Effective Connectivity Analysis Research Articles

InfoFlowNet: A multi-head attention-based self-supervised learning model with surrogate approach for uncovering brain effective connectivity

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, which gauges the directional influences among brain regions, is pivotal for these studies. This research introduces InfoFlowNet, a novel self-supervised learning model designed to infer causal relationships in electroencephalogram (EEG) data, aiming at capturing the complex information flow among brain regions. Specifically, InfoFlowNet employs convolution operations and multi-head self-attention mechanisms with a masking feature, which ignores self-connections to focus on inter-regional dynamics. Additionally, a new causal magnitude estimation is introduced by assessing the impact of shuffled surrogate data on signal prediction to quantify causal influence. Experiments using synthetic data and real EEG datasets demonstrate that InfoFlowNet effectively uncovers time-varying causal relationships. Compared with the Granger causality model (GCM) and the temporal causal discovery framework (TCDF), InfoFlowNet shows superior sensitivity in detecting significant causal edges. For instance, in a psychomotor vigilance task, InfoFlowNet identified 8 out of 16 significant causal edges, significantly outperforming GCM and TCDF. Furthermore, InfoFlowNet successfully passes the whiteness test on the model's residuals, and advanced analyses illustrate the advantages of utilizing multi-head attention and masking mechanisms to capture effective connectivity. In sum, InfoFlowNet represents a significant advancement in the analysis of effective connectivity, offering a deeper understanding of brain network dynamics. The model's ability to discern complex causal interactions supports its potential application in broader neuroscientific research and applications.

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

Retention of verbal and nonverbal information in the working memory. An analysis of functional and effective connectivity

In this work we estimated differences in the structure of brain systems that ensure encoding and retention in working memory (WM) of two types of information: verbal (letters) and non-verbal (segments of an open broken line) sequences presented either statically or dynamically. Brain systems were characterized by the strength of functional and effective connections between eight approximately bilaterally symmetrical cortical loci, including the dorsolateral prefrontal cortex (dlPFC) and regions of the temporal (STG), parietal (IPS), and occipital (v2) cortices. Using an 8-channel vector autoregressive model in the space of cortical EEG sources, it was shown in a group of subjects in whom high-density EEG was recorded that: (1) the brain organization of the WM when holding a sequence of letters differs from that when holding a sequence of broken line segments; (2) the brain organization of the WM depends on the mode of presentation of sequences: the strength of the functional connection is different during dynamic and static presentation of the sequence; (3) differences in the structure of functional and effective connections are not of a pronounced frequency-selective nature and are observed in all studied EEG frequency ranges from theta (4–8 Hz) to high-frequency gamma (50–60 Hz); (4) the most reliable differences between the task of retaining a sequence of letters and the task of retaining a sequence of broken line segments are observed in the alpha and beta frequency ranges during static visual presentation of sequences in the strength of functional connectivity measured using coherence between the left hemisphere dlPFC and the right hemisphere STG, as well as in theta range between the right hemisphere dlPFC and the left visual cortex v2; (5) the most reliable difference between static and dynamic presentation modes is observed in the task of holding broken line segments in the gamma frequency range (50–60 Hz) between the dlPFC in the right hemisphere and the left visual cortex v2.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

Speech and neuroimaging effects following HiCommunication: a randomized controlled group intervention trial in Parkinson's disease.

Speech, voice and communication changes are common in Parkinson's disease. HiCommunication is a novel group intervention for speech and communication in Parkinson's disease based on principles driving neuroplasticity. In a randomized controlled trial, 95 participants with Parkinson's disease were allocated to HiCommunication or an active control intervention. Acoustic analysis was performed pre-, post- and six months after intervention. Intention-to-treat analyses with missing values imputed in linear multilevel models and complimentary per-protocol analyses were performed. The proportion of participants with a clinically relevant increase in the primary outcome measure of voice sound level was calculated. Resting-state functional MRI was performed pre- and post-intervention. Spectral dynamic causal modelling and the parametric empirical Bayes methods were applied to resting-state functional MRI data to describe effective connectivity changes in a speech-motor-related network of brain regions. From pre- to post-intervention, there were significant group-by-time interaction effects for the measures voice sound level in text reading (unstandardized b = 2.3, P = 0.003), voice sound level in monologue (unstandardized b = 2.1, P = 0.009), Acoustic Voice Quality Index (unstandardized b = -0.5, P = 0.016) and Harmonics-to-Noise Ratio (unstandardized b = 1.3, P = 0.014) post-intervention. For 59% of the participants, the increase in voice sound level after HiCommunication was clinically relevant. There were no sustained effects at the six-month follow-up. In the effective connectivity analysis, there was a significant decrease in inhibitory self-connectivity in the left supplementary motor area and increased connectivity from the right supplementary motor area to the left paracentral gyrus after HiCommunication compared to after the active control intervention. In conclusion, the HiCommunication intervention showed promising effects on voice sound level and voice quality in people with Parkinson's disease, motivating investigations of barriers and facilitators for implementation of the intervention in healthcare settings. Resting-state brain effective connectivity was altered following the intervention in areas implicated, possibly due to reorganization in brain networks.

Functional and Effective Connectivity Underlying Semantic Verbal Fluency.

Semantic verbal fluency (SVF) impairment is present in several neurological disorders. Although activation in SVF-related areas has been reported, how these regions are connected and their functional roles in the network remain divergent. We assessed SVF static and dynamic functional connectivity (FC) and effective connectivity in healthy participants using functional magnetic resonance imaging. We observed activation in the inferior frontal (IFG), middle temporal (pMTG) and angular gyri (AG), anterior cingulate (AC), insular cortex, and regions of the superior, middle, and medial frontal gyri (SFG, MFG, MidFG). Our static FC analysis showed a highly interconnected task and resting state network. Increased connectivity of AC with the pMTG and AG was observed for the task. The dynamic FC analysis provided circuits with connections similarly modulated across time and regions related to category identification, language comprehension, word selection and recovery, word generation, inhibition of speaking, speech planning, and articulatory planning of orofacial movements. Finally, the effective connectivity analysis provided a network that best explained our data, starting at the AG and going to the pMTG, from which there was a division between the ventral and dorsal streams. The SFG and MFG regions were connected and modulated by the MidFG, while the inferior regions formed the ventral stream. Therefore, we successfully assessed the SVF network, exploring regions associated with the entire processing, from category identification to word generation. The methodological approach can be helpful for further investigation of the SVF network in neurological disorders.

Elucidating the underlying components of metacognitive systematic bias in the human dorsolateral prefrontal cortex and inferior parietal cortex

Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.

Neural representation of phonological wordform in temporal cortex.

While the neural bases of the earliest stages of speech categorization have been widely explored using neural decoding methods, there is still a lack of consensus on questions as basic as how wordforms are represented and in what way this word-level representation influences downstream processing in the brain. Isolating and localizing the neural representations of wordform is challenging because spoken words activate a variety of representations (e.g., segmental, semantic, articulatory) in addition to form-based representations. We addressed these challenges through a novel integrated neural decoding and effective connectivity design using region of interest (ROI)-based, source-reconstructed magnetoencephalography/electroencephalography (MEG/EEG) data collected during a lexical decision task. To identify wordform representations, we trained classifiers on words and nonwords from different phonological neighborhoods and then tested the classifiers' ability to discriminate between untrained target words that overlapped phonologically with the trained items. Training with word neighbors supported significantly better decoding than training with nonword neighbors in the period immediately following target presentation. Decoding regions included mostly right hemisphere regions in the posterior temporal lobe implicated in phonetic and lexical representation. Additionally, neighbors that aligned with target word beginnings (critical for word recognition) supported decoding, but equivalent phonological overlap with word codas did not, suggesting lexical mediation. Effective connectivity analyses showed a rich pattern of interaction between ROIs that support decoding based on training with lexical neighbors, especially driven by right posterior middle temporal gyrus. Collectively, these results evidence functional representation of wordforms in temporal lobes isolated from phonemic or semantic representations.

Aberrant Brain Triple-Network Effective Connectivity Patterns in Type2 Diabetes Mellitus.

Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

The influence of the precuneus on the medial temporal cortex determines the subjective quality of memory during the retrieval of naturalistic episodes.

Memory retrieval entails dynamic interactions between the medial temporal lobe and areas in the parietal and frontal cortices. Here, we tested the hypothesis that effective connectivity between the precuneus, in the medial parietal cortex, and the medial temporal cortex contributes to the subjective quality of remembering objects together with information about their rich spatio-temporal encoding context. During a 45min encoding session, the participants were presented with pictures of objects while they actively explored a virtual town. The following day, under fMRI, participants were presented with images of objects and had to report whether: they recognized the object and could remember the place/time of encoding, the object was familiar only, or the object was new. The hippocampus/parahippocampus, the precuneus and the ventro-medial prefrontal cortex activated when the participants successfully recognized objects they had seen in the virtual town and reported that they could remember the place/time of these events. Analyses of effective connectivity showed that the influence exerted by the precuneus on the medial temporal cortex mediates this effect of episodic recollection. Our findings demonstrate the role of the inter-regional connectivity in mediating the subjective experience of remembering and underline the relevance of studying memory in contextually-rich conditions.

The effective connectivity analysis of fMRI based on asymmetric detection of transfer brain entropy.

It is important to explore causal relationships in functional magnetic resonance imaging study. However, the traditional effective connectivity analysis method is easy to produce false causality, and the detection accuracy needs to be improved. In this paper, we introduce a novel functional magnetic resonance imaging effective connectivity method based on the asymmetry detection of transfer entropy, which quantifies the disparity in predictive information between forward and backward time, subsequently normalizing this disparity to establish a more precise criterion for detecting causal relationships while concurrently reducing computational complexity. Then, we evaluate the effectiveness of this method on the simulated data with different level of nonlinearity, and the results demonstrated that the proposed method outperforms others methods on the detection of both linear and nonlinear causal relationships, including Granger Causality, Partial Granger Causality, Kernel Granger Causality, Copula Granger Causality, and traditional transfer entropy. Furthermore, we applied it to study the effective connectivity of brain functional activities in seafarers. The results showed that there are significantly different causal relationships between different brain regions in seafarers compared with non-seafarers, such as Temporal lobe related to sound and auditory information processing, Hippocampus related to spatial navigation, Precuneus related to emotion processing as well as Supp_Motor_Area associated with motor control and coordination, which reflects the occupational specificity of brain function of seafarers.

Combining electrodermal activity analysis and dynamic causal modeling to investigate the visual-odor multimodal integration during face perception

Objective. This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective is to investigate how the emotional valence of odors influences the cortical connectivity underlying face processing and the role of face-induced sympathetic arousal in this visual-olfactory multimodal integration. Approach. To this aim, we combine electrodermal activity (EDA) analysis and dynamic causal modeling to examine changes in cortico-cortical interactions. Results. The results reveal that stimuli arising sympathetic EDA responses are associated with a more negative N170 amplitude, which may be a marker of heightened arousal in response to faces. Hedonic odors, on the other hand, lead to a more negative N1 component and a reduced the vertex positive potential when they are unpleasant or pleasant. Concerning connectivity, unpleasant odors strengthen the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus, which is involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses after a stimulus is correlated with an inhibition of this same connection and an enhancement of the backward connection from ITG to the fusiform face gyrus. Significance. These findings suggest that unpleasant odors may enhance the interpretation of emotional expressions and mental states, while faces capable of eliciting sympathetic arousal prioritize identity processing.

Multilevel testing of constraints induced by structural equation modeling in fMRI effective connectivity analysis: A proof of concept

In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.

Alteration in temporal-cerebellar effective connectivity can effectively distinguish stable and progressive mild cognitive impairment.

Stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI) represent two distinct subtypes of mild cognitive impairment (MCI). Early and effective diagnosis and accurate differentiation between sMCI and pMCI are crucial for administering targeted early intervention and preventing cognitive decline. This study investigated the intrinsic dysconnectivity patterns in sMCI and pMCI based on degree centrality (DC) and effective connectivity (EC) analyses, with the goal of uncovering shared and distinct neuroimaging mechanisms between subtypes. Resting-state functional magnetic resonance imaging combined with DC analysis was used to explore the functional connectivity density in 42 patients with sMCI, 31 patients with pMCI, and 82 healthy control (HC) participants. Granger causality analysis was used to assess changes in EC based on the significant clusters found in DC. Furthermore, correlation analysis was conducted to examine the associations between altered DC/EC values and cognitive function. Receiver operating characteristic curve analysis was performed to determine the accuracy of abnormal DC and EC values in distinguishing sMCI from pMCI. Compared with the HC group, both pMCI and sMCI groups exhibited increased DC in the left inferior temporal gyrus (ITG), left posterior cerebellum lobe (CPL), and right cerebellum anterior lobe (CAL), along with decreased DC in the left medial frontal gyrus. Moreover, the sMCI group displayed reduced EC from the right CAL to bilateral CPL, left superior temporal gyrus, and bilateral caudate compared with HC. pMCI demonstrated elevated EC from the right CAL to left ITG, which was linked to episodic memory and executive function. Notably, the EC from the right CAL to the right ITG effectively distinguished sMCI from pMCI, with sensitivity, specificity, and accuracy of 0.5806, 0.9512, and 0.828, respectively. This study uncovered shared and distinct alterations in DC and EC between sMCI and pMCI, highlighting their involvement in cognitive function. Of particular significance are the unidirectional EC disruptions from the cerebellum to the temporal lobe, which serve as a discriminating factor between sMCI and pMCI and provide a new perspective for understanding the temporal-cerebellum. These findings offer novel insights into the neural circuit mechanisms involving the temporal-cerebellum connection in MCI.

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an “emotional” brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a “rational brain”. These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the “emotional/sensory brain”, as well as over-inhibition of the “rational brain” by the “emotional/sensory brain”. The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the “rational brain” to the “emotional/sensory brain” in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Inter- and Intra- Hemispheric Interactions in Reading Ambiguous Words

The present study investigated how the brain processes words with multiple meanings. Specifically, we examined the inter- and intra-hemispheric connectivity of unambiguous words compared to two types of ambiguous words: homophonic homographs, which have multiple meanings mapped to a single phonological representation and orthography, and heterophonic homographs, which have multiple meanings mapped to different phonological representations but the same orthography. Using a semantic relatedness judgment task and effective connectivity analysis via Dynamic Causal Modeling (DCM) on previously published fMRI data (Bitan et al., 2017), we found that the two hemispheres compete in orthographic processing during the reading of unambiguous words. For heterophonic homographs, we observed increased connectivity within the left hemisphere, highlighting the importance of top-down re-activation of orthographic representations by phonological ones for considering alternative meanings. For homophonic homographs, we found a flow of information from the left to the right hemisphere and from the right to the left, indicating that the brain retrieves different meanings using different pathways. These findings provide novel insights into the complex mechanisms involved in language processing and shed light on the different communication patterns within and between hemispheres during the processing of ambiguous and unambiguous words.

Effective Connectivity Changes among Brain Hierarchical Architecture of Pre-Supplementary Motor Area in Taxi Drivers

Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain’s intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain’s intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM.

Effective connectivity analysis of verbal working memory advantage across materials for pathological smartphone users by fNIRS

Previous studies have found working memory (WM) advantages of the pathological smartphone use (PSU) group, but most of which were emphasized in the network-related domain. Whether the advantages can transfer to other domains has yet to be confirmed. In particular, exploring from a brain mechanism perspective is necessary. Using the classical N-back paradigm, this study selected network-related words and neutral words as materials combined with fNIRS to probe the verbal WM characteristics of the PSU group. The results showed that β in channel 3, channel 4, and channel 5 were significantly lower in the PSU group than those in the control group The analysis of the region of interest revealed that the PSU group showed significantly lower β in the l-DLPFC and frontopolar. Granger Causality results showed that functional connectivity between frontopolar and R-DLPFC for the PSU group was significantly higher than for the control group in the network word condition. These results demonstrate that the PSU group has an advantage in WM, transferring from the network-related stimulus to the neutral stimulus. The advantages of network stimulus were related to bidirectional connectivity between frontopolar and R-DLPFC. Also, the l-DLPFC and frontopolar are associated with the cross-material consistency of WM.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner.

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex.

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.