Resting-State Functional MRI of Healthy Adults: Temporal Dynamic Brain Coactivation Patterns

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach

Bipolar disorder (BD) is a neuropsychiatric disorder characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture the evolving processes of emotion and cognition in BD. Nevertheless, prior investigations of dFC typically centered on larger time scales, limiting the sensitivity to transient changes. This study employed hidden Markov model (HMM) analysis to delve deeper into the moment-to-moment temporal patterns of brain activity in BD. We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. Additionally, we investigated the relationship between altered brain dynamics and structural disruption through the ridge regression (RR) algorithm. The results demonstrated that BD spent less time in a hyperconnected state with higher network efficiency and lower segregation. Conversely, BD spent more time in anticorrelated states featuring overall negative correlations, particularly among pairs of default mode network (DMN) and sensorimotor network (SMN), DMN and insular-opercular ventral attention networks (ION), subcortical network (SCN) and SMN, as well as SCN and ION. Interestingly, the hypoactivation of the cognitive control network in BD may be associated with the structural disruption primarily situated in the frontal and parietal lobes. This study investigated the dynamic mechanisms of brain network dysfunction in BD and offered fresh perspectives for exploring the physiological foundation of altered brain dynamics.

Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment

INTRODUCTION

Brain network dynamics have been extensively explored in patients with amnestic mild cognitive impairment (aMCI); however, differences in single- and multiple-domain aMCI (SD-aMCI and MD-aMCI) remain unclear.

METHODS

Using multicenter datasets, coactivation patterns (CAPs) were constructed and compared among normal control (NC), SD-aMCI, MD-aMCI, and Alzheimer's disease (AD) patients based on individual high-order cognitive network (HOCN) and primary sensory network (PSN) parcellations. Correlations between spatiotemporal characteristics and neuropsychological scores were analyzed.

RESULTS

Compared to NC, SD-aMCI showed temporal alterations in HOCN-dominant CAPs, while MD-aMCI showed alterations in PSN-dominant CAPs. In addition, transitions from SD-aMCI to AD may involve PSN, while MD-aMCI to AD involves both PSN and HOCN. Results were generally consistent across datasets from Chinese and White populations.

DISCUSSION

The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between aMCI subtypes and AD, highlighting the necessity of aMCI subtype classification in AD studies.

HIGHLIGHTS

Individual functional network parcellations and coactivation pattern (CAP) analysis were performed to characterize spatiotemporal differences between single- and multiple-domain amnestic mild cognitive impairment (SD-aMCI and MD-aMCI), and between distinct aMCI subtypes and Alzheimer's disease (AD). The analysis of multicenter datasets converged on four pairs of recurrent CAPs, including primary sensory networks (PSN)-dominant CAPs, high-order cognitive networks (HOCN)-dominant CAPs, and PSN-HOCN-interacting CAPs. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between distinct aMCI subtypes and AD.

Altered resting-state and dynamic functional connectivity of hypothalamic in first-episode depression: A functional magnetic resonance imaging study

The hypothalamus is an important component of the hypothalamic-pituitary-adrenal axis and an important brain region of the limbic system. Twenty-four first depressive episode(FDE) patients and 25 healthy controls were recruited for this study. The hypothalamus was used as a seed to observe the characteristics of resting state and dynamic functional connectivity (FC) changes in FDE patients, and further observed the correlation between the different brain regions and clinical symptoms. The results found that compared with the HC group, the FDE group showed sFC was increased of the left hypothalamus with right superior parietal gyrus and right middle temporal gyrus, and dFC was increased of the left hypothalamus with left inferior occipital gyrus. And sFC was increased of the right hypothalamus with right orbital part of inferior frontal gyrus, right supplementary motor area, and right middle temporal gyrus, and the dFC was also increased of right hypothalamus with right superior parietal gyrus and left middle temporal gyrus. In addition,there was a negative correlation between dFC values of the right hypothalamus with the right superior parietal gyrus and clinical symptoms in the FDE group. This study provides new insights into understanding the altered neuropathological mechanisms of the hypothalamic circuit in FDE.

Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study

BACKGROUND

Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored.

METHODS

We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up.

RESULTS

FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health.

CONCLUSIONS

This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.

Age Prediction Using Resting-State Functional MRI

The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.

A comprehensive survey of complex brain network representation

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain networks and relate these features to different clinical measures or demographical variables. With the enormous successes in deep learning techniques, graph learning methods have played significant roles in brain network analysis. In this survey, we first provide a brief overview of neuroimaging-derived brain networks. Then, we focus on presenting a comprehensive overview of both traditional methods and state-of-the-art deep-learning methods for brain network mining. Major models, and objectives of these methods are reviewed within this paper. Finally, we discuss several promising research directions in this field.

Atypical Hierarchical Connectivity Revealed by Stepwise Functional Connectivity in Aging

Hierarchical functional structure plays a crucial role in brain function. We aimed to investigate how aging affects hierarchical functional structure and to evaluate the relationship between such effects and molecular, microvascular, and cognitive features. We used resting-state functional magnetic resonance imaging (fMRI) data from 95 older adults (66.94 ± 7.23 years) and 44 younger adults (21.8 ± 2.53 years) and employed an innovative graph-theory-based analysis (stepwise functional connectivity (SFC)) to reveal the effects of aging on hierarchical functional structure in the brain. In the older group, an SFC pattern converged on the primary sensory-motor network (PSN) rather than the default mode network (DMN). Moreover, SFC decreased in the DMN and increased in the PSN at longer link-steps in aging, indicating a reconfiguration of brain hub systems during aging. Subsequent correlation analyses were performed between SFC values and molecular, microvascular features, and behavioral performance. Altered SFC patterns were associated with dopamine and serotonin, suggesting that altered hierarchical functional structure in aging is linked to the molecular fundament with dopamine and serotonin. Furthermore, increased SFC in the PSN, decreased SFC in the DMN, and accelerated convergence rate were all linked to poorer microvascular features and lower executive function. Finally, a mediation analysis among SFC features, microvascular features, and behavioral performance indicated that the microvascular state may influence executive function through SFC features, highlighting the interactive effects of SFC features and microvascular state on cognition.

Left frontal eye field encodes sound locations during passive listening

Previous studies reported that auditory cortices (AC) were mostly activated by sounds coming from the contralateral hemifield. As a result, sound locations could be encoded by integrating opposite activations from both sides of AC ("opponent hemifield coding"). However, human auditory "where" pathway also includes a series of parietal and prefrontal regions. It was unknown how sound locations were represented in those high-level regions during passive listening. Here, we investigated the neural representation of sound locations in high-level regions by voxel-level tuning analysis, regions-of-interest-level (ROI-level) laterality analysis, and ROI-level multivariate pattern analysis. Functional magnetic resonance imaging data were collected while participants listened passively to sounds from various horizontal locations. We found that opponent hemifield coding of sound locations not only existed in AC, but also spanned over intraparietal sulcus, superior parietal lobule, and frontal eye field (FEF). Furthermore, multivariate pattern representation of sound locations in both hemifields could be observed in left AC, right AC, and left FEF. Overall, our results demonstrate that left FEF, a high-level region along the auditory "where" pathway, encodes sound locations during passive listening in two ways: a univariate opponent hemifield activation representation and a multivariate full-field activation pattern representation.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Neuroinflammation mechanisms of neuromodulation therapies for anxiety and depression

Mood disorders are associated with elevated inflammation, and the reduction of symptoms after multiple treatments is often accompanied by pro-inflammation restoration. A variety of neuromodulation techniques that regulate regional brain activities have been used to treat refractory mood disorders. However, their efficacy varies from person to person and lack reliable indicator. This review summarizes clinical and animal studies on inflammation in neural circuits related to anxiety and depression and the evidence that neuromodulation therapies regulate neuroinflammation in the treatment of neurological diseases. Neuromodulation therapies, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), photobiomodulation (PBM), transcranial ultrasound stimulation (TUS), deep brain stimulation (DBS), and vagus nerve stimulation (VNS), all have been reported to attenuate neuroinflammation and reduce the release of pro-inflammatory factors, which may be one of the reasons for mood improvement. This review provides a better understanding of the effective mechanism of neuromodulation therapies and indicates that inflammatory biomarkers may serve as a reference for the assessment of pathological conditions and treatment options in anxiety and depression.

Efficacy and safety of simultaneous rTMS-tDCS over bilateral angular gyrus on neuropsychiatric symptoms in patients with moderate Alzheimer's disease: A prospective, randomized, sham-controlled pilot study

BACKGROUND

Treating neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) remains highly challenging. Noninvasive brain stimulation using repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) is of considerable interest in this context.

OBJECTIVE

To investigate the efficacy and safety of a novel technique involving simultaneous application of rTMS and tDCS (rTMS-tDCS) over bilateral angular gyrus (AG, P5/P6 electrode site) for AD-related NPS.

METHODS

Eighty-four AD patients were randomized to receive rTMS-tDCS, single-rTMS, single-tDCS, or sham stimulation for 4 weeks, with evaluation at week-4 (W4, immediately after treatment) and week-12 (W12, follow-up period) after initial examination. Primary outcome comprising Neuropsychiatric Inventory (NPI) score and secondary outcomes comprising mini-mental state examination (MMSE), AD assessment scale-cognitive subscale (ADAS-cog), and Pittsburgh sleep quality index (PSQI) scores were collected and analyzed by a two-factor (time and treatment), mixed-design ANOVA.

RESULTS

rTMS-tDCS produced greater improvement in NPI scores than single-tDCS and sham at W4 and W12 (both P < 0.017) and trended better than single-rTMS (W4: P = 0.058, W12: P = 0.034). rTMS-tDCS improved MMSE scores compared with single-tDCS at W4 (P = 0.011) and sham at W4 and W12 (both P < 0.017). rTMS-tDCS also significantly improved PSQI compared with single-rTMS and sham (both P < 0.017). Interestingly, rTMS-tDCS-induced NPI/PSQI improvement was significantly associated with MMSE/ADAS-cog improvement. tDCS- and/or rTMS-related adverse events appeared slightly and briefly.

CONCLUSIONS

rTMS-tDCS application to bilateral AG can effectively improve AD-related NPS, cognitive function, and sleep quality with considerable safety.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Background

Electroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR).

Materials and Methods

We proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy.

Results

We compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants.

Conclusion

The accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems.

Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson's Patients

Previous studies have demonstrated a close relationship between early Parkinson's disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson's disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson's disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson's disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson's disease aged 50-75 years from the Parkinson's Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson's patients is critical for clarifying the mechanisms of early diagnosis of the disease.