Functional Connectivity Networks Research Articles

Alterations of Sensory-related Functional Brain Network Connectivity in Nrsn2 Homozygous Knockout Mice

Alterations of Sensory-related Functional Brain Network Connectivity in Nrsn2 Homozygous Knockout Mice

Modeling functional connectivity changes during an auditory language task using line graph neural networks

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p < 0.0001). When applying the same models to the second dataset, both line GNNs also showed statistically significant improvements over their traditional counterparts with little to no overfitting. We believe this shows that line GNN models demonstrate promising utility in FC studies.

Mapping multi-regional functional connectivity of astrocyte-neuronal networks during behaviors

Mapping multi-regional functional connectivity of astrocyte-neuronal networks during behaviors

The altered hypothalamic network functional connectivity in chronic insomnia disorder and regulation effect of acupuncture: a randomized controlled neuroimaging study.

The hypothalamus has been recognized as a core structure in the sleep-wake cycle. However, whether the neuroplasticity of the hypothalamus is involved in the acupuncture treatment of insomnia remains elusive. We recruited 42 patients with chronic insomnia disorder (CID) and 23 matched healthy controls (HCs), with CID patients randomly assigned to receive real acupuncture (RA) or sham acupuncture (SA) for four weeks. Insomnia severity was evaluated using the Pittsburgh Sleep Quality Index (PSQI) score, and the resting-state functional connectivity (rsFC) of the hypothalamus was assessed via functional magnetic resonance imaging (fMRI). In the cross-sectional investigation, CID patients showed increased rsFC between the medial hypothalamus (MH) and left lateral orbital frontal cortex (LOFC), and bilateral medial orbital frontal cortex (MOFC) compared to HCs. In the longitudinal experiment, PSQI scores significantly decreased in the RA group (p = 0.03) but not in the SA group. Interestingly, the increased MH-LOFC connectivity was found to be reduced following RA treatment. In addition, the altered rsFC of MH-LOFC significantly correlated with clinical improvement in the RA group (r = -0.692, p = 0.006). This randomized neuroimaging study provides preliminary evidence that acupuncture may improve insomnia symptoms by restoring circuits associated with hypothalamic subregions. This trial has been registered on the Chinese Clinical Trial Registry ( www.chictr.org.cn ) with the identifier (ChiCTR1800017092). Registered date: 11/07/2018.

Combined graph convolutional networks with a multi-connection pattern to identify tremor-dominant Parkinson's disease and essential tremor with resting tremor.

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder.

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

Brain functional connectivity under teleoperation latency: a fNIRS study

IntroductionLong-distance robot teleoperation faces high latencies that pose cognitive challenges to human operators. Latency between command, execution, and feedback in teleoperation can impair performance and affect operators’ mental state. The neural underpinnings of these effects are not well understood.MethodsThis study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed.ResultsThe presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns.ConclusionThe study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.

A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study

IntroductionRight-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.MethodsFirstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.ResultsOur experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.DiscussionThe results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

NIMG-57. EMBEDDED GRAPH DERIVED FROM PSEUDO-RESTING-STATE FUNCTIONAL MRI CAN PREDICT COGNITIVE IMPAIRMENT IN GLIOMA

Abstract Resting-state brain network analyses are of great interest in studying the neurocognition and measuring the functional connectivity (FC) patterns of glioma patients. However, resting-state functional MRI (rs-fMRI) is not assessed in clinical routine due to the scan time and cost. The current study used pseudo-resting-state functional MRI (pseudo-rs-fMRI) derived from dynamic susceptibility contrast (DSC) perfusion MRI to predict cognitive impairment in glioma. 37 glioma patients were enrolled consecutively in the current study with DSC perfusion MRI acquired and neurocognition assessed, where 24 glioma patients also got rs-fMRI collected. The pseudo-rs-fMRI was created by voxel-wise subtracting the Gamma-variate modeled signal of contrast agent bolus from the original DSC perfusion signal. Following the pre-processing of pseudo-rs-fMRI and full rs-fMRI, the functional connectivity network (FCN) was established for each patient. Graph embedding was implemented to learn and extract features of each node in the binarized FCN, and the decision tree classification algorithm was applied to distinguish cognitive impairment. FCNs of 24 patients with DSC perfusion and rs-fMRI acquired were used to train the model, and FCNs of 13 patients with only DSC perfusion MRI acquired were used to test the model. The AUC (area under the receiver operating characteristic curve) was used to evaluate the model’s performance. The similarity and mean-square-difference between average FCNs extracted from rs-fMRI and pseudo-rs-fMRI were 0.6769 and 0.0263, respectively. Classifier trained using 24 FCNs of pseudo-rs-fMRI has an AUC of 0.750 in identifying cognitively impaired patients in the testing cohort. Combining FCNs of both rs-fMRI and pseudo-rs-fMRI to train the classifier resulted in a higher AUC of 0.833 in the testing cohort. To summary, DSC perfusion MRI-derived pseudo-rs-fMRI can be used to predict cognitive impairment in patients with gliomas. External validation would be required.

NIMG-11. DISCOVERING POSTOPERATIVE APHASIA RECOVERY IN GLIOMA PATIENTS: THE ROLE OF THE RIGHT BROCA’S HOMOLOG AND VERIFICATION

Abstract OBJECTIVE This study aimed to elucidate the neurological mechanisms that support the recovery of postoperative aphasia by examining alterations in language and extended networks in patients with glioma. Additionally, we sought to determine the causal relationship between the right Broca’s homolog and language recovery using neuro-navigated repetitive transcranial magnetic stimulation (nrTMS). METHODS Sixty-two patients with left hemisphere gliomas were retrospectively analyzed and categorized into aphasia and non-aphasia groups based on Aphasia Quotient (AQ) scores. Resting-state functional MRI (rs-fMRI) data were collected and analyzed to investigate differences in functional connectivity (FC) and topological properties of language-related networks. Additionally, eight patients with surgery-induced aphasia were prospectively treated with nrTMS, targeting the node identified from the retrospective analysis. RESULTS Significant increases in FC between the left auditory cortex and the right inferior frontal gyrus in the Executive Control Network were observed in the non-aphasia group. Nodal properties, such as the nodal efficiency of the right Broca’s homolog, were independently associated with postoperative language function. The nrTMS treatment group demonstrated significant improvements in AQ scores, with correlations between increased nodal efficiency and positive language outcomes. CONCLUSION The right hemisphere, specifically the right inferior frontal gyrus, plays a compensatory role in maintaining language function after glioma resection. These findings suggest the therapeutic potential of nrTMS for enhancing postoperative language recovery, offering insights into individualized patient care and rehabilitation strategies for patients with glioma.

Aberrant dynamic functional network connectivity in patients with diffuse axonal injury

Diffuse axonal injury (DAI) results in aberrant functional connectivity and is significantly linked to cognitive impairment. Nevertheless, the network mechanisms influencing neurocognitive function following DAI remain unclear. This study aimed to examine the characteristics of static and dynamic functional network connectivity (FNC) in patients with DAI. Resting-state functional magnetic resonance imaging data were collected from 26 patients with DAI and 27 healthy controls. Resting-state networks were extracted using independent component analysis. We evaluated the connectivity strength through spatial maps and static FNC, and then further dynamic properties were identified using a sliding time-window approach and k-means clustering, and investigated their associations with clinical variables. Patients with DAI showed stronger intra-network spatial maps in the default mode network and subcortical network than healthy controls, but static inter-network functional connectivity remained stable. Furthermore, three recurring states for dynamic connectivity were identified in all participants, and state 1 occurred most frequently in patients with DAI and exhibited higher fractional time, and as well as longer mean dwell time, which was positively associated with MMSE scores. Meanwhile, patients with DAI exhibited mostly increased functional connectivity strength of dynamic FNC in all states, particularly within the default mode network and visual network. These findings suggest that patients with DAI are characterized by altered dynamic FNC and temporal properties, which provide distinct complementary information different from static functional connectivity, and new insights into the neural pathophysiology of DAI associated with cognitive impairment.

Resting state functional magnetic resonance imaging in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hemopoietic stem cell transplantation

BACKGROUND: Multiple sclerosis is a chronic autoimmune disease characterized by multifocal foci of demyelination in the central nervous system, usually affecting people of working age. The disease causes damage to the blood-brain barrier, the development of multifocal inflammation, destruction of the myelin sheath of axons and various degrees of damage. It is clinically manifested by restriction of motor activity, visual acuity, as well as other symptoms leading to loss of performance and disability of the patient. AIM: determination of changes in the functional connectivity of brain neural networks in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hematopoietic stem cell transplantation by performing functional magnetic resonance imaging at rest. MATERIALS AND METHODS: The data of functional magnetic resonance imaging of patients with multiple sclerosis were analyzed in dynamics before and after the use of high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation. The study involved 25 patients with a verified diagnosis of multiple sclerosis. Each underwent complex magnetic resonance imaging at two time points (before and after high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation) with a difference of 12 months, which included structural magnetic resonance imaging - in order to exclude the presence of pathological foci in the brain (in addition to foci of multiple sclerosis) and functional magnetic resonance imaging.-resonance imaging at rest — to assess functional connectivity. Also, according to the method generally accepted in classical neurology, a clinical neurological examination was performed. RESULTS: At the stage of comparing data on the two groups obtained using functional magnetic resonance imaging at rest, changes in functional activity were detected in various parts of the brain, presumably responsible for clinical differences in the studied groups. CONCLUSION: Currently, the links between brain structures and morphological changes that cause cognitive impairment in multiple sclerosis are being studied. To predict the progression of the disease, the development of biomarkers, including those based on functional magnetic resonance imaging, is required. Evaluating changes in the functional connectivity of brain neural networks can help personalize therapeutic and rehabilitation approaches.

Change in striatal functional connectivity networks across 2 years due to stimulant exposure in childhood ADHD: results from the ABCD sample.

Widely prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), stimulants (e.g., methylphenidate) have been studied for their chronic effects on the brain in prospective designs controlling dosage and adherence. While controlled approaches are essential, they do not approximate real-world stimulant exposure contexts where medication interruptions, dosage non-compliance, and polypharmacy are common. Brain changes in real-world conditions are largely unexplored. To fill this gap, we capitalized on the observational design of the Adolescent Brain Cognitive Development (ABCD) study to examine effects of stimulants on large-scale bilateral cortical networks' resting-state functional connectivity (rs-FC) with 6 striatal regions (left and right caudate, putamen, and nucleus accumbens) across two years in children with ADHD. Bayesian hierarchical regressions revealed associations between stimulant exposure and change in rs-FC of multiple striatal-cortical networks, affiliated with executive and visuo-motor control, which were not driven by general psychotropic medication. Of these connections, three were selective to stimulants versus stimulant naive: reduced rs-FC between caudate and frontoparietal network, and between putamen and frontoparietal and visual networks. Comparison with typically developing children in the ABCD sample revealed stronger rs-FC reduction in stimulant-exposed children for putamen and frontoparietal and visual networks, suggesting a normalizing effect of stimulants. 14% of stimulant-exposed children demonstrated reliable reduction in ADHD symptoms, and were distinguished by stronger rs-FC reduction between right putamen and visual network. Thus, stimulant exposure for a two-year period under real-world conditions modulated striatal-cortical functional networks broadly, had a normalizing effect on a subset of networks, and was associated with potential therapeutic effects involving visual attentional control.

Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.

Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.

Enhancing perceptual, attentional, and working memory demands through variable practice schedules: insights from high-density EEG multi-scale analyses.

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We used high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway, anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and working memory processes, as well as large-scale functional networks sustaining more general attentional and executive processes.

Static and Dynamic Functional Network Connectivity inParkinson's Disease Patients With Postural Instability andGait Disorder.

The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC). We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms. Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores. Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.

Altered functional connectivity of unimodal sensory and multisensory integration networks is related to symptom severity in autism spectrum disorders

BackgroundAtypical sensory processing is a prevalent feature in autism spectrum disorders (ASD) and constitutes a core diagnostic criterion in the Diagnostic and Statistical Manual of Mental of Disorders, 5th edition (DSM-5). However, neurocognitive underpinnings of atypical unimodal and multimodal sensory processing and their relationships with autism symptoms remain unclear. MethodsThis study examined intrinsic functional connectivity (FC) patterns among five unimodal sensory and multisensory integration (MSI) networks in ASD with a large multi-site dataset (n = 646), and investigated the relationships among altered FC, atypical sensory processing, social communicative deficits, and overall autism symptoms with correlation and mediation analyses. ResultsRelative to typically developing (TD) controls, the ASD group demonstrated increased FC of the olfactory network, decreased FC within the MSI network, and decreased FC of the MSI-unimodal-sensory networks. Furthermore, altered FC was positively associated with autism symptom severity, and such associations were completely mediated by atypical sensory processing and social communicative deficits. ConclusionsASD-specific olfactory overconnectivity and MSI-unimodal-sensory underconnectivity lend support to the Intense World Theory and Weak Central Coherence Theory, suggesting olfactory hypersensitivity at the expense of multisensory integration as potential neural mechanisms underlying atypical sensory processing in ASD. These atypical FC patterns further suggest potential targets for psychological and neuromodulatory interventions.

Different stimulation targets of rTMS modulate specific triple-network and hippocampal-cortex functional connectivity

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is widely applied to treat Alzheimer's disease (AD). Various treatment targets are currently being explored in clinical research. However, target diversity can result in considerable heterogeneity. ObjectiveThis study aimed to investigate whether different rTMS targets can enhance cognitive domains by modulating functional connectivity (FC) of the hippocampus (HIP) and triple network, which comprises the salience network (SN), central executive network (CEN), and default mode network (DMN). MethodsWe enrolled 63 patients with AD, of whom 48 and 15 underwent rTMS targeting the left dorsolateral prefrontal cortex (dlPFC) and the bilateral angular gyrus (ANG), respectively. We examined the network-level FC differences within the triple-network before and after treatment. Additionally, we utilized HIP as a seed for voxel-level analysis. We compared the similarities and differences in the effects of dlPFC and ANG rTMS. ResultsrTMS targeting the dlPFC primarily influenced the FC of the CEN, whereas rTMS targeting the ANG primarily influenced the SN and DMN. Moreover, the right temporal lobe within the DMN exhibited reduced FC with the left HIP following both therapies. The results of least absolute shrinkage and selection operator (LASSO) analysis indicated that hippocampal-cortex FC played a dominant role in the therapeutic effect. The observed FC changes significantly correlated with improvements in multiple cognitive scales. ConclusionrTMS targeting different regions affected the FC of specific networks. Both stimulation targets modulate the FC of hippocampal-cortex to influence therapeutic outcomes. Classification of patients based on damaged networks can further inform subsequent treatment strategies.

Alterations of Excitation-Inhibition Balance and Brain Network Dynamics Support Sensory Deprivation Theory in Presbycusis.

Sensory deprivation theory is an important hypothesis involving potential pathways between hearing loss and cognitive impairment in patients with presbycusis. The theory suggests that prolonged auditory deprivation in presbycusis, including neural deafferentation, cortical reallocation, and atrophy, causes long-lasting changes and reorganization in brain structure and function. However, neurophysiological changes underlying the cognition-ear link have not been explored. In this study, we recruited 98 presbycusis patients and 60 healthy controls and examined the differences between the two groups in gamma-aminobutyric acid (GABA) and glutamate (Glu) levels in bilateral auditory cortex, excitation-inhibition (E/I) balance (Glu/GABA ratio), dynamic functional network connectivity (dFNC), hearing ability and cognitive performance. Then, correlations with each other were investigated and variables with statistical significance were further analyzed using the PROCESS Macro in SPSS. GABA levels in right auditory cortex and Glu levels in bilateral auditory cortex were lower but E/I balance in right auditory cortex were higher in presbycusis patients compared to healthy controls. Hearing assessments and cognitive performance were worse in presbycusis patients. Three recurring connectivity states were identified after dFNC analysis: State 1 (least frequent, middle-high dFNC strength with negative functional connectivity), State 2 (high dFNC strength), and State 3 (most frequent, low dFNC strength). The occurrence and dwell time of State 3 were higher, on the other hand, the dwell time of State 2 decreased in patients with presbycusis compared to healthy controls. In patients with presbycusis, worse hearing assessments and cognition were correlated with decreased GABA levels, increased E/I balance, and aberrant dFNC, decreased GABA levels and increased E/I balance were correlated with decreased occurrence and dwell time in State 3. In the mediation model, the fractional windows, as well as dwell time in State 3, mediated the relationship between the E/I balance in right auditory cortex and episodic memory (Auditory Verbal Learning Test, AVLT) in presbycusis. Moreover, in patients with presbycusis, we found that worse hearing loss contribute to lower GABA levels, higher E/I balance, and further impact aberrant dFNC, which caused lower AVLT scores. Overall, the results suggest that a shift in E/I balance in right auditory cortex plays an important role in cognition-ear link reorganization and provide evidence for sensory deprivation theory, enhancing our understanding the connection between neurophysiological changes and cognitive impairment in presbycusis. In presbycusis patients, E/I balance may serve as a potential neuroimaging marker for exploring and predicting cognitive impairment.

A subset of brain regions within adult functional connectivity networks demonstrates high reliability across early development.

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios.