Decoding time-resolved neural representations of orientation ensemble perception.

The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation.

Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study.

AIMS: Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia. METHODS: A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia. RESULTS: The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity. CONCLUSIONS: This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.

Multivariate pattern analysis of medical imaging-based Alzheimer's disease.

Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.

Multivariate pattern analysis of cooperation and competition in constructive action.

The neural underpinning of cooperative and competitive constructive activity has been investigated using mass-univariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure.

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data.

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Testing two-step models of negative quantification using a novel machine learning analysis of EEG.

The sentences "More than half of the students passed the exam" and "Fewer than half of the students failed the exam" describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. "Fewer than half of the dots are blue") that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with fewer than half. We provide an alternative interpretation of our results in line with an expectation-based pragmatic account.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Shared neural codes of recognition memory.

Recognition memory research has identified several electrophysiological indicators of successful memory retrieval, known as old-new effects. These effects have been observed in different sensory domains using various stimulus types, but little attention has been given to their similarity or distinctiveness and the underlying processes they may share. Here, a data-driven approach was taken to investigate the temporal evolution of shared information content between different memory conditions using openly available EEG data from healthy human participants of both sexes, taken from six experiments. A test dataset involving personally highly familiar and unfamiliar faces was used. The results show that neural signals of recognition memory for face stimuli were highly generalized starting from around 200 ms following stimulus onset. When training was performed on non-face datasets, an early (around 200-300 ms) to late (post-400 ms) differentiation was observed over most regions of interest. Successful cross-classification for non-face stimuli (music and object/scene associations) was most pronounced in late period. Additionally, a striking dissociation was observed between familiar and remembered objects, with shared signals present only in the late window for correctly remembered objects, while cross-classification for familiar objects was successful in the early period as well. These findings suggest that late neural signals of memory retrieval generalize across sensory modalities and stimulus types, and the dissociation between familiar and remembered objects may provide insight into the underlying processes.

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Unveiling the neural dynamics of conscious perception in rapid object recognition.

Our brain excels at recognizing objects, even when they flash by in a rapid sequence. However, the neural processes determining whether a target image in a rapid sequence can be recognized or not remains elusive. We used electroencephalography (EEG) to investigate the temporal dynamics of brain processes that shape perceptual outcomes in these challenging viewing conditions. Using naturalistic images and advanced multivariate pattern analysis (MVPA) techniques, we probed the brain dynamics governing conscious object recognition. Our results show that although initially similar, the processes for when an object can or cannot be recognized diverge around 180 ms post-appearance, coinciding with feedback neural processes. Decoding analyses indicate that gist perception (partial conscious perception) can occur at ∼120 ms through feedforward mechanisms. In contrast, object identification (full conscious perception of the image) is resolved at ∼190 ms after target onset, suggesting involvement of recurrent processing. These findings underscore the importance of recurrent neural connections in object recognition and awareness in rapid visual presentations.

Observing the suppression of individual aversive memories from conscious awareness.

When reminded of an unpleasant experience, people often try to exclude the unwanted memory from awareness, a process known as retrieval suppression. Here we used multivariate decoding (MVPA) and representational similarity analyses on EEG data to track how suppression unfolds in time and to reveal its impact on item-specific cortical patterns. We presented reminders to aversive scenes and asked people to either suppress or to retrieve the scene. During suppression, mid-frontal theta power within the first 500 ms distinguished suppression from passive viewing of the reminder, indicating that suppression rapidly recruited control. During retrieval, we could discern EEG cortical patterns relating to individual memories-initially, based on theta-driven visual perception of the reminders (0 to 500 ms) and later, based on alpha-driven reinstatement of the aversive scene (500 to 3000 ms). Critically, suppressing retrieval weakened (during 360 to 600 ms) and eventually abolished item-specific cortical patterns, a robust effect that persisted until the reminder disappeared (780 to 3000 ms). Representational similarity analyses provided converging evidence that retrieval suppression weakened the representation of target scenes during the 500 to 3000 ms reinstatement window. Together, rapid top-down control during retrieval suppression abolished cortical patterns of individual memories, and precipitated later forgetting. These findings reveal a precise chronometry on the voluntary suppression of individual memories.

Decoding the task specificity of post-error adjustments: Features and determinants.

Errors typically trigger post-error adjustments aimed at improving subsequent reactions within a single task, but little work has focused on whether these adjustments are task-general or task-specific across different tasks. We collected behavioral and electrophysiological (EEG) data when participants performed a psychological refractory period paradigm. This paradigm required them to complete Task 1 and Task 2 separated by a variable stimulus onset asynchrony (SOA). Behaviorally, post-error slowing and post-error accuracy exhibited task-general features at short SOAs but some task-specific features at long SOAs. EEG results manifest that task-general adjustments had a short-lived effect, whereas task-specific adjustments were long-lasting. Moreover, error awareness specifically conduced to the improvement of subsequent sensory processing and behavior performance in Task 1 (the task where errors occurred). These findings demonstrate that post-error adjustments rely on both transient, task-general interference and longer-lasting, task-specific control mechanisms simultaneously, with error awareness playing a crucial role in determining these mechanisms. We further discuss the contribution of central resources to the task specificity of post-error adjustments.

Oral Oxytocin Blurs Sex Differences in Amygdala Responses to Emotional Scenes.

BACKGROUND: Sex differences are shaped both by innate biological differences and the social environment and are frequently observed in human emotional neural responses. Oral administration of oxytocin (OXT), as an alternative and noninvasive intake method, has been shown to produce sex-dependent effects on emotional face processing. However, it is unclear whether oral OXT produces similar sex-dependent effects on processing continuous emotional scenes. METHODS: The current randomized, double-blind, placebo-controlled neuropsychopharmacological functional magnetic resonance imaging experiment was conducted in 147 healthy participants (OXT = 74, men/women = 37/37; placebo = 73, men/women = 36/37) to examine the oral OXT effect on plasma OXT concentrations and neural response to emotional scenes in both sexes. RESULTS: At the neuroendocrine level, women showed lower endogenous OXT concentrations than men, but oral OXT increased OXT concentrations equally in both sexes. Regarding neural activity, emotional scenes evoked opposite valence-independent effects on right amygdala activation (women > men) and its functional connectivity with the insula (men > women) in men and women in the placebo group. This sex difference was either attenuated (amygdala response) or even completely eliminated (amygdala-insula functional connectivity) in the OXT group. Multivariate pattern analysis confirmed these findings by developing an accurate sex-predictive neural pattern that included the amygdala and the insula under the placebo but not the OXT condition. CONCLUSIONS: The results of the current study suggest a pronounced sex difference in neural responses to emotional scenes that was eliminated by oral OXT, with OXT having opposite modulatory effects in men and women. This may reflect oral OXT enhancing emotional regulation to continuous emotional stimuli in both sexes by facilitating appropriate changes in sex-specific amygdala-insula circuitry.

Decoding Remapped Spatial Information in the Peri-Saccadic Period.

It has been suggested that, prior to a saccade, visual neurons predictively respond to stimuli that will fall in their receptive fields after completion of the saccade. This saccadic remapping process is thought to compensate for the shift of the visual world across the retina caused by eye movements. To map the timing of this predictive process in the brain, we recorded neural activity using electroencephalography during a saccade task. Human participants (male and female) made saccades between two fixation points while covertly attending to oriented gratings briefly presented at various locations on the screen. Data recorded during trials in which participants maintained fixation were used to train classifiers on stimuli in different positions. Subsequently, data collected during saccade trials were used to test for the presence of remapped stimulus information at the post-saccadic retinotopic location in the peri-saccadic period, providing unique insight into when remapped information becomes available. We found that the stimulus could be decoded at the remapped location ∼180 ms post-stimulus onset, but only when the stimulus was presented 100-200 ms before saccade onset. Within this range, we found that the timing of remapping was dictated by stimulus onset rather than saccade onset. We conclude that presenting the stimulus immediately before the saccade allows for optimal integration of the corollary discharge signal with the incoming peripheral visual information, resulting in a remapping of activation to the relevant post-saccadic retinotopic neurons.

Erratum: NeuroDecodeR: a package for neural decoding in R.

[This corrects the article DOI: 10.3389/fninf.2023.1275903.].

Have you been there before? Decoding recognition of spatial scenes from fMRI signals in precuneus.

One potential application of forensic "brain reading" is to test whether a suspect has previously experienced a crime scene. Here, we investigated whether it is possible to decode real life autobiographic exposure to spatial locations using fMRI. In the first session, participants visited four out of eight possible rooms on a university campus. During a subsequent scanning session, subjects passively viewed pictures and videos from these eight possible rooms (four old, four novel) without giving any responses. A multivariate searchlight analysis was employed that trained a classifier to distinguish between "seen" versus "unseen" stimuli from a subset of six rooms. We found that bilateral precuneus encoded information that can be used to distinguish between previously seen and unseen rooms and that also generalized to the two stimuli left out from training. We conclude that activity in bilateral precuneus is associated with the memory of previously visited rooms, irrespective of the identity of the room, thus supporting a parietal contribution to episodic memory for spatial locations. Importantly, we could decode whether a room was visited in real life without the need of explicit judgments about the rooms. This suggests that recognition is an automatic response that can be decoded from fMRI data, thus potentially supporting forensic applications of concealed information tests for crime scene recognition.

Brain representations of lexical ambiguity: Disentangling homonymy, polysemy, and their meanings.

In human languages, it is a common phenomenon for a single word to have multiple meanings. This study used fMRI to investigate how the brain processed different types of lexical ambiguity, and how it differentiated the meanings of ambiguous words. We focused on homonyms and polysemy that differed in the relatedness among multiple meanings. Participants (N = 35) performed a prime-target semantic relatedness task, where a specific meaning of an ambiguous word was primed. Results showed that homonyms elicited greater activation in bilateral dorsal prefrontal and posterior parietal cortices than polysemous words, suggesting that these regions may be more engaged in cognitive control when the meanings of ambiguous words are unrelated. Multivariate pattern analysis further revealed that meanings of homonyms with different syntactic categories were represented differently in the frontal and temporal cortices. The findings highlighted the importance of semantic relations and grammatical factors in the brain's representation of lexical ambiguities.

Spatial functional reorganizations can serve as potential biomarkers of post facial palsy synkinesis.

Facial palsy can result in a serious complication known as facial synkinesis, causing both physical and psychological harm to the patients. There is growing evidence that patients with facial synkinesis have brain abnormalities, but the brain mechanisms and underlying imaging biomarkers remain unclear. Here, we employed functional magnetic resonance imaging (fMRI) to investigate brain function in 31 unilateral post facial palsy synkinesis patients and 25 healthy controls during different facial expression movements and at rest. Combining surface-based mass-univariate analysis and multivariate pattern analysis, we identified diffused activation and intrinsic connection patterns in the primary motor cortex and the somatosensory cortex on the patient's affected side. Further, we classified post facial palsy synkinesis patients from healthy subjects with favorable accuracy using the support vector machine based on both task-related and resting-state functional magnetic resonance imaging data. Together, these findings indicate the potential of the identified functional reorganizations to serve as neuroimaging biomarkers for facial synkinesis diagnosis.

Mapping the neural mechanism that distinguishes between holistic thinking and analytic thinking.

Holistic and analytic thinking are two distinct modes of thinking used to interpret the world with relative preferences varying across cultures. While most research on these thinking styles has focused on behavioral and cognitive aspects, a few studies have utilized functional magnetic resonance imaging (fMRI) to explore the correlations between brain metrics and self-reported scale scores. Other fMRI studies used single holistic and analytic thinking tasks. As a single task may involve processing in spurious low-level regions, we used two different holistic and analytic thinking tasks, namely the frame-line task and the triad task, to seek convergent brain regions to distinguish holistic and analytic thinking using multivariate pattern analysis (MVPA). Results showed that brain regions fundamental to distinguish holistic and analytic thinking include the bilateral frontal lobes, bilateral parietal lobes, bilateral precentral and postcentral gyrus, bilateral supplementary motor areas, bilateral fusiform, bilateral insula, bilateral angular gyrus, left cuneus, and precuneus, left olfactory cortex, cingulate gyrus, right caudate and putamen. Our study maps brain regions that distinguish between holistic and analytic thinking and provides a new approach to explore the neural representation of cultural constructs. We provide initial evidence connecting culture-related brain regions with language function to explain the origins of cultural differences in cognitive styles.

Connectivity analyses for task-based fMRI.

Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.