Bayesian estimation of covariate assisted principal regression for brain functional connectivity.

This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.

Intimate relationships regulate female brain activity in a competitive context: evidence from EEG and functional connectivity analysis.

Competition is common in life, and intimate relationships are essential. Understanding how intimate relationships impact an individual's competitive process is crucial. This study explored the impact of competitor gender on female competition using electroencephalography analysis. The results revealed that females exhibited a smaller median of the absolute value of reaction time difference (DRT) between their partners and their competitors when their partners were absent compared to when their partners were present. Additionally, females showed greater average amplitudes of N2 posterior contralateral component (N2pc) and Late Positive Potential (LPP), increased activation of the alpha frequency band, and enhanced theta frequency band functional connectivity between the central parietal lobe and occipital lobe. Furthermore, when competing with individuals of the same gender as opposed to individuals of the opposite gender, females exhibited greater average amplitudes of percentage of wins and N2pc. A significant negative correlation was noted between the DRT and the average wave amplitudes of N2pc and LPP. These findings suggest that females are more engaged in competitive tasks when partners are not present and have improved decision-making when competing with same-gender individuals. This study provides evidence for the influence of lovers on female competition, helping females adapt to social competition and promoting healthy relationships.

High segregation and diminished global integration in large-scale brain functional networks enhances the perceptual binding of cross-modal stimuli.

Speech perception requires the binding of spatiotemporally disjoint auditory-visual cues. The corresponding brain network-level information processing can be characterized by two complementary mechanisms: functional segregation which refers to the localization of processing in either isolated or distributed modules across the brain, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using functional magnetic resonance imaging recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation-integration. We controlled the inter-subject variability of illusory/cross-modal perception parametrically, by introducing temporal lags in the incongruent auditory-visual articulations of speech sounds within the McGurk paradigm. The states of segregation-integration balance were captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sensory signals defined as the perceptual binding network (PBN) was identified using standardized parametric statistical approaches and their temporal correlations with all other brain areas were computed. With increasing illusory perception, the majority of the nodes of PBN showed decreased cooperation with the rest of the brain, reflecting states of high segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of segregation-integration were cross-validated.

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

Deleterious effect of sustained neuroinflammation in pediatric traumatic brain injury.

INTRODUCTION: Despite improved management of traumatic brain injury (TBI), it still leads to lifelong sequelae and disability, particularly in children. Chronic neuroinflammation (the so-called tertiary phase), in particular, microglia/macrophage and astrocyte reactivity, is among the main mechanisms suspected of playing a role in the generation of lesions associated with TBI. The role of acute neuroinflammation is now well understood, but its persistent effect and impact on the brain, particularly during development, are not. Here, we investigated the long-term effects of pediatric TBI on the brain in a mouse model. METHODS: Pediatric TBI was induced in mice on postnatal day (P) 7 by weight-drop trauma. The time course of neuroinflammation and myelination was examined in the TBI mice. They were also assessed by magnetic resonance, functional ultrasound, and behavioral tests at P45. RESULTS: TBI induced robust neuroinflammation, characterized by acute microglia/macrophage and astrocyte reactivity. The long-term consequences of pediatric TBI studied on P45 involved localized scarring astrogliosis, persistent microgliosis associated with a specific transcriptomic signature, and a long-lasting myelination defect consisting of the loss of myelinated axons, a decreased level of myelin binding protein, and severe thinning of the corpus callosum. These results were confirmed by reduced fractional anisotropy, measured by diffusion tensor imaging, and altered inter- and intra-hemispheric connectivity, measured by functional ultrasound imaging. In addition, adolescent mice with pediatric TBI showed persistent social interaction deficits and signs of anxiety and depressive behaviors. CONCLUSIONS: We show that pediatric TBI induces tertiary neuroinflammatory processes associated with white matter lesions and altered behavior. These results support our model as a model for preclinical studies for tertiary lesions following TBI.

Differential network knockoff filter with application to brain connectivity analysis.

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

The underlying neuropsychological and neural correlates of the impaired Chinese reading skills in children with attention deficit hyperactivity disorder.

Impaired basic academic skills (e.g., word recognition) are common in children with Attention Deficit Hyperactivity Disorder (ADHD). The underlying neuropsychological and neural correlates of impaired Chinese reading skills in children with ADHD have not been substantially explored. Three hundred and two children with ADHD (all medication-naïve) and 105 healthy controls underwent the Chinese language skill assessment, and 175 also underwent fMRI scans (84 ADHD and 91 controls). Between-group and mediation analyses were applied to explore the interrelationships of the diagnosis of ADHD, cognitive dysfunction, and impaired reading skills. Five ADHD-related brain functional networks, including the default mode network (DMN) and the dorsal attention network (DAN), were built using predefined regions of interest. Voxel-based group-wise comparisons were performed. The ADHD group performed worse than the control group in word-level reading ability tests, with lower scores in Chinese character recognition (CR) and word chains (WS) (all P < 0.05). With full-scale IQ and sustained attention in the mediation model, the direct effect of ADHD status on the CR score became insignificant (P = 0.066). The underlying neural correlates for the orthographic knowledge (OT) and CR differed between the ADHD and the control group. The ADHD group tended to recruit more DMN regions to maintain their reading performance, while the control group seemed to utilize more DAN regions. Children with ADHD generally presented impaired word-level reading skills, which might be caused by impaired sustained attention and lower IQ. According to the brain functional results, we infer that ADHD children might utilize a different strategy to maintain their orthographic knowledge and character recognition performance.

A scoping review of resting-state brain functional alterations in Type 2 diabetes.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.

Person-Based Similarity Index for Cognition and Its Neural Correlates in Late Adulthood: Implications for Cognitive Reserve.

Healthy aging is typically associated with some level of cognitive decline, but there is substantial variation in such decline among older adults. The mechanisms behind such heterogeneity remain unclear but some have suggested a role for cognitive reserve. In this work, we propose the "person-based similarity index" for cognition (PBSI-Cog) as a proxy for cognitive reserve in older adults, and use the metric to quantify similarity between the cognitive profiles of healthy older and younger participants. In the current study, we computed this metric in 237 healthy older adults (55-88 years) using a reference group of 156 younger adults (18-39 years) taken from the Cambridge Center for Ageing and Neuroscience dataset. Our key findings revealed that PBSI-Cog scores in older adults were: 1) negatively associated with age (rho = -0.25, P = 10-4) and positively associated with higher education (t = 2.4, P = 0.02), 2) largely explained by fluid intelligence and executive function, and 3) predicted more by functional connectivity between lower- and higher-order resting-state networks than brain structural morphometry or education. Particularly, we found that higher segregation between the sensorimotor and executive networks predicted higher PBSI-Cog scores. Our results support the notion that brain network functional organization may underly variability in cognitive reserve in late adulthood.

The default mode network is associated with changes in internalizing and externalizing problems differently in adolescent boys and girls.

Internalizing and externalizing problems that emerge during adolescence differentially increase boys' and girls' risk for developing psychiatric disorders. It is not clear, however, whether there are sex differences in the intrinsic functional architecture of the brain that underlie changes in the severity of internalizing and externalizing problems in adolescents. Using resting-state fMRI data and self-reports of behavioral problems obtained from 128 adolescents (73 females; 9-14 years old) at two timepoints, we conducted multivoxel pattern analysis to identify resting-state functional connectivity markers at baseline that predict changes in the severity of internalizing and externalizing problems in boys and girls 2 years later. We found sex-differentiated involvement of the default mode network in changes in internalizing and externalizing problems. Whereas changes in internalizing problems were associated with the dorsal medial subsystem in boys and with the medial temporal subsystem in girls, changes in externalizing problems were predicted by hyperconnectivity between core nodes of the DMN and frontoparietal network in boys and hypoconnectivity between the DMN and affective networks in girls. Our results suggest that different neural mechanisms predict changes in internalizing and externalizing problems in adolescent boys and girls and offer insights concerning mechanisms that underlie sex differences in the expression of psychopathology in adolescence.

Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation.

(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.

Rapid encoding of musical tones discovered in whole-brain connectivity.

Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Functional connectivity analyses performed using phase synchronisation and graph theoretical measures showed a large network of brain areas recruited during encoding of sounds, comprising primary and secondary auditory cortices, frontal operculum, insula, hippocampus and basal ganglia. Moreover, our results highlighted the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. Further, we discovered that individual differences along cognitive abilities and musicianship modulated the degree centrality of the brain areas implicated in the encoding process. Indeed, participants with higher musical expertise presented a stronger centrality of superior temporal gyrus and insula, while individuals with high working memory abilities showed a stronger centrality of frontal operculum. In conclusion, our study revealed the rapid unfolding of brain network dynamics responsible for the encoding of sounds and their relationship with individual differences, showing a complex picture which extends beyond the well-known involvement of auditory areas. Indeed, our results expanded our understanding of the general mechanisms underlying auditory pattern encoding in the human brain.

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking.

Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also included two widely-used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode network, but also by higher coupling between the two canonical brain networks. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and a data-driven approach, we provide initial empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

Connectome-based evidence for creative thinking as an emergent property of ordinary cognitive operations.

Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel "travelling pattern prediction analysis", here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

EEG-Based Sleep Staging Analysis with Functional Connectivity.

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

Breakdown of the affective-cognitive network in functional dystonia.

Previous studies suggested that brain regions subtending affective-cognitive processes can be implicated in the pathophysiology of functional dystonia (FD). In this study, the role of the affective-cognitive network was explored in two phenotypes of FD: fixed (FixFD) and mobile dystonia (MobFD). We hypothesized that each of these phenotypes would show peculiar functional connectivity (FC) alterations in line with their divergent disease clinical expressions. Resting state fMRI (RS-fMRI) was obtained in 40 FD patients (12 FixFD; 28 MobFD) and 43 controls (14 young FixFD-age-matched [yHC]; 29 old MobFD-age-matched [oHC]). FC of brain regions of interest, known to be involved in affective-cognitive processes, and independent component analysis of RS-fMRI data to explore brain networks were employed. Compared to HC, all FD patients showed reduced FC between the majority of affective-cognitive seeds of interest and the fronto-subcortical and limbic circuits; enhanced FC between the right affective-cognitive part of the cerebellum and the bilateral associative parietal cortex; enhanced FC of the bilateral amygdala with the subcortical and posterior cortical brain regions; and altered FC between the left medial dorsal nucleus and the sensorimotor and associative brain regions (enhanced in MobFD and reduced in FixFD). Compared with yHC and MobFD patients, FixFD patients had an extensive pattern of reduced FC within the cerebellar network, and between the majority of affective-cognitive seeds of interest and the sensorimotor and high-order function ("cognitive") areas with a unique involvement of dorsal anterior cingulate cortex connectivity. Brain FC within the affective-cognitive network is altered in FD and presented specific features associated with each FD phenotype, suggesting an interaction between brain connectivity and clinical expression of the disease.

Brain morphological and functional features in cognitive subgroups of schizophrenia.

AIM: Previous studies have reported different brain morphologies in different cognitive subgroups of patients with schizophrenia. We aimed to examine the brain structures and functional connectivity in these cognitive subgroups of schizophrenia. METHODS: We compared brain structures among healthy controls and cognitively deteriorated and preserved subgroups of patients with schizophrenia according to the decline in IQ. Connectivity analyses between subcortical regions and other brain areas were performed using resting-state functional magnetic resonance imaging among the groups. RESULTS: Whole brain and total cortical gray matter, right fusiform gyrus, left pars orbitalis gyrus, right pars triangularis, left superior temporal gyrus and left insula volumes, and bilateral cortical thickness were decreased in the deteriorated group compared to the control and preserved groups. Both schizophrenia subgroups had increased left lateral ventricle, right putamen and left pallidum, and decreased bilateral hippocampus, left precentral gyrus, right rostral middle frontal gyrus, and bilateral superior frontal gyrus volumes compared with controls. Hyperconnectivity between the thalamus and a broad range of brain regions was observed in the deteriorated group compared to connectivity in the control group, and this hyperconnectivity was less evident in the preserved group. We also found hyperconnectivity between the accumbens and the superior and middle frontal gyri in the preserved group compared with connectivity in the deteriorated group. CONCLUSION: These findings provide evidence of prominent structural and functional brain abnormalities in deteriorated patients with schizophrenia, suggesting that cognitive subgroups in schizophrenia might be useful biotypes to elucidate brain pathophysiology for new diagnostic and treatment strategies.

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population's structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint.

Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a "chronnectome" approach (recurring, time-varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472 at-risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD-related pathogenetic mutations and 223 mutation non-carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding-time window correlation to rs-fMRI data, and meta-state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta-states, shifting less often across them, and travelling through a narrowed meta-state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials.