Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem

Functional brain networks predicting sustained attention are not specific to perceptual modality

Sustained attention is essential for daily life and can be directed to information from different perceptual modalities, including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study, we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults' visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N 1 = 29, N 2 = 60, N 3 = 17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

Deviant sound frequency and time stimuli in auditory oddball tasks reveal persistent aberrant brain activity in patients with psychosis and symptomatic remission

The detection of rare or deviant stimuli shares common brain circuits involved in temporal processing and salience, critical for cognitive control. Disruption in these processes may contribute to the mechanisms of the disease and explain cognitive deficits observed in psychosis and related disorders. We designed a neuroimaging study, using oddball task-based functional sequences (fMRI) and diffusion tensor imaging (DTI), comparing healthy controls (HC, n = 14, 7 females) and patients with stable psychosis (PSY, n = 20, 10 females). The PSY individuals had schizophrenia or bipolar disorder diagnosis (ICD-10), meeting symptom remission criteria in the last 6 months. Two variants of the auditory oddball paradigm were employed, focusing on sound frequency (SF) and time discrimination (TD) tasks, adapted for fMRI. We used a general linear model to analyze fMRI data and a random effects model for group analysis, complemented by an exploratory statistical agnostic mapping analysis. DTI data were processed using FSL (FMRIB Software Library) and TBSS (Tract Based Spatial Statistics). Distinct activation patterns between groups were observed, with increased brain activity in PSY in TD and SF oddball tasks. In response to increased task difficulty, HC predominantly activated cerebellar regions, whereas PSY relied more on frontal regions. Reduced fractional anisotropy in PSY correlated with lower performance scores in the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery (MCCB). The study underscores aberrant brain activity and white matter deficits in stable psychosis patients, highlighting distinct responses to cognitive challenges compared to HC. These findings may support the hypothesis of cognitive dysmetria as a potential underlying mechanism in psychosis and highlight future therapeutic strategies, including non-invasive brain stimulation techniques.

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking

It has been indicated that extreme sport activities result in a highly rewarding experience, despite also providing fear, stress and anxiety. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate-HR, and breathing rate-BR) and neural (electroencephalographic-EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m-low-walk- and 45 m-high-walk-from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network-VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network-DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a 'task-constantly-opened-to-subtle-information experience', rather than a 'task-focused experience'.

Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI

Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.

Pupillary response is associated with the reset and switching of functional brain networks during salience processing

The interface between processing internal goals and salient events in the environment involves various top-down processes. Previous studies have identified multiple brain areas for salience processing, including the salience network (SN), dorsal attention network, and the locus coeruleus-norepinephrine (LC-NE) system. However, interactions among these systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, EEG, and fMRI during an auditory oddball paradigm. The analyses of EEG and fMRI data uncovered spatiotemporally organized target-associated neural correlates. By modeling the target-modulated effective connectivity, we found that the target-evoked pupillary response is associated with the network directional couplings from late to early subsystems in the trial, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with the pupil-indexed LC-NE system in the reset and switching of cortical networks, and shed light on their implications in various cognitive processes and neurological diseases.

The effects of first-dose methylphenidate on the neural signatures of visual selective attention in children with attention-deficit/hyperactivity disorder

Although methylphenidate (MPH) has been shown to significantly improve selective attention in children with attention-deficit/hyperactivity disorder (ADHD), the neural mechanism of this effect remains unclear. We investigated the effects of first-dose MPH on the neural signatures of visual selective attention in children with ADHD. We measured the impact of first-dose MPH on electrophysiological indexes from eighteen children with ADHD (8.9-15.2 years; 15 boys) while they performed a visual search task. MPH was administered in a double-blind placebo-controlled crossover design. MPH led to decreases in behavioral error rates and reaction times. For the electrophysiological indexes, MPH significantly increased the target-elicited N2pc amplitude and posterior P3 amplitude during the selective attention process. The trial-based correlation analysis revealed that the enhanced N2pc (more negative) and P3 (more positive) promoted the behavioral response speed for children with ADHD. The lower individual P3 amplitude was associated with higher severity of inattention symptoms. The severer inattention symptoms were related to weaker MPH effect on N2pc amplitude. These findings suggest that N2pc and P3 are closely related to the mechanism of MPH in the ADHD treatment.

Sensory Target Detection at Local and Global Timescales Reveals a Hierarchy of Supramodal Dynamics in the Human Cortex

To ensure survival in a dynamic environment, the human neocortex monitors input streams from different sensory organs for important sensory events. Which principles govern whether different senses share common or modality-specific brain networks for sensory target detection? We examined whether complex targets evoke sustained supramodal activity while simple targets rely on modality-specific networks with short-lived supramodal contributions. In a series of hierarchical multisensory target detection studies (n = 77, of either sex) using EEG, we applied a temporal cross-decoding approach to dissociate supramodal and modality-specific cortical dynamics elicited by rule-based global and feature-based local sensory deviations within and between the visual, somatosensory, and auditory modality. Our data show that each sense implements a cortical hierarchy orchestrating supramodal target detection responses, which operate at local and global timescales in successive processing stages. Across different sensory modalities, simple feature-based sensory deviations presented in temporal vicinity to a monotonous input stream triggered a mismatch negativity-like local signal which decayed quickly and early, whereas complex rule-based targets tracked across time evoked a P3b-like global neural response which generalized across a late time window. Converging results from temporal cross-modality decoding analyses across different datasets, we reveal that global neural responses are sustained in a supramodal higher-order network, whereas local neural responses canonically thought to rely on modality-specific regions evolve into short-lived supramodal activity. Together, our findings demonstrate that cortical organization largely follows a gradient in which short-lived modality-specific as well as supramodal processes dominate local responses, whereas higher-order processes encode temporally extended abstract supramodal information fed forward from modality-specific cortices.SIGNIFICANCE STATEMENT Each sense supports a cortical hierarchy of processes tracking deviant sensory events at multiple timescales. Conflicting evidence produced a lively debate around which of these processes are supramodal. Here, we manipulated the temporal complexity of auditory, tactile, and visual targets to determine whether cortical local and global ERP responses to sensory targets share cortical dynamics between the senses. Using temporal cross-decoding, we found that temporally complex targets elicit a supramodal sustained response. Conversely, local responses to temporally confined targets typically considered modality-specific rely on early short-lived supramodal activation. Our finding provides evidence for a supramodal gradient supporting sensory target detection in the cortex, with implications for multiple fields in which these responses are studied (e.g., predictive coding, consciousness, and attention).

N-methyl-d-aspartate receptor antagonism modulates P300 event-related potentials and associated activity in salience and central executive networks

Impairments in auditory information processing in schizophrenia as indexed electrophysiologically by P300 deficits during novelty (P3a) and target (P3b) processing are linked to N -methyl- D -aspartate receptor (NMDAR) dysfunction. This study in 14 healthy volunteers examined the effects of a subanesthetic dose of the NMDAR antagonist ketamine on P300 and their relationship to psychomimetic symptoms and cortical source activity (with eLORETA). Ketamine reduced early (e- P3a) and late (l-P3a) novelty P300 at sensor (scalp)-level and at source-level in the salience network. Increases in dissociation symptoms were negatively correlated with ketamine-induced P3b changes, at sensor-level and source-level, in both salience and central executive networks. These P3a alterations during novelty processing, and the symptom-related P3b changes during target processing support a model of NMDAR hypofunction underlying disrupted auditory attention in schizophrenia.

Multimodal neuroimaging markers of variation in cognitive ability in older HIV+ men

OBJECTIVE

This study used converging methods to examine the neural substrates of cognitive ability in middle-aged and older men with well-controlled HIV infection.

METHODS

Seventy-six HIV+ men on antiretroviral treatment completed an auditory oddball task and an inhibitory control (Simon) task while time-locked high-density EEG was acquired; 66 had usable EEG data from one or both tasks; structural MRI was available for 43. We investigated relationships between task-evoked EEG responses, cognitive ability and immunocompromise. We also explored the structural correlates of these EEG markers in the sub-sample with complete EEG and MRI data (N = 27).

RESULTS

EEG activity was associated with cognitive ability at later (P300) but not earlier stages of both tasks. Only the oddball task P300 was reliably associated with HIV severity (nadir CD4). Source localization confirmed that the tasks engaged partially distinct circuits. Thalamus volume correlated with oddball task P300 amplitude, while globus pallidus volume was related to the P300 in both tasks.

INTERPRETATION

This is the first study to use task-evoked EEG to identify neural correlates of individual differences in cognition in men living with well-controlled HIV infection, and to explore the structural basis of the EEG markers. We found that EEG responses evoked by the oddball task are more reliably related to cognitive performance than those evoked by the Simon task. We also provide preliminary evidence for a subcortical contribution to the effects of HIV infection severity on P300 amplitudes. These results suggest brain mechanisms and candidate biomarkers for individual differences in cognition in HIV.

Restricted function-on-function linear regression model

The usual function-on-function linear regression model depicts the association between functional variables in the whole rectangular region and the value of response curve at any point is influenced by the entire trajectory of the predictor curve. But in addition to this, there are cases where the value of the response curve at a point is only influenced by the value of the predictor curve in a subregion, such as the historical relationship and the short-term association. We will consider the restricted function-on-function regression model, where the value of response curve at any point is influenced by a subtrajectory of the predictor. We have two major purposes. First, we propose a novel estimation procedure that is more accurate and computational efficient for the restricted function-on-function model with a given subregion. Second, as the subregion is seldom specified in practice, we propose a subregion selection procedure that can lead to models with better interpretation and predictive performance. Algorithms are developed for both model estimation and subregion selection.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning

OBJECTIVE

The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs).

METHODS

EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects.

RESULTS

We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification.

CONCLUSION

The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings.

SIGNIFICANCE

We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Supramodal Mechanisms of the Cognitive Control Network in Uncertainty Processing

Information processing under conditions of uncertainty requires the involvement of cognitive control. Despite behavioral evidence of the supramodal function (i.e., independent of sensory modality) of cognitive control, the underlying neural mechanism needs to be directly tested. This study used functional magnetic imaging together with visual and auditory perceptual decision-making tasks to examine brain activation as a function of uncertainty in the two stimulus modalities. The results revealed a monotonic increase in activation in the cortical regions of the cognitive control network (CCN) as a function of uncertainty in the visual and auditory modalities. The intrinsic connectivity between the CCN and sensory regions was similar for the visual and auditory modalities. Furthermore, multivariate patterns of activation in the CCN predicted the level of uncertainty within and across stimulus modalities. These findings suggest that the CCN implements cognitive control by processing uncertainty as abstract information independent of stimulus modality.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FC_EEG to second range of FC_fMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

Decomposing Simon task BOLD activation using a drift-diffusion model framework

The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks. In this study we use our freely available framework for fitting and comparing non-standard SSMs to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and a conflict counteraction parameter to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable. We find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.

Decreased resting gamma activity in adult attention deficit/hyperactivity disorder

Objectives: To delineate task-free gamma activity in adult ADHD and healthy control subjects based on high-density EEG recordings. Relationship of gamma activity with symptom severity was also examined, since gamma activity is considered to be an index of network functions in the brain that underlie higher-order cognitive processes.Methods: Spontaneous EEG was recorded in adult ADHD subjects (N = 42; 25 methylphenidate-naïve and 17 on methylphenidate treatment) and controls (N = 59) with eyes open. EEG absolute power gamma was investigated in the gamma₁ (30.25-39 Hz) and gamma₂ (39.25-48 Hz) frequency bands.Results: Gamma₁ and gamma₂ activity was diminished in ADHD compared with healthy control subjects. The difference between ADHD and controls was the most pronounced in the right centroparietal region for both gamma₁ and gamma₂. Inverse associations were found between gamma₁ and gamma₂ activity and ADHD symptoms in centroparietal scalp regions.Conclusions: Gamma activity is reduced in adult ADHD, and the reduction has a predominantly right centroparietal distribution. Our findings are consistent with childhood ADHD literature with respect to diminished posterior gamma activity in patients, which may reflect altered dorsal attention network functions. Gamma abnormalities might provide a link between neurophysiological functioning and neuropsychological deficiencies, thereby offering an opportunity to investigate the neurobiological mechanisms that underlie the clinical symptoms of ADHD.

Association of β-Amyloid Burden With Sleep Dysfunction and Cognitive Impairment in Elderly Individuals With Cognitive Disorders

IMPORTANCE

Evidence shows that sleep dysfunction and β-amyloid (Aβ) deposition work synergistically to impair brain function in individuals with normal cognition, increasing the risk of developing dementia later in life. However, whether Aβ continues to play an integral role in sleep dysfunction after the onset of cognitive decline in individuals with dementia is unclear.

OBJECTIVE

To determine whether Aβ deposition in the brain is associated with subjective measures of sleep quality and cognition in elderly individuals with cognitive disorders.

DESIGN, SETTING, AND PARTICIPANTS

A nested survey study was conducted at the Cognitive Disorders and Comprehensive Alzheimer Disease Center of Thomas Jefferson University Hospital in Philadelphia, Pennsylvania. Participants included patients aged 65 years and older with cognitive disorders verified by neuropsychological testing. Eligible participants were identified from a referral center-based sample of patients who underwent fluorine 18-labeled florbetaben positron emission tomography imaging at Thomas Jefferson University Hospital as part of the multicenter Imaging Dementia-Evidence for Amyloid Scanning study. Data collection and analysis occurred between November 2018 and March 2019.

MAIN OUTCOMES AND MEASURES

Sleep quality was measured via responses to sleep questionnaires, Aβ deposition was measured via fluorine 18-labeled florbetaben positron emission tomography, and cognition was measured via Mini-Mental State Examination (MMSE) performance.

RESULTS

Of the 67 eligible participants, 52 (77.6%) gave informed consent to participate in the study. Of the 52 enrolled participants (mean [SD] age, 76.6 [7.4] years), 27 (51.9%) were women. Daytime sleepiness was associated with Aβ deposition in the brainstem (B = 0.0063; 95% CI, 0.001 to 0.012; P = .02), but not MMSE performance (B = -0.01; 95% CI, -0.39 to 0.37; P = .96). The number of nocturnal awakenings was associated with Aβ deposition in the precuneus (B = 0.11; 95% CI, 0.06 to 0.17; P < .001) and poor MMSE performance (B = -2.13; 95% CI, -3.13 to -1.13; P < .001). Mediation analysis demonstrated an indirect association between Aβ deposition and poor MMSE performance that relied on nocturnal awakenings as an intermediary (B = -3.99; 95% CI, -7.88 to -0.83; P = .01).

CONCLUSIONS AND RELEVANCE

Nighttime sleep disruption may mediate the association between Aβ and cognitive impairment, suggesting that there is an underlying sleep-dependent mechanism that links Aβ burden in the brain to cognitive decline. Further elucidation of this mechanism may improve understanding of disease processes associated with Aβ accumulation.

Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis

In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.

Unravelling intra- and intersegmental neuronal connectivity between central pattern generating networks in a multi-legged locomotor system

Animal walking results from a complex interplay of central pattern generating networks (CPGs), local sensory signals expressing position, velocity and forces generated in the legs, and coordinating signals between neighboring legs. In particular, the CPGs control the activity of motoneuron (MN) pools which drive the muscles of the individual legs and are thereby responsible for the generation of rhythmic leg movements. The rhythmic activity of the CPGs as well as their connectivity can be modified by the aforementioned sensory signals. However, the precise nature of the interaction between the CPGs and these sensory signals has remained generally largely unknown. Experimental methods aiming at finding out details of these interactions often apply cholinergic agonists such as pilocarpine in order to induce rhythmic activity in the CPGs. Using this general approach, we removed the influence of sensory signals and investigated the putative connections between CPGs controlling the upward/downward movement in the different legs of the stick insect. The experimental data, i.e. the measured MN activities, underwent connectivity analysis using Dynamic Causal Modelling (DCM). This method can uncover the underlying coupling structure and strength between pairs of segmental CPGs. For the analysis we set up different coupling schemes (models) for DCM and compared them using Bayesian Model Selection (BMS). Models with contralateral connections in each segment and ipsilateral connections on both sides, as well as the coupling from the meta- to the ipsilateral prothoracic ganglion were preferred by BMS to all other types of models tested. Moreover, the intrasegmental coupling strength in the mesothoracic ganglion was the strongest and most stable in all three ganglia.

Modeling of the BOLD signal using event-related simultaneous EEG-fMRI and convolutional sparse coding analysis

In this work, we employ simultaneous EEG-fMRI data acquired during a visually-guided attention task along with convolutional sparse coding (CSC) analysis to extract transient events from the EEG. Subsequently, we use these events in a standard voxel-wise fMRI analysis and compare the resultant activation maps with maps obtained using the subjects' response time (RT) in detection of visual target stimuli. We also employ FIR models to obtain HRF estimates using the detected CSC events. Our results show concordance between the resultant activation maps and consistent HRF shapes for most of the subjects, suggesting that CSC can be used as a tool for the detection of reliable events in the EEG.

Single-Trial EEG-fMRI Reveals the Generation Process of the Mismatch Negativity

Although research on the mismatch negativity (MMN) has been ongoing for 40 years, the generation process of the MMN remains largely unknown. In this study, we used a single-trial electro-encephalography (EEG)-functional magnetic resonance imaging (fMRI) coupling method which can analyze neural activity with both high temporal and high spatial resolution and thus assess the generation process of the MMN. We elicited the MMN with an auditory oddball paradigm while recording simultaneous EEG and fMRI. We divided the MMN into five equal-durational phases. Utilizing the single-trial variability of the MMN, we analyzed the neural generators of the five phases, thereby determining the spatiotemporal generation process of the MMN. We found two distinct bottom-up prediction error propagations: first from the auditory cortex to the motor areas and then from the auditory cortex to the inferior frontal gyrus (IFG). Our results support the regularity-violation hypothesis of MMN generation.

Spatiotemporal Dynamics of Attention Networks Revealed by Representational Similarity Analysis of EEG and fMRI

The fronto-parietal attention networks have been extensively studied with functional magnetic resonance imaging (fMRI), but spatiotemporal dynamics of these networks are not well understood. We measured event-related potentials (ERPs) with electroencephalography (EEG) and collected fMRI data from identical experiments where participants performed visual and auditory discrimination tasks separately or simultaneously and with or without distractors. To overcome the low temporal resolution of fMRI, we used a novel ERP-based application of multivariate representational similarity analysis (RSA) to parse time-averaged fMRI pattern activity into distinct spatial maps that each corresponded, in representational structure, to a short temporal ERP segment. Discriminant analysis of ERP-fMRI correlations revealed 8 cortical networks-2 sensory, 3 attention, and 3 other-segregated by 4 orthogonal, temporally multifaceted and spatially distributed functions. We interpret these functions as 4 spatiotemporal components of attention: modality-dependent and stimulus-driven orienting, top-down control, mode transition, and response preparation, selection and execution.

Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks

Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an encoding model, we investigated the reverse model: if the stimuli's surprise is assumed as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. to tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al. in 2016, and applied it to three datasets, one with visual, one with auditory, and one with both visual and auditory stimuli. We demonstrated that our decoding model performs very well for both of the sensory modalities with or without the presence of the subject's motor response.

fMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Attentional responses on an auditory oddball predict false memory susceptibility

Attention and memory are highly integrated processes. Building on prior behavioral investigations, this study assesses the link between individual differences in low-level neural attentional responding and false memory susceptibility on the misinformation effect, a paradigm in which false event memories are induced via misleading post-event information. Twenty-four subjects completed the misinformation effect paradigm after which high-density (256-channel) EEG data was collected as they engaged in an auditory oddball task. Temporal-spatial decomposition was used to extract two attention-related components from the oddball data, the P3b and Classic Slow Wave. The P3b was utilized as an index of individual differences in salient target attentional responding while the slow wave was adopted as an index of variability in task-level sustained attention. Analyses of these components show a significant negative relationship between slow-wave responses to oddball non-targets and perceptual false memory endorsements, suggestive of a link between individual differences in levels of sustained attention and false memory susceptibility. These findings provide the first demonstrated link between individual differences in basic attentional responses and false memory. These results support prior behavioral work linking attention and false memory and highlight the integration between attentional processes and real-world episodic memory.

Effects of Transcutaneous Vagus Nerve Stimulation (tVNS) on the P300 and Alpha-Amylase Level: A Pilot Study

Recent research suggests that the P3b may be closely related to the activation of the locus coeruleus-norepinephrine (LC-NE) system. To further study the potential association, we applied a novel technique, the non-invasive transcutaneous vagus nerve stimulation (tVNS), which is speculated to increase noradrenaline levels. Using a within-subject cross-over design, 20 healthy participants received continuous tVNS and sham stimulation on two consecutive days (stimulation counterbalanced across participants) while performing a visual oddball task. During stimulation, oval non-targets (standard), normal-head (easy) and rotated-head (difficult) targets, as well as novel stimuli (scenes) were presented. As an indirect marker of noradrenergic activation we also collected salivary alpha-amylase (sAA) before and after stimulation. Results showed larger P3b amplitudes for target, relative to standard stimuli, irrespective of stimulation condition. Exploratory post hoc analyses, however, revealed that, in comparison to standard stimuli, easy (but not difficult) targets produced larger P3b (but not P3a) amplitudes during active tVNS, compared to sham stimulation. For sAA levels, although main analyses did not show differential effects of stimulation, direct testing revealed that tVNS (but not sham stimulation) increased sAA levels after stimulation. Additionally, larger differences between tVNS and sham stimulation in P3b magnitudes for easy targets were associated with larger increase in sAA levels after tVNS, but not after sham stimulation. Despite preliminary evidence for a modulatory influence of tVNS on the P3b, which may be partly mediated by activation of the noradrenergic system, additional research in this field is clearly warranted. Future studies need to clarify whether tVNS also facilitates other processes, such as learning and memory, and whether tVNS can be used as therapeutic tool.

Detecting sub-second changes in brain activation patterns during interictal epileptic spike using simultaneous EEG-fMRI

OBJECTIVE

Epileptic spikes are associated with rapidly changing brain activation involving the epileptic foci and other brain regions in the "epileptic network". We aim to resolve these activation changes using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings.

METHODS

Simultaneous EEG-fMRI recordings from 9 patients with epilepsy were used in the analysis. Our method employed the whole scalp EEG data to generate regressors for the analysis of fMRI data using the general linear model.

RESULTS

We were able to resolve, with milliseconds temporal resolution, changes in activation patterns involving suspected epileptic foci and other brain regions in the epileptic network during spike and slow wave. Using summary maps (called SSWAS maps) which show the activation frequency of voxels, we found that suspected epileptic foci tend to be significantly active during this interval. SSWAS maps also enabled the detection of the epileptic foci in 4 of 5 patients where the conventional event-timing-based analysis failed to identify.

CONCLUSION

These findings demonstrated the efficacy of the method and the potential application of SSWAS maps to identify epileptic foci.

SIGNIFICANCE

The method could help resolve activation changes during epileptic spike and could provide insights into the underlying pathophysiology of these changes.

Task-evoked pupil dilation and BOLD variance as indicators of locus coeruleus dysfunction

Pupillary responses during cognitive tasks are linked to functioning of the locus coeruleus (LC). The LC is an early site of abnormal tau deposition, which may contribute to key aspects of Alzheimer's disease (AD) pathophysiology. We previously found attenuation of pupillary responses to increases in cognitive load in individuals with mild cognitive impairment (MCI), suggesting pupillary responses may provide a biomarker of early risk for AD associated with LC dysfunction. The LC modulates cortical activity through two modes of operation: tonic and phasic. Early LC damage has been predicted to result in a state of persistent high tonic LC activity that may disrupt task-related phasic activity. To further examine whether pupillary responses are associated with early LC dysfunction, we measured pupil dilation during a digit span task as a measure of phasic activity, and low frequency BOLD variance (LFBV) during resting-state fMRI in key nodes of the ventral attention network (VAN) as a measure of cortical reactivity related to LC tonic activity in 358 middle-aged men. Individuals with greater LFBV in VAN nodes, i.e., higher tonic brain activity at rest, showed a smaller increase in pupil dilation from low to moderate cognitive loads. Thus, higher tonic LFBV activity at rest was related to reduced task-appropriate phasic dilation increases. The results support predictions from prominent models of LC functioning in which early LC dysfunction leads to persistent high tonic rates of activity during rest and lower signal-to-noise of phasic responses during task performance. Taken together with previous findings of early AD pathophysiology in LC and reduced phasic dilation responses to increased cognitive load in individuals with MCI, the present results suggest that pupillary responses may index early LC dysfunction and should receive further study as a potential biomarker of risk for AD.

The spatiotemporal pattern of pure tone processing: A single-trial EEG-fMRI study

Although considerable research has been published on pure tone processing, its spatiotemporal pattern is not well understood. Specifically, the link between neural activity in the auditory pathway measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) markers of pure tone processing in the P1, N1, P2, and N4 components is not well established. In this study, we used single-trial EEG-fMRI as a multi-modal fusion approach to integrate concurrently acquired EEG and fMRI data, in order to understand the spatial and temporal aspects of the pure tone processing pathway. Data were recorded from 33 subjects who were presented with stochastically alternating pure tone sequences with two different frequencies: 200 and 6400 Hz. Brain network correlated with trial-to-trial variability of the task-discriminating EEG amplitude was identified. We found that neural responses responding to pure tone perception are spatially along the auditory pathway and temporally divided into three stages: (1) the early stage (P1), wherein activation occurs in the midbrain, which constitutes a part of the low level auditory pathway; (2) the middle stage (N1, P2), wherein correlates were found in areas associated with the posterodorsal auditory pathway, including the primary auditory cortex and the motor cortex; (3) the late stage (N4), wherein correlation was found in the motor cortex. This indicates that trial-by-trial variation in neural activity in the P1, N1, P2, and N4 components reflects the sequential engagement of low- and high-level parts of the auditory pathway for pure tone processing. Our results demonstrate that during simple pure tone listening tasks, regions associated with the auditory pathway transiently correlate with trial-to-trial variability of the EEG amplitude, and they do so on a millisecond timescale with a distinct temporal ordering.

Electrophysiological correlates reflect the integration of model-based and model-free decision information

In this study, we investigated the interplay of habitual (model-free) and goal-directed (model-based) decision processes by using a two-stage Markov decision task in combination with event-related potentials (ERPs) and computational modeling. To manipulate the demands on model-based decision making, we applied two experimental conditions with different probabilities of transitioning from the first to the second stage of the task. As we expected, when the stage transitions were more predictable, participants showed greater model-based (planning) behavior. Consistent with this result, we found that stimulus-evoked parietal (P300) activity at the second stage of the task increased with the predictability of the state transitions. However, the parietal activity also reflected model-free information about the expected values of the stimuli, indicating that at this stage of the task both types of information are integrated to guide decision making. Outcome-related ERP components only reflected reward-related processes: Specifically, a medial prefrontal ERP component (the feedback-related negativity) was sensitive to negative outcomes, whereas a component that is elicited by reward (the feedback-related positivity) increased as a function of positive prediction errors. Taken together, our data indicate that stimulus-locked parietal activity reflects the integration of model-based and model-free information during decision making, whereas feedback-related medial prefrontal signals primarily reflect reward-related decision processes.

Coupling and dynamics of cortical and autonomic signals are linked to central inhibition during the wake-sleep transition

Maintaining temporal coordination across physiological systems is crucial at the wake-sleep transition. As shown in recent studies, the degree of coordination between brain and autonomic arousal influences attention, which highlights a previously unrecognised point of potential failure in the attention system. To investigate how cortical and autonomic dynamics are linked to the attentive process we analysed electroencephalogram, electrocardiogram and skin conductance data of 39 healthy adults recorded during a 2-h resting-state oddball experiment. We related cross-correlations to fluctuation periods of cortical and autonomic signals and correlated obtained measures to event-related potentials N1 and P2, reflecting excitatory and inhibitory processes. Increasing alignment of cortical and autonomic signals and longer periods of vigilance fluctuations corresponded to a larger and earlier P2; no such relations were found for N1. We compared two groups, with (I) and without measurable (II) delay in cortico-autonomic correlations. Individuals in Group II had more stable vigilance fluctuations, larger and earlier P2 and fell asleep more frequently than individuals in Group I. Our results support the hypothesis of a link between cortico-autonomic coupling and dynamics and central inhibition. Quantifying this link could help refine classification in psychiatric disorders with attention and sleep-related symptoms, particularly in ADHD, depression, and insomnia.

The impact of individuation on the bases of human empathic responding

While there is substantial overlap in the neural systems underlying empathy for people we know as opposed to strangers, social distance has been shown to significantly moderate empathic neural responses towards the negative experiences of others. Intriguingly however, variance in empathic neural responses towards known and unknown targets has not been reflected by behavioral differences as indexed by self-reported empathic ratings. One explanation for this disconnect is that empathic evaluations of known and unknown individuals draw on different bases (e.g. target identity/reactions) within the empathic process. To test this hypothesis, we utilized high density EEG to assess how individuating targets with personal names moderated the link between behavioral pain ratings and attentional processing oriented towards (a) initial target processing and (b) subsequent expressions target discomfort. Consistent with prior findings, no differences in pain ratings between individuated and unindividuated targets was observed. However, individual mean pain rating differences for individuated targets was strongly positively related to attentional processing levels, indexed by the P300, during the initial presentation of those targets, a relationship absent for unindividuated targets. In contrast, pain ratings for unindividuated targets was positively related to levels of attentional processing, indexed by the Late Positive Potential (LPP), during the subsequent discomfort expression stage. Furthermore, the LPP response to individuated target discomfort was positively linked to behavioral measures of emotional expressivity whereas the LPP response to unindividuated target discomfort was positively associated with cognitive appraisal. These findings suggest that individuation can significantly shift the bases of empathic responding.

Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo-mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

Genetic correlates of the development of theta event related oscillations in adolescents and young adults

The developmental trajectories of theta band (4-7Hz) event-related oscillations (EROs), a key neurophysiological constituent of the P3 response, were assessed in 2170 adolescents and young adults ages 12 to 25. The theta EROs occurring in the P3 response, important indicators of neurocognitive function, were elicited during the evaluation of task-relevant target stimuli in visual and auditory oddball tasks. Associations between the theta EROs and genotypic variants of 4 KCNJ6 single nucleotide polymorphisms (SNPs) were found to vary with age, sex, scalp location, and task modality. Three of the four KCNJ6 SNPs studied here were found to be significantly associated with the same theta EROs in adults in a previous family genome wide association study. Since measures of the P3 response have been found to be a useful endophenotypes for the study of a number of clinical and behavioral disorders, studies of genetic effects on its development in adolescents and young adults may illuminate neurophysiological factors contributing to the onset of these conditions.

Spatiotemporal mapping of epileptic spikes using simultaneous EEG-functional MRI

Epileptic spikes occur on the sub-second timescale and are known to involve not only epileptic foci but also large-scale distributed brain networks. There is likely to be a sequence of neural activity in multiple brain regions that occurs within the duration of a single spike, but standard electroencephalography-functional magnetic resonance imaging analyses, which use only the timing of the spikes to model the functional magnetic resonance imaging data, cannot determine the sequence of these activations. Our aim in this study is to temporally resolve these spatial activations to observe the spatiotemporal dynamics of the spike-related neural activity at a sub-second timescale. We studied eight focal epilepsy patients (age 11-42 years, six female) and used amplitude features of the electroencephalogram specific to different spike components (early and late peaks and troughs) to encode temporal information into our functional magnetic resonance imaging models. This enables us to associate each activation with a specific model of each of the spike components to infer the temporal order of these spike-related spatial activations. In seven of eight patients the distributed networks were associated with the late spike component. The focal activations were more variably coupled with time epochs, but tended to precede the distributed network effects. We also found that incorporating electroencephalogram features into the models increased sensitivity and in six patients revealed additional regions unseen in the standard analysis result. This included strong bilateral thalamus activation in two patients. We demonstrate the clinical utility of this approach in a patient who recently underwent a successful surgical resection of the region where we saw enhanced activation using electroencephalogram amplitude information specific to the early spike component. This focal cluster of activation was larger and more precisely tracked the anatomy compared to what was seen using the standard timing-based analysis. Our novel electroencephalography-functional magnetic resonance imaging data fusion approach, which utilizes information based on the single spike variability across all electroencephalogram channels, has the potential to help us better understand epileptic networks and aid in the interpretation of functional magnetic resonance imaging activation maps during treatment planning.

Brain substrates underlying auditory speech priming in healthy listeners and listeners with schizophrenia

BACKGROUND

Under 'cocktail party' listening conditions, healthy listeners and listeners with schizophrenia can use temporally pre-presented auditory speech-priming (ASP) stimuli to improve target-speech recognition, even though listeners with schizophrenia are more vulnerable to informational speech masking.

METHOD

Using functional magnetic resonance imaging, this study searched for both brain substrates underlying the unmasking effect of ASP in 16 healthy controls and 22 patients with schizophrenia, and brain substrates underlying schizophrenia-related speech-recognition deficits under speech-masking conditions.

RESULTS

In both controls and patients, introducing the ASP condition (against the auditory non-speech-priming condition) not only activated the left superior temporal gyrus (STG) and left posterior middle temporal gyrus (pMTG), but also enhanced functional connectivity of the left STG/pMTG with the left caudate. It also enhanced functional connectivity of the left STG/pMTG with the left pars triangularis of the inferior frontal gyrus (TriIFG) in controls and that with the left Rolandic operculum in patients. The strength of functional connectivity between the left STG and left TriIFG was correlated with target-speech recognition under the speech-masking condition in both controls and patients, but reduced in patients.

CONCLUSIONS

The left STG/pMTG and their ASP-related functional connectivity with both the left caudate and some frontal regions (the left TriIFG in healthy listeners and the left Rolandic operculum in listeners with schizophrenia) are involved in the unmasking effect of ASP, possibly through facilitating the following processes: masker-signal inhibition, target-speech encoding, and speech production. The schizophrenia-related reduction of functional connectivity between the left STG and left TriIFG augments the vulnerability of speech recognition to speech masking.