We present an electrophysiological dataset collected from the amygdalae of nine participants attending a visual dynamic stimulation of emotional aversive content. The participants were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Participants were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). The dataset contains the simultaneous recording of intracranial EEG (iEEG) and neuronal spike times and waveforms, and localization information for iEEG electrodes. Participant characteristics and trial information are provided. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals. This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.
Objective: Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach: We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results: We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance: We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.
Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈−3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.
Objective: The aim of the study was to show that short-lasting (90 s) transcranial alternating current stimulation (tACS) at 20 Hz delivered over the left primary motor cortex (M1) is able to change the shape of recruitment curve of the corticospinal pathway.
Methods: The corticospinal pathway was studied during tACS by means of the relationship between the intensity of transcranial magnetic stimulation (TMS) delivered over the left M1 and corresponding motor evoked potentials (MEPs) recorded from the right first dorsal interosseus muscle (FDI), in nine healthy subjects. In order to extract characteristics of the input–output relationship that have particular physiological relevance, data were fitted to the Boltzmann sigmoidal function by the Levenberg–Marquardt nonlinear, least mean squares algorithm.
Results: The β-rhythm tACS influenced the shape and parameters of the input–output relation, so that the initial segment of the conditioned curve (from threshold to 30% of maximum muscle size) diverged, while the subsequent segment converged to overlap the unconditioned control curve.
Discussion: β-rhythm tACS conditions only a definite subset of corticospinal elements influencing less than 30% of the entire motoneuronal pool. The fact that β-rhythm tACS mainly affects the most excitable motoneurons could explain the observed antikinetic effect of the tACS at β-rhythm applied in the motor regions.
Interpretation of the neural networks architectures for decoding the signals of the brain usually reduced to the analysis of spatial and temporal weights. We propose a theoretically justified method of their interpretation within the simple architecture based on a priori knowledge of the subject area. This architecture is comparable in decoding quality to the winner of the BCI IV competition and allows for automatic engineering of physiologically meaningful features. To demonstrate the operation of the algorithm, we performed Monte Carlo simulations and received a significant improvement in the restoration of patterns for different noise levels and also investigated the relation between the decoding quality and patterns reconstruction fidelity.
The spatial accuracy of transcranial magnetic stimulation (TMS) may be as small as a few millimeters. Despite such great potential, navigated TMS (nTMS) mapping is still underused for the assessment of motor plasticity, particularly in clinical settings. Here, we investigate the within-limb somatotopy gradient as well as absolute and relative reliability of three hand muscle cortical representations (MCRs) using a comprehensive grid-based sulcus-informed nTMS motor mapping. We enrolled 22 young healthy male volunteers. Two nTMS mapping sessions were separated by 5–10 days. Motor evoked potentials were obtained from abductor pollicis brevis (APB), abductor digiti minimi, and extensor digitorum communis. In addition to individual MRI-based analysis, we studied normalized MNI MCRs. For the reliability assessment, we calculated intraclass correlation and the smallest detectable change. Our results revealed a somatotopy gradient reflected by APB MCR having the most lateral location. Reliability analysis showed that the commonly used metrics of MCRs, such as areas, volumes, centers of gravity (COGs), and hotspots had a high relative and low absolute reliability for all three muscles. For within-limb TMS somatotopy, the most common metrics such as the shifts between MCR COGs and hotspots had poor relative reliability. However, overlaps between different muscle MCRs were highly reliable. We, thus, provide novel evidence that inter-muscle MCR interaction can be reliably traced using MCR overlaps while shifts between the COGs and hotspots of different MCRs are not suitable for this purpose. Our results have implications for the interpretation of nTMS motor mapping results in healthy subjects and patients with neurological conditions.
People often change their beliefs by succumbing to an opinion of the majority. Such changes are often referred to as majority influence or conformity. While some previous studies have focused on the reinforcement learning mechanisms of conformity or on its internalization, others have reported evidence of changes in sensory processing evoked by majority opinion. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the remote effects of agreement and disagreement with the majority. During the first session, participants rated the trustworthiness of faces and subsequently learned how the majority of their peers had previously rated each face. To identify the neural correlates of the post-effect of agreeing or disagreeing with the group, we recorded MEG activity while participants rated faces during the next session. We found MEG traces of past disagreement or agreement with the peer group at the parietal cortices as early as approximately 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger if the participant’s rating had previously differed from the ratings of his or her peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex starting at about 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of remote effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex
Magnetoencephalography (MEG) is a neuroimaging method ideally suited for non-invasive studies of brain dynamics. MEG’s spatial resolution critically depends on the approach used to solve the ill-posed inverse problem in order to transform sensor signals into cortical activation maps. Over recent years non-globally optimized solutions based on the use of adaptive beamformers (BF) gained popularity.
When operating in the environment with a small number of uncorrelated sources the BFs perform optimally and yield high spatial resolution. However, the BFs are known to fail when dealing with correlated sources acting like poorly tuned spatial filters with low signal-to-noise ratio (SNR) of the output timeseries and often meaningless cortical maps of power distribution.
This fact poses a serious limitation on the broader use of this promising technique especially since fundamental mechanisms of brain functioning, its inherent symmetry and task-based experimental paradigms result into a great deal of correlation in the activity of cortical sources. To cope with this problem, we developed a novel data covariance modification approach that allows for building beamformers that maintain high spatial resolution when operating in the environments with correlated sources.
At the core of our method is a projection operation applied to the vectorized sensor-space covariance matrix. This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al., 2018) we refer to the family of algorithms presented here as ReciPSIICOS.
We assess the performance of the novel approach using realistically simulated MEG data and show its superior performance in comparison to the classical BF approaches and well established MNE as a method immune to source synchrony by design. We have also applied our approach to the MEG datasets from the two experiments involving two different auditory tasks.
The analysis of experimental MEG datasets showed that beamformers from ReciPSIICOS family, but not the classical BF, discovered the expected bilateral focal sources in the primary auditory cortex and detected motor cortex activity associated with the audio-motor task. In most cases MNE managed well but as expected produced more spatially diffuse source distributions. Notably, ReciPSIICOS beamformers yielded cortical activity estimates with SNR several times higher than that obtained with the classical BF, which may indirectly indicate the severeness of the signal cancellation problem when applying classical beamformers to MEG signals generated by synchronous sources.
Using movies and narratives as naturalistic stimuli in human neuroimaging studies has yielded significant ad- vances in understanding of cognitive and emotional functions. The relevant literature was reviewed, with em- phasis on how the use of naturalistic stimuli has helped advance scientific understanding of human memory, attention, language, emotions, and social cognition in ways that would have been difficult otherwise. These ad- vances include discovering a cortical hierarchy of temporal receptive windows, which supports processing of dynamic information that accumulates over several time scales, such as immediate reactions vs. slowly emerging patterns in social interactions. Naturalistic stimuli have also helped elucidate how the hippocampus supports segmentation and memorization of events in day-to-day life and have afforded insights into attentional brain mechanisms underlying our ability to adopt specific perspectives during natural viewing. Further, neuroimaging studies with naturalistic stimuli have revealed the role of the default-mode network in narrative-processing and in social cognition. Finally, by robustly eliciting genuine emotions, these stimuli have helped elucidate the brain basis of both basic and social emotions apparently manifested as highly overlapping yet distinguishable patterns of brain activity.
Background and Purpose Despite the continuing efforts in multimodal assessment of the motor system after stroke, conclusive findings on the complementarity of functional and structural metrics of the corticospinal tract (CST) integrity and the role of the contralesional hemisphere are still missing. The aim of this work was to find the best combination of the motor system parameters, allowing classification of patients into three predefined groups of upper limb motor recovery.
Methods 35 chronic ischemic stroke patients (47 [26–66] y.o., 29 [6–58] months post-stroke) with only supratentorial lesion and unilateral upper extremity weakness were enrolled. Patients were divided into three groups depending on the upper limb motor recovery. Non-parametric statistical tests and regression analysis were used to investigate the relationships among structural and functional motor system parameters, probed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS). In addition, stratification rules were tested, using a decision tree classifier to identify parameters explaining motor recovery.
Results Fractional anisotropy (FA) ratio in the internal capsule (IC) and absence/presence of motor evoked potentials (MEPs), were equally discriminative of the worst motor outcome group (96% accuracy). MEP presence diverged for two investigated hand muscles. Concurrently, for the three recovery groups’ classification, the best parameter combination was: IC FA ratio and Fréchet distance between the contralesional and ipsilesional CST FA profiles (91% accuracy). No other metrics had any additional value for patients’ classification.
Conclusions This study demonstrates that IC FA ratio and MEPs absence are equally important markers for poor recovery. Importantly, we found that MEPs should be controlled in more than one hand muscle. Finally, we show that better separation between different motor recovery groups may be achieved when considering the whole CST FA profile.
While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.
Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew’s correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.
Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that allows interaction with endogenous cortical oscillatory rhythms by means of external sinusoidal potentials. The physiological mechanisms underlying tACS effects are still under debate. Whereas online (e.g., ongoing) tACS over the motor cortex induces robust state-, phase- and frequency-dependent effects on cortical excitability, the offline effects (i.e. after-effects) of tACS are less clear. Here, we explored online and offline effects of tACS in two single-blind, sham-controlled experiments. In both experiments we used neuronavigated transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) as a probe to index changes of cortical excitability and delivered M1 tACS at 10 Hz (alpha), 20 Hz (beta) and sham (30 s of low-frequency transcranial random noise stimulation; tRNS). Corticospinal excitability was measured by single pulse TMS-induced motor evoked potentials (MEPs). tACS was delivered online in Experiment 1 and offline in Experiment 2. In Experiment 1, the increase of MEPs size was maximal with the 20 Hz stimulation, however in Experiment 2 neither the 10 Hz nor the 20 Hz stimulation induced tACS offline effects. These findings support the idea that tACS affects cortical excitability only during online application, at least when delivered on the scalp overlying M1, thereby contributing to the development of effective protocols that can be applied to clinical populations.
The confidence in our retrieved memories, i.e. retrospective confidence, is a metamemory process we perform daily. There is a wide variety of applied research focused on these metamemory judgements and very diverse studies including a wide a range of clinical populations. Yet, the neural correlates that support its functioning are not well defined. We used the activation likelihood estimation method (ALE) on the 18 eligible studies in the functional magnetic resonance imaging literature available on the topic. The main analysis of confidence revealed concordant bilateral activation in the parahippocampal gyrus, left middle frontal gyrus, right amygdala and right caudate. These activations support the involvement of a frontoparietal network in metamemory evaluations and the requirement of the medial temporal lobe. Activations for the exploratory high > low subtraction analysis were also noticed in the main analysis on retrospective confidence, whereas our exploratory low > high subtraction analyses showed distinctive activations of the right precuneus, previously attributed to low confident evaluations.
Selective serotonin reuptake inhibitors (SSRI) are commonly used to treat depression during pregnancy. SSRIs cross the placenta and may influence the maturation of the foetal brain. Clinical and preclinical findings suggest long-term consequences of SSRI perinatal exposure for the offspring. The mechanisms of SSRI effects on developing brain remain largely unknown and there are no directional approaches for prevention of the consequences of maternal SSRI treatment during pregnancy. The heptapeptide Semax (MEHFPGP) is a synthetic analogue of ACTH(4-10) which exerts marked nootropic and neuroprotective activities. The aim of the present study was to investigate the long-term effects of neonatal exposure to the SSRI fluvoxamine (FA) in white rats. Additionally, the study examined the potential for Semax to prevent the negative consequences of neonatal FA exposure. Rat pups received FA or vehicle injections on postnatal days 1-14, a time period equivalent to 27-40 weeks of human foetal age. After FA treatment, rats were administered with Semax or vehicle on postnatal days 15-28. During the 2nd month of life, the rats underwent behavioural testing, and monoamine levels in brain structures were measured. It was shown that neonatal FA exposure leads to the impaired emotional response to stress and novelty and delayed acquisition of food-motivated maze task in adolescent and young adult rats. Furthermore, FA exposure induced alterations in the monoamine levels in brains of 1- and 2- month-old rats. Semax administration reduced the anxiety-like behaviour, improved learning abilities and normalized the levels of brain biogenic amines impaired by the FA exposure. The results demonstrate that early-life FA exposure in rat pups produces long-term disturbances in their anxiety-related behaviour, learning abilities, and brain monoamines content. Semax exerts a favourable effect on behaviour and biogenic amine system of rats exposed to the antidepressant. Thus, peptide Semax can prevent behavioural deficits caused by altered 5-HT levels during development.
The article discusses the problem of organizing information in the human mind, in particular, the fundamental possibility of identifying any structural units in the array of perceived and processed information, the possibility of identifying the smallest, “elementary” unit of information in relation to consciousness. The process of perception is considered as a process during which the information received from receptors is consistently generalized, compared with previously accumulated experience and becomes material for the formation of concepts of a high level of abstraction. The process of “understanding” of concepts is considered as a process opposite to their formation – a process during which the collision of consciousness with a previously acquired concept leads to the re-deployment of numerous images and associations that previously became the material for its formation. The problem of identifying key characteristics, the most significant associative connections in relation to the normal psyche and pathology of the schizophrenic spectrum is considered.
Previous studies on the acquisition of semantics in the aspectual domain have suggested that a difficult case for achieving a targetlike representation in a second language arises when learners need to preempt a first language (L1) option (Gabriele, 2009). This study investigates this issue by focusing on a learning scenario where predicate-level variability exists in the L1 input. We investigate whether Japanese learners of English can learn to invalidate event cancellation readings (Tsujimura, 2003) in English and how such knowledge develops with increasing English proficiency. We address these questions by examining how Japanese learners of English interpret accomplishment predicates that allow an event cancellation reading in Japanese but not in English. A truth-value judgment task was administered to 60 beginner, 96 intermediate, and 40 advanced Japanese learners of English as well as 20 L1 English and 20 L1 Japanese speakers. Our results showed that Japanese learners of English progressed toward a targetlike representation of aspectual entailment. We argue that such progress follows two parallel routes: a grammatical route rooted in the learners’ growing awareness of the English determiner and number morphology combined with a statistical route rooted in the learners’ inferences based on missing data.
An increased propensity for risk taking is a hallmark of adolescent behavior with significant health and social consequences. Here, we elucidated cortical and subcortical regions associated with risky and risk-averse decisions and outcome evaluation using the Balloon Analog Risk Task in a large sample of adolescents (n=256, 56% female, age 14 ± 0.6), including the level of risk as a parametric modulator. We also identified sex differences in neural activity. Risky decisions engaged regions that are parts of the salience, dorsal attention, and frontoparietal networks, but only the insula was sensitive to increasing risks in parametric analyses. During risk-averse decisions, the same networks covaried with parametric levels of risk. The dorsal striatum was engaged by both risky and risk-averse decisions, but was not sensitive to escalating risk. Negative-outcome processing showed greater activations than positive-outcome processing. Insula, lateral orbitofrontal cortex, middle, rostral, and superior frontal areas, rostral and caudal anterior cingulate cortex were activated only by negative outcomes, with a subset of regions associated with negative outcomes showing greater activation in females. Taken together, these results suggest that safe decisions are predicted by more accurate neural representation of increasing risk levels, whereas reward-related processes play a relatively minor role.
Anxiety results in sub-optimal motor learning, but the precise mechanisms through which this effect occurs remain unknown. Using a motor sequence learning paradigm with separate phases for initial exploration and reward-based learning, we show that anxiety states in humans impair learning by attenuating the update of reward estimates. Further, when such estimates are perceived as unstable over time (volatility), anxiety constrains adaptive behavioral changes. Neurally, anxiety during initial exploration increased the amplitude and the rate of long bursts of sensorimotor and prefrontal beta oscillations (13–30 Hz). These changes extended to the subsequent learning phase, where phasic increases in beta power and burst rate following reward feedback were linked to smaller updates in reward estimates, with a higher anxiety-related increase explaining the attenuated belief updating. These data suggest that state anxiety alters the dynamics of beta oscillations during reward processing, thereby impairing proper updating of motor predictions when learning in unstable environments.