Connected Brain Regions Research Articles

A combined DTI-fMRI approach for optimizing the delineation of posteromedial versus anterolateral entorhinal cortex.

In the entorhinal cortex (EC), attempts have been made to identify the human homologue regions of the medial (MEC) and lateral (LEC) subregions using either functional magnetic resonance imaging (fMRI) or diffusion tensor imaging (DTI). However, there are still discrepancies between entorhinal subdivisions depending on the choice of connectivity seed regions and the imaging modality used. While DTI can be used to follow the white matter tracts of the brain, fMRI can identify functionally connected brain regions. In this study, we used both DTI and resting-state fMRI in 103 healthy adults to investigate both structural and functional connectivity between the EC and associated cortical brain regions. Differential connectivity with these regions was then used to predict the locations of the human homologues of MEC and LEC. Our results from combining DTI and fMRI support a subdivision into posteromedial (pmEC) and anterolateral (alEC) EC and reveal a confined border between the pmEC and alEC. Furthermore, the EC subregions obtained by either imaging modality showed similar distinct whole-brain connectivity profiles. Optimizing the delineation of the human homologues of MEC and LEC with a combined, cross-validated DTI-fMRI approach allows to define a likely border between the two subdivisions and has implications for both cognitive and translational neuroscience research.

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical

Structural projections to the Nucleus Accumbens link to impulsive components of human risk preference

Abstract Functional responses in the Nucleus Accumbens (NAcc) to risk- and reward-related cues can predict real-life risk-taking behavior. Since NAcc activity depends on neurotransmission from connected brain regions, projections to the NAcc may also predict risk preference. To quantify risk preference, we employed latent variables previously derived in a comprehensive, independent study examining the psychometric structure of risk preference, which yielded a general risk preference factor as well as several specific factors, including a factor capturing impulsivity. Informed by previous work, we preregistered a set of hypotheses concerning the association between different risk preference factors and fractional anisotropy (or FA, which is sensitive to fiber coherence) for projections to the NAcc from Medial PreFrontal Cortex (MPFC), Anterior Insula, Amygdala, and an inferior tract from the Ventral Tegmental Area (iVTA). We tested our hypotheses in a community sample of 125 healthy human adults. As predicted, bilateral iVTA-NAcc tract FA showed a negative correlation with a psychometric factor that captured impulsivity, generalizing findings from prior research. Also as predicted, FA of the bilateral Amygdala-NAcc tract was positively associated with the impulsivity factor. Contrary to predictions, however, we observed no robust associations between the general risk preference factor and FA for projections from bilateral MPFC, right Anterior Insula, or bilateral Amygdala to the NAcc. Notably, exploratory unilateral analyses revealed an association between the general risk preference factor and left MPFC-NAcc tract FA. Taken together, these findings suggest that impulse control as a facet of risk preference maps onto specific neurobiological targets, while more general facets of risk preference may be supported by structural properties of lateral fronto-striatal projections. Although the exact associated functional mechanisms remain to be fully clarified, conNAcctomic approaches like the one presented here could pave the way for further research into the physiological foundations of risk preference and related constructs.

Differential Resting-State Brain Characteristics of Skeleton Athletes and Non-Athletes: A Preliminary Resting-State fMRI Study.

(1) Background: This study investigates the resting-state brain characteristics of skeleton athletes compared to healthy age-matched non-athletes, using resting-state fMRI to investigate long-term skeleton-training-related changes in the brain. (2) Methods: Eleven skeleton athletes and twenty-three matched novices with no prior experience with skeleton were recruited. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity analyses were explored to investigate resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to elucidate differences in resting-state brain function between the two groups. (3) Results: Compared to the control group, skeleton athletes exhibited significantly higher ALFF in the left fusiform, left inferior temporal gyrus, right inferior frontal gyrus, left middle temporal gyrus, left and right insula, left Rolandic operculum, left inferior frontal gyrus, and left superior temporal gyrus. Skeleton athletes exhibit stronger functional connectivity in brain regions associated with cognitive and motor control (superior frontal gyrus, insula), as well as those related to reward learning (putamen), visual processing (precuneus), spatial cognition (inferior parietal), and emotional processing (amygdala), during resting-state brain function. (4) Conclusions: The study contributes to understanding how motor training history shapes skeleton athletes' brains, which have distinct neural characteristics compared to the control population, indicating potential adaptations in brain function related to their specialized training and expertise in the sport.

Association of bacterial overgrowth in the small intestine with cortical thickness and functional connectivity in Parkinson's disease involving mild cognitive impairment.

This study explored potential associations of bacterial overgrowth in the small intestine, as detected based on levels of hydrogen and methane in breath after lactulose consumption, with cortical thickness and resting-state functional connectivity in different brain regions. Prospective comparison of 35 patients with Parkinson's disease (PD) involving mild cognitive impairment, 35 patients with PD with normal cognitive function and 17 healthy controls showed the largest level of hydrogen alone and the largest combined level of hydrogen and methane in patients with mild cognitive impairment. The comparison also revealed a significant negative correlation between those levels and thickness of the right insular cortex. Mild cognitive patients showed different functional connectivity between the right insula and cognition-related brain networks from normal cognitive patients. Our results suggest that bacterial overgrowth in the small intestine may contribute to cortical thinning and alterations in resting-state functional connectivity in PD involving mild cognitive impairment. These insights support and deepen previous observations implicating the gut-brain axis in the neurological disorder.

Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection.

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog.

Anatomical and Pathological Review of Alzheimer's, Huntington's, and Pick's Disease: A Public Study on the Awareness of Neurological Disorders

Neurodegenerative diseases (NDDs) include Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), Huntington's Disease, and Pick's disease. These conditions are characterized by a progressive decline in the structure and function of the nervous system. The objective of this paper is to provide a brief overview of these diseases, outline the anatomical changes they cause in the brain, and evaluate the level of public awareness about their prevalence and impact. Neuroimaging studies show that Alzheimer's disease initially leads to the degeneration of neuronal connections in brain regions associated with memory, such as the entorhinal cortex and hippocampus. ALS is characterized by the degradation of motor neurons in the spinal cord and brainstem, leading to muscle weakness and atrophy. In contrast, Parkinson's disease involves the decay of dopaminergic neurons in the substantia nigra, resulting in motor control impairments. Brain structure changes are linked to the accumulation of abnormal protein aggregates such as amyloid beta in Alzheimer's disease and alpha-synuclein in Parkinson's disease, interfering with cellular functions and causing neuronal death. Besides addressing brain structure, this paper presents results from a survey assessing public awareness of neurodegenerative diseases. The findings suggest that while the public is well-informed about Alzheimer's and Parkinson's disease, there is still a significant knowledge gap regarding less common conditions like ALS and Huntington's disease. The significance of implementing thorough educational approaches to increase public knowledge is highlighted by these results. This study seeks to narrow the divide between scientific knowledge and public awareness of neurodegenerative diseases by combining anatomical understanding with public perceptions.

Cortical activation and functional connectivity during a verbal fluency task in patients with chronic insomnia: A multi-channel NIRS study

BackgroundPatients with chronic insomnia exhibit varying degrees of cognitive dysfunction. Functional connectivity of different brain regions contributes to the understanding of underlying cognitive processes in the cerebral cortex. However, this has not yet been studied in patients with chronic insomnia. This study aimed to elucidate the differences between brain activity patterns in patients with chronic insomnia and healthy controls (HCs) using a verbal fluency task (VFT). MethodsWe recruited 84 patients with chronic insomnia and 81 HCs. Oxy-haemoglobin (Oxy-Hb) concentrations in the brains of the participants were monitored using functional near-infrared spectroscopy (fNIRS) while performing the VFT. ResultsDuring the task period, no significant difference was observed between the VFT results of the two groups; patients with chronic insomnia showed significantly less cortical activation in haemodynamic responses of oxy-Hb at channels and brain regions mainly located in the prefrontal cortex compared to HCs (FDR-corrected p < 0.05). Moreover, the average channel-to-channel connectivity strength of patients in the chronic insomnia group was lower than that of those in the HC group (t = −6.717, p < 0.001). ConclusionOur study provides neurological evidence for the dynamic detection of executive function in patients with chronic insomnia. Compared to HCs, patients with chronic insomnia exhibit weaker levels of brain activity and reduced task-related functional connectivity.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents.

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( ) but not verbal IQ scores( ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

Enhanced Cognition and Modulation of Brain Connectivity in Mild Neurocognitive Disorder: The Promise of Transcranial Pulse Stimulation.

Existing pharmacological treatments for mild neurocognitive disorder (NCD) offer limited effectiveness and adverse side effects. Transcranial pulse stimulation (TPS) utilizing ultrashort ultrasound pulses reaches deep brain regions and may circumvent conductivity issues associated with brain stimulation. This study addresses the gap in TPS research for mild NCD during a critical intervention period before irreversible cognitive degradation. Our objective was to explore the effectiveness and tolerability of TPS in older adults with mild NCD. In an open-label study, 17 older adults (including 10 females and 7 males) with mild NCD underwent TPS for two weeks with three sessions per week. Cognitive evaluations and fMRI scans were conducted pre- and post-intervention. The results indicated changes in functional connectivity in key brain regions, correlating with cognitive improvement at B = 0.087 (CI, 0.007-0.167; p = 0.038). However, cortical thickness measurements showed no significant differences. Here we show that TPS can enhance cognitive function within mild NCD. This proof-of-concept study suggests that TPS has potential as a non-invasive therapy used to attenuate cognitive decline, encouraging further investigation in larger randomized trials. The findings could influence clinical practice by introducing TPS as an adjunctive treatment option and potentially impact policy by promoting its inclusion in new treatment strategies for mild NCD.

Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder.

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

Impact of Electric Field Magnitude in the Left Dorsolateral Prefrontal Cortex on Changes in Intrinsic Functional Connectivity Using Transcranial Direct Current Stimulation: A Randomized Crossover Study.

This study investigated whether the electric field magnitude (E-field) delivered to the left dorsolateral prefrontal cortex (L-DLPFC) changes resting-state brain activity and the L-DLPFC resting-state functional connectivity (rsFC), given the variability in tDCS response and lack of understanding of how rsFC changes. Twenty-one healthy participants received either 2 mA anodal or sham tDCS targeting the L-DLPFC for 10 min. Brain imaging was conducted before and after stimulation. The fractional amplitude of low-frequency fluctuation (fALFF), reflecting resting brain activity, and the L-DLPFC rsFC were analyzed to investigate the main effect of tDCS, main effect of time, and interaction effects. The E-field was estimated by modeling tDCS-induced individual electric fields and correlated with fALFF and L-DLPFC rsFC. Anodal tDCS increased fALFF in the left rostral middle frontal area and decreased fALFF in the midline frontal area (FWE p < 0.050), whereas sham induced no changes. Overall rsFC decreased after sham (positive and negative connectivity, p = 0.001 and 0.020, respectively), with modest and nonsignificant changes after anodal tDCS (p = 0.063 and 0.069, respectively). No significant differences in local rsFC were observed among the conditions. Correlations were observed between the E-field and rsFC changes in the L-DLPFC (r = 0.385, p = 0.115), left inferior parietal area (r = 0.495, p = 0.037), and right lateral visual area (r = 0.683, p = 0.002). Single-session tDCS induced resting brain activity changes and may help maintain overall rsFC. The E-field in the L-DLPFC is associated with rsFC changes in both proximal and distally connected brain regions to the L-DLPFC.

Pathologic Substrates of Structural Brain Network Resilience and Topology in Parkinson Disease Decedents.

In Parkinson disease (PD), α-synuclein spreading through connected brain regions leads to neuronal loss and brain network disruptions. With diffusion-weighted imaging (DWI), it is possible to capture conventional measures of brain network organization and more advanced measures of brain network resilience. We aimed to investigate which neuropathologic processes contribute to regional network topologic changes and brain network resilience in PD. Using a combined postmortem MRI and histopathology approach, PD and control brain donors with available postmortem in situ 3D T1-weighted MRI, DWI, and brain tissue were selected from the Netherlands Brain Bank and Normal Aging Brain Collection Amsterdam. Probabilistic tractography was performed, and conventional network topologic measures of regional eigenvector centrality and clustering coefficient, and brain network resilience (change in global efficiency upon regional node failure) were calculated. PSer129 α-synuclein, phosphorylated-tau, β-amyloid, neurofilament light-chain immunoreactivity, and synaptophysin density were quantified in 8 cortical regions. Group differences and correlations were assessed with rank-based nonparametric tests, with age, sex, and postmortem delay as covariates. Nineteen clinically defined and pathology-confirmed PD (7 F/12 M, 81 ± 7 years) and 15 control (8 F/7 M, 73 ± 9 years) donors were included. With regional conventional measures, we found lower eigenvector centrality only in the parahippocampal gyrus in PD (d = -1.08, 95% CI 0.003-0.010, p = 0.021), which did not associate with underlying pathology. No differences were found in regional clustering coefficient. With the more advanced measure of brain network resilience, we found that the PD brain network was less resilient to node failure of the dorsal anterior insula compared with the control brain network (d = -1.00, 95% CI 0.0012-0.0015, p = 0.018). This change was not directly driven by neuropathologic processes within the dorsal anterior insula or in connected regions but was associated with higher Braak α-synuclein staging (rs = -0.40, p = 0.036). Although our cohort might suffer from selection bias, our results highlight that regional network disturbances are more complex to interpret than previously believed. Regional neuropathologic processes did not drive regional topologic changes, but a global increase in α-synuclein pathology had a widespread effect on brain network reorganization in PD.

Medial prefrontal cortex connectivity with the nucleus accumbens is related to HIV serostatus, perceptions of psychological stress, and monocyte expression of TNF-a

Post-menopausal persons living with HIV (PWH) report elevated levels of psychological stress and monocyte activation compared to persons living without HIV (PWOH). Resting state functional connectivity (rsFC) of mesolimbic brain regions underpinning stress and emotion regulation are susceptible to inflammatory insult. Although psychological stress is elevated, rsFC reduced, and CD16+ monocytes overexpressed in the brains of PWH, it is unclear whether the relationships amongst these variables differ compared to PWOH.An ethnically diverse sample of postmenopausal women, 24 PWH and 30 PWOH provided self-report mood surveys and provided peripheral blood specimens to quantify LPS-stimulated CD16+/− expression of TNF-α via flow cytometric analysis. An anatomical and resting state functional MRI scan were used to derive time-series metrics of connectivity between the medial prefrontal cortex (mPFC) and the nucleus accumbens (NAcc) as well as the amygdala.A positive association was observed between levels of perceived stress and CD16+/− TNF-α in both LPS-stimulated and unstimulated cells. PLWH showed lower connectivity between mPFC and NAcc. In turn, lower rsFC between these regions predicted greater psychological stress and proportion of CD16−, but not CD16+, cells expression of TNF-α.Neuroimmune effects of monocyte inflammation on the functional connectivity of mesolimbic regions critical for discrimination of uncertainty-safety and reward signals were observed in an ethnically diverse sample of postmenopausal women living with and without HIV. PWH showed lower mPFC-NAcc functional connectivity, which in turn was associated with greater perceived stress.

Altered Effective Connectivity of the Pain Matrix in Herpes Zoster and Postherpetic Neuralgia Patients: Granger Causality Analysis of Resting-State fMRI.

Shingles can cause long-term pain and negative emotions, along with changes in brain function. In this study, Granger Causality Analysis (GCA) was used to compare herpes zoster (HZ) and postherpetic neuralgia (PHN) differences in effective connections within the "pain matrix" between patients and healthy controls to further understand patterns of interaction between brain regions and explore the relationship between changes in effective connections and clinical features. Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 55 HZ; 55 PHN; and 50 age-, sex- matched healthy controls (HCs). The brain regions associated with the pain matrix are used as the seeds of effective connectivity. GCA was used to analyze effective connections in brain regions that differed significantly between groups. Then the correlation between GCA values and clinical indicators was studied. Compared with HC, GCA values between the thalamus and the amygdala, between the thalamus and the precentral gyrus, from the thalamus to the postcentral gyrus, and from the parahippocampal gyrus to the amygdala, anterior cingulate gyrus were significantly reduced in HZ patients. Compared with HC, GCA values between the insular and the postcentral gyrus, from the insular to the inferior parietal lobe, and from the postcentral gyrus to the amygdala were significantly reduced in PHN patients. Compared with HZ, GCA values between the inferior parietal lobe and the parahippocampal gyrus, between the inferior parietal lobe and the anterior cingulate gyrus, and from the anterior cingulate gyrus to the amygdala were significantly increased in PHN patients. The visual analogue scale (VAS) score of PHN patients was positively correlated with the GCA value from the central posterior lobe to the insula. PHN and HZ patients showed a broad reduction in effective connections, mainly reflected in abnormal pain pathway regulation, pain perception, negative emotion and memory production, providing new perspectives to understand the neuroimaging mechanisms of shingles.

The effects of musicality on brain network topology in the context of Alzheimer’s disease and memory decline

Abstract Music’s role in modulating brain structure, particularly in neurodegenerative contexts such as Alzheimer’s Disease (AD), has been increasingly recognized. While previous studies have hinted at the potential neuroplastic benefits of musical engagement and training, the mechanisms through which music impacts structural connectivity in neurodegenerative pathways remain underexplored. We aimed to examine the impact of music perception skills, active musical engagement, and musical training on structural connectivity in areas relating to memory, emotion, and learning in individuals with worsening memory impairment, investigating the potential neuroplastic effects of music. Employing diffusion tensor imaging (DTI) based structural connectivity and graph theoretical analysis, we investigated brain topological features in 78 participants aged 42 to 85 with a range of memory impairments. Participants were assessed for musical training, engagement, and perception skills. The study analyzed regional and local network topological metrics to examine the influence of musical activities on graph metrics, while controlling for stages of objective memory impairment (SOMI) and diagnosis, separately. This study aimed to elucidate the effects of musical perception skills, active musical engagement, and musical training on structural connectivity within memory, emotion, and learning-related brain areas in individuals with varying degrees of memory impairment. We found enhanced structural connectivity of the right hippocampus and the right posterior cingulate cortex was associated with stronger local network metrics, such as clustering coefficient and betweenness centrality, with increased music perception skills like melody and beat perception. Musical training specifically impacted the clustering coefficient of the right hippocampus and the node degree of the right mid cingulate gyrus. Active musical engagement influenced the eigenvector centrality of the right hippocampus. Furthermore, musical training was associated with enhanced global metrics, such as global efficiency and characteristic path length. Our study integrates diffusion magnetic resonance imaging (MRI) and graph theoretical analysis to reveal significant effects of musical activities on structural connectivity in key brain regions. The results highlight the potential of musical activities to serve as a non-invasive modulatory tool for cognitive resilience, especially in memory impairment and neurodegeneration contexts. These insights contribute to the understanding of delaying AD onset and aiding early-stage patients through music-based interventions, emphasizing the importance of musical engagement in maintaining cognitive and brain health.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Whole-brain model replicates sleep-like slow-wave dynamics generated by stroke lesions

Focal brain injuries, such as stroke, cause local structural damage as well as alteration of neuronal activity in distant brain regions. Experimental evidence suggests that one of these changes is the appearance of sleep-like slow waves in the otherwise awake individual. This pattern is prominent in areas surrounding the damaged region and can extend to connected brain regions in a way consistent with the individual's specific long-range connectivity patterns. In this paper we present a generative whole-brain model based on (f)MRI data that, in combination with the disconnection mask associated with a given patient, explains the effects of the sleep-like slow waves originated in the vicinity of the lesion area on the distant brain activity. Our model reveals new aspects of their interaction, being able to reproduce functional connectivity patterns of stroke patients and offering a detailed, causal understanding of how stroke-related effects, in particular slow waves, spread throughout the brain. The presented findings demonstrate that the model effectively captures the links between stroke occurrences, sleep-like slow waves, and their subsequent spread across the human brain.

Enhancing local representation learning through global–local integration with functional connectivity for EEG-based emotion recognition

Emotion recognition based on electroencephalogram (EEG) signals is crucial in understanding human affective states. Current research has limitations in extracting local features. The representation capabilities of local features are limited, making it difficult to comprehensively capture emotional information. In this study, a novel approach is proposed to enhance local representation learning through global–local integration with functional connectivity for EEG-based emotion recognition. By leveraging the functional connectivity of brain regions, EEG signals are divided into global embeddings that represent comprehensive brain connectivity patterns throughout the entire process and local embeddings that reflect dynamic interactions within specific brain functional networks at particular moments. Firstly, a convolutional feature extraction branch based on the residual network is designed to extract local features from the global embedding. To further improve the representation ability and accuracy of local features, a multidimensional collaborative attention (MCA) module is introduced. Secondly, the local features and patch embedded local embeddings are integrated into the feature coupling module (FCM), which utilizes hierarchical connections and enhanced cross-attention to couple region-level features, thereby enhancing local representation learning. Experimental results on three public datasets show that compared with other methods, this method improves accuracy by 4.92% on the DEAP, by 1.11% on the SEED, and by 7.76% on the SEED-IV, demonstrating its superior performance in emotion recognition tasks.

A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data.

Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information. We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals. Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features. The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.