus provides important new insights on fundamental issues that have been long-debated in cognitive neuroscience concerning both object-related processing and the role of attention.