fMRI resting state networks define distinct modes of long-distance interactions in the human brain

Alterations within and between intrinsic connectivity networks in cognitive interference resolution

Cognitive interference resolution (CIR) is the process of maintaining goal-directed focus despite the presence of distractions. While CIR has been extensively studied through localized activation analyses, its network-level dynamics remain underexplored with sufficient methodological diversity. In this study, we investigated the task-modulated intrinsic connectivity networks (ICNs) and their dynamic interactions with detailed subnetwork segmentation during CIR using fMRI data from 27 healthy adults performing the Multi-Source Interference Task (MSIT). We applied high-order group independent component analysis (ICA) to extract ICN subcomponents, followed by task-modulated component identification and dynamic functional connectivity analysis to examine network interactions. Our results reveal that the dorsal attention network (DAN) and cognitive control network (CCN) show increased activation and connectivity, while the default mode network (DMN) and limbic network exhibit decreased activation and connectivity. Additionally, the visual and cerebellum networks emerge as key intermediaries in CIR, as DAN and CCN strengthen their connectivity with these networks rather than directly interacting with each other. Furthermore, network reconfiguration patterns suggest functional segregation within the somatomotor network and CCN, indicating specialized subcomponent contributions. These findings provide a granular understanding of ICN activations and dynamic inter-network communication during CIR, offering new insights into the flexible reorganization of brain networks in response to cognitive interference.

Altered static and dynamic functional network connectivity and combined Machine learning in asthma

Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structural and functional abnormalities in the brains of individuals with asthma. However, earlier research has primarily focused on static changes in brain activity, neglecting the effects of asthma on the dynamic characteristics of functional brain networks. This study included 31 asthma patients and 31 healthy controls (HCs). Independent component analysis (ICA) was employed to extract changes in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) from the acquired data. Compared to the HC group, the overall functional connectivity (FC) within the visual network (VN) in asthma patients declined, whereas the FC in the auditory network (AN) and cerebellar network (CN) increased. Additionally, functional network connectivity (FNC) analysis revealed enhanced connectivity between the VN and AN, as well as between the VN and executive control network (ECN), while AN-AN functional connectivity was reduced. The dFNC was primarily characterized by abnormal connections among the default mode network (DMN), AN, and other brain regions. The support vector machine (SVM) model based on FC and FNC demonstrates excellent performance in distinguishing asthma patients from HCs. Our findings highlight significant alterations in functional connectivity within the sFNC and dFNC of asthma patients. These results enhance our understanding of the potential neurobiological mechanisms underlying emotional deficits and cognitive impairments in asthma patients. Furthermore, they provide additional neuroimaging evidence that may be helpful for researchers in identifying potential neurobiological markers to differentiate asthma patients from HCs.

Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Autism and Schizophrenia Spectrum Disorders

BACKGROUND

Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), autistic, and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition.

METHODS

FALFF from 495 participants (185 TDC, 68 autism, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participant's average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps.

RESULTS

Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and autistic participants compared with TDCs. Limited differences were observed between autistic and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the autism and SSD groups was also significantly higher compared with TDC.

CONCLUSIONS

Similar patterns of fALFF and individual variability in autism and SSD suggest some common neurobiological features across these related heterogeneous conditions.

Unraveling the mesoscale functional connectivity of the human primary visual cortex using high-resolution functional MRI

Despite their importance in shaping visual perception, functional connectivity between ocular dominance columns (ODCs), the building blocks of neuronal processing within the human primary visual cortex (V1), remains largely unknown. Using high-resolution functional MRI (fMRI), we localized ODCs and assessed their resting-state functional connectivity (rs-FC) in 11 human adults (3 females). Consistent with anatomical studies in animals, we found stronger rs-FC in the middle compared to deep and superficial cortical depths and selectively stronger rs-FC between ODCs with alike compared to unalike ocular polarity. Beyond what was known from animal models, and consistent with human perceptual biases, we found stronger intra- and interhemispheric rs-FC in peripheral (compared to central) and in dorsal (compared to ventral) V1 subregions. Lastly, we found a significant correlation between rs-FC patterns and ODC maps, supporting the hypothesis that ODC maps can be predicted from rs-FC patterns within V1. These results highlight the heterogeneity in functional connectivity between ODCs across cortical depths and V1 subfields, underscoring their likely association with human perceptual biases.

Significance Statement

Our findings provide evidence for selective mesoscale rs-FC between ODCs, aligning with prior anatomical findings in animals.Beyond what is known from animal studies, we demonstrate that the mesoscale rs-FC pattern varies across V1 subregions, aligning with the expected heterogeneity in global visual processing across visual subfields.We provide evidence for the predictability of ODC maps from the rs-FC pattern, establishing one of the first steps toward leveraging rs-FC for segmentation of the visual cortex at mesoscale levels.

A novel method for functional brain networks based on static cerebral blood flow

Cerebral blood flow (CBF) offers a quantitative and reliable measurement for brain activity and is increasingly used to study functional networks. However, current methods evaluate inter-regional relations mainly based on CBF temporal dynamics, which suffers from low signal-to-noise ratio and poor temporal resolution. Here we proposed a method to construct functional brain networks by estimating shape similarity (index by Jensen-Shannon divergence) in probability distributions of regional static CBF measured by arterial spin labeling perfusion imaging over a scanning period. Based on CBF data of 30 healthy participants from 10 visits, we found that the CBF networks exhibited non-trivial topological features (e.g., small-world organization, modular architecture, and hubs) and showed low-to-fair test-retest reliability and high between-subject consistency. We further found that interregional CBF similarities were depended on anatomical distance and differed between high- and lower-order subnetworks. Moreover, interregional CBF similarities within high-order subnetworks showed significantly lower reliability than those within low-order subnetworks. Finally, we showed that nodal degree of the CBF networks were related to regional sizes and CBF levels and spatially aligned with maps of the dopamine transporter and metabolic glutamate receptor 5 intensities, expression levels of genes primarily enriched in cholesterol-related pathways and endothelial cells, and meta-analytic activations related to memory, language, and executive function. Altogether, our proposed method provide a novel, relatively reliable, and neurobiologically meaningful means to study functional network organization of the human brain.

GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. However, to date, the empirical cognitive characterization of these networks has been limited. The present study introduces the Groupe d'Imagerie Neurofonctionnelle Network Atlas, a comprehensive brain atlas featuring 33 resting-state networks. Based on the resting-state data of 1812 participants, the atlas was developed by classifying independent components extracted individually, ensuring consistent between-subject detection. We further explored the cognitive relevance of each GINNA network using Neurosynth-based meta-analytic decoding and generative null hypothesis testing. Significant cognitive terms for each network were then synthesized into appropriate cognitive processes through the consensus of six authors. The GINNA atlas showcases a diverse range of topological profiles, reflecting a broad spectrum of the known human cognitive repertoire. The processes associated with each network are named according to the standard Cognitive Atlas ontology, thus providing opportunities for empirical validation.

Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke

Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients. Fifty stroke patients and 50 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Initially, the independent component analysis (ICA) method was utilized to extract the resting-state network (RSN). Subsequently, the disparities in static functional network connectivity both within and between networks among the two groups were computed and juxtaposed. Following this, five consistent and robust dynamic functional network connectivity (dFNC) states were derived by integrating the sliding time window method with k-means cluster analysis, and the distinctions in dFNC between the groups across different states, along with the intergroup variations in three dynamic temporal metrics, were assessed. Finally, a support vector machine (SVM) approach was employed to discriminate stroke patients from HCs using FC and FNC as classification features. Comparing the stroke group to the healthy control (HC) group, the stroke group exhibited reduced intra-network functional connectivity (FC) in the right superior temporal gyrus of the ventral attention network (VAN), the left calcarine of the visual network (VN), and the left precuneus of the default mode network (DMN). Regarding static functional network connectivity (FNC), we identified increased connectivity between the executive control network (ECN) and dorsal attention network (DAN), salience network (SN) and DMN, SN-ECN, and VN-ECN, along with decreased connectivity between DAN-DAN, ECN-SN, SN-SN, and DAN-VN between the two groups. Noteworthy differences in dynamic FNC (dFNC) were observed between the groups in states 3 to 5. Moreover, stroke patients demonstrated a significantly higher proportion of time and longer mean dwell time in state 4, alongside a decreased proportion of time in state 5 compared to HC. Finally, utilizing FC and FNC as features, stroke patients could be distinguished from HC with an accuracy exceeding 70% and an area under the curve ranging from 0.8284 to 0.9364. In conclusion, our study reveals static and dynamic changes in large-scale brain networks in stroke patients, potentially linked to abnormalities in visual, cognitive, and motor functions. This investigation offers valuable insights into the neural mechanisms underpinning the functional deficits observed in stroke, thereby aiding in the diagnosis and development of targeted therapeutic interventions for affected individuals.

The Brain's Aging Resting State Functional Connectivity

Resting state networks (RSNs) of the brain are characterized as correlated spontaneous time-varying fluctuations in the absence of goal-directed tasks. These networks can be local or large-scale spanning the brain. The study of the spatiotemporal properties of such networks has helped understand the brain's fundamental functional organization under healthy and diseased states. As we age, these spatiotemporal properties change. Moreover, RSNs exhibit neural plasticity to compensate for the loss of cognitive functions. This narrative review aims to summarize current knowledge from functional magnetic resonance imaging (fMRI) studies on age-related alterations in RSNs. Underlying mechanisms influencing such changes are discussed. Methodological challenges and future directions are also addressed. By providing an overview of the current state of knowledge in this field, this review aims to guide future research endeavors aimed at promoting healthy brain aging and developing effective interventions for age-related cognitive impairment and neurodegenerative diseases.

Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles

Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.

Near-critical corticothalamic eigenmodes: Effects of nonuniform connectivity on modes, activity, and communication channels

The effects of nonuniformities in axonal connectivity on natural modes of brain activity are explored to determine their contributions to modal eigenvalues, structure, and communication and to clarify the limits of validity of widely used uniform-connectivity approximations. Preferred channels of communication are demonstrated that are supported by natural modes of mean connectivity and resulting activity. The effects of axonal tracts on these modes are calculated using perturbation methods, and it is found that modes and their spectra are only moderately perturbed by even the largest white matter tracts. However, perturbations of activity are greatly magnified when modes are near-critical and realistic connectivity and gain perturbations can then enable rapid responses to stimuli on the observed timescales of evoked responses. It is thus argued that dynamic mode-mode communication channels complement ones based on white matter tracts and that both rely on near-criticality to have their observed effects.

Born Connected: Do Infants Already Have Adult-Like Multi-Scale Connectivity Networks?

The human brain undergoes remarkable development with the first six postnatal months witnessing the most dramatic structural and functional changes, making this period critical for in-depth research. rsfMRI studies have identified intrinsic connectivity networks (ICNs), including the default mode network, in infants. Although early formation of these networks has been suggested, the specific identification and number of ICNs reported in infants vary widely, leading to inconclusive findings. In adults, ICNs have provided valuable insights into brain function, spanning various mental states and disorders. A recent study analyzed data from over 100,000 subjects and generated a template of 105 multi-scale ICNs enhancing replicability and generalizability across studies. Yet, the presence of these ICNs in infants has not been investigated. This study addresses this significant gap by evaluating the presence of these multi-scale ICNs in infants, offering critical insight into the early stages of brain development and establishing a baseline for longitudinal studies. To accomplish this goal, we employ two sets of analyses. First, we employ a fully data-driven approach to investigate the presence of these ICNs from infant data itself. Towards this aim, we also introduce burst independent component analysis (bICA), which provides reliable and unbiased network identification. The results reveal the presence of these multi-scale ICNs in infants, showing a high correlation with the template (rho > 0.5), highlighting the potential for longitudinal continuity in such studies. We next demonstrate that reference-informed ICA-based techniques can reliably estimate these ICNs in infants, highlighting the feasibility of leveraging the NeuroMark framework for robust brain network extraction. This approach not only enhances cross-study comparisons across lifespans but also facilitates the study of brain changes across different age ranges. In summary, our study highlights the novel discovery that the infant brain already possesses ICNs that are widely observed in older cohorts.

Oculomotor functional connectivity associated with motor sequence learning

Acquisition of learned motor sequences involves saccades directed toward the goal to gather visual information prior to reaching. While goal-directed actions involve both eye and hand movements, the role of brain areas controlling saccades during motor sequence learning is still unclear. This study aimed to determine whether resting-state functional connectivity of oculomotor regions is associated with behavioral changes resulting from motor sequence learning. We investigated connectivity between oculomotor control regions and candidate regions involved in oculomotor control and motor sequence learning. Twenty adults had brain scans before 3 days of motor task practice and after a 24-hour retention test, which was used to assess sequence-specific learning. During testing, both saccades and reaches were tracked. Stronger connectivity in multiple oculomotor regions prior to motor task practice correlated with greater sequence-specific learning for both saccades and reaches. A more negative connectivity change involving oculomotor regions from pre- to post-training correlated with greater sequence-specific learning for both saccades and reaches. Overall, oculomotor functional connectivity was associated with the magnitude of behavioral change resulting from motor sequence learning, providing insight into the function of the oculomotor system during motor sequence learning.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Altered functional connectivity of resting-state networks and the correlation with clinical characteristics in intermittent exotropia adult patients: a resting-state magnetic resonance imaging study

BACKGROUND

The pathogenesis of intermittent exotropia (IXT) remains unclear. The study aims to investigate alterations of resting-state networks (RSNs) in IXT adult patients using resting-state functional magnetic resonance imaging (rs-fMRI) data to explore the potential neural mechanisms.

METHODS

Twenty-six IXT adult patients and 22 age-, sex-, handedness-, and education-matched healthy controls (HCs) underwent fMRI scanning and ophthalmological examinations. Brain areas with significant functional connectivity (FC) differences between the IXT and HC groups were selected as regions of interest (ROI) and mean z-scores were calculated to control for individual differences.

RESULTS

Compared with HCs, IXT patients exhibited altered FC in various brain regions within RSNs involved in binocular fusion, stereopsis, ocular movement, emotional processes and social cognition, including the default mode network (DMN), the dorsal attention network (DAN), the visual network (VN), the sensorimotor network (SMN), the executive control network (ECN), the frontoparietal network (FPN) and the auditory network (AN). The degree of exodeviation was positively correlated with FC value of left middle occipital gyrus (MOG) within the VN. Correspondingly, we found a negative correlation between the degree of exodeviation and the FC value of left angular gyrus (AG) within FPN (P < 0.05). The FNC analysis between different RSNs also provides evidence on visual-motor cortical plasticity.

CONCLUSIONS

IXT patients showed widespread changes of brain activity within RSNs related to binocular fusion, stereopsis, oculomotor control, emotional processes, and social cognition. These findings extend our current understanding of the neuropathological mechanisms of IXT.

TRIAL REGISTRATION

Beginning date of the trial: 2021-09-01. Date of registration:2021-07-18. Trial registration number: ChiCTR 2,100,048,852. Trial registration site: http://www.chictr.org.cn/index.aspx .

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called leading eigenvector dynamics analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, intergroup differences in brain dynamic activity indicators are examined, and the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual region activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state and more active in limbic region activity state and visual region activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.NEW & NOTEWORTHY Alzheimer's disease (AD) is a neurodegenerative disease, but few studies have explored the characteristics of abnormal dynamic brain activity in AD patients. Here, our report reveals the abnormal dynamic activity of the patients' resting-state network, providing new insights into the dynamic analysis of brain networks and helping to gain a deeper understanding of the abnormal spatial dynamic patterns in AD patients.

Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning

Background

Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA).

Methods

Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups.

Results

In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292.

Conclusion

In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.

Neuroimaging of motor recovery after ischemic stroke - functional reorganization of motor network

The long-term motor outcome of acute stroke patients may be correlated to the reorganization of brain motor network. Abundant neuroimaging studies contribute to understand the pathological changes and recovery of motor networks after stroke. In this review, we summarized how current neuroimaging studies have increased understanding of reorganization and plasticity in post stroke motor recovery. Firstly, we discussed the changes in the motor network over time during the motor-activation and resting states, as well as the overall functional integration trend of the motor network. These studies indicate that the motor network undergoes dynamic bilateral hemispheric functional reorganization, as well as a trend towards network randomization. In the second part, we summarized the current study progress in the application of neuroimaging technology to early predict the post-stroke motor outcome. In the third part, we discuss the neuroimaging techniques commonly used in the post-stroke recovery. These methods provide direct or indirect visualization patterns to understand the neural mechanisms of post-stroke motor recovery, opening up new avenues for studying spontaneous and treatment-induced recovery and plasticity after stroke.

Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies

Glioblastoma multiforme (GBM) is one of the most aggressive forms of brain tumor, characterized by a daunting prognosis with a life expectancy hovering around 12-16 months. Despite a century of relentless research, only a select few drugs have received approval for brain tumor treatment, largely due to the formidable barrier posed by the blood-brain barrier. The current standard of care involves a multifaceted approach combining surgery, irradiation, and chemotherapy. However, recurrence often occurs within months despite these interventions. The formidable challenges of drug delivery to the brain and overcoming therapeutic resistance have become focal points in the treatment of brain tumors and are deemed essential to overcoming tumor recurrence. In recent years, a promising wave of advanced treatments has emerged, offering a glimpse of hope to overcome the limitations of existing therapies. This review aims to highlight cutting-edge technologies in the current and ongoing stages of development, providing patients with valuable insights to guide their choices in brain tumor treatment.

Altered functional connectivity of insular subregions in subjective cognitive decline

Objective

Recent research has highlighted the insula as a critical hub in human brain networks and the most susceptible region to subjective cognitive decline (SCD). However, the changes in functional connectivity of insular subregions in SCD patients remain poorly understood. The present study aims to clarify the altered functional connectivity patterns within insular subregions in individuals with SCD using resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

In this study, we collected rs-fMRI data from 30 patients with SCD and 28 healthy controls (HCs). By defining three subregions of the insula, we mapped whole-brain resting-state functional connectivity (RSFC). We identified several distinct RSFC patterns of the insular subregions. Specifically, for positive connectivity, three cognitive-related RSFC patterns were identified within the insula, suggesting anterior-to-posterior functional subdivisions: (1) a dorsal anterior zone of the insula that shows RSFC with the executive control network (ECN); (2) a ventral anterior zone of the insula that shows functional connectivity with the salience network (SN); and (3) a posterior zone along the insula that shows functional connectivity with the sensorimotor network (SMN).

Results

Compared to the controls, patients with SCD exhibited increased positive RSFC to the sub-region of the insula, demonstrating compensatory plasticity. Furthermore, these abnormal insular subregion RSFCs are closely correlated with cognitive performance in the SCD patients.

Conclusion

Our findings suggest that different insular subregions exhibit distinct patterns of RSFC with various functional networks, which are affected differently in patients with SCD.

Fractional amplitude of low-frequency fluctuations in sensory-motor networks and limbic system as a potential predictor of treatment response in patients with schizophrenia

BACKGROUND

Previous investigations have revealed substantial differences in neuroimaging characteristics between healthy controls (HCs) and individuals diagnosed with schizophrenia (SCZ). However, we are not entirely sure how brain activity links to symptoms in schizophrenia, and there is a need for reliable brain imaging markers for treatment prediction.

METHODS

In this longitudinal study, we examined 56 individuals diagnosed with 56 SCZ and 51 HCs. The SCZ patients underwent a three-month course of antipsychotic treatment. We employed resting-state functional magnetic resonance imaging (fMRI) along with fractional Amplitude of Low Frequency Fluctuations (fALFF) and support vector regression (SVR) methods for data acquisition and subsequent analysis.

RESULTS

In this study, we initially noted lower fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, coupled with higher fALFF values in the left hippocampus and right putamen in SCZ patients compared to the HCs at baseline. However, when comparing fALFF values in brain regions with abnormal baseline fALFF values for SCZ patients who completed the follow-up, no significant differences in fALFF values were observed after 3 months of treatment compared to baseline data. The fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, and the left postcentral gyrus were useful in predicting treatment effects.

CONCLUSION

Our findings suggest that reduced fALFF values in the sensory-motor networks and increased fALFF values in the limbic system may constitute distinctive neurobiological features in SCZ patients. These findings may serve as potential neuroimaging markers for the prognosis of SCZ patients.

The Necessity of Taking Culture and Context into Account When Studying the Relationship between Socioeconomic Status and Brain Development

Decades of research has revealed a relationship between childhood socioeconomic status (SES) and brain development at the structural and functional levels. Of particular note is the distinction between income and maternal education, two highly correlated factors which seem to influence brain development through distinct pathways. Specifically, while a families' income-to-needs ratio is linked with physiological stress and household chaos, caregiver education influences the day-to-day language environment a child is exposed to. Variability in either one of these environmental experiences is related to subsequent brain development. While this work has the potential to inform public policies in a way that benefits children, it can also oversimplify complex factors, unjustly blame low-SES parents, and perpetuate a harmful deficit perspective. To counteract these shortcomings, researchers must consider sociodemographic differences in the broader cultural context that underlie SES-based differences in brain development. This review aims to address these issues by (a) identifying how sociodemographic mechanisms associated with SES influence the day-to-day experiences of children, in turn, impacting brain development, while (b) considering the broader cultural contexts that may differentially impact this relationship.

Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data

Resting-state fMRI (rs-fMRI) data is used to study the intrinsic functional connectivity (FC) in the human brain. In the past decade, interest has focused on studying the temporal dynamics of FC on short timescales, ranging from seconds to minutes. These studies of time-varying FC (TVFC) have enabled the classification of whole-brain dynamic FC profiles into distinct "brain states", defined as recurring whole-brain connectivity profiles reliably observed across subjects and sessions. The analysis of rs-fMRI data is complicated by the fact that the measured BOLD signal consists of changes induced by neuronal activation, as well as non-neuronal nuisance fluctuations that should be removed prior to further analysis. Thus, the data undergoes significant preprocesing prior to analysis. In previous work [24], we illustrated the potential pitfalls involved with using modular preprocessing pipelines, showing how later preprocessing steps can reintroduce correlation with signal previously removed from the data. Here we show that the problem runs deeper, and that certain statistical analysis techniques can potentially interact with preprocessing and reintroduce correlations with previously removed signal. One such technique is the popular sliding window analysis, used to compute TVFC. In this paper, we discuss the problem both theoretically and empirically in application to test-retest rs-fMRI data. Importantly, we show that we are able to obtain essentially the same brain states and state transitions when analyzing motion induced signal as we do when analyzing the preprocessed but windowed data. Our results cast doubt on whether the estimated brain states obtained using sliding window analysis are neuronal in nature, or simply reflect non-neuronal nuisance signal variation (e.g., motion).

Graph theoretical analysis and independent component analysis of diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study

AIMS

This study aimed to investigate the resting-state functional connectivity and topologic characteristics of brain networks in patients with diabetic optic neuropathy (DON).

METHODS

Resting-state functional magnetic resonance imaging scans were performed on 23 patients and 41 healthy control (HC) subjects. We used independent component analysis and graph theoretical analysis to determine the topologic characteristics of the brain and as well as functional network connectivity (FNC) and topologic properties of brain networks.

RESULTS

Compared with HCs, patients with DON showed altered global characteristics. At the nodal level, the DON group had fewer nodal degrees in the thalamus and insula, and a greater number in the right rolandic operculum, right postcentral gyrus, and right superior temporal gyrus. In the internetwork comparison, DON patients showed significantly increased FNC between the left frontoparietal network (FPN-L) and ventral attention network (VAN). Additionally, in the intranetwork comparison, connectivity between the left medial superior frontal gyrus (MSFG) of the default network (DMN) and left putamen of auditory network was decreased in the DON group.

CONCLUSION

DON patients altered node properties and connectivity in the DMN, auditory network, FPN-L, and VAN. These results provide evidence of the involvement of specific brain networks in the pathophysiology of DON.

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment

Lifestyle interventions have positive neuroprotective effects in aging. However, there are still open questions about how changes in resting-state functional connectivity (rsFC) contribute to cognitive improvements. The Projecte Moviment is a 12-week randomized controlled trial of a multimodal data acquisition protocol that investigated the effects of aerobic exercise (AE), computerized cognitive training (CCT), and their combination (COMB). An initial list of 109 participants was recruited from which a total of 82 participants (62% female; age = 58.38 ± 5.47) finished the intervention with a level of adherence > 80%. Only in the COMB group, we revealed an extended network of 33 connections that involved an increased and decreased rsFC within and between the aDMN/pDMN and a reduced rsFC between the bilateral supplementary motor areas and the right thalamus. No global and especially local rsFC changes due to any intervention mediated the cognitive benefits detected in the AE and COMB groups. Projecte Moviment provides evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.

Greater Early Posttrauma Activation in the Right Inferior Frontal Gyrus Predicts Recovery From Posttraumatic Stress Disorder Symptoms

BACKGROUND

Posttraumatic stress disorder (PTSD) has been associated with altered emotion processing and modulation in specific brain regions, i.e., the amygdala, insula, and medial prefrontal and anterior cingulate cortices. Functional alterations in these regions, recorded shortly after trauma exposure, may predict changes in PTSD symptoms.

METHODS

Survivors (N = 104) of a traumatic event, predominantly a motor vehicle accident, were included. Functional magnetic resonance imaging was used to assess brain activation 1, 6, and 14 months after trauma exposure (T1, T2, and T3, respectively). Participants performed the Shifted-attention Emotional Appraisal Task, which probes 3 affective processes: implicit emotional processing (of emotional faces), emotion modulation by attention shifting (away from these faces), and emotion modulation by appraisal (of the participants' own emotional response to these faces). We defined regions of interest based on task-related activations, extracted beta weights from these regions of interest, and submitted them to a series of analyses to examine relationships between neural activation and PTSD severity over the 3 time points.

RESULTS

At T1, a regression model containing activations in the left dorsolateral prefrontal cortex, bilateral inferior frontal gyrus (IFG), and medial prefrontal cortex during emotion modulation by appraisal significantly predicted change in PTSD symptoms. More specifically, greater right IFG activation at T1 was associated with greater reduction in symptom severity (T1-T3). Exploratory analysis also found that activation of the right IFG increased from T1 to T3.

CONCLUSIONS

The results suggest that greater early posttrauma activation during emotion appraisal in the right IFG, a region previously linked to cognitive control in PTSD, predicts recovery from PTSD symptoms.

Activation network improves spatiotemporal modelling of human brain communication processes

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Increased functional connectivity between the auditory cortex and the frontoparietal network compensates for impaired visuomotor transformation after early auditory deprivation

Early auditory deprivation leads to a reorganization of large-scale brain networks involving and extending beyond the auditory system. It has been documented that visuomotor transformation is impaired after early deafness, associated with a hyper-crosstalk between the task-critical frontoparietal network and the default-mode network. However, it remains unknown whether and how the reorganized large-scale brain networks involving the auditory cortex contribute to impaired visuomotor transformation after early deafness. Here, we asked deaf and early hard of hearing participants and normal hearing controls to judge the spatial location of a visual target. Compared with normal hearing controls, the superior temporal gyrus showed significantly increased functional connectivity with the frontoparietal network and the default-mode network in deaf and early hard of hearing participants, specifically during egocentric judgments. However, increased superior temporal gyrus-frontoparietal network and superior temporal gyrus-default-mode network coupling showed antagonistic effects on egocentric judgments. In deaf and early hard of hearing participants, increased superior temporal gyrus-frontoparietal network connectivity was associated with improved egocentric judgments, whereas increased superior temporal gyrus-default-mode network connectivity was associated with deteriorated performance in the egocentric task. Therefore, the data suggest that the auditory cortex exhibits compensatory neuroplasticity (i.e. increased functional connectivity with the task-critical frontoparietal network) to mitigate impaired visuomotor transformation after early auditory deprivation.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

Introduction

Functional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis.

Materials and methods

In this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities.

Results

Our data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 % similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data.

Conclusion

Our analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.

Aortic valve Replacement compared to Transcatheter Implant and its relationship with COgnitive Impairment (ARTICO) evaluated with neuropsychological and advanced neuroimaging: a longitudinal cohort study

BACKGROUND

Aortic stenosis is the most common valvulopathy in Western countries. The treatment of choice had been surgery aortic valve replacement (SAVR), but the improvement in endovascular approaches as transcatheter aortic valve implantation (TAVI), initially reserved for patients with very high surgical risk, has been extended to high and intermediate, and recently also to low-risk patients. Stroke and vascular cognitive impairment are the most important complications. It is not entirely clear which technique is best to avoid these complications as well as their impact. Our goal is to evaluate changes in cognitive performance in the early (1-month) and late (1-year) postoperative period in patients undergoing SAVR or TAVI, by extensive neuropsychological study (NRP) and advanced Magnetic Resonance Imaging (MRI). Specifically, to compare early and late cognitive changes after the intervention between both groups, the occurrence of stroke during follow-up and to compare the appearance of silent vascular lesions and changes in brain activity and functional connectivity with functional MRI during follow-up between both groups.

METHODS/DESIGN

Prospective longitudinal cohort study. A non-selected representative sample of 80 subjects, 40 SAVR and 40 TAVI to obtain a final sample of 36 eligible subjects in each group, ranging from 70 to 85 years old, with indication for aortic replacement and intermediate or high surgical risk will be studied. At baseline, within one month before the treatment, all individuals will undergo an extensive NRP and advanced MRI study. These studies will also be performed 1-month and 1-year after treatment, to assess the appearance of new vascular lesions, as well as changes in cognitive performance with respect to baseline.

DISCUSSION

This study aims to evaluate changes in cognitive performance as well as both clinical and silent vascular events occurring in the early (1-month) and late (1-year) periods after SAVR and TAVI. We will also analyze the correlation between neuropsychological and neuroimaging approaches in order to evaluate cognition. Therefore, it may provide high-quality data of cognitive changes and vascular events for both techniques, and be useful to tailor interventions to individual characteristics and ultimately aiding in decision-making.

TRIAL REGISTRATION

This study is register in Clinicaltrials.gov (NCT05235529) on 11th February 2022.

Modulation of limbic resting-state networks by subthalamic nucleus deep brain stimulation

Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson's disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific modulation induced by STN-DBS using Leading Eigenvector Dynamics Analysis (LEiDA). We calculated the occupancy of resting-state networks (RSNs) in functional MRI data from 10 patients with Parkinson's disease implanted with STN-DBS and statistically compared between ON and OFF conditions. STN-DBS was found to specifically modulate the occupancy of networks overlapping with limbic RSNs. STN-DBS significantly increased the occupancy of an orbitofrontal limbic subsystem with respect to both DBS OFF (p = 0.0057) and 49 age-matched healthy controls (p = 0.0033). Occupancy of a diffuse limbic RSN was increased with STN-DBS OFF when compared with healthy controls (p = 0.021), but not when STN-DBS was ON, which indicates rebalancing of this network. These results highlight the modulatory effect of STN-DBS on components of the limbic system, particularly within the orbitofrontal cortex, a structure associated with reward processing. These results reinforce the value of quantitative biomarkers of RSN activity in evaluating the disseminated impact of brain stimulation techniques and the personalization of therapeutic strategies.

Differences in the Default Mode Network of Temporal Lobe Epilepsy Patients Detected by Hilbert-Huang Transform Based Dynamic Functional Connectivity

Resting-state functional connectivity, constructed via functional magnetic resonance imaging, has become an essential tool for exploring brain functions. Aside from the methods focusing on the static state, investigating dynamic functional connectivity can better uncover the fundamental properties of brain networks. Hilbert-Huang transform (HHT) is a novel time-frequency technique that can adapt to both non-linear and non-stationary signals, which may be an effective tool for investigating dynamic functional connectivity. To perform the present study, we investigated time-frequency dynamic functional connectivity among 11 brain regions of the default mode network by first projecting the coherence into the time and frequency domains, and subsequently by identifying clusters in the time-frequency domain using k-means clustering. Experiments on 14 temporal lobe epilepsy (TLE) patients and 21 age and sex-matched healthy controls were performed. The results show that functional connections in the brain regions of the hippocampal formation, parahippocampal gyrus, and retrosplenial cortex (Rsp) were reduced in the TLE group. However, the connections in the brain regions of the posterior inferior parietal lobule, ventral medial prefrontal cortex, and the core subsystem could hardly be detected in TLE patients. The findings not only demonstrate the feasibility of utilizing HHT in dynamic functional connectivity for epilepsy research, but also indicate that TLE may cause damage to memory functions, disorders of processing self-related tasks, and impairment of constructing a mental scene.

Altered Resting-State Brain Activity and Functional Connectivity in Post-Stroke Apathy: An fMRI Study

Apathy is a common neuropsychiatric disease after stroke and is linked to a lower quality of life while undergoing rehabilitation. However, it is still unknown what are the underlying neural mechanisms of apathy. This research aimed to explore differences in the cerebral activity and functional connectivity (FC) of subjects with post-stroke apathy and those without it. A total of 59 individuals with acute ischemic stroke and 29 healthy subjects with similar age, sex, and education were recruited. The Apathy Evaluation Scale (AES) was used to evaluate apathy at 3 months after stroke. Patients were split into two groups-PSA (n = 21) and nPSA (n = 38)-based on their diagnosis. The fractional amplitude of low-frequency fluctuation (fALFF) was used to measure cerebral activity, as well as region-of-interest to region-of-interest analysis to examine functional connectivity among apathy-related regions. Pearson correlation analysis between fALFF values and apathy severity was performed in this research. The values of fALFF in the left middle temporal regions, right anterior and middle cingulate regions, middle frontal region, and cuneus region differed significantly among groups. Pearson correlation analysis showed that the fALFF values in the left middle temporal region (p < 0.001, r = 0.66) and right cuneus (p < 0.001, r = 0.48) were positively correlated with AES scores in stroke patients, while fALFF values in the right anterior cingulate (p < 0.001, r = -0.61), right middle frontal gyrus (p < 0.001, r = -0.49), and middle cingulate gyrus (p = 0.04, r = -0.27) were negatively correlated with AES scores in stroke patients. These regions formed an apathy-related subnetwork, and functional connectivity analysis unveiled that altered connectivity was linked to PSA (p < 0.05). This research found that abnormalities in brain activity and FC in the left middle temporal region, right middle frontal region, right cuneate region, and right anterior and middle cingulate regions in stroke patients were associated with PSA, revealing a possible neural mechanism and providing new clues for the diagnosis and treatment of PSA.

Neuroimaging-Derived Biomarkers of the Antidepressant Effects of Ketamine

Major depressive disorder is a highly prevalent psychiatric disorder. Despite an extensive range of treatment options, about a third of patients still struggle to respond to available therapies. In the last 20 years, ketamine has gained considerable attention in the psychiatric field as a promising treatment of depression, particularly in patients who are treatment resistant or at high risk for suicide. At a subanesthetic dose, ketamine produces a rapid and pronounced reduction in depressive symptoms and suicidal ideation, and serial treatment appears to produce a greater and more sustained therapeutic response. However, the mechanism driving ketamine's antidepressant effects is not yet well understood. Biomarker discovery may advance knowledge of ketamine's antidepressant action, which could in turn translate to more personalized and effective treatment strategies. At the brain systems level, neuroimaging can be used to identify functional pathways and networks contributing to ketamine's therapeutic effects by studying how it alters brain structure, function, connectivity, and metabolism. In this review, we summarize and appraise recent work in this area, including 51 articles that use resting-state and task-based functional magnetic resonance imaging, arterial spin labeling, positron emission tomography, structural magnetic resonance imaging, diffusion magnetic resonance imaging, or magnetic resonance spectroscopy to study brain and clinical changes 24 hours or longer after ketamine treatment in populations with unipolar or bipolar depression. Though individual studies have included relatively small samples, used different methodological approaches, and reported disparate regional findings, converging evidence supports that ketamine leads to neuroplasticity in structural and functional brain networks that contribute to or are relevant to its antidepressant effects.

Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence

The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients' cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted，and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

Task-based functional connectivity of the Useful Field of View (UFOV) fMRI task

Declines in processing speed performance occur in aging and are a critical marker of functional independence in older adults. Numerous studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline in older adults. Despite its efficacy, little is known about the neural correlates of this task. The current study is the first to investigate the coherence of functional connectivity during UFOV task completion. A total of 336 participants completed the UFOV task while undergoing task-based functional magnetic resonance imaging (fMRI). Ten spherical regions of interest (ROIs), selected a priori, were created based on regions with the greatest peak BOLD activation patterns in the UFOV fMRI task and regions that have been shown to significantly relate to UFOV fMRI task performance. We used a weighted ROI-to-ROI connectivity analysis to model task-specific functional connectivity strength between these a priori selected ROIs. We found that our UFOV fMRI network was functionally connected during task performance and was significantly associated to UFOV fMRI task performance. Within-network connectivity of the UFOV fMRI network showed comparable or better predictive power in accounting for UFOV accuracy compared to 7 resting state networks, delineated by Yeo and colleagues. Finally, we demonstrate that the within-network connectivity of UFOV fMRI task accounted for scores on a measure of "near transfer", the Double Decision task, better than the aforementioned resting state networks. Our data elucidate functional connectivity patterns of the UFOV fMRI task. This may assist in future targeted interventions that aim to improve synchronicity within the UFOV fMRI network.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

Brief diesel exhaust exposure acutely impairs functional brain connectivity in humans: a randomized controlled crossover study

BACKGROUND

While it is known that exposure to traffic-related air pollution causes an enormous global toll on human health, neurobiological underpinnings therein remain elusive. The study addresses this gap in knowledge.

METHODS

We performed the first controlled human exposure study using functional MRI with an efficient order-randomized double-blind crossover study of diesel exhaust (DE) and control (filtered air; FA) in 25 healthy adults (14 males, 11 females; 19-49 years old; no withdrawals). Analyses were carried out using a mixed effects model in FLAME. Z (Gaussianised T/F) statistic images were thresholded non-parametrically using clusters determined by Z > 2.3 and a (corrected) cluster significance threshold of p = 0.05.

RESULTS

All 25 adults went through the exposures and functional MRI imaging were collected. Exposure to DE yielded a decrease in functional connectivity compared to exposure to FA, shown through the comparison of DE and FA in post-exposure measurement of functional connectivity.

CONCLUSION

We observed short-term pollution-attributable decrements in default mode network functional connectivity. Decrements in brain connectivity causes many detrimental effects to the human body so this finding should guide policy change in air pollution exposure regulation.

TRIAL REGISTRATION

University of British Columbia Clinical Research Ethics Board (# H12-03025), Vancouver Coastal Health Ethics Board (# V12-03025), and Health Canada's Research Ethics Board (# 2012-0040).

Frequency-dependent functional alterations in people living with HIV with early stage of HIV-associated neurocognitive disorder

Background

HIV enters the brain soon after seroconversion and causes HIV-associated neurocognitive disorder (HAND). However, the pathogenesis of this insidious impairment at an early stage remains unclear.

Objectives

To explore functional integration and segregation changes at the early stages of HAND, voxel-level indices of regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF), and voxel-mirrored homotopic connectivity (VMHC) under two different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz) were analyzed.

Methods

Ninety-eight people living with HIV (PLWH) and 44 seronegative controls underwent resting-state functional magnetic resonance imaging. Furthermore, all PLWHs underwent neuropsychological and daily functioning tests. The main effect of the group and the interaction between the group and frequency band were investigated. Finally, the relationship between the altered indices and the cognitive domains was explored.

Results

A significant group-by-frequency interaction was demonstrated in the right thalamus for ReHo; for VMHC, the interaction was observed in the bilateral precuneus and paracentral gyrus. The post hoc Bonferroni test indicated that the alteration of ReHo and VMHC could only be detected in slow-5. PLWH showed significantly reduced ALFF in both the frequency bands in the right occipital gyrus and right calcarine. Moreover, some altered functional integration and segregation indices are related to impaired cognitive function.

Conclusion

People living with HIV displayed aberrant functional integration and segregation at the early stages of HAND, which is linked to cognitive function. The frequency band of slow-5 might be more sensitive for detecting insidious damage at an early stage.

Recent Technical Developments in ASL: A Review of the State of the Art

This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.

Extracting default mode network based on graph neural network for resting state fMRI study

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.

The expanding horizons of network neuroscience: From description to prediction and control

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

Aberrant functional connectivity within the salience network is related to cognitive deficits and disorganization in psychosis

In schizophrenia and schizoaffective disorder cognitive deficits are a reliable characteristic predicting a poor functional outcome. It has been theorized that both the default mode network (DMN) and the salience network (SN) play a crucial role in cognitive processes and aberrant functional connectivity within these networks in psychotic patients has been reported. The goal of this study was to reveal potential links between aberrant functional connectivity within these networks and impaired cognitive performance in psychosis. We chose two approaches for cognitive assessment, first the MATRICS Consensus Cognitive Battery (MCCB) combined into a global score and second the disorganization factor derived from a five-factor model of the Positive and Negative Syndrome Scale (PANSS) known to be relevant for cognitive performance. DMN and SN were identified using independent component analysis on resting-state functional magnetic resonance imaging data. We found significantly decreased connectivity within the right supplementary motor area (SMA) and bilateral putamen in patients with psychosis (n = 70; 27F/43M) compared to healthy controls (n = 72; 28F/44M). Within patients, linear regression analysis revealed that aberrant SMA connectivity was associated with impaired global cognition, while dysfunctional bilateral putamen connectivity predicted disorganization. There were no significant changes in connectivity within the DMN. Results support the hypothesis that SN dysfunctional connectivity is important in the pathobiology of cognitive deficits in psychosis. For the first time we were able to show the involvement of dysfunctional SMA connectivity in this context. We interpret the decreased SN connectivity as evidence of reduced functionality in recruiting brain areas necessary for cognitive processing.

Disrupted intra- and inter-network connectivity in unilateral acute tinnitus with hearing loss

Purpose

Currently, the underlying neurophysiological mechanism of acute tinnitus is still poorly understood. This study aimed to explore differences in brain functional connectivity (FC) within and between resting-state networks (RSNs) in acute tinnitus patients with hearing loss (ATHL). Furthermore, it also evaluated the correlations between FC alterations and clinical characteristics.

Methods

Two matched groups of 40 patients and 40 healthy controls (HCs) were included. Independent component analysis (ICA) was employed to obtain RSNs and FC differences were calculated within RSNs. In addition, the relationships between networks were conducted using functional network connectivity (FNC) analysis. Finally, an analysis of correlation was used to evaluate the relationship between FNC abnormalities and clinical data.

Results

Results of this study found that seven major RSNs including the auditory network (AN), cerebellum network (CN), default mode network (DMN), executive control network (ECN), sensorimotor network (SMN), ventral attention network (VAN), and visual network (VN) were extracted using the group ICA in both groups. Furthermore, it was noted that the ATHL group showed aberrant FC within the CN, ECN, and VN as compared with HCs. Moreover, different patterns of network interactions were observed between groups, including the SMN-ECN, SMN-CN, ECN-AN, DMN-VAN, and DMN-CN connections. The correlations between functional disconnection and clinical characteristics in ATHL were also found in this study.

Conclusion

In conclusion, this study indicated widespread alterations of intra- and inter-network connectivity in ATHL, suggesting that multiple large-scale network dysfunctions and interactions are involved in the early stage. Furthermore, our findings may provide new perspectives to understand the neuropathophysiological mechanism of acute tinnitus.

Parcellation-based tractographic modeling of the salience network through meta-analysis

BACKGROUND

The salience network (SN) is a transitory mediator between active and passive states of mind. Multiple cortical areas, including the opercular, insular, and cingulate cortices have been linked in this processing, though knowledge of network connectivity has been devoid of structural specificity.

OBJECTIVE

The current study sought to create an anatomically specific connectivity model of the neural substrates involved in the salience network.

METHODS

A literature search of PubMed and BrainMap Sleuth was conducted for resting-state and task-based fMRI studies relevant to the salience network according to PRISMA guidelines. Publicly available meta-analytic software was utilized to extract relevant fMRI data for the creation of an activation likelihood estimation (ALE) map and relevant parcellations from the human connectome project overlapping with the ALE data were identified for inclusion in our SN model. DSI-based fiber tractography was then performed on publicaly available data from healthy subjects to determine the structural connections between cortical parcellations comprising the network.

RESULTS

Nine cortical regions were found to comprise the salience network: areas AVI (anterior ventral insula), MI (middle insula), FOP4 (frontal operculum 4), FOP5 (frontal operculum 5), a24pr (anterior 24 prime), a32pr (anterior 32 prime), p32pr (posterior 32 prime), and SCEF (supplementary and cingulate eye field), and 46. The frontal aslant tract was found to connect the opercular-insular cluster to the middle cingulate clusters of the network, while mostly short U-fibers connected adjacent nodes of the network.

CONCLUSION

Here we provide an anatomically specific connectivity model of the neural substrates involved in the salience network. These results may serve as an empiric basis for clinical translation in this region and for future study which seeks to expand our understanding of how specific neural substrates are involved in salience processing and guide subsequent human behavior.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

Brain Reactions to Opening and Closing the Eyes: Salivary Cortisol and Functional Connectivity

This study empirically assessed the strength and duration of short-term effects induced by brain reactions to closing/opening the eyes on a few well-known resting-state networks. We also examined the association between these reactions and subjects’ cortisol levels. A total of 55 young adults underwent 8-min resting-state fMRI (rs-fMRI) scans under 4-min eyes-closed and 4-min eyes-open conditions. Saliva samples were collected from 25 of the 55 subjects before and after the fMRI sessions and assayed for cortisol levels. Our empirical results indicate that when the subjects were relaxed with their eyes closed, the effect of opening the eyes on conventional resting-state networks (e.g., default-mode, frontal-parietal, and saliency networks) lasted for roughly 60-s, during which we observed a short-term increase in activity in rs-fMRI time courses. Moreover, brain reactions to opening the eyes had a pronounced effect on time courses in the temporo-parietal lobes and limbic structures, both of which presented a prolonged decrease in activity. After controlling for demographic factors, we observed a significantly positive correlation between pre-scan cortisol levels and connectivity in the limbic structures under both conditions. Under the eyes-closed condition, the temporo-parietal lobes presented significant connectivity to limbic structures and a significantly positive correlation with pre-scan cortisol levels. Future research on rs-fMRI could consider the eyes-closed condition when probing resting-state connectivity and its neuroendocrine correlates, such as cortisol levels. It also appears that abrupt instructions to open the eyes while the subject is resting quietly with eyes closed could be used to probe brain reactivity to aversive stimuli in the ventral hippocampus and other limbic structures.