Search

The neurobiological basis of brand and product attachment has received much attention in consumer neuroscience research, although it remains unclear. In this study, we conducted functional MRI experiments involving female users of famous luxury brand cosmetics as participants, based on the regions of interest involved in human attachment and object attachment. The results showed that the left ventral pallidum (VP), which is involved in positive reward, and the right posterior cingulate cortex (PCC), which is involved in self-concept, a key concept in object attachment, are the core regions in cosmetic attachment. Moreover, the performed psychophysiological interaction analyses showed that VP-temporoparietal junction connectivity positively correlated with activity in the dorsal raphe nucleus, and PCC–anterior hippocampus (aHC) connectivity positively correlated with subjective evaluation of attachment. The former suggests that object attachment is a human-like attachment and a stronger tendency of anthropomorphism is associated with stronger feelings of security. The latter suggests that the individual’s concept of attachment as well as the relationships with the attached cosmetics are represented in the aHC, and the PCC–aHC associations produce subjective awareness of the attachment relationships. These associations between memory and reward systems have been shown to play critical roles in cosmetic attachment.

Over the past years navigated repetitive transcranial magnetic stimulation (nrTMS) had become increasingly important for the preoperative examination and mapping of eloquent brain areas. Among other applications it was demonstrated that the detection of neuropsychological function, such as arithmetic processing or face recognition, is feasible with nrTMS. In order to investigate the mapping of further brain functions, this study aims to investigate the cortical mapping of categorization function via nrTMS. 20 healthy volunteers purely right-handed, with German as mother tongue underwent nrTMS mapping using 5 Hz/10 pulses. 52 cortical spots spread over each hemisphere were stimulated. The task consisted of 80 pictures of living and non-living images, which the volunteers were instructed to categorize while the simulation pulses were applied. The highest error rates for all errors of all subjects were observed in the left hemisphere’s posterior middle frontal gyrus (pMFG) with an error rate of 60%, as well as in the right pMFG and posterior supra marginal gyrus (pSMG) (45%). In total the task processing of non-living objects elicited more errors in total, than the recognition of living objects. nrTMS is able to detect cortical categorization function. Moreover, the observed bihemispheric representation, as well as the higher error incidence for the recognition of non-living objects is well in accordance with current literature. Clinical applicability for preoperative mapping in brain tumor patients but also in general neuroscience has to be evaluated as the next step.

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.

Preadolescence is a period of increased vulnerability for anxiety, especially among Latina girls. Reduced microstructure (fractional anisotropy; FA) of white matter tracts between limbic and prefrontal regions may underlie regulatory impairments in anxiety. However, developmental research on the association between anxiety and white matter microstructure is mixed, possibly due to interactive influences with puberty. In a sample of 39 Latina girls (8–13 years), we tested whether pubertal stage moderated the association between parent- and child-reported anxiety symptoms and FA in the cingulum and uncinate fasciculus. Parent- but not child-reported anxiety symptoms predicted lower cingulum FA, and this effect was moderated by pubertal stage, such that this association was only significant for prepubertal girls. Neither anxiety nor pubertal stage predicted uncinate fasciculus FA. These findings suggest that anxiety is associated with disruptions in girls’ cingulum white matter microstructure and that this relationship undergoes maturational changes during puberty.

Everyday cognitive functioning is characterized by constant alternations between different modes of information processing, driven by constant fluctuations in environmental demands. At the neural level, this is realized through corresponding dynamic shifts in functional activation and network connectivity. A distinction is often made between resting and task processing and between task-negative and task-positive functional networks. The Default Mode Network (DMN) is classically considered as a resting state (i.e. task-negative) network, upregulated in the absence of cognitive demands. In contrast, task-positive networks have been labelled the Extrinsic Mode Network (EMN). We investigated changes in brain activation and functional network connectivity in an experimental situation of repeated alterations between levels of cognitive effort, following a block-design. Using fMRI and a classic Stroop paradigm, participants switched back and forth between periods of no effort (resting), low effort (word reading, i.e. automatic processing based on learned internal representations and rules) and high effort (color naming, i.e. cognitively controlled perceptual processing of specific features of external stimuli). Results showed an expected EMN-activation for task versus resting contrasts, and DMN-activation for rest versus task contrasts. The DMN was in addition more strongly activated during periods of low effort contrasted with high effort, suggesting a gradual up- and down-regulation of the DMN network, depending on the level of demand and the type of processing required. The often reported “anti-correlation” between DMN and EMN was strongest during periods of low effort, indicating intermittent contributions of both networks. Taken together, these results challenge the traditional view of the DMN as solely a task-negative network. Instead, both the EMN and DMN may contribute to low-effort cognitive processing. In contrast, periods of resting and high effort are dominated by the DMN and EMN, respectively.

The brain operates at millisecond timescales but despite of that, the study of its functional networks is approached with time invariant methods. Equally, for a variety of brain conditions treatment is delivered with fixed temporal protocols unable to monitor and follow the rapid progression and therefore the cycles of a disease. To facilitate the understanding of brain network dynamics we developed Neurocraft, a user friendly software suite. Neurocraft features a highly novel signal processing engine fit for tracking evolving network states with superior time and frequency resolution. A variety of analytics like dynamic connectivity maps, force-directed representations and propagation models, allow for the highly selective investigation of transient pathophysiological dynamics. In addition, machine-learning tools enable the unsupervised investigation and selection of key network features at individual and group-levels. For proof of concept, we compared six seizure-free and non seizure-free focal epilepsy patients after resective surgery using Neurocraft. The network features were calculated using 50 intracranial electrodes on average during at least 120 epileptiform discharges lasting less than one second, per patient. Powerful network differences were detected in the pre-operative data of the two patient groups (effect size = 1.27), suggesting the predictive value of dynamic network features. More than one million patients are treated with cardiac and neuro modulation devices that are unable to track the hourly or daily changes in a subject’s disease. Decoding the dynamics of transition from normal to abnormal states may be crucial in the understanding, tracking and treatment of neurological conditions. Neurocraft provides a user-friendly platform for the research of microscale brain dynamics and a stepping stone for the personalised device-based adaptive neuromodulation in real-time.

Purpose Functional MRI (fMRI) enables evaluation of language cortical organization and plays a central role in surgical planning. Diffusion Tensor Imaging (DTI) or Tractography, allows evaluation of the white matter fibers involved in language. Unlike fMRI, DTI does not rely on the patient's cooperation. In monolinguals, there is a good correlation between the lateralization of language on fMRI and on DTI. Our objective is to delineate the arcuate fasciculus (AF) in right- and left-handed trilinguals and determine if the AF laterality on DTI is correlated to language lateralization on fMRI.Methods 15 right and 15 left-handed trilingual volunteers underwent fMRI and DTI. Laterality Index was determined on fMRI (fMRI-LI). Mean Diffusivity, Fractional Anisotropy (FA), Number of Fibers, Fiber Length, Fiber Volume and Laterality Index (DTI-LI) of the AF were calculated on DTI.Results 28 of the 30 subjects presented a bilateral AF. Most subjects (52%) were found to have a bilateral language lateralization of the AF on DTI. Only 4 subjects had bilateral lateralization of language on fMRI. The right AF demonstrated lower diffusivity than the left AF in the total participants, the right-handed, and the left-handed subjects. FA, Volume and Length of the AF were not significantly different between the two hemispheres. No correlation was found between the DTI-LI of the AF and the fMRI-LI.Conclusion A prominent role of the right AF and a bilateral structural organization of the AF was present in our multilingual population regardless of their handedness. While in prior studies DTI was able to determine language lateralization in monolingual subjects, this was not possible in trilingual highly educated subjects.

Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.

Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose “Brain Spacetime” as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.

Prior diffusion tensor imaging (DTI) studies have investigated white matter (WM) changes in patients with primary restless legs syndrome (RLS), but the results were inconsistent. Here, we proposed using tract-specific statistical analysis (TSSA) to find alterations in specific WM tracts to clarify the pathophysiological mechanisms of RLS. We enrolled 30 patients with RLS and 31 age- and sex- matched controls who underwent brain magnetic resonance imaging, neuropsychological tests, and polysomnography. Fractional anisotropy (FA) maps obtained from whole-brain diffusion tensor imaging and TSSA were used to localize WM changes in patients with RLS. Subsequently, a comparison of FA values for each tract between patients and controls was performed. The associations between FA values and clinical, polysomnographic, and neuropsychological parameters in RLS patients were assessed. RLS patients demonstrated decreased FA values in the left corticospinal tract (CST) and cingulum, and in the right anterior thalamic radiation (ATR) and inferior fronto-occipital fasciculus (IFO). Patients’ attention/executive function and visual memory scores positively correlated with FA values in the right ATR, and anxiety levels negatively correlated with FA values in the right IFO. Additionally, the number of periodic leg movements and movement arousal index were negatively correlated with FA values in the left CST. The TSSA method identified previously unknown tract-specific alterations in patients with RLS and significant associations with distinct clinical manifestations of RLS.

Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach for their analysis. However, many such analyses rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.

Recent research has begun to identify the neural mechanisms underlying the beneficial impact of mindfulness meditation training (MMT) on health and cognition. However, little is known about the effects of MMT on the global interplay of large-scale networks (LSNs) in the brain. In the present study, healthy, meditation-naïve adults (N = 46) underwent resting state fMRI prior to and upon completing 31 days of MMT or an active control intervention. Independent component analysis, sliding time window, and seed-based correlation analyses were performed to assess training-related changes in functional connectivity (FC) within and between networks with relevance to mindfulness meditation. Across sliding time window analyses and seed-based correlation analyses, we found increased FC between nodes of the default mode network (DMN) and nodes of the salience network (SN) in participants of the MMT. Seed-based correlation analyses revealed further connectivity increases between the SN and key regions of the central executive network (CEN). These results indicate, that, among multiple LSNs, one month of mindfulness meditation effectively increases interconnectivity between networks of the triple network model (DMN, SN, CEN), hereby introducing a potential mechanistic concept underlying the beneficial impact of MMT.Clinical trial registration: This study is listed as a clinical trial on the ISRCTN registry with trial ID ISRCTN95197731 (date of first registration: 15/02/2022).

Evidence suggests that selective serotonin reuptake inhibitors (SSRIs) reorganize neural networks via a transient window of neuroplasticity. While previous findings support an effect of SSRIs on intrinsic functional connectivity, little is known regarding the influence of SSRI-administration on connectivity during sequence motor learning. To investigate this, we administered 20 mg escitalopram or placebo for 1-week to 60 healthy female participants undergoing concurrent functional magnetic resonance imaging and sequence motor training in a double-blind randomized controlled design. We assessed task-modulated functional connectivity with a psycho-physiological interaction (PPI) analysis in the thalamus, putamen, cerebellum, dorsal premotor, primary motor, supplementary motor, and dorsolateral prefrontal cortices. Comparing an implicit sequence learning condition to a control learning condition, we observed decreased connectivity between the thalamus and bilateral motor regions after 7 days of escitalopram intake. Additionally, we observed a negative correlation between plasma escitalopram levels and PPI connectivity changes, with higher escitalopram levels being associated with greater thalamo-cortico decreases. Our results suggest that escitalopram enhances network-level processing efficiency during sequence motor learning, despite no changes in behaviour. Future studies in more diverse samples, however, with quantitative imaging of neurochemical markers of excitation and inhibition, are necessary to further assess neural responses to escitalopram.

Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren–Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13–27%) performance improvement compared to the state-of-the-art methods.

Functional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.

Electroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.

Predicting language therapy outcomes in bilinguals with aphasia (BWA) remains challenging due to the multiple pre- and poststroke factors that determine the deficits and recovery of their two languages. Computational models that simulate language impairment and treatment outcomes in BWA can help predict therapy response and identify the optimal language for treatment. Here we used the BiLex computational model to simulate the behavioral profile of language deficits and treatment response of a retrospective sample of 13 Spanish-English BWA who received therapy in one of their languages. Specifically, we simulated their prestroke naming ability and poststroke naming impairment in each language, and their treatment response in the treated and the untreated language. BiLex predicted treatment effects accurately and robustly in the treated language and captured different degrees of cross-language generalization in the untreated language in BWA. Our cross-validation approach further demonstrated that BiLex generalizes to predict treatment response for patients whose data were not used in model training. These findings support the potential of BiLex to predict therapy outcomes for BWA and suggest that computational modeling may be helpful to guide individually tailored rehabilitation plans for this population.

We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

Graph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

Probable mechanism behind the neuronal ephaptic coupling is investigated based on the introduction of “Brain”-triggered potential excitation signal smartly with a specific very low frequency (VLF) waves as a neuronal motor toolkit. Detection of this electric motor toolkit is attributed to in-vitro precise analyses of a neural network of snail, along to the disconnected snail’s neuronal network as a control. This is achieved via rapid (real-time) electrical signals acquisition by blind patch-clamp method during micro-electrode implanting in the neurons at the gigaseal conditions by the surgery operations. This process is based on its waveform (potential excitation signal) detection by mathematical curve fitting process. The characterized waveform of this electrical signal is “Saw Tooth” that is smartly stimulated, alternatively, by the brain during triggering the action potential’s (AP’s) hyperpolarization zone at a certain time interval at the several µs levels. Triggering the neuron cells results in (1) observing a positive shift (10.0%, depending on the intensity of the triggering wave), and (2) major promotion in the electrical current from sub nano (n) to micro (µ) amper (nA, µA) levels. Direct tracing the time domain (i.e., electrical signal vs. time) and estimation of the frequency domain (diagram of electrical response vs. the applied electrical frequencies) by the “Discrete Fast Fourier Transform” algorithm approve the presence of bilateral and reversible electrical currents between axon and dendrite. This mechanism therefore opens a novel view about the neuronal motor toolkit mechanism, versus the general knowledge about the unilateral electrical current flow from axon to dendrite operations in as neural network. The reliability of this mechanism is evaluated via (1) sequential modulation and demodulation of the snail’s neuron network by a simulation electrical functions and sequentially evaluation of the neuronal current sensitivity between pA and nA (during the promotion of the signal-to-noise ratio, via averaging of 30 ± 1 (n = 15) and recycling the electrical cycles before any neuronal response); and (2) operation of the process on the differentiated stem cells. The interstice behavior is attributed to the effective role of Ca2+ channels (besides Na+ and K+ ionic pumping), during hyper/hypo calcium processes, evidenced by inductively coupled plasma as the selected analytical method. This phenomenon is also modeled during proposing quadrupole well potential levels in the neuron systems. This mechanism therefore points to the microprocessor behavior of neuron networks. Stimulation of the neuronal system based on this mechanism, not only controls the sensitivity of neuron electrical stimulation, but also would open a light window for more efficient operating the neuronal connectivity during providing interruptions by phenomena such as neurolysis as well as an efficient treatment of neuron-based disorders.

Referrals for gender dysphoria (GD), characterized by a distressful incongruence between gender identity and at-birth assigned sex, are steadily increasing. The underlying neurobiology, and the mechanisms of the often-beneficial cross-sex hormone treatment are unknown. Here, we test hypothesis that own body perception networks (incorporated in the default mode network—DMN, and partly in the salience network—SN), are different in trans-compared with cis-gender persons. We also investigate whether these networks change with cross-sex hormone treatment. Forty transmen (TrM) and 25 transwomen (TrW) were scanned before and after cross-sex hormone institution. We used our own developed Body Morph test (BM), to assess the perception of own body as self. Fifteen cisgender persons were controls. Within and between-group differences in functional connectivity were calculated using independent components analysis within the DMN, SN, and motor network (a control network). Pretreatment, TrM and TrW scored lower “self” on the BM test than controls. Their functional connections were weaker in the anterior cingulate-, mesial prefrontal-cortex (mPFC), precuneus, the left angular gyrus, and superior parietal cortex of the DMN, and ACC in the SN “Self” identification and connectivity in the mPFC in both TrM and TrW increased from scan 1 to 2, and at scan 2 no group differences remained. The neurobiological underpinnings of GD seem subserved by cerebral structures composing major parts of the DMN.

We explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18–84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p

To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications.

Stability is a physical attribute that stands opposite the change. However, it is still unclear how the arrangement of links called topology affects network stability. In this study, we tackled this issue in the resting-state brain network using structural balance. Structural balance theory employs the quality of triadic associations between signed links to determine the network stability. In this study, we showed that negative links of the resting-state network make hubs to reduce balance-energy and push the network into a more stable state compared to null-networks with trivial topologies. In this regard, we created a global measure entitled ‘tendency to make hub’ to assess the hubness of the network. Besides, we revealed nodal degrees of negative links have an exponential distribution that confirms the existence of negative hubs. Our findings indicate that the arrangement of negative links plays an important role in the balance (stability) of the resting-state brain network.

Brain organoids grown from human pluripotent stem cells self-organize into cytoarchitectures resembling the developing human brain. These three-dimensional models offer an unprecedented opportunity to study human brain development and dysfunction. Characterization currently sacrifices spatial information for single-cell or histological analysis leaving whole-tissue analysis mostly unexplored. Here, we present the SCOUT pipeline for automated multiscale comparative analysis of intact cerebral organoids. Our integrated technology platform can rapidly clear, label, and image intact organoids. Algorithmic- and convolutional neural network-based image analysis extract hundreds of features characterizing molecular, cellular, spatial, cytoarchitectural, and organoid-wide properties from fluorescence microscopy datasets. Comprehensive analysis of 46 intact organoids and ~ 100 million cells reveals quantitative multiscale “phenotypes" for organoid development, culture protocols and Zika virus infection. SCOUT provides a much-needed framework for comparative analysis of emerging 3D in vitro models using fluorescence microscopy.

Although it is increasingly recognized that evaluation is a key phase for a two-fold creativity model, the neural model is not yet well understood. To this end, we constructed a theoretical model of creative evaluation and supported it with neural evidence through event-related potentials (ERPs) technology during a creative advertising task. Participants were required to evaluate the relationship between target words and advertising that systematically varied in novelty and usefulness. The ERPs results showed that (a) the novelty-usefulness and novelty-only conditions evoked a larger N1-P2 amplitude, reflecting an automatic attentional bias to novelty, and (b) these two novelty conditions elicited a larger N200-500 amplitude, reflecting an effort to process the novel content; (c) the novelty-usefulness and usefulness-only conditions induced a larger LPC amplitude, reflecting that valuable associations were formed through retrieval of relevant memories. These results propose a neural model of creative evaluation in advertising: the N1-P2, N200-500, and LPC should be the key indices to define three sub-processes of novelty perception, conception expansion, and value selection, respectively.

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient’s subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state—which results in a loss of this anticorrelation—is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).

One of the typical campus scenes is the social interaction between college couples, and the lesson couples must keep learning is to adapt to each other. This fMRI study investigated the shopping interactions of 30 college couples, one lying inside and the other outside the scanner, beholding the same item from two connected PCs, making preference ratings and subsequent buy/not-buy decisions. The behavioral results showed the clear modulation of significant others’ preferences onto one’s own decisions, and the contrast of the “shop-together vs. shop-alone”, and the “congruent (both liked or disliked the item, 68%) vs. incongruent (one liked but the other disliked, and vice versa)” together trials, both revealed bilateral temporal parietal junction (TPJ) among other reward-related regions, likely reflecting mentalizing during preference harmony. Moreover, when contrasting “own-high/other-low vs. own-low/other-high” incongruent trials, left anterior inferior parietal lobule (l-aIPL) was parametrically mapped, and the “yield (e.g., own-high/not-buy) vs. insist (e.g., own-low/not-buy)” modulation further revealed left lateral-IPL (l-lIPL), together with left TPJ forming a local social decision network that was further constrained by the mediation analysis among left TPJ–lIPL–aIPL. In sum, these results exemplify, via the two-person fMRI, the neural substrate of shopping interactions between couples. ispartof: Scientific Reports vol:10 issue:1 pages:1-11 ispartof: location:England status: published

Age-related hearing loss has been related to a compensatory increase in audio-visual integration and neural reorganization including alterations in functional resting state connectivity. How these two changes are linked in elderly listeners is unclear. The current study explored modulatory effects of hearing thresholds and audio-visual integration on resting state functional connectivity. We analysed a large set of resting state data of 65 elderly participants with a widely varying degree of untreated hearing loss. Audio-visual integration, as gauged with the McGurk effect, increased with progressing hearing thresholds. On the neural level, McGurk illusions were negatively related to functional coupling between motor and auditory regions. Similarly, connectivity of the dorsal attention network to sensorimotor and primary motor cortices was reduced with increasing hearing loss. The same effect was obtained for connectivity between the salience network and visual cortex. Our findings suggest that with progressing untreated age-related hearing loss, functional coupling at rest declines, affecting connectivity of brain networks and areas associated with attentional, visual, sensorimotor and motor processes. Especially connectivity reductions between auditory and motor areas were related to stronger audio-visual integration found with increasing hearing loss.

International audience; Different types of carbon materials are biocompatible with neural cells and can promote maturation. The mechanism of this effect is not clear. Here we have tested the capacity of a carbon material composed of amorphous sp 3 carbon backbone, embedded with a percolating network of sp 2 carbon domains to sustain neuronal cultures. We found that cortical neurons survive and develop faster on this novel carbon material. After 3 days in culture, there is a precocious increase in the frequency of neuronal activity and in the expression of maturation marker KCC2 on carbon films as compared to a commonly used glass surface. Accelerated development is accompanied by a dramatic increase in neuronal dendrite arborization. The mechanism for the precocious maturation involves the activation of intracellular calcium oscillations by the carbon material already after 1 day in culture. Carbon-induced oscillations are independent of network activity and reflect intrinsic spontaneous activation of developing neurons. Thus, these results reveal a novel mechanism for carbon material-induced neuronal survival and maturation. Brain trauma as well as neurodegenerative diseases are the leading cause of irreversible disability and low quality of life in the elderly population 1. A way to combat neurodegeneration is to promote reparation of neuronal networks, rewiring of neuronal connections, and eventual restoration or substitution of the lost functionality 2. One putative therapeutic avenue is providing scaffolds-special materials that support targeted differentiation of neuronal stem cells and neurite outgrowth of regenerating neurons. Carbon-derived materials possess numerous properties that make them usable as scaffolds 3. Carbon nanotubes (CNTs) and graphene are among the most studied carbon materials for biological applications. These materials enhance neuronal stem cell differentiation 4-6 , as well as promote neuronal survival, neuronal activity, and neuronal process outgrowth 7-13. Neurons cultured on CNTs have increased levels of neuronal K +-Cl − cotransporter KCC2, a key component in the functional maturation of inhibitory synaptic 14 and glutamatergic 15-17 transmission. Downregulation of this protein is also implicated in reactive plasticity following brain trauma 18. CNTs have been suggested to improve the electrical responsiveness of neurons by facilitating local electronic shortcuts between somas and dendrites 19. The ability of CNTs to form tight contacts with neurons is beneficial for neuron-electrode interfaces 20-24. 3D gra-phene substrates support growth and differentiation of neurons 25-27 that in combination with anti-inflammatory properties 28,29 makes graphene a next-generation neuronal tissue scaffold. Despite the importance of novel carbon materials for future engineering, we do not fully understand the mechanisms underlining the trophic action of carbon scaffolds. In this work, we propose a novel mechanism by which a new type of sputtered carbon material accelerates neuronal maturation. The carbon film material consists of conducting nanoscale sp 2 carbon islands embedded in a diamond-like sp 3 carbon matrix. We demonstrate OPEN

Gliomas grow and invade along white matter fiber tracts. This study assessed the effects of motor cortex gliomas on the cerebral white matter fiber bundle skeleton. The motor cortex glioma group included 21 patients, and the control group comprised 14 healthy volunteers. Both groups underwent magnetic resonance imaging-based 3.0 T diffusion tensor imaging. We used tract-based spatial statistics to analyze the characteristics of white matter fiber bundles. The left and right motor cortex glioma groups were analyzed separately from the control group. Results were statistically corrected by the family-wise error rate. Compared with the controls, patients with left motor cortex gliomas exhibited significantly reduced fractional anisotropy and an increased radial diffusivity in the corpus callosum. The alterations in mean diffusivity (MD) and the axial diffusivity (AD) were widely distributed throughout the brain. Furthermore, atlas-based analysis showed elevated MD and AD in the contralateral superior fronto-occipital fasciculus. Motor cortex gliomas significantly affect white matter fiber microstructure proximal to the tumor. The range of affected white matter fibers may extend beyond the tumor-affected area. These changes are primarily related to early stage tumor invasion.

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

Conservatives are more sensitive to threatening/anxious situations in perceptual and cognitive levels, experiencing emotional responses and stress, while liberals are more responsive to but tolerant of ambiguous and uncertain information. Interestingly, conservatives have greater psychological well-being and are more satisfied with their lives than liberals despite their psychological vulnerability to stress caused by threat and anxiety sensitivities. We investigated whether conservatives have greater resilience and self-regulation capacity, which are suggested to be psychological buffers that enhance psychological well-being, than liberals and moderates. We also explored associations between intrinsic functional brain organization and these psychological resources to expand our neurobiological understanding of self-regulatory processes in neuropolitics. We found that conservatives, compared to liberals and moderates, had greater psychological resilience and self-regulation capacity that were attributable to greater impulse control and causal reasoning. Stronger intrinsic connectivities between the orbitofrontal cortex (OFC) and precuneus and between the insula and frontal pole/OFC in conservatives were correlated with greater resilience and self-regulation capacity. These results suggest the neural underpinnings that may allow conservatives to manage the psychological stress and achieve greater life satisfaction. This study provides neuroscientific evidence for the different responses of liberals and conservatives to politically relevant social issues.

Anxiety is a mental state characterized by an intense sense of tension, worry or apprehension, relative to something adverse that might happen in the future. Researchers differentiate aspects of anxiety into state and trait, respectively defined as a more transient reaction to an adverse situation, and as a more stable personality attribute in experiencing events. It is yet unclear whether brain structural and functional features may distinguish these aspects of anxiety. To study this, we assessed 42 healthy participants with the State-Trait Anxiety Inventory and then investigated with MRI to characterize structural grey matter covariance and resting-state functional connectivity (rs-FC). We found several differences in the structural–functional patterns across anxiety types: (1) trait anxiety was associated to both structural covariance of Default Mode Network (DMN), with an increase in dorsal nodes and a decrease in its ventral part, and to rs-FC of DMN within frontal regions; (2) state anxiety, instead, was widely related to rs-FC of Salience Network and of DMN, specifically in its ventral nodes, but not associated with any structural pattern. In conclusion, our study provides evidence of a neuroanatomical and functional distinction between state and trait anxiety. These neural features may be additional markers in future studies evaluating early diagnosis or treatment effects.

Este artículo se encuentra disponible en la siguiente URL: https://www.nature.com/articles/s41598-020-68025-y.pdf En este artículo también participan: Alberto Oliverio y Andrea Mele. Goal-directed navigation can be based on world-centered (allocentric) or body-centered (egocentric) representations of the environment, mediated by a wide network of interconnected brain regions, including hippocampus, striatum and prefrontal cortex. The relative contribution of these regions to navigation from novel or familiar routes, that demand a different degree of flexibility in the use of the stored spatial representations, has not been completely explored. To address this issue, we trained mice to find a reward relying on allocentric or egocentric information, in a modified version of the cross-maze task. Then we used Zif268 expression to map brain activation when well-trained mice were required to find the goal from a novel or familiar location. Successful navigation was correlated with the activation of CA1, posterior-dorsomedial striatum, nucleus accumbens core and infralimbic cortex when allocentric-trained mice needed to use a novel route. Allocentric navigation from a familiar route activated dorsomedial striatum, nucleus accumbens, prelimbic and infralimbic cortex. None of the structures analyzed was significantly activated in egocentric-trained mice, irrespective of the starting position. These data suggest that a flexible use of stored allocentric information, that allows goal finding even from a location never explored during training, induces a shift from fronto-striatal to hippocampal circuits.

Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurological outcome. RS-fMRI was used to evaluate functional and effective connectivity within the default mode network in a cohort of 90 comatose patients and their impact on functional neurological outcome after 3 months. The RS-fMRI processing protocol comprises the evaluation of functional and effective connectivity within the default mode network. Seed-to-voxel and ROI-to-ROI feature analysis was performed as starting point for a supervised machine-learning approach. Classification of the Cerebral Performance Category (CPC) 1–3 (good to acceptable outcome) versus CPC 4–5 (adverse outcome) achieved a positive predictive value of 91.7%, sensitivity of 90.2%, and accuracy of 87.8%. A direct link to the level of consciousness and outcome after 3 months was identified for measures of segregation in the precuneus, in medial and right frontal regions. Thalamic connectivity appeared significantly reduced in patients without conscious response. Decreased within-network connectivity in the default mode network and within cortico-thalamic circuits correlated with clinical outcome after 3 months. Our results indicate a potential role of these markers for decision-making in comatose patients early after cardiac arrest.

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network’s activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.