Analyzing combined eye-tracking/EEG experiments with (non)linear deconvolution models

Fixation-related potentials (FRPs), neural responses aligned to saccade offsets, are a promising tool to study the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of combined eye-tracking and EEG experiments: (i) the synchronization of data streams, (ii) the removal of ocular artifacts, (iii) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, (iv) and the fact that numerous low-level stimulus and saccade properties also influence the post-saccadic neural responses. While effective solutions exist for the first two problems, the latter ones are only beginning to be addressed. In the current paper, we present and review a unified framework to deconvolve overlapping potentials and control for linear and nonlinear confounds on the FRPs. An open software implementation is provided for all suggested procedures. We then demonstrate the advantages of this analysis approach for three commonly studied free viewing paradigms: face perception, scene viewing, and natural sentence reading. First, for a traditional ERP face recognition experiment, we show how deconvolution can separate stimulus-ERPs from the overlapping muscle and brain potentials produced by small (micro)saccades on the face. Second, in natural scene viewing, we isolate multiple non-linear influences of saccade parameters on the FRP. Finally, for a reading experiment using the classic boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing the spurious overlap effects caused by the difference in average fixation time. Our results suggest a principal way of measuring reliable fixation-related potentials during natural vision.

https://www.biorxiv.org/content/10.1101/735530v1

Agency and responsibility over virtual movements controlled through different paradigms of brain-computer interface

Agency is the attribution of an action to the self and is a prerequisite for experiencing responsibility over its consequences. Here we investigated agency and responsibility by studying the control of movements of an embodied avatar, via brain computer interface (BCI) technology, in immersive virtual reality. After induction of virtual body ownership by visuomotor correlations, healthy participants performed a motor task with their virtual body. We compared the passive observation of the subject's 'own' virtual arm performing the task with (1) the control of the movement through activation of sensorimotor areas (motor imagery) and (2) the control of the movement through activation of visual areas (steady-state visually evoked potentials). The latter two conditions were carried out using a brain-computer interface (BCI) and both shared the intention and the resulting action. We found that BCI-control of movements engenders the sense of agency, which is strongest for sensorimotor areas activation. Furthermore, increased activity of sensorimotor areas, as measured using EEG, correlates with levels of agency and responsibility. We discuss the implications of these results for the neural bases of agency, but also in the context of novel therapies involving BCI and the ethics of neurotechnology.

https://www.biorxiv.org/content/10.1101/735548v1

The CMU Array. A 3D Nano-Printed, Fully Customizable Ultra-High-Density Microelectrode Array

Microelectrode arrays (MEAs) provide the means to record electrophysiological activity fundamental to both basic and clinical neuroscience (e.g. brain computer interfaces). Despite recent advances, current MEAs have significant limitations, including recording density, fragility, expense, and the inability to optimize the probe to individualized study or patient needs. Here we address the technological limitations through the utilization of the newest developments in 3D nanoparticle printing. Our CMU Arrays possess previously impossible electrode densities (> 6000 channels/cm2) with tip diameters as small as 10um. Most importantly, the probes are entirely customizable owing to the adaptive manufacturing process. Any combination of individual shank lengths, impedances, and layouts are possible. This is achieved in part via our new multi-layer, multi material, custom 3D-printed circuit boards, a fabrication advancement in itself. This device design enables new experimental avenues of targeted, large-scale recording of electrical signals from a variety of biological tissues.

https://www.biorxiv.org/content/10.1101/742346v1

Low-frequency Neural Activity Reflects Rule-based Chunking during Speech Listening

Cortical activity tracks the rhythms of phrases and sentences during speech comprehension, which has been taken as strong evidence that the brain groups words into multi-word chunks. It has prominently been argued, in contrast, that the tracking phenomenon could be explained as the neural tracking of word properties. Here we distinguish these two hypotheses based on novel tasks in which we dissociate word properties from the chunk structure of a sequence. Two tasks separately require listeners to group semantically similar or semantically dissimilar words into chunks. We demonstrate that neural activity actively tracks task-related chunks rather than passively reflecting word properties. Furthermore, without an explicit 'chunk processing task,' neural activity barely tracks chunks defined by semantic similarity - but continues to robustly track syntactically well-formed meaningful sentences. These results suggest that cortical activity tracks multi-word chunks constructed by either long-term syntactic rules or temporary task-related rules. The properties of individual words are likely to contribute only in a minor way, contrary to recent claims.

https://www.biorxiv.org/content/10.1101/742585v1

Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

https://www.biorxiv.org/content/10.1101/740597v1

Frequency specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators

Various processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major role in human cognition. In this study, we used the memory retrieval task to investigate the underlying mechanism of recognition memory. The connectivity between brain regions is estimated from scalp electroencephalography signals that were recorded from twenty-three healthy subject participated in recognition memory task to correctly classify old/new words. Multivariate autoregressive models (MVAR) are used for the determination of Granger causality to estimate the effective connectivity in the time-frequency domain. We use GPDC and dDTF methods because they have almost resolved the previous problems in estimations. Results show that brain regions in the old condition have greater global connectivity in the theta and gamma band compared to the new words retrieval. Connectivity within and between the brain hemisphere may be related to correct rejection. The left frontal has a crucial role in recollection. theta and gamma specific connectivity pattern between temporal, parietal and frontal cortex may disclose the retrieval mechanism. old/new comparison resulted in the different patterns of network connection. These results and other evidence emphasize the role of frequency of causal network interactions in the memory process.

https://www.biorxiv.org/content/10.1101/739573v1

Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging

Numerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.

https://www.biorxiv.org/content/10.1101/743799v1

Neocortical activity tracks syllable and phrasal structure of self-produced speech

To gain novel insights into how the human brain processes self-produced auditory information during reading aloud, we investigated the coupling between neuromagnetic activity and the temporal envelope of the heard speech sounds (i.e., speech brain tracking) in a group of adults who 1) read a text aloud, 2) listened to a recording of their own speech (i.e., playback), and 3) listened to another speech recording. Coherence analyses revealed that, during reading aloud, the reader's brain tracked the slow temporal fluctuations of the speech output. Specifically, auditory cortices tracked phrasal structure (<1 Hz) but to a lesser extent than during the two speech listening conditions. Also, the tracking of syllable structure (4-8 Hz) occurred at parietal opercula during reading aloud and at auditory cortices during listening. Directionality analyses based on renormalized partial directed coherence revealed that speech brain tracking at <1 Hz and 4-8 Hz is dominated by speech-to-brain directional coupling during both reading aloud and listening, meaning that speech brain tracking mainly entails auditory feedback processing. Nevertheless, brain-to-speech directional coupling at 4-8 Hz was enhanced during reading aloud compared with listening, likely reflecting speech monitoring before production. Altogether, these data bring novel insights into how auditory verbal information is tracked by the human brain during perception and self-generation of connected speech.
https://www.biorxiv.org/content/10.1101/744151v1

Phase-amplitude markers of synchrony and noise: A resting-state and TMS-EEG study of schizophrenia

The electroencephalogram (EEG) of schizophrenia patients exhibits several well-known abnormalities. For brain responses evoked by a stimulus in a variety of behavioral paradigms, these alterations appear as a reduction of signal-to-noise ratio and of phase-locking, and as a facilitated excitability. Here we observe these effects in EEG using transcranial magnetic stimulation (TMS)-evoked potentials, comparing also to the resting-state EEG. To ensure veracity of our results we used three weekly sessions and analyzed both resting state and TMS-EEG data. In addition to confirming the known results for the stimulus response we also show a broadening of the amplitude distribution in the resting-state EEG of patients relative to that of controls, indicative of lower signal to noise. Specifically, we evaluated the instantaneous amplitude in narrow-band filtered EEG, using a form of mean-normalized variation (quartile coefficient, QC) as a marker for amplitude fluctuations. We find that on time scales of seconds to tens of seconds, amplitude fluctuations in the alpha and beta frequency bands in the resting state of healthy controls are larger than their fluctuations in schizophrenia patients. According to our marker, the narrow-band-filtered amplitude fluctuations in the patient group are more similar to the theoretical limit of narrow-band Gaussian noise. Our results thus support the neuronal noise hypothesis of schizophrenia, which states that the ability of neuronal populations to form locally and temporally correlated activity is reduced in schizophrenia due to inherent noise in all brain activity.

https://www.biorxiv.org/content/10.1101/740621v1

Stimjim: open source hardware for precise electrical stimulation

Electrical stimulation is a simple and powerful tool to perturb and evoke neuronal activity in order to understand the function of neurons and neural circuits. Despite this, devices that can provide precise current or voltage stimulation are expensive and closed-source. Here, we introduce Stimjim, a capable and inexpensive ($200 USD) open-source instrument for electrical stimulation that combines both function generation and electrical isolation. Stimjim provides microsecond temporal resolution with microampere or millivolt scale precision on two electrically isolated output channels. We demonstrate Stimjim's utility both in vitro by precisely stimulating brain slices, and in vivo by training mice to perform intracranial self-stimulation (ICSS) for brain stimulation reward. During ICSS, Stimjim enables the experimenter to smoothly tune the strength of reward-seeking behavior by varying either the output frequency or amplitude. We envision Stimjim will enable new kinds of experiments due to its open-source and scalable nature.

https://bitbucket.org/natecermak/stimjim/src/master/

https://www.biorxiv.org/content/10.1101/757716v1

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Time-resolved analyses of magnetoencephalographic (MEG) signals explained this behavioral difference by the neural interaction between inferred beliefs and incoming evidence, an effect originating from the medial temporal lobe (MTL).

https://www.biorxiv.org/content/10.1101/755223v1

Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

The activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes.

https://www.biorxiv.org/content/10.1101/810861v1