Consistent resting-state networks across healthy subjects

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

Influence of binge drinking on the resting state functional connectivity of university Students: A follow-up study

Binge Drinking (BD) is characterized by consuming large amounts of alcohol on one occasion, posing risks to brain function. Nonetheless, it remains the most prevalent consumption pattern among students. Cross-sectional studies have explored the relationship between BD and anomalies in resting-state functional connectivity (RS-FC), but the medium/long-term consequences of BD on RS-FC during developmental periods remain relatively unexplored. In this two-year follow-up study, the impact of sustained BD on RS-FC was investigated in 44 college students (16 binge-drinkers) via two fMRI sessions at ages 18-19 and 20-21. Using a seed-to-voxel approach, RS-FC differences were examined in nodes of the main brain functional networks vulnerable to alcohol misuse, according to previous studies. Group differences in RS-FC were observed in four of the explored brain regions. Binge drinkers, compared to the control group, exhibited, at the second assessment, decreased connectivity between the right SFG (executive control network) and right precentral gyrus, the ACC (salience network) and right postcentral gyrus, and the left amygdala (emotional network) and medial frontal gyrus/dorsal ACC. Conversely, binge drinkers showed increased connectivity between the right Nacc (reward network) and four clusters comprising bilateral middle frontal gyrus (MFG), right middle cingulate cortex, and right MFG extending to SFG. Maintaining a BD pattern during critical neurodevelopmental years impacts RS-FC, indicating mid-to-long-term alterations in functional brain organization. This study provides new insights into the neurotoxic effects of adolescent alcohol misuse, emphasizing the need for longitudinal studies addressing the lasting consequences on brain functional connectivity.

Alterations in static and dynamic functional network connectivity in chronic low back pain: a resting-state network functional connectivity and machine learning study

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

Dynamics and concordance alterations of intrinsic brain activity indices in stroke-induced Broca's aphasia varies based on first language: A resting-state fMRI analysis

OBJECTIVE

This study aimed to investigate the changes in intrinsic brain activity (IBA) among individuals with Broca aphasia (BA) after a stroke.

METHODS

We collected information from 60 participants. The participants were categorized into four groups according to language (Uyghur and Chinese) and BA status (BA and healthy): Uyghur aphasia patients (UA), Uyghur healthy control subjects (UH), Chinese aphasia patients (CA), and Chinese healthy control subjects (CH). Each group comprised 15 individuals. The shifting dynamics and concordance of regional IBA indices were examined via sliding time-window analysis. A two-way analysis of variance (ANOVA) was conducted with the IBA indices to test for regions with interactions between language and BA status. Partial correlation analysis was employed to evaluate the relationships between various indices and language behaviors.

RESULTS

Participants with head motion exceeding 3 mm translation or 3° rotation were excluded, leaving 9, 12, 13, and 15 participants in the UA, CA, UH, and CH groups, respectively. Seven IBA indices were activated in 16 brain regions in the four groups. In detail, two-way ANOVA revealed a significant interaction between language and BA status in four IBA dynamic indices (amplitude of low-frequency fluctuations (ALFF), Regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC)) in 11 brain regions (P < 0.000). For the other three dynamic indices (fractional amplitude of low-frequency fluctuation (fALFF), Voxel-mirrored homotopic connectivity (VMHC), and Global signal connectivity (GSCorr)), no interaction was observed among the four groups. However, the main effect analysis of the BA state demonstrated significant differences across a total of six brain regions (P < 0.000). The concordance alterations in fALFF, ReHo, VMHC, DC, and GSCorr in the right calcarine fissure and the surrounding cortex were significantly lower in CA than in CH (P = 0.000), significantly higher in UA than in CA (P = 0.025), and significantly lower in UH than CH (P = 0.000).

CONCLUSION

In conclusion, alterations in IBA dynamics and concordance were observed in individuals from UA, UH, CA, and CH. These findings suggest that the IBA dynamic index varies across brain regions of BA patients with different local languages, providing a novel perspective for investigating brain alterations by analyzing temporal dynamics using rs-fMRI data.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs)

BACKGROUND

Mental illnesses extract personal and societal costs, leading to significant challenges in cognitive function, emotional regulation, and social behavior. These disorders are thought to result from disruptions in how different brain regions communicate with each other. Despite advances in neuroimaging, current methods are not always precise enough to fully understand the complexity of these disruptions. More advanced approaches are needed to better identify and characterize the specific brain network alterations linked to different psychiatric conditions.

METHODS

We employed a hierarchical approach to derive Double Functionally Independent Primitives (dFIPs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. dFIPs represent independent patterns of functional network connectivity (FNC) across the brain. Our study utilized a large multi-site dataset comprising 5805 individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and healthy controls. We analyzed how combinations of dFIPs differentiate psychiatric diagnoses.

RESULTS

Distinct dFIP patterns emerged for each disorder. Schizophrenia was characterized by heightened cerebellar connectivity and reduced cerebellar-subcortical connectivity. In ASD, sensory domain hyperconnectivity was prominent. Some dFIPs displayed disorder-specific connectivity patterns, while others exhibited commonalities across multiple conditions. These findings underscore the utility of dFIPs in revealing neural connectivity alterations unique to each disorder, serving as unique fingerprints for different mental disorders.

CONCLUSIONS

Our study demonstrates that dFIPs provide a novel, data-driven method for identifying disorder-specific functional connectivity patterns in psychiatric conditions. These distinct neural signatures offer potential biomarkers for mental illnesses, contributing to a deeper understanding of the neurobiological underpinnings of these disorders.

Aberrant white matter structural connectivity of nucleus accumbens in patients with major depressive disorder: A probabilistic fibre tracing study

BACKGROUND

Extensive neuroimaging studies have established that functional abnormalities and morphological alterations in the nucleus accumbens (NAc) are implicated in major depressive disorder (MDD), but changes in its white matter structural connectivity (SC) remain unclear. We aimed to elucidate the changes in the white matter fibre connectivity of the NAc in MDD patients.

METHODS

This study used probabilistic fibre tracking to analyze the diffusion tensor imaging (DTI) data of 125 MDD patients and 129 healthy controls (HCs), calculating the strength of SC (sSC) from bilateral NAc to the entire brain and its correlation with depressive symptoms.

RESULTS

Compared to HCs, MDD exhibited increased sSC between the left NAc (L.NAc) and regions involving the left middle frontal gyrus, bilateral cingulate gyrus (CG), bilateral hippocampus, left caudate, left medial superior occipital gyrus, right globus pallidus, right superior and middle temporal gyrus, right precuneus, right insula, and right posterior parietal thalamus. Enhanced sSC was also observed between the right NAc (R.NAc) and the left temporal lobe, left posterior superior temporal sulcus (pSTS), bilateral lateral occipital cortex, left hippocampus, right putamen and right ventral occipital cortex. The sSC of L.NAc-left CG and R.NAc-left pSTS was positively correlated with HAMD scores in MDD.

CONCLUSIONS

Abnormal white matter connectivity of the NAc primarily affects the cortico-limbic circuit, cortico-basal ganglia circuit, and the temporal-occipital cortical regions in patients with MDD, along with the asymmetrical features of the inter-hemispheric SC related to NAc. These alteration may underlie the dysfunction of reward processing and emotion regulation in MDD.

Inverse relationship between nodal strength and nodal power: Insights from separate resting fMRI and EEG datasets

BACKGROUND

Regional neural response and network properties have traditionally been studied separately. However, growing evidence suggests a close interplay between regional activity and inter-regional connectivity. This study aimed to examine the relationship between global functional connectivity and regional spontaneous activity, termed the global-to-local relationship.

NEW METHOD

Resting-state fMRI data were parcellated using MOSI (modular analysis and similarity measurements), enabling multi-resolution functional partitioning. For each parcellated cluster, the mean amplitude of low-frequency fluctuations (node power) and its average functional connectivity with the remaining cortex (node strength) were computed. Correlation analyses assessed their relationship. A supplementary analysis was conducted on EEG data (1-30 Hz).

RESULTS

A significant negative correlation between node strength and regional power was observed in MRI datasets. One-sample t-tests confirmed robustness across different MOSI resolutions, with individual P values at the level 10-4 to 10-5. The negative relationship was also found in EEG data but was restricted to delta (1-4 Hz) and theta (4-8 Hz) bands.

COMPARISON WITH EXISTING METHODS

This study introduces two key novel aspects. First, it applies MOSI to the entire cortex, enhancing the comprehensiveness of network analysis. Second, it examines the global influence on regional neural activity, rather than limiting the focus to a specific network.

CONCLUSIONS

A robust negative relationship between node strength and node power was consistently observed across both MRI and EEG datasets, particularly in lower frequency bands (up to 8 Hz). These findings suggest a framework for investigating how global connectivity shapes regional neural activity, with inhibitory coupling as a potential underlying mechanism.

Biomarker identification in bipolar disorder

Bipolar disorder (BD) is a severe psychiatric condition whose pathophysiology is complex and multifactorial. Genetic, environmental and social risk factors play a role in its development as well as in its progressive course. Research is currently focusing on the identification of the biological basis underlying these processes in order to suggest novel biomarkers capable to predict BD etiopathogenesis and staging. Staging has been recognized as of great value for the treatment and management of many illnesses and might also be suitable for mental health issues, particularly in disorders like BD, which progress from an initial mild phase to a more severe and thus difficult-to-treat situation. Thus, it would be of great help the characterization of to suggest better treatment requirements and improve prognosis across the different stages of the illness. Here, we summarize current research on the biological hypotheses of BD and the biomarkers associated with its progression, reviewing clinical studies available in the literature.

Spontaneous mesoscale calcium dynamics reflect the development of the modular functional architecture of the mouse cerebral cortex

The mature cerebral cortex operates through the segregation and integration of specialized functions to generate complex cognitive states. In the mouse, the anatomical and functional correlates of this organization arise during the perinatal period and are critically shaped by neural activity. Understanding how early activity patterns distribute, interact, and generate large-scale cortical dynamics is essential to elucidate the proper development of the cortex. Here, we investigate spontaneous mesoscale cortical dynamics during the first two postnatal weeks by performing wide-field calcium imaging in GCaMP6s transgenic mice. Our results demonstrate a marked change in the spatiotemporal features of spontaneous cortical activity across fine stages of postnatal development. Already after birth, the cortical hemisphere presents a primordial macroscopic organization, which undergoes a steady refinement based on the parcellation of the cortex. As calcium activity transitions from large, widespread events to swift waves between the first and second postnatal week, significant topographic differences emerge across different cortical regions. Functional connectivity profiles of the cortex gradually segregate into main subnetworks and give rise to a highly modular network topology at the end of the second postnatal week. Overall, spontaneous mesoscale activity reflects the maturation of cortical networks, and reveals critical breakpoints in the development of the functional architecture of the cortex.

Investigating brain network dynamics in state-dependent stimulation: a concurrent Electroencephalography and Transcranial Magnetic Stimulation study using Hidden Markov Models

BACKGROUND

Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization.

OBJECTIVE

We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability.

METHODS

This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined.

RESULTS

We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network.

CONCLUSIONS

These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.

Large-scale functional network connectivity alterations in adolescents with major depression and non-suicidal self-injury

Non-suicidal self-injury (NSSI) is prevalent among adolescent populations worldwide, yet its neuropathological mechanisms remain unclear. This study aimed to investigate brain functional differences in NSSI patients by utilizing large-scale functional networks and examining their correlation with clinical outcomes. Cross-sectional clinical and functional magnetic resonance imaging (fMRI) data were collected from 42 patients and 47 healthy controls. Independent component analysis (ICA) was utilized to investigate changes in both intra-network and inter-network functional connectivity. We then investigated the potential association between functional network connectivity and clinical self-injurious behavior. The results revealed significant abnormalities in intra-network functional connectivity within the left middle cingulum gyrus, right angular gyrus, and middle frontal gyrus in patients with NSSI. Additionally, we found altered inter-network connectivity patterns, particularly between higher-order cognitive networks and primary sensory networks, suggesting potential disruptions in multisensory integration and emotional regulation in these patients. This study revealed significant alterations in large-scale functional network connectivity in adolescents with depression and NSSI, particularly in networks related to emotion regulation and cognitive control. These findings provide novel perspectives on the neurobiological mechanisms of NSSI and suggest possible avenues for early intervention and treatment.

Investigation of the static and dynamic brain network mechanisms in patients with rhegmatogenous retinal detachment based on independent component analysis

BACKGROUND

Previous neuroimaging studies have identified substantial structural and functional abnormalities in the brains of patients with rhegmatogenous retinal detachment (RRD). Nonetheless, there remains a paucity of comprehensive research on the alterations in functional connectivity (FC) within large-scale static and dynamic brain networks in these patients.

METHODS

This study utilized independent component analysis (ICA) to investigate alterations in large-scale brain network FC in patients with RRD. Additionally, it employed support vector machine (SVM) to classify RRD patients and healthy controls (HCs) and examined the relationship between abnormal brain regions and clinical ophthalmic parameters.

RESULTS

The RRD patients demonstrated significantly increased FC in the default mode network (DMN) and visual network (VN) compared to the HCs, whereas the FC in the auditory network (AN) and the sensorimotor network (SMN) was significantly decreased. Analysis of dynamic functional network connectivity (dFNC) revealed that the fraction of time (FT) spent in state 4 was significantly greater in RRD patients compared to HCs. SVM analysis showed that the AUC for classification using AN and FNC features reached 0.828 and 0.819, respectively. Additionally, the FC value of the right medial superior frontal gyrus (R-SFGmed) in RRD patients was positively correlated with axial length (r = 0.401, p = 0.038).

CONCLUSION

This study revealed that patients with RRD exhibit both damage and adaptive remodeling in their brain functional networks. Alterations in the AN and FNC may serve as potential neuroimaging biomarkers for distinguishing RRD patients from HCs, providing crucial neuroimaging evidence for understanding the mechanisms underlying visual loss in RRD.

Mapping the current trends and hotspots of transcranial magnetic stimulation-based addiction treatment from 2001-2023: A bibliometric analysis

BACKGROUND

The prevalence of addiction makes it a significant public health issue. Recently, transcranial magnetic stimulation (TMS) has garnered significant attention as a promising treatment for addiction.

AIM

To analyze development trends and research hotspots in TMS-based addiction treatment using a bibliometric approach.

METHODS

Articles on TMS-based addiction treatment from 2001 to 2023 were sourced from the Science Citation Index Expanded in the Web of Science Core Collection. CiteSpace software, VOSviewer, the "bibliometrix" R software package, and the bibliometric online analysis platform were used to analyze the current publication trends and hotspots.

RESULTS

Total 190 articles on TMS-based addiction treatment were identified, with clinical studies being the most prevalent. The United States led in both publication volume and international collaborations. Medical University of South Carolina and Zangen A were the most productive institution and author, respectively. Neurobiology, alcohol use disorder, and repetitive TMS were the most recent research hotspots.

CONCLUSION

Future research should focus on the neurobiological mechanisms underlying TMS-based addiction treatment. This study offers comprehensive insights and recommendations for advancing research on TMS-based addiction treatment.

Beyond Pairwise Connections in Complex Systems: Insights into the Human Multiscale Psychotic Brain

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study aims to bridge this gap by applying a matrix-based entropy functional to estimate total correlation, a mathematical framework that incorporates multivariate information measures extending beyond pairwise interactions. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of interactions beyond pairwise relationships in the human multiscale brain. Additionally, this approach holds promise for psychiatric studies, providing a new lens through which to explore beyond pairwise brain network interactions. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding higher-order brain dynamics. This framework not only enhances our understanding of complex brain functions but also offers new opportunities for investigating pathophysiology, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, the methodology of analyzing multiway interactions beyond pairwise connections can be applied to any signal analysis. In this study, we specifically explore its application to neural signals, demonstrating its power in uncovering complex multiway interaction patterns of brain activity, which provide a window to explore connectivity beyond pairwise interactions in the multiscale functionality of the brain.

Hubs and interaction: the brain's meta-loop

We must reconcile the needs of the internal world and the demands of the external world to make decisions relevant to homeostasis, well-being, and flexible behavior. Engagement with the internal (eg interoceptive) world is linked to medial brain systems, whereas the extrapersonal space (eg exteroceptive) is associated with lateral brain systems. Using Human Connectome Project data, we found three association tracts connecting the action-related frontal lobe with perception-related posterior lobes. A lateral dorsal tract and a medial dorsal tract interact independently with a ventral tract at frontal and posterior hubs. The two frontal and the two posterior hubs are interconnected, forming a meta-loop that integrates lateral and medial brain systems. The four anatomical hubs correspond to the common nodes of the intrinsic cognitive brain networks such as the default mode network. These functional networks depend on the integration of both realms. Thus, the positioning of functional cognitive networks can be understood as the intersection of long anatomical association tracts. The strength of structural connectivity within lateral and medial brain systems correlates with performance on behavioral tests assessing theory of mind. The meta-loop provides an anatomical framework to associate neurological and psychiatric symptoms with functional and structural changes.

Facial Symmetry Enhancement and Brain Network Modifications in Facial Palsy Patients after Botulinum Toxin Type A Treatment

BACKGROUND

Facial palsy, often resulting from trauma or iatrogenic treatments, leads to significant aesthetic and functional impairment. Surgical interventions, such as masseteric-to-facial nerve transfer combined with static suspension, are frequently recommended to restore facial nerve function and symmetry.

METHODS

This study examined the impact of botulinum toxin type A (BoNT-A) treatment on the unaffected side with regard to facial symmetry and brain connectivity in patients with severe oral commissure droop from facial nerve damage. Patients were divided into 2 groups: 1 group received BoNT-A injections on the unaffected side, and the other did not.

RESULTS

The authors' findings revealed that BoNT-A treatment not only improved facial symmetry but also induced significant modifications in brain functional network connectivity. These modifications extended beyond the sensorimotor network, involving high-level cognitive processes, and exhibited a significant correlation with the degree of facial asymmetry.

CONCLUSIONS

These results provide valuable insights into the mechanisms underlying the positive effects of BoNT-A intervention on motor recovery and brain plasticity in facial palsy patients. Furthermore, the study emphasizes the importance of a multidisciplinary approach to facial palsy rehabilitation. Understanding these intricate interactions between facial symmetry restoration and brain network adaptations may pave the way for more effective treatments and improved quality of life for individuals dealing with facial palsy.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, II.

Quantitative spinal cord imaging: Early ALS diagnosis and monitoring of disease progression

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of motor neurons in the cortex, brainstem, and spinal cord. This degeneration leads to muscular weakness, progressively impairing motor functions and ultimately resulting in respiratory failure. The clinical, genetic, and pathological heterogeneity of ALS, combined with the absence of reliable biomarkers, significantly challenge the efficacy of therapeutic trials. Despite these hurdles, neuroimaging, and particularly spinal cord imaging, has emerged as a promising tool. It provides insights into the involvement of both upper and lower motor neurons. Quantitative spinal imaging has the potential to facilitate early diagnosis, enable accurate monitoring of disease progression, and refine the design of clinical trials. In this review, we explore the utility of spinal cord imaging within the broader context of developing spinal imaging biomarkers in ALS. We focus on a both diagnostic and prognostic biomarker in ALS, highlighting its pivotal role in elucidating the disease's underlying pathology. We also discuss the existing limitations and future avenues for research, aiming to bridge the translational gap between academic research and its application in clinical practice and therapeutic trials.

Structure-function coupling in network connectivity and associations with negative affectivity in a group of transdiagnostic adolescents

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.

Measuring and interpreting individual differences in fetal, infant, and toddler neurodevelopment

As scientists interested in fetal, infant, and toddler (FIT) neurodevelopment, our research questions often focus on how individual children differ in their neurodevelopment and the predictive value of those individual differences for long-term neural and behavioral outcomes. Measuring and interpreting individual differences in neurodevelopment can present challenges: Is there a "standard" way for the human brain to develop? How do the semantic, practical, or theoretical constraints that we place on studying "development" influence how we measure and interpret individual differences? While it is important to consider these questions across the lifespan, they are particularly relevant for conducting and interpreting research on individual differences in fetal, infant, and toddler neurodevelopment due to the rapid, profound, and heterogeneous changes happening during this period, which may be predictive of long-term outcomes. This article, therefore, has three goals: 1) to provide an overview about how individual differences in neurodevelopment are studied in the field of developmental cognitive neuroscience, 2) to identify challenges and considerations when studying individual differences in neurodevelopment, and 3) to discuss potential implications and solutions moving forward.

Periodontitis associated with brain function impairment in middle-aged and elderly individuals with normal cognition

BACKGROUND

The present study aimed to investigate changes in intranetwork functional connectivity (FC) and internetwork FC in middle-aged and elderly individuals with normal cognition (NC) and varying degrees of periodontitis to determine the effects of periodontitis on brain function.

METHODS

Periodontal findings and resting-state functional magnetic resonance imaging data were acquired from 51 subjects with NC. Independent component analysis and correlation analysis were used for the statistical analysis of the data.

RESULTS

Differences in intranetwork FC were observed among groups in the anterior default-mode network (aDMN), dorsal attention network and dorsal sensorimotor network (dSMN). Compared with the nonperiodontitis (NP) group or the mild-periodontitis group, the analysis of internetwork FC showed increased FC between the auditory network and the ventral attention network (VAN), between the aDMN and the salience network (SN), and between the SN and the VAN and decreased FC between the posterior default-mode network and the right frontoparietal network in the moderate-to-severe periodontitis group. Additionally, internetwork FC between the dSMN and the VAN was also increased in the moderate-to-severe periodontitis group compared to the NP group. The altered intra- and internetwork FC were significantly correlated with the periodontal clinical index.

CONCLUSION

Our results confirmed that periodontitis was associated with both intra- and internetwork FC changes even in NC. The present study indicates that periodontitis might be a potential risk factor for brain damage and provides a theoretical clue and a new treatment target for the early prevention of Alzheimer disease.

PLAIN LANGUAGE SUMMARY

Recent research has proposed that periodontitis is a potential risk factor for Alzheimer disease (AD). However, the relationship between periodontitis and the brain function of middle-aged and elderly individuals with normal cognition (NC) remains unclear. Analyzing the effect of periodontitis on brain function in the NC stage can provide clues to AD development and help achieve early prevention of dementia. The present study aimed to investigate changes in brain functional connectivity (FC) in NC with different severity of periodontitis to determine the effects of periodontitis on brain function. Both changed intranetwork FC and internetwork FC were found in the moderate-to-severe periodontitis group, and periodontitis was associated with brain network function impairment in NC. The present study indicates that periodontitis might be a potential risk factor for brain damage even in NC stage, and provides a theoretical clue and a new treatment target for the early prevention of AD.

Abnormal characteristics in disorders of consciousness: A resting-state functional magnetic resonance imaging study

AIMS

To explore the functional brain imaging characteristics of patients with disorders of consciousness (DoC).

METHODS

This prospective cohort study consecutively enrolled 27 patients in minimally conscious state (MCS), 23 in vegetative state (VS), and 25 age-matched healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was employed to evaluate the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC). Sliding windows approach was conducted to construct dynamic FC (dFC) matrices. Moreover, receiver operating characteristic analysis and Pearson correlation were used to distinguish these altered characteristics in DoC.

RESULTS

Both MCS and VS exhibited lower ALFF, ReHo, and DC values, along with reduced FC in multiple brain regions compared with HC. Furthermore, the values in certain regions of VS were lower than those in MCS. The primary differences in brain function between patients with varying levels of consciousness were evident in the cortico-striatopallidal-thalamo-cortical mesocircuit. Significant differences in the temporal properties of dFC (including frequency, mean dwell time, number of transitions, and transition probability) were also noted among the three groups. Moreover, these multimodal alterations demonstrated high classificatory accuracy (AUC > 0.8) and were correlated with the Coma Recovery Scale-Revised (CRS-R).

CONCLUSION

Patients with DoC displayed abnormal patterns in local and global dynamic and static brain functions. These alterations in rs-fMRI were closely related to the level of consciousness.

Cognitive Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome-Aetiology and Potential Treatments

Systemic infection and inflammation impair mental function through a combination of altered attention and cognition. Here, we comprehensively review the relevant literature and report personal clinical observations to discuss the relationship between infection, peripheral inflammation, and cerebral and cognitive dysfunction in patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Cognitive dysfunction in ME/CFS could result from low-grade persistent inflammation associated with raised pro-inflammatory cytokines. This may be caused by both infectious and non-infectious stimuli and lead to altered regional cerebral blood flow accompanied by disturbed neuronal function. Immune dysregulation that manifests as a subtle immunodeficiency or the autoimmunity targeting of one or more neuronal receptors may also be a contributing factor. Efforts to reduce low-grade systemic inflammation and viral reactivation and to improve mitochondrial energy generation in ME/CFS have the potential to improve cognitive dysfunction in this highly disabling condition.

Research Progress in the Pathogenesis of Cognitive Dysfunction in White Matter Hyperintensities: A Narrative Review

Cerebral small vessel disease is a common disease endangering human health due to its insidious and repeated onset and progressive aggravation. White matter hyperintensities (WMHs) are one of the classic imaging markers of cerebral small vessel disease. The term 'WMHs' was first proposed by Hachinski in 1987. The WMHs in our study mainly refer to cerebral white matter damage caused by various vascular factors, known as vascularized white matter hyperintensity. WMHs are significantly correlated with stroke, cognitive dysfunction, emotional disturbance, and gait abnormality, and have drawn widespread attention. This article reviews the research progress on the pathogenesis of cognitive dysfunction associated with WMHs and provides a theoretical reference for understanding the pathogenesis of WMHs and the early assessment of associated cognitive dysfunction.

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.

Correspondence between thalamic injury-induced changes in resting-state fMRI of monkeys and their sensorimotor behaviors and neural activities

Resting state functional MRI (rsfMRI) exploits variations in blood-oxygenation-level-dependent (BOLD) signals to infer resting state functional connectivity (FC) within and between brain networks. However, there have been few reports quantifying and validating the results of rsfMRI analyses with other metrics of brain circuits. We measured longitudinal changes in FC both within and between brain networks in three squirrel monkeys after focal lesions of the thalamic ventroposterior lateral nucleus (VPL) that were intended to disrupt the input to somatosensory cortex and impair manual dexterity. Local field potential signals were recorded to assess electrophysiological changes during each animal's recovery, and behavioral performances were measured longitudinally using a sugar-pellet grasping task. Finally, end-point histological evaluations were performed on brain tissue slices to quantify the VPL damage. The rsfMRI data analysis showed significant decrease in FC measures both within and between networks immediately post-injury, which started to recover at different time-points for each animal. The trajectories of FC recovery for each animal mirrored their individual behavioral recovery time-courses. Electrophysiological measurements of inter-electrode coherences and end-point histological measures also aligned well with the graded injury effects measured using rsfMRI-based FC. A simple algorithm employing FC measures from the somatosensory network could accurately predict each monkeys' behavioral recovery timeframe after four weeks post-injury. Whole brain between-network FC measures further revealed that the injury effects were not limited to thalamocortical connections but were rather more widespread. Overall, this study provides evidence of the validity of rsfMRI based FC measures as indicators of the functional integrity and behavioral relevance following an injury to a specific brain circuit.

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder

BACKGROUND

Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index.

METHODS

Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited. Brain activities during eyes-closed period were collected using 64-channel electroencephalography. The differences in microstate features between OCD patients and HCs were compared, and the relationship between the microstate features and clinical symptoms were explored. Key microstate features were selected for machine learning modeling to achieve targeted classifications.

RESULTS

The probability of transition from microstate B to C was significantly lower in OCD patients compared to HCs, and the obsessive thoughts factor scores were significantly correlated with the duration of microstate A, the occurrence of microstate B, and the transition probability from microstate C to B. The occurrence rate of microstate C was significantly negatively correlated with the Hamilton rating scale for anxiety (HAMA) scores. The AUC (Area Under the Receiver Operating Characteristic Curve) of the machine learning model in the test set classification between the above two groups and between OCD patients with more than moderate/mild anxiety symptoms could achieve 70.43% and 77.13%, respectively.

CONCLUSION

EEG microstate characteristics were altered in OCD patients, and these changes were closely associated with obsessive thoughts and anxiety symptoms. Besides, the machine learning classification model based on microstate features has limited ability to identify OCD, and further optimization on this classification approach is still needed in the future.

Measuring the effects of motion corruption in fetal fMRI

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non-stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level-dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19-39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component-based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.

TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting-State Functional MRI Fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

Deep-learning based Embedding of Functional Connectivity Profiles for Precision Functional Mapping

Spatial correlation of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial correlation is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial correlation is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.

fMRI data acquisition and analysis for task-free, anesthetized rats

Templates for the acquisition of large datasets such as the Human Connectome Project guide the neuroimaging community to reproducible data acquisition and scientific rigor. By contrast, small animal neuroimaging often relies on laboratory-specific protocols, which limit cross-study comparisons. The establishment of broadly validated protocols may facilitate the acquisition of large datasets, which are essential for uncovering potentially small effects often seen in functional MRI (fMRI) studies. Here, we outline a procedure for the acquisition of fMRI datasets in rats and describe animal handling, MRI sequence parameters, data conversion, preprocessing, quality control and data analysis. The procedure is designed to be generalizable across laboratories, has been validated by using datasets across 20 research centers with different scanners and field strengths ranging from 4.7 to 17.2 T and can be used in studies in which it is useful to compare functional connectivity measures across an extensive range of datasets. The MRI procedure requires 1 h per rat to complete and can be carried out by users with limited expertise in rat handling, MRI and data processing.

Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal

OBJECTIVE

Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. This study introduces the Time Reversal (TR) pretraining method to address these challenges. TR aims to learn temporal dependencies in data, leveraging large datasets for pretraining and applying this knowledge to improve schizophrenia classification on smaller datasets.

METHODS

We pretrained an LSTM-based model with attention using the TR approach, focusing on learning the direction of time in fMRI data, achieving over 98 % accuracy on HCP and UK Biobank datasets. For downstream schizophrenia classification, TR-pretrained weights were transferred to models evaluated on FBIRN, COBRE, and B-SNIP datasets. Saliency maps were generated using Integrated Gradients (IG) to provide post hoc explanations for pretraining, while Earth Mover's Distance (EMD) quantified the temporal dynamics of salient features in the downstream tasks.

RESULTS

TR pretraining significantly improved schizophrenia classification performance across all datasets: median AUC scores increased from 0.7958 to 0.8359 (FBIRN), 0.6825 to 0.7778 (COBRE), and 0.6341 to 0.7224 (B-SNIP). The saliency maps revealed more concentrated and biologically meaningful salient features along the time axis, aligning with the episodic nature of schizophrenia. TR consistently outperformed baseline pretraining methods, including OCP and PCL, in terms of AUC, balanced accuracy, and robustness.

CONCLUSIONS

This study demonstrates the dual benefits of the TR method: enhanced predictive performance and improved interpretability. By aligning model predictions with meaningful temporal patterns in brain activity, TR bridges the gap between deep learning and clinical relevance. These findings emphasize the potential of explainable AI tools for aiding clinicians in diagnostics and treatment planning, especially in conditions characterized by disrupted temporal dynamics.

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.

Detection of respiratory frequency rhythm in human alpha phase shifts: topographic distributions in wake and drowsy states

Introduction

The relationship between brain activity and respiration is recently attracting increasing attention, despite being studied for a long time. Respiratory modulation was evidenced in both single-cell activity and field potentials. Among EEG and intracranial measurements, the effect of respiration was prevailingly studied on amplitude/power in all frequency bands.

Methods

Since phases of EEG oscillations received less attention, we applied our previously published carrier frequency (CF) mathematical model of human alpha oscillations on a group of 10 young healthy participants in wake and drowsy states, using a 14-channel average reference montage. Since our approach allows for a more precise calculation of CF phase shifts (CFPS) than any individual Fourier component, by using a 2-s moving Fourier window, we validated the new method and studied, for the first time, temporal waveforms CFPS(t) and their oscillatory content through FFT (CFPS(t)).

Results

Although not appearing equally in all channel pairs and every subject, a clear peak in the respiratory frequency region, 0.21-0.26 Hz, was observed (max at 0.22 Hz). When five channel pairs with the most prominent group averaged amplitudes at 0.22 Hz were plotted in both states, topographic distributions changed significantly-from longitudinal, connecting frontal and posterior channels in the wake state to topographically split two separate regions-frontal and posterior in the drowsy state. In addition, in the drowsy state, 0.22-Hz amplitudes decreased for all pairs, while statistically significant reduction was obtained for 20/91 (22%) pairs.

Discussion

These results potentially evidence, for the first time, the respiratory frequency modulation of alpha phase shifts, as well as the significant impact of wakeful consciousness on the observed oscillations.

Network Abnormalities in Ischemic Stroke: A Meta-analysis of Resting-State Functional Connectivity

Aberrant large-scale resting-state functional connectivity (rsFC) has been frequently documented in ischemic stroke. However, it remains unclear about the altered patterns of within- and across-network connectivity. The purpose of this meta-analysis was to identify the altered rsFC in patients with ischemic stroke relative to healthy controls, as well as to reveal longitudinal changes of network dysfunctions across acute, subacute, and chronic phases. A total of 24 studies were identified as eligible for inclusion in the present meta-analysis. These studies included 269 foci observed in 58 contrasts (558 patients with ischemic stroke; 526 healthy controls; 38.84% female). The results showed: (1) within-network hypoconnectivity in the sensorimotor network (SMN), default mode network (DMN), frontoparietal network (FPN), and salience network (SN), respectively; (2) across-network hypoconnectivity between the SMN and both of the SN and visual network, and between the FPN and both of the SN and DMN; and (3) across-network hyperconnectivity between the SMN and both of the DMN and FPN, and between the SN and both of the DMN and FPN. Meta-regression showed that hypoconnectivity between the DMN and the FPN became less pronounced as the ischemic stroke phase progressed from the acute to the subacute and chronic phases. This study provides the first meta-analytic evidence of large-scale rsFC dysfunction in ischemic stroke. These dysfunctional biomarkers could help identify patients with ischemic stroke at risk for cognitive, sensory, motor, and emotional impairments and further provide potential insight into developing diagnostic models and therapeutic interventions for rehabilitation and recovery.

Alterations in local activity and whole-brain functional connectivity in human immunodeficiency virus-associated neurocognitive disorders: a resting-state functional magnetic resonance imaging study

Background

Approximately half of human immunodeficiency virus (HIV) patients experience HIV-associated neurocognitive disorders (HAND); however, the neurophysiological mechanisms underlying HAND remain unclear. This study aimed to evaluate changes in functional brain activity patterns during the early stages of HIV infection by comparing local and global indicators using resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

A total of 165 people living with HIV (PLWH) but without neurocognitive disorders (PWND), 173 patients with asymptomatic neurocognitive impairment (ANI), and 100 matched healthy controls (HCs) were included in the study. A cross-sectional study of the participants was conducted. The metrics of functional segregation and integration were computed, using graph theory to explore differences across methodologies. Brain functional changes in the PWND and ANI groups were assessed, and correlations between the rs-fMRI metrics, clinical data, and cognitive function were examined.

Results

As cognitive function declined, changes reflected by regional homogeneity (ReHo) were primarily observed in the default mode network (DMN). In the DMN and visual network (VIS), amplitude of low-frequency fluctuation (ALFF) decreases were mainly observed in the parieto-occipital lobes, while increases were mainly observed in the limbic network (LIM). Reductions in fractional ALFF (fALFF) were mainly observed in the somatomotor network (SMN) and LIM, while increases were observed in the DMN and LIM. Unlike local indicators, global functional connectivity (FC) significantly decreased in both the PWND and ANI groups compared to the HC group. The ANI group showed partial increases in FC compared to the PWND group, with major changes observed in the DMN, VIS, and LIM. Notably, FC between the right insula and right supramarginal gyrus decreased significantly following HIV infection, while FC between the right caudate nucleus and the left middle frontal gyrus declined further in the ANI group. Graph theory further confirmed the significance of the DMN, and revealed changes in the eigenvector centrality mapping (ECM) values of the frontoparietal network (FPN) and dorsal attention network (DAN).

Conclusions

HIV patients exhibit complex changes in both local and global brain activity, regardless of cognitive impairment. Widespread abnormalities primarily involve the DMN, VIS, and LIM. Changes in FC along the fronto-striatal pathway may play a crucial role in the decline of cognitive function in individuals with HAND. Our findings provide new insights that may assist in the early detection of brain damage in the early stages of HIV infection. The use of multiple methodologies may offer a more comprehensive and effective approach, enabling the early detection of brain damage in HIV patients.

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.

State-dependent inter-network functional connectivity development in neonatal brain from the developing human connectome project

Although recent studies have consistently reported the emergence of resting-state networks in early infancy, the changes in inter-network functional connectivity with age are controversial and the alterations in its dynamics remain unclear at this stage. This study aimed to investigate dynamic functional network connectivity (dFNC) using resting-state functional MRI in 244 full-term (age: 37-44 weeks) and 36 preterm infants (age: 37-43 weeks) from the dHCP dataset. We evaluated whether early dFNC exhibits age-dependent changes and is influenced by preterm birth. Gestational age (GA) and postnatal age (PNA) showed different effects on variance of FNC change over time during fMRI scan in resting-state networks, especially among high-order association networks. These variances were significantly reduced by preterm birth. Moreover, two states of weakly-connected (State Ⅰ) and strongly-connected (State Ⅱ) FNC were identified. The fraction window and dwell time in State Ⅰ, and the transition from State Ⅱ to State Ⅰ, all showed significantly negative correlations with both GA and PNA. Preterm-born infants spent a longer time in the weakly-connected state compared to term-born infants. These findings suggest a state-dependent development of dynamic FNC across brain networks in the early stages, gradually reconfiguring towards a more flexible and dynamic system with stronger connections.

Neurovascular coupling over cortical brain areas and resting state network connectivity with and without rigidified carotid artery

Significance

Neurovascular coupling (NVC) is key to research as hemodynamics can reflect neuronal activation and is often used in studies regarding the resting state network (RSN). However, several circumstances, including diseases that reduce blood vessel elasticity, can diminish NVC. In these cases, hemodynamic proxies might not accurately reflect the neuronal RSN.

Aim

We aim to investigate in resting state if (1) NVC differs over brain regions, (2) NVC remains intact with a mild rigidification of the carotid artery, (3) hemodynamic-based RSN reflects neuronal-based RSN, and (4) RSN differs with a mildly rigidified artery.

Approach

We rigidified the right common carotid artery of mice ( n = 15 ) by applying aCaCl 2 -soaked cloth to it (NaCl for Sham, n = 17 ). With simultaneous GCaMP and intrinsic optical imaging, we compared neuronal activation to hemodynamic changes over the entire cortex.

Results

NVC parameters did not differ between the CaCl and Sham groups. Likewise, GCaMP and hemodynamic RSN showed similar connections in both groups. However, the parameters of NVC differed over brain regions. Retrosplenial regions had a slower response and a higher HbR peak than sensory and visual regions, and the motor cortex showed less HbO influx than sensory and visual regions.

Conclusions

NVC in a resting state differs over brain regions but is not altered by mild rigidification of the carotid artery.

The phobic brain: Morphometric features correctly classify individuals with small animal phobia

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro-networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies.

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.

Synchronization in spiking neural networks with short and long connections and time delays

Synchronization is fundamental for information processing in oscillatory brain networks and is strongly affected by time delays via signal propagation along long fibers. Their effect, however, is less evident in spiking neural networks given the discrete nature of spikes. To bridge the gap between these different modeling approaches, we study the synchronization conditions, dynamics underlying synchronization, and the role of the delay of a two-dimensional network model composed of adaptive exponential integrate-and-fire neurons. Through parameter exploration of neuronal and network properties, we map the synchronization behavior as a function of unidirectional long-range connection and the microscopic network properties and demonstrate that the principal network behaviors comprise standing or traveling waves of activity and depend on noise strength, E/I balance, and voltage adaptation, which are modulated by the delay of the long-range connection. Our results show the interplay of micro- (single neuron properties), meso- (connectivity and composition of the neuronal network), and macroscopic (long-range connectivity) parameters for the emergent spatiotemporal activity of the brain.

Symmetry breaking organizes the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain's capacity to generate certain dynamics characteristic of health and pathology.

Stepwise pathways from the olfactory cortex to central hub regions in the human brain

The human brain is organized as a hierarchical global network. Functional connectivity research reveals that sensory cortices are connected to corresponding association cortices via a series of intermediate nodes linked by synchronous neural activity. These sensory pathways and relay stations converge onto central cortical hubs such as the default-mode network (DMN). The DMN regions are believed to be critical for representing concepts and, hence, language acquisition and use. Although prior research has established that major senses are placed at a similar distance from the DMN-five to six connective steps-it is still unknown how the olfactory system functionally connects to the large-scale cortical hubs of the human brain. In this study, we investigated the connective distance from olfactory seed areas to the DMN. The connective distance involves a series of three to four intermediate steps. Furthermore, we parcellated the olfactory cortical subregions and found evidence of two distinct olfactory pathways. One emerges from the anterior olfactory nucleus and olfactory tubercle; it involves early access to the orbitofrontal cortex, known for processing reward and multisensory signals. The other emerges from the frontal and temporal regions of the piriform cortex, involving the anterior insula, intermediate frontal sulcus, and parietal operculum. The results were confirmed in a replication cohort. Our results provide evidence that olfaction has unique early access to the central cortical networks via dual pathways.

Resting-State Network Plasticity Following Category Learning Depends on Sensory Modality

Learning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting-state functional connectivity is an effective method for detecting short-term brain alterations induced by various modality-based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty-four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting-state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

Connectome embedding in multidimensional graph spaces

Connectomes' topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated various connectomes (structural/functional, binary/weighted). We observed that nodal properties are correlated (i.e., they carry similar information) at whole-brain and subnetwork level. We conducted an exploratory machine learning analysis to test whether high-dimensional network information differs between sensory and association areas. Brain regions of sensory and association networks were classified with an 80-86% accuracy in a 10-dimensional (10D) space. We observed the largest gain in machine learning accuracy going from a 2D to 3D space, with a plateauing accuracy toward 10D space, and nonlinear Gaussian kernels outperformed linear kernels. Finally, we quantified the Euclidean distance between nodes in a 10D graph space. The multidimensional Euclidean distance was highest across subjects in the default mode network (in structural networks) and frontoparietal and temporal lobe areas (in functional networks). To conclude, we propose a new framework for quantifying network features in high-dimensional spaces that may reveal new network properties of the brain.

Interactions of transcranial magnetic stimulation with brain oscillations: a narrative review

Brain responses to transcranial magnetic stimulation (TMS) can be recorded with electroencephalography (EEG) and comprise TMS-evoked potentials and TMS-induced oscillations. Repetitive TMS may entrain endogenous brain oscillations. In turn, ongoing brain oscillations prior to the TMS pulse can influence the effects of the TMS pulse. These intricate TMS-EEG and EEG-TMS interactions are increasingly attracting the interest of researchers and clinicians. This review surveys the literature of TMS and its interactions with brain oscillations as measured by EEG in health and disease.

Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety

Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.

Exercise intervention on the brain structure and function of patients with mild cognitive impairment: systematic review based on magnetic resonance imaging studies

Objective

This systematic review evaluates the impact of exercise intervention in MCI patients and discusses the potential neural mechanisms.

Methods

A systematic search and screening of relevant literature was conducted in English and Chinese databases. Based on predefined keywords and criteria, 24 articles were assessed and analyzed.

Results

Structurally, a significant increase was observed in the hippocampal and gray matter volumes of MCI patients following exercise intervention, with a trend of improvement in cortical thickness and white matter integrity. Functionally, after the exercise intervention, there were significant changes in the local spontaneous brain activity levels, cerebral blood flow, and functional connectivity during rest and memory encoding and retrieval tasks in MCI patients.

Conclusion

Exercise may contribute to delaying neurodegenerative changes in brain structure and function in patients with MCI. However, the underlying neural mechanisms require further research.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023482419.

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.