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A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.

Recordings from Parkinson's disease (PD) patients show strong beta-band oscillations (13-35 Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100 Hz) ameliorates motor symptoms and reduces beta activity in the basal ganglia and motor cortex, the effects of low-frequency DBS (<30 Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, whichmight be causal or epiphenomenal. Here, we investigated how subthalamic nucleus (STN) beta-band DBS affects cortical beta oscillations and motor performance. We recorded the magnetoencephalogram of 14 PD patients (nine males) with DBS implants while on their usual medication. Following a baseline recording (DBS OFF), we applied bipolar DBS at beta frequencies (10, 16, 20, 26, and 30 Hz) via the left electrode in a cyclic fashion, turning stimulation on (5 s) and off (3 s) repeatedly. Cyclic stimulation was applied at rest and during right-hand finger tapping. In the baseline recording, we observed a negative correlation between the strength of hemispheric beta power lateralization and the tap rate. Importantly, beta-band DBS accentuated the lateralization and reduced the tap rate proportionally. The change in lateralization was specific to the alpha/beta range (8-26 Hz), outlasted stimulation, and did not depend on the stimulation frequency, suggesting a remote-induced response rather than entrainment. Our study demonstrates that cortical beta oscillations can be manipulated by STN beta-band DBS. This manipulation has consequences for motor performance, supporting a causal role of beta oscillations. [ABSTRACT FROM AUTHOR]

The extraction and analysis of pitch underpin speech and music recognition, sound segregation, and other auditory tasks. Perceptually, pitch can be represented as a helix composed of two factors: height monotonically aligns with frequency, while chroma cyclically repeats at doubled frequencies. Although the early perceptual and neurophysiological mechanisms for extracting pitch from acoustic signals have been extensively investigated, the equally essential subsequent stages that bridge to high-level auditory cognition remain less well understood. How does the brain represent perceptual attributes of pitch at higher-order processing stages, and how are the neural representations formed over time? We used a machine learning approach to decode time-resolved neural responses of human listeners (10 females and 7 males) measured by magnetoencephalography across different pitches, hypothesizing that different pitches sharing similar neural representations would result in reduced decoding performance. We show that pitch can be decoded from lower-frequency neural responses within auditory-frontal cortical regions. Specifically, linear mixed-effects modeling reveals that height and chroma explain the decoding performance of delta band (0.5-4 Hz) neural activity at distinct latencies: a long-lasting height effect precedes a transient chroma effect, followed by a recurrence of height after chroma, indicating sequential processing stages associated with unique perceptual and neural characteristics. Furthermore, the localization analyses of the decoder demonstrate that height and chroma are associated with overlapping cortical regions, with differences observed in the right orbital and polar frontal cortex. The data provide a perspective motivating new hypotheses on the mechanisms of pitch representation. [ABSTRACT FROM AUTHOR]

Background/Objectives: The main objective of this systematic review was to explore the role of magnetoencephalography (MEG) in the diagnosis, assessment, and monitoring of mild traumatic brain injury (mTBI) and post-concussion syndrome (PCS). We aimed to evaluate the potential of some MEG biomarkers in detecting subtle brain abnormalities often missed by conventional imaging techniques. Methods: A systematic review was conducted using 25 studies that administered MEG to examine mTBI and PCS patients. The quality of the studies was assessed based on selection, comparability, and outcomes. Studies were analyzed for their methodology, evaluated parameters, and the clinical implications of using MEG for mTBI diagnosis. Results: MEG detected abnormal brain oscillations, including increased delta, theta, and gamma waves and disruptions in functional connectivity, particularly in the default mode and frontoparietal networks of patients suffering from mTBI. MEG consistently revealed abnormalities in mTBI patients even when structural imaging was normal. The use of MEG in monitoring recovery showed significant reductions in abnormal slow-wave activity corresponding to clinical improvements. Machine learning algorithms applied to MEG data demonstrated high sensitivity and specificity in distinguishing mTBI patients from healthy controls and predicting clinical outcomes. Conclusions: MEG provides a valuable diagnostic and prognostic tool for mTBI and PCS by identifying subtle neurophysiological abnormalities. The high temporal resolution and the ability to assess functional brain networks make MEG a promising complement to conventional imaging. Future research should focus on integrating MEG with other neuroimaging modalities and standardizing MEG protocols for clinical use. [ABSTRACT FROM AUTHOR]

Oral communication regularly takes place amidst background noise, requiring the ability to selectively attend to a target speech stream. Musical training has been shown to be beneficial for this task. Regarding the underlying neural mechanisms, recent studies showed that the speech envelope is tracked by neural activity in auditory cortex, which plays a role in the neural processing of speech, including speech in noise. The neural tracking occurs predominantly in two frequency bands, the delta and the theta bands. However, much regarding the specifics of these neural responses, as well as their modulation through musical training, still remain unclear. Here, we investigated the delta- and theta-band cortical tracking of the speech envelope of target and distractor speech using magnetoencephalography (MEG) recordings. We thereby assessed both musicians and nonmusicians to explore potential differences between these groups. The cortical speech tracking was quantified through source-reconstructing the MEG data and subsequently relating the speech envelope in a certain frequency band to the MEG data using linear models. We thereby found the theta-band tracking to be dominated by early responses with comparable magnitudes for target and distractor speech, whereas the delta band tracking exhibited both earlier and later responses that were modulated by selective attention. Almost no significant differences emerged in the neural responses between musicians and nonmusicians. Our findings show that only the speech tracking in the delta but not in the theta band contributes to selective attention, but that this mechanism is essentially unaffected by musical training. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder that manifests through motor and non-motor symptoms. Understanding the alterations in brain connectivity associated with PD remains a challenge that is crucial for enhancing diagnosis and clinical management. Methods: This study utilized Magnetoencephalography (MEG) to investigate brain connectivity in PD patients compared to healthy controls (HCs) by applying eigenvector centrality (EC) measures across different frequency bands. Results: Our findings revealed significant differences in EC between PD patients and HCs in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands. To go into further detail, in the alpha frequency band, PD patients in the frontal lobe showed higher EC values compared to HCs. Additionally, we found statistically significant correlations between EC measures and clinical impairment scores (UPDRS-III). Conclusions: The proposed results suggest that MEG-derived EC measures can reveal important alterations in brain connectivity in PD, potentially serving as biomarkers for disease severity. [ABSTRACT FROM AUTHOR]

Studies of perception have long shown that the brain adds information to its sensory analysis of the physical environment. A touchstone example for humans is language use: to comprehend a physical signal like speech, the brain must add linguistic knowledge, including syntax. Yet, syntactic rules and representations are widely assumed to be atemporal (i.e., abstract and not bound by time), so they must be translated into time-varying signals for speech comprehension and production. Here, we test 3 different models of the temporal spell-out of syntactic structure against brain activity of people listening to Dutch stories: an integratory bottom-up parser, a predictive top-down parser, and a mildly predictive left-corner parser. These models build exactly the same structure but differ in when syntactic information is added by the brain—this difference is captured in the (temporal distribution of the) complexity metric "incremental node count." Using temporal response function models with both acoustic and information-theoretic control predictors, node counts were regressed against source-reconstructed delta-band activity acquired with magnetoencephalography. Neural dynamics in left frontal and temporal regions most strongly reflect node counts derived by the top-down method, which postulates syntax early in time, suggesting that predictive structure building is an important component of Dutch sentence comprehension. The absence of strong effects of the left-corner model further suggests that its mildly predictive strategy does not represent Dutch language comprehension well, in contrast to what has been found for English. Understanding when the brain projects its knowledge of syntax onto speech, and whether this is done in language-specific ways, will inform and constrain the development of mechanistic models of syntactic structure building in the brain. Comprehending spoken language requires the encoding of information beyond what is presented in the raw speech signals. Using MEG recordings during natural story listening, this study shows that the human brain projects its knowledge of syntax onto speech in a predictive and language-dependent way. [ABSTRACT FROM AUTHOR]

Perceptual awareness results from an intricate interaction between external sensory input and the brain's spontaneous activity. Pre-stimulus ongoing activity influencing conscious perception includes both brain oscillations in the alpha (7 to 14 Hz) and beta (14 to 30 Hz) frequency ranges and aperiodic activity in the slow cortical potential (SCP, <5 Hz) range. However, whether brain oscillations and SCPs independently influence conscious perception or do so through shared mechanisms remains unknown. Here, we addressed this question in 2 independent magnetoencephalography (MEG) data sets involving near-threshold visual perception tasks in humans using low-level (Gabor patches) and high-level (objects, faces, houses, animals) stimuli, respectively. We found that oscillatory power and large-scale SCP activity influence conscious perception through independent mechanisms that do not have shared variance. In addition, through mediation analysis, we show that pre-stimulus oscillatory power and SCP activity have different relations to pupil size—an index of arousal—in their influences on conscious perception. Together, these findings suggest that oscillatory power and SCPs independently contribute to perceptual awareness, with distinct relations to pupil-linked arousal. Conscious perception is influenced by brain oscillations and slow cortical potentials within the brain, but it is not clear if they influence perception via shared mechanisms. This study shows that oscillations and aperiodic brain activity influence conscious perception independent of each other. [ABSTRACT FROM AUTHOR]

The human visual system is equipped to rapidly and implicitly learn and exploit the statistical regularities in our environment. Within visual search, contextual cueing demonstrates how implicit knowledge of scenes can improve search performance. This is commonly interpreted as spatial context in the scenes becoming predictive of the target location, which leads to a more efficient guidance of attention during search. However, what drives this enhanced guidance is unknown. First, it is under debate whether the entire scene (global context) or more local context drives this phenomenon. Second, it is unclear how exactly improved attentional guidance is enabled by target enhancement and distractor suppression. In the present magnetoencephalography experiment, we leveraged rapid invisible frequency tagging to answer these two outstanding questions. We found that the improved performance when searching implicitly familiar scenes was accompanied by a stronger neural representation of the target stimulus, at the cost specifically of those distractors directly surrounding the target. Crucially, this biasing of local attentional competition was behaviorally relevant when searching familiar scenes. Taken together, we conclude that implicitly learned spatial predictive context improves how we search our environment by sharpening the attentional field. [ABSTRACT FROM AUTHOR]

Human vocabularies include specific words to communicate interpersonal behaviors, a core linguistic function mainly afforded by social verbs (SVs). This skill has been proposed to engage dedicated systems subserving social knowledge. Yet, neurocognitive evidence is scarce, and no study has examined spectro-temporal and spatial signatures of SV access. Here, we combined magnetoencephalography and time-resolved decoding methods to characterize the neural dynamics underpinning SVs, relative to nonsocial verbs (nSVs), via a lexical decision task. Time-frequency analysis revealed stronger beta (20 Hz) power decreases for SVs in right fronto-temporal sensors at early stages. Time-resolved decoding showed that beta oscillations significantly discriminated SVs and nSVs between 180 and 230 ms. Sources of this effect were traced to the right anterior superior temporal gyrus (a key hub underpinning social conceptual knowledge) as well as parietal, pre/motor and prefrontal cortices supporting nonverbal social cognition. Finally, representational similarity analyses showed that the observed fronto-temporal neural patterns were specifically predicted by verbs' socialness, as opposed to other psycholinguistic dimensions such as sensorimotor content, emotional valence, arousal, and concreteness. Overall, verbal conveyance of socialness seems to involve distinct neurolinguistic patterns, partly shared by more general sociocognitive and lexicosemantic processes. [ABSTRACT FROM AUTHOR]

Simple Summary: This study used the superlet transform and the cluster depth test to compute the time–frequency representation (TFR) of the oscillatory differences between neural activities recorded by magnetoencephalography with optically pumped magnetometers while participants were listening to congruent and incongruent Chinese semantics. Then, the differences were parameterized based on the definition of local events. The results showed the TFRs of the differences in oscillatory activity occurring during various semantic processing tasks. The specific times, frequencies, and brain regions in which these differences occurred were demonstrated in detail. These results revealed the specific manifestations of the differences in neural oscillation activities during the cognition of semantically congruent and incongruent stimuli, which also revealed the potential causes of the differences in N400m neural activity and mismatch activities from the perspective of neural oscillations. Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time–frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time–frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics. Subsequently, ODTFR was parameterized based on the definition of local events. Finally, this study calculated the specific time–frequency values at which oscillation differences occurred in multiple auditory-language-processing regions. It was found that these oscillatory differences appeared in most regions and were mainly concentrated in the beta band. The average peak frequency of these oscillatory differences was 15.7 Hz, and the average peak time was 457 ms. These findings offer a fresh perspective on the neural mechanisms underlying the processing of distinct Chinese semantics and provide references and insights for analyzing language-related brain activities recorded by OPM-MEG. [ABSTRACT FROM AUTHOR]

Abstract Background Changes in amyloid beta (Aβ) and phosphorylated tau brain levels are known to affect brain network organization but very little is known about how plasma markers can relate to these measures. We aimed to address the relationship between centrality network changes and two plasma pathology markers: phosphorylated tau at threonine 231 (p-tau231), a proxy for early Aβ change, and neurofilament light chain (Nfl), a marker of axonal degeneration. Methods One hundred and four cognitively unimpaired individuals were divided into a high pathology load (33 individuals; HP) group and a low pathology (71 individuals; LP) one. All participants underwent a magnetoencephalography (MEG) recording, a neuropsychological evaluation and plasma sampling. With the MEG recordings, a compound centrality score for each brain source was calculated that considered both intra- and inter-band links. For each group, the relationship between this centrality score and the two plasma markers was studied by means of correlation analyses. Furthermore, the relationship between the centrality score and the plasma markers among the HP and LP groups was compared. Lastly, we investigated whether hubs were more intensely affected by these changes. Results Increasing concentrations of p-tau231, which is a proxy of Aβ pathology, were associated with greater theta centrality score of posterior areas that increased their connectedness in the theta range with the remaining areas, regardless of the latter’s frequency range. The opposite relationship was found for left areas that decreased their centrality score in the gamma frequency range. These results only emerged for HP individuals, who showed a significantly different relationship between centrality and p-tau231 compared to LP individuals. Hubs’ centrality score in the theta band was significantly more affected by p-tau231 levels compared to less central regions. Conclusions Early brain network reorganizations in cognitively unimpaired individuals are associated with elevated plasma p-tau231, a proxy for very early Aβ changes, only among individuals who show signs of a higher pathology load. Posterior centrality score increases in the theta band are congruent with previous literature and theoretical models, while gamma centrality score losses could be associated with inhibitory neuron dysfunction. Hubs were more intensely affected by p-tau231, and changed to a higher degree, thus corroborating hubs’ vulnerability.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of taus elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These tau ICs showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trials needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing early-stage (-100 to 0 ms) M1 activity during ~l min recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and upper-gamma (60-90 Hz) bands in 13 healthy participants (26 datasets) and three presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) or gamma/upper-gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In three presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement-related brain-muscle coupling above the movement frequency and its harmonics.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

BackgroundAlzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).MethodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).ResultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.ConclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Abstract Background Autism is associated with alterations of social communication, such as during face-to-face interactions. This study aimed to probe face processing in autistics with normal IQ utilizing magnetoencephalography to examine event-related fields within the fusiform gyrus during face perception. Methods A case–control cohort of 22 individuals diagnosed with autism and 20 age-matched controls (all male, age 29.3 ± 6.9 years) underwent magnetoencephalographic scanning during an active task while observing neutral faces, face-like pareidolic objects, and non-face objects. The fusiform face area was identified using a face localizer for each participant, and the cortical activation pattern was normalized onto an average brain for subsequent analysis. Results Early post-stimulus activation amplitudes (before 100–200 ms) indicated differentiation between stimuli containing fundamental facial features and non-face objects in both groups. In contrast, later activation (400–550 ms) differentiated real faces from both pareidolic and non-face objects across both groups and faces from objects in controls but not in autistics. There was no effect of autistic-like traits. Conclusions The absence of group differences in early activation suggest intact face detection in autistics possessing a normal IQ. Later activation captures a greater degree of the complexity and social information from actual faces. Although both groups distinguished faces from pareidolic and non-face objects, the control group exhibited a slightly heightened differentiation at this latency, indicating a potential disadvantage for autistics in real face processing. The subtle difference in late-stage face processing observed in autistic individuals may reflect specific cognitive mechanisms related to face perception in autism.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of brain–computer interfaces (BCIs). Multimodal BCIs have been able to gain significant traction given their potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), and single-unit activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with a discussion on the available literature, highlighting recent advances, challenges, and future directions for each of these modalities.

Abstract Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the automatic detection of spikes using deep learning in single center. In this study, we performed a multi-center study using six MEG centers to improve the performance of the automated detection of neuromagnetically recorded epileptic spikes, which we previously developed using deep learning. Data from four centers were used for training and evaluation (internal data), and the remaining two centers were used for evaluation only (external data). We used a five-fold subject-wise cross-validation technique to train and evaluate the models. A comparison showed that the multi-center model outperformed the single-center model in terms of performance. The multi-center model achieved an average ROC-AUC of 0.9929 and 0.9426 for the internal and external data, respectively. The median distance between the neurophysiologist-analyzed and automatically analyzed dipoles was 4.36 and 7.23 mm for the multi-center model for internal and external data, respectively, indicating accurate detection of epileptic spikes. By training data from multiple centers, automated analysis can improve spike detection and reduce the analysis workload for neurophysiologists. This study suggests that the multi-center model has the potential to detect spikes within 1 cm of a neurophysiologist’s analysis. This multi-center model can significantly reduce the number of hours required by neurophysiologists to detect spikes.

Objective Methods Results Significance The most common medically resistant epilepsy (MRE) involves the temporal lobe (TLE), and children designated as temporal plus epilepsy (TLE+) have a five‐times increased risk of postoperative surgical failure. This retrospective, blinded, cross‐sectional study aimed to correlate visual and computational analyses of magnetoencephalography (MEG) virtual sensor waveforms with surgical outcome and epilepsy classification (TLE and TLE+).Patients with MRE who underwent MEG and iEEG monitoring and had at least 1 year of postsurgical follow‐up were included in this retrospective analysis. User‐defined virtual sensor (UDvs) beamforming was completed with virtual sensors placed manually and symmetrically in the bilateral amygdalohippocampi, inferior/middle/superior temporal gyri, insula, suprasylvian operculum, orbitofrontal cortex, and temporoparieto‐occipital junction. Additionally, MEG effective connectivity was computed and quantified using eigenvector centrality (EC) to identify hub regions. More conventional MEG methods (equivalent current dipole [ECD], standardized low‐resolution brain electromagnetic tomography, synthetic aperture magnetometry beamformer), UDvs beamformer, and EC hubs were compared to iEEG.Eighty patients (38 female, 42 male) with MRE (mean age = 11.3 ± 6.2 years, range = 1.0–31.5) were identified and included. Twenty‐five patients (31.3%) were classified as TLE, whereas 55 (68.8%) were TLE+. When modeling the association between MEG method, iEEG, and postoperative surgical outcome (odds of a worse [International League Against Epilepsy (ILAE) class > 2] outcome), a significant result was seen only for UDvs beamformer (odds ratio [OR] = 1.22, 95% confidence interval [CI] = 1.01–1.48). Likewise, when the relationship between MEG method, iEEG, and classification (TLE and TLE+) was modeled, only UDvs beamformer had a significant association (OR = 1.47, 95% CI = 1.13–1.92). When modeling the association between EC hub location and resection/ablation to postoperative surgical outcome (odds of a good [ILAE 1–2] outcome), a significant association was seen (OR = 1.22, 95% CI = 1.05–1.43).This study demonstrates a concordance between UDvs beamforming and iEEG that is related to both postsurgical seizure outcome and presurgical classification of epilepsy (TLE and TLE+). UDvs beamforming could be a complementary approach to the well‐established ECD, improving invasive electrode and surgical resection planning for patients undergoing epilepsy surgery evaluations and treatments. [ABSTRACT FROM AUTHOR]

The study of the cortical basis of reading has greatly benefited from the use of naturalistic paradigms that permit eye movements. However, due to the short stimulus lengths used in most naturalistic reading studies, it remains unclear how reading of texts comprising more than isolated sentences modulates cortical processing. To address this question, we used magnetoencephalography to study the spatiospectral distribution of oscillatory activity during naturalistic reading of multi-page texts. In contrast to previous results, we found abundant activity in the right hemisphere in several frequency bands, whereas reading-related modulation of neural activity in the left hemisphere was quite limited. Our results show that the role of the right hemisphere may be importantly emphasized as the reading process extends beyond single sentences. [ABSTRACT FROM AUTHOR]

Speech production and perception involve complex neural dynamics in the human brain. Using magnetoencephalography, our study explores the interaction between cortico-cortical and cortico-subcortical connectivities during these processes. Our connectivity findings during speaking revealed a significant connection from the right cerebellum to the left temporal areas in low frequencies, which displayed an opposite trend in high frequencies. Notably, high-frequency connectivity was absent during the listening condition. These findings underscore the vital roles of cortico-cortical and cortico-subcortical connections within the speech production and perception network. The results of our new study enhance our understanding of the complex dynamics of brain connectivity during speech processes, emphasizing the distinct frequency-based interactions between various brain regions. [ABSTRACT FROM AUTHOR]

As speech-related symptoms of Landau–Kleffner syndrome (LKS) are often refractory to pharmacotherapy, and resective surgery is rarely available due to the involvement of the vital cortex, multiple subpial transection (MST) was suggested to improve patient outcome and preserve cortical functions. Here, we analyze the reports about MST use in LKS, regarding its impact on seizures, language, behavior, EEG, cognition, and reported adverse effects. In conditions like LKS, surgery is not a popular treatment option and presumably should be considered sooner. Candidates for MST should be selected carefully, optimally with the unilateral onset of epileptic activity. Laterality can be assessed using a methohexital suppression test (MHXT), electrical intracarotid amobarbital test, or magnetoencephalography. After MST, a significant percentage of LKS patients present seizure-free status, normalization of EEG patterns, and rapid behavior improvement. Data comprising language outcomes are mixed, with improvement reported in 23.8–100% of cases, and no superiority was found in the only study comparing MST with a non-surgical group. Cognitive outcomes are not well described. The risk linked to MST is described as low, with cerebral edema and transient neurological deficits being the most common complications. MST successfully improves seizure, EEG, and behavioral outcomes in LKS patients. However, its beneficial impact on language and cognition is not well proven. It is generally a safe neurological operation. [ABSTRACT FROM AUTHOR]

Background: Autism is associated with alterations of social communication, such as during face-to-face interactions. This study aimed to probe face processing in autistics with normal IQ utilizing magnetoencephalography to examine event-related fields within the fusiform gyrus during face perception. Methods: A case–control cohort of 22 individuals diagnosed with autism and 20 age-matched controls (all male, age 29.3 ± 6.9 years) underwent magnetoencephalographic scanning during an active task while observing neutral faces, face-like pareidolic objects, and non-face objects. The fusiform face area was identified using a face localizer for each participant, and the cortical activation pattern was normalized onto an average brain for subsequent analysis. Results: Early post-stimulus activation amplitudes (before 100–200 ms) indicated differentiation between stimuli containing fundamental facial features and non-face objects in both groups. In contrast, later activation (400–550 ms) differentiated real faces from both pareidolic and non-face objects across both groups and faces from objects in controls but not in autistics. There was no effect of autistic-like traits. Conclusions: The absence of group differences in early activation suggest intact face detection in autistics possessing a normal IQ. Later activation captures a greater degree of the complexity and social information from actual faces. Although both groups distinguished faces from pareidolic and non-face objects, the control group exhibited a slightly heightened differentiation at this latency, indicating a potential disadvantage for autistics in real face processing. The subtle difference in late-stage face processing observed in autistic individuals may reflect specific cognitive mechanisms related to face perception in autism. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Intracranial single‐pulse electrical stimulation (SPES) can elicit cortico‐cortical evoked potentials. Their investigation with intracranial EEG is biased by the limited number and selected location of electrodes, which could be circumvented by simultaneous non‐invasive whole‐scalp recording. This study aimed at investigating the ability of magnetoencephalography (MEG) to characterize cortico‐cortical evoked fields (CCEFs) and effective connectivity between the epileptogenic zone (EZ) and non‐epileptogenic zone (i.e., non‐involved [NIZ]).A total of 301 SPES trains (at 0.9 Hz during 120 s) were performed in 10 patients with refractory focal epilepsy. MEG signals were denoised, epoched, averaged, and decomposed using independent component analysis. Significant response deflections and significant source generators were detected. Peak latency/amplitude were compared between each different cortical/subcortical structure of the NIZ containing more than five SPES, and then between the EZ and corresponding brain structures in the NIZ.MEG detected and localized polymorphic/polyphasic CCEFs, including one to eight significant consecutive deflections. The latency and amplitude of CCEFs within the NIZ differed significantly depending on the stimulated brain structure. Compared with the corresponding NIZ, SPES within the extratemporal EZ demonstrated delayed CCEF latency, whereas SPES within the temporal EZ showed decreased CCEF amplitude. SPES within the EZ elicited a significantly higher rate of CCEFs within the stimulated lobe compared with those within the NIZ.This study reveals polymorphic CCEFs with complex spatiotemporal dynamics both within the NIZ and EZ. It highlights significant differences in effective connectivity of the epileptogenic network. These cortico‐cortical evoked responses could thus contribute to increasing the yield of intracranial recordings. [ABSTRACT FROM AUTHOR]

While humans typically saccade every ~250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes~50 ms to initiate and execute a saccade, this leaves only~200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements. Using multivariate pattern analysis, we demonstrate that future parafoveal images could be decoded at the feature and category level with peak decoding at ~110 and ~165 ms, respectively, while the decoding of fixated objects at the feature and category level peaked at~100 and~145 ms. The decoding of features and categories was contingent on the objects being saccade goals. In sum, we provide insight on the neuronal mechanism of presaccadic attention by demonstrating that feature- and category-specific information of foveal and parafoveal objects can be extracted in succession within a ~200 ms intersaccadic interval. These findings rule out strict serial or parallel processing accounts but are consistent with a pipeline mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (TBI) sustained in a deployment environment (deployment TBI) can be associated with increased severity of long-term symptom presentation, despite the general expectation of full recovery from a single mild TBI. The heterogeneity in the effects of deployment TBI on the brain can be difficult for a case–control design to capture. The functional connectome of the brain is an approach robust to heterogeneity that allows global measurement of effects using a common set of outcomes. The present study evaluates how differences in the functional connectome relate to remote symptom presentation following combat deployment and determines if deployment TBI, blast exposure, or post-traumatic stress disorder (PTSD) are associated with these neurological differences. Participants included 181 Iraq and Afghanistan combat-exposed Veterans, approximately 9.4 years since deployment. Structured clinical interviews provided diagnoses and characterizations of TBI, blast exposure, and PTSD. Self-report measures provided characterization of long-term symptoms (psychiatric, behavioral health, and quality of life). Resting-state magnetoencephalography was used to characterize the functional connectome of the brain individually for each participant. Linear regression identified factors contributing to symptom presentation including relevant covariates, connectome metrics, deployment TBI, blast exposure PTSD, and conditional relationships. Results identified unique contributions of aspects of the connectome to symptom presentation. Furthermore, several conditional relationships were identified, demonstrating that the connectome was related to outcomes in the presence of only deployment-related TBI (including blast-related TBI, primary blast TBI, and blast exposure). No conditional relationships were identified for PTSD; however, the main effect of PTSD on symptom presentation was significant for all models. These results demonstrate that the connectome captures aspects of brain function relevant to long-term symptom presentation, highlighting that deployment-related TBI influences symptom outcomes through a neurological pathway. These findings demonstrate that changes in the functional connectome associated with deployment-related TBI are relevant to symptom presentation over a decade past the injury event, providing a clear demonstration of a brain-based mechanism of influence. [ABSTRACT FROM AUTHOR]

Objective: Early detection and prediction of Alzheimer's Disease are paramount for elucidating neurodegenerative processes and enhancing cognitive resilience. Structural Magnetic Resonance Imaging (sMRI) provides insights into brain morphology, while resting-state Magnetoencephalography (rsMEG) elucidates functional aspects. However, inherent disparities between these multimodal neuroimaging modalities pose challenges to the effective integration of multimodal features. Approach: To address these challenges, we propose a deep learning-based multimodal classification framework for Alzheimer's disease, which harnesses the fusion of pivotal features from sMRI and rsMEG to augment classification precision. Utilizing the BioFIND dataset, classification trials were conducted on 163 Mild Cognitive Impairment cases and 144 cognitively Healthy Controls. Results: The study findings demonstrate that the InterFusion method, combining sMRI and rsMEG data, achieved a classification accuracy of 0.827. This accuracy significantly surpassed the accuracies obtained by rsMEG only at 0.710 and sMRI only at 0.749. Moreover, the evaluation of different fusion techniques revealed that InterFusion outperformed both EarlyFusion with an accuracy of 0.756 and LateFusion with an accuracy of 0.801. Additionally, the study delved deeper into the role of different frequency band features of rsMEG in fusion by analyzing six frequency bands, thus expanding the diagnostic scope. Discussion: These results highlight the value of integrating resting-state rsMEG and sMRI data in the early diagnosis of Alzheimer's disease, demonstrating significant potential in the field of neuroscience diagnostics. [ABSTRACT FROM AUTHOR]

The spontaneous oscillations within the brain are intimately linked to the hierarchical structures of the cortex, as evidenced by the cross-cortical gradient between parametrized spontaneous oscillations and cortical locations. Despite the significance of both peak frequency and peak time in characterizing these oscillations, limited research has explored the relationship between peak time and cortical locations. And no studies have demonstrated that the cross-cortical gradient can be measured by optically pumped magnetometer-based magnetoencephalography (OPM-MEG). Therefore, the cross-cortical gradient of parameterized spontaneous oscillation was analyzed for oscillations recorded by OPM-MEG using restricted maximum likelihood estimation with a linear mixed-effects model. It was validated that OPM-MEG can measure the cross-cortical gradient of spontaneous oscillations. Furthermore, results demonstrated the difference in the cross-cortical gradient between spontaneous oscillations during eye-opening and eye-closing conditions. The methods and conclusions offer potential to integrate electrophysiological and structural information of the brain, which contributes to the analysis of oscillatory fluctuations across the cortex recorded by OPM-MEG. [ABSTRACT FROM AUTHOR]

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions. [ABSTRACT FROM AUTHOR]

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of brain–computer interfaces (BCIs). Multimodal BCIs have been able to gain significant traction given their potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), and single-unit activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with a discussion on the available literature, highlighting recent advances, challenges, and future directions for each of these modalities. [ABSTRACT FROM AUTHOR]

Like all domains of cognition, language processing is affected by top–down knowledge. Classic evidence for this is missing blatant errors in the signal. In sentence comprehension, one instance is failing to notice word order errors, such as transposed words in the middle of a sentence: “you that read wrong” (Mirault et al., 2018). Our brains seem to fix such errors, since they are incompatible with our grammatical knowledge, but how do our brains do this? Following behavioral work on inner transpositions, we flashed four-word sentences for 300 ms using rapid parallel visual presentation (Snell and Grainger, 2017). We compared magnetoencephalography responses to fully grammatical and reversed sentences (24 human participants: 21 females, 4 males). The left lateral language cortex robustly distinguished grammatical and reversed sentences starting at 213 ms. Thus, the influence of grammatical knowledge begun rapidly after visual word form recognition (Tarkiainen et al., 1999). At the earliest stage of this neural “sentence superiority effect,” inner transpositions patterned between grammatical and reversed sentences, showing evidence that the brain initially “noticed” the error. However, 100 ms later, inner transpositions became indistinguishable from grammatical sentences, suggesting at this point, the brain had “fixed” the error. These results show that after a single glance at a sentence, syntax impacts our neural activity almost as quickly as higher-level object recognition is assumed to take place (Cichy et al., 2014). The earliest stage involves detailed comparisons between the bottom–up input and grammatical knowledge, while shortly afterward, top–down knowledge can override an error in the stimulus. [ABSTRACT FROM AUTHOR]

Humans possess a remarkable ability to rapidly access diverse information from others’ faces with just a brief glance, which is crucial for intricate social interactions. While previous studies using event-related potentials/fields have explored various face dimensions during this process, the interplay between these dimensions remains unclear. Here, by applying multivariate decoding analysis to neural signals recorded with optically pumped magnetometer magnetoencephalography, we systematically investigated the temporal interactions between invariant and variable aspects of face stimuli, including race, gender, age, and expression. First, our analysis revealed unique temporal structures for each face dimension with high test–retest reliability. Notably, expression and race exhibited a dominant and stably maintained temporal structure according to temporal generalization analysis. Further exploration into the mutual interactions among face dimensions uncovered age effects on gender and race, as well as expression effects on race, during the early stage (∼200–300 ms post-face presentation). Additionally, we observed a relatively late effect of race on gender representation, peaking ∼350 ms after the stimulus onset. Taken together, our findings provide novel insights into the neural dynamics underlying the multidimensional aspects of face perception and illuminate the promising future of utilizing OPM-MEG for exploring higher-level human cognition. [ABSTRACT FROM AUTHOR]

In everyday tasks, our focus of attention shifts seamlessly between contents in the sensory environment and internal memory representations. Yet, research has mainly considered external and internal attention in isolation. We used magnetoencephalography to compare the neural dynamics of shifting attention to visual contents within vs. between the external and internal domains. Participants performed a combined perception and working-memory task in which two sequential cues guided attention to upcoming (external) or memorized (internal) sensory information. Critically, the second cue could redirect attention to visual content within the same or alternative domain as the first cue. Multivariate decoding unveiled distinct patterns of human brain activity when shifting attention within vs. between domains. Brain activity distinguishing withinfrom between-domain shifts was broadly distributed and highly dynamic. Intriguingly, crossing domains did not invoke an additional stage prior to shifting attention. Alpha lateralization, a canonical marker of shifting spatial attention, showed no delay when cues redirected attention to the same vs. alternative domain. Instead, evidence suggested that neural states associated with a given domain linger and influence subsequent shifts of attention within vs. between domains. Our findings provide critical insights into the neural dynamics that govern attentional shifts between perception and working memory. [ABSTRACT FROM AUTHOR]

Stroke causes pronounced and widespread slowing of neural activity. Despite decades of work exploring these abnormal neural dynamics and their associated functional impairments, their causes remain largely unclear. To close this gap in understanding, we applied a neurophysiological corticothalamic circuit model to simulate magnetoencephalography (MEG) power spectra recorded from chronic stroke patients. Comparing model-estimated physiological parameters to those of controls, patients demonstrated significantly lower intrathalamic inhibition in the lesioned hemisphere, despite the absence of direct damage to the thalamus itself. We hypothesized that this disinhibition could instead be related to secondary degeneration of the thalamus, for which growing evidence exists in the literature. Further analyses confirmed that spectral slowing correlated significantly with overall secondary degeneration of the ipsilesional thalamus, encompassing decreased thalamic volume, altered tissue microstructure, and decreased blood flow. Crucially, this relationship was mediated by model-estimated thalamic disinhibition, suggesting a causal link between secondary thalamic degeneration and abnormal brain dynamics via thalamic disinhibition. Finally, thalamic degeneration was correlated significantly with poorer cognitive and language outcomes, but not lesion volume, reinforcing that thalamus damage may account for additional individual variability in poststroke disability. Overall, our findings indicate that the frequently observed poststroke slowing reflects a disruption of corticothalamic circuit dynamics due to secondary thalamic dysfunction, and highlights the thalamus as an important target for understanding and potentially treating poststroke brain dysfunction. [ABSTRACT FROM AUTHOR]

Introduction: Much research on the treatment of paranoia has involved cognitive-behavioural interventions that address explicit social cognition processes. However, much of human cognition is preverbal or implicit, raising the possibility that such social judgements are implicated in paranoia. One type of implicit social cognition that has been investigated concerning paranoia is implicit self-esteem with some evidence that it may be possible to change implicit self-esteem using techniques based on conditioning theory. Therefore, the primary purpose of this research is to further evaluate the potential of this approach. At the same time, as a secondary purpose, we introduce a novel way of measuring social cognition that, we argue, has utility for investigating the psychological processes involved in paranoia. Method: We conducted two proof-of-concept studies of a novel brief intervention based on evaluative conditioning, targeting implicit cognition. The first study was conducted with a large non-clinical sample, while the second study included a small series of psychotic patients. As part of our proof-of-concept evaluation of the potential of evaluative conditioning, we attempted to probe for neurophysiological changes following the intervention using magnetoencephalography in an exploratory way in the clinical sample. Results: Our results revealed that both non-clinical and clinical participants in the experimental group showed a significant change in how they evaluated themselves in the social cognition task, which could be related to the perception of social information in a less threatening way. In addition, clinical participants in the experimental group showed changes in brain activity during the social cognition task, particularly in regions involved in emotional reactivity and mentalization processes. Discussion: Our results are encouraging, suggesting that implicit cognition is manipulable, that such manipulation affects underlying neurophysiological mechanisms, and that there may be an impact on paranoid symptoms. However, much more work is required to determine whether this approach can produce meaningful clinical change and be delivered in routine clinical settings. Finally, it is important to note that we are not claiming the clinical effectiveness of our intervention, which is in a very early stage of development. Our goal here is to demonstrate clinical possibilities that warrant further investigation [ABSTRACT FROM AUTHOR]