Brain Network Connectivity 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

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.

Pilot Study of Intensive Pain Rehabilitation, Sleep, and Small-World Brain Networks in Adolescents with Chronic Pain

Background: Approximately 25% of adolescents live with chronic pain, with many reporting symptoms of functional impairment and poor sleep quality. Both chronic pain and poor sleep quality can negatively impact brain functional connectivity and efficiency. Better sleep quality may improve pain outcomes through its relationship with brain functional connectivity. Methods: This pilot prospective cohort study used data from 24 adolescents with chronic pain (aged 10–18 years) participating in an Intensive Interdisciplinary Pain Treatment (IIPT) at the Alberta Children’s Hospital. Data were collected within the first couple of weeks prior to starting IIPT and on the last day of the 3-week IIPT program. Sleep quality was assessed using the modified Adolescent Sleep-Wake Scale. Resting-state functional MRI data were obtained, and graph-theory metrics were applied to assess small-world brain networks. Questionnaires were used to obtain self-reported functional disability data. Paired t-tests were applied to evaluate changes in outcomes from pre- to post-IIPT, and moderation analyses were used to examine the relationships between sleep, small-world brain network connectivity, and functional disability. Results: Total sleep quality (p = 0.005) increased, and functional disability (p = 0.020) decreased, between baseline and discharge from IIPT. Small-world brain networks did not change pre- to post-IIPT (p > 0.05). Unlike adolescents with high small-worldness (p = 0.665), adolescents with low to moderate small-world brain characteristics (1SD below or at the mean) who reported better sleep quality reported less functional disability (all p ≤ 0.001) over time. Conclusions: The IIPT program was associated with improvements in sleep quality and functional disability. Better sleep quality together with greater small-worldness was associated with less pain-related disability. This suggests that it is equally important for IIPTs to target sleep problems in adolescents with chronic pain, as this may have a key role in producing long-term improvements in pain outcomes.

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.

Altered gait speed and brain network connectivity in Parkinson's disease.

Slow gait speed and disrupted brain network connectivity are common in patients with Parkinson's disease (PD). This study aimed to clarify the relationship between gait speed and clinical characteristics in PD, and explore the underlying brain network mechanisms. Forty-two PD patients and 20 healthy controls (HC) were recruited. Statistical independent component analysis and correlation analysis were employed to investigate underlying neural mechanisms and relationships. PD patients exhibited significantly slower gait speed, which showed a significant negative correlation with postural instability and gait disturbance scores. Network connectivity analysis revealed decreased intranetwork functional connectivity (FC) within visual network (VN) and cerebellum network (CN), but increased internetwork FC between CN and both sensorimotor network (SMN) and frontoparietal network (FPN) in PD patients compared to HC. The slow gait speed PD subgroup demonstrated increased intranetwork FC within SMN and VN, along with decreased FC between VN and both FPN and default mode network. Correlation analyses revealed negative correlation between gait speed and FC of CN and positive correlation to FC of CN-SMN. Our study identified relationships between gait speed and clinical characteristics, and corresponding network connectivity alterations in PD patients, providing insights into the neural mechanisms underlying gait impairments in PD.

Characteristic activation pattern and network connectivity of prefrontal cortex in patients with type 2 diabetes mellitus and major depressive disorder during a verbal fluency task: A functional near-infrared spectroscopy study based on network-based statistic prediction.

Type 2 diabetes (T2DM) and major depressive disorder (MDD) together occur frequently among the elderly population. However, the inconsistency in assessments and limited medical resources in the community make it challenging to identify depression in patients with T2DM. This cross-sectional study aimed to investigate the activation pattern and network connectivity of prefrontal cortex (PFC) during a verbal fluency task (VFT) in patients with T2DM and MDD using functional near-infrared spectroscopy (fNIRS). Three parallel groups (T2DM with MDD group, T2DM group, and healthy group) with 100 participants in each group were included in the study. Recruitment took place from August 1, 2020, to December 31, 2023. Due to the close association between the PFC and depressive emotions, fNIRS was used to monitor brain activation and network connectivity of PFC in all participants during a task of Chinese-language phonological VFT. Network-based statistic prediction (NBS-predict) was adopted as data analysis method. Patients in the T2DM with MDD group showed characteristic activation pattern and network connectivity in contrast with patients with T2MD and healthy controls, including decreased activation in PFC, and decreased network connectivity of right dorsolateral prefrontal cortex (DLPFC). Furthermore, the network connectivity of the right DLPFC in patients with T2DM and MDD was negatively correlated with scores of Hamilton Depression Scale-24 (HAMD-24). There was a distinctive activation pattern and network connectivity of the prefrontal cortex in patients with T2DM and MDD. The right DLPFC could serve as a potential target for the diagnosis and intervention of MDD in patients with T2DM.

Aging-dependent loss of functional connectivity in a mouse model of Alzheimer's disease and reversal by mGluR5 modulator.

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier’s performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.

Shedding Light on the Aftermath: Childhood Maltreatment's Role in Modifying the Association Between Recent Life Stress and Resting-State Network Connectivity.

Childhood maltreatment has been demonstrated to impact brain development. However, whether childhood maltreatment can influence the effects of recent stress on brain networks remains unclear. This study aimed to investigate whether childhood maltreatment moderates the longitudinal relationship between recent life stress and within- and between-network connectivity in key brain networks, including the anterior salience (ASN), central executive (CEN), default mode (DMN), and emotional regulation network (ERN). A cohort of 172 individuals from the Neuroscience in Psychiatry Network (NSPN) underwent MRI scans at two specific time points and undertook evaluations of childhood maltreatment and recent life stress. The results showed that childhood abuse moderated the association of recent life stress with the within-network connectivity of ASN and ERN but not DMN and CEN. Furthermore, recent life stress significantly interacted with childhood abuse to be associated with the between-network connectivity of ASN-DMN, ASN-CEN, ASN-ERN, DMN-ERN and CEN-ERN. Overall, among youth exposed to higher degrees of childhood abuse, greater recent life stress was longitudinally associated with increased network connectivity. Understanding these interactions can provide valuable insights for developing prevention strategies and interventions aimed at mitigating the lasting impact of childhood maltreatment on brain development and overall well-being.

Small world properties of schizophrenia and OCD patients derived from fNIRS based functional brain network connectivity metrics

Individuals suffering from obsessive compulsive disorder (OCD) and schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of functional integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain at rest is known to exhibit small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. The aim of this study was to compare the small-world properties of prefrontal cortical functional networks of healthy subjects with OCD and SCZ patient groups by use of hemodynamic data obtained with functional near infrared spectroscopy (fNIRS). 13 healthy subjects and 47 patients who were clinically diagnosed with either OCD (N = 21) or SCZ (N = 26) completed a Stroop test while their prefrontal cortex (PFC) hemodynamics were monitored with fNIRS. The Stroop test had a block design consisting of neutral, congruent and incongruent stimuli. For each subject and stimuli type, FCMs were derived separately which were then used to compute small world features that included (i) global efficiency (GE), (ii) clustering coefficient (CC), (iii) modularity (Q), and (iv) small-world parameter (σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma $$\\end{document}). Small-world features of patients exhibited random networks which were indicated by higher GE and lower CC values when compared to healthy controls, implying a higher neuronal operational cost.

Brain functional alteration and cognitive performance in cardiovascular diseases: a systematic review of fMRI studies.

Functional magnetic resonance imaging (fMRI) is a useful tool to evaluate brain inefficiencies secondary to cardiovascular diseases (CVDs); nevertheless, limited fMRI studies have been conducted to investigate the effect of CVDs on brain functional changes and cognitive function. This systematic review aims to explore, synthesise, and report fMRI outcomes (resting state and task-based) and cognitive performance in patients with CVDs. Two reviewers independently searched published literature until April 2024 on ScienceDirect, PubMed, Web of Science, and ClinicalTrials.gov adhering to the PRISMA protocol. A total of 26 eligible studies were considered for full-text screening, of which 10 were included in this review. The methodological quality was assessed by mixed methods appraisal tool and was reported as empirically fair. Among 336 subjects with CVDs, aged between 49.90 ± 6.10 to 72.20 ± 5.70 years, the majority had coronary artery diseases (n = 177, 52.68%) and hypertension (n = 200, 59.52%), and approximately half of them were females (n = 169, 50.30%). Based on the qualitative synthesis, subjects with CVDs demonstrated an increased cognitive decline (reduced Mini-Mental State Examination/Montreal Cognitive Assessment mean values) and attenuated task performance (lower mean 2-back task scores and slower reaction time). Results also indicated impaired brain activity at the supplementary motor area associated with poor ejection fraction; reduced default mode network suppression linked to high low-density lipoprotein cholesterol; lower regional homogeneity and amplitude of low-frequency fluctuation values; and reduced functional connectivity. In summary, alterations in brain networks connectivity may have contributed to an impaired cognitive performance in patients with cardiovascular diseases. It can be extrapolated that CVDs tend to alter the brain network connectivity and result in cognitive impairment and poorer task performance. However, for future imaging studies, more stringent and homogenous demographic data are highly recommended.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis

BackgroundIndividuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.MethodsWe recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.ResultsThe MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.ConclusionMUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

18F]FDG PET for mapping the cerebral glucose metabolic characteristics of drug-sensitive and drug-resistant epilepsy in pediatric patients.

This study aimed to investigate [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) mapping for cerebral glucose metabolism in drug-sensitive and drug-resistant pediatric epilepsy patients. This retrospective study enrolled 40 patients and 25 controls. Patients were categorized into drug-sensitive epilepsy (n = 22) and drug-resistant epilepsy (n = 18) according to the seizure frequency at follow-up. All patients underwent two [18F]FDG PET scans separated by a minimum of one year. Absolute asymmetry index (|AI|) was calculated for assessing metabolic differences and changes in epileptic foci. Statistical Parametric Mapping (SPM) was utilized to reveal voxel-wise metabolic characteristics and alterations throughout the brain. Network analysis based on graph theory was used to investigate network-level differences between the two patient groups. The drug-sensitive group showed a lower |AI| at both baseline (P = 0.038) and follow-up (P = 0.003) PET scans than the drug-resistant group. |AI| decreased in the drug-sensitive group and increased in the drug-resistant group across scans, but these trends were not statistically significant (P = 0.240 and P = 0.450, respectively). Both groups exhibited hypometabolism at baseline. The drug-sensitive group showed less hypometabolic brain regions than the drug-resistant group. The drug-sensitive maintained stable level of hypometabolism between the two scans, whereas the drug-resistant group showed an increasing hypometabolism. Network analysis demonstrated that the drug-sensitive group had a higher global efficiency, average degree, and clustering, along with a shorter characteristic path length compared to the drug-resistant group. For the first time, this study revealed in vivo cerebral glucose metabolic pattern of nonsurgical pediatric epilepsy patients treated by antiepileptic drugs. Especially, drug-resistant epilepsy patients represented significantly extensive and progressive hypometabolism with inefficient brain network connectivity compared with drug-sensitive epilepsy. [18F]FDG PET imaging may be a potential visual approach for theranostics of epilepsy patients.

8607 Gender Affirming Hormone Therapy: Influence of Brain Network Connectivity Changes on Affect

Abstract Disclosure: J. Andreano: None. A. Lopez: None. M. Misra: None. Background: Previous research indicates that acute gonadal treatments and fluctuations in ovarian hormone levels over the course of the menstrual cycle can alter patterns of communication between brain regions, as well as producing significant changes in self-reported affect. Recent research from our laboratory indicates changes in both brain connectivity and affect with gender affirming hormone therapy (GAHT). However, the relationship between these changes with GAHT are not well understood. Additionally, it is difficult to separate influences of GAHT and transgender identity in understanding differences in connectvity and affect from cisgender controls. A better understanding of the influence of GAHT on intrinsic network connectivity could point the way towards improved GAHT treatment regimens that minimize affective side effects. Objective: To assess relationships between changes in brain network connectivity and affective changes following GAHT, identifying regions in which changes of connectivity are associated with changes in self-reported affect, and to examine differences in brain network connectivity between transgender participants and cisgender controls. Methods: 14 transgender individuals receiving GAHT (estradiol n =7, testosterone n = 7)) were scanned using functional magnetic imaging (fMRI) at rest at two timepoints: baseline, prior to treatment, and 6 months into GAHT. Additionally, 20 cisgender controls (10 male, 10 female) were scanned once each. The time course of neural activity was computed for each voxel in the brain, then tested for correlation with each other voxel to develop a whole-brain connectome for each participant. Maps of average connectivity to two seeds known to associate with two major networks in the brain (posterior cingulate cortex for Default Mode Network, and anterior insula for Salience Network) were then generated for each participant. Within transgender participants, these maps were then compared between the baseline and 6 month timepoints. Additionally, baseline scans were also compared to scans from cisgender controls corresponding to biological sex. Results: Within-subjects analyses of transgender participants showed diverse associations between changes in seed-based connectivity and changes in self-reported anxiety and depression following GAHT in both the default mode and salience networks. Between subjects comparisons showed significantly higher connectivity within the salience network in trans men relative to cis women, as well as greater inter-network connectivity in this group. In contrast, cis men showed greater connectivity within both default mode and salience networks relative to trans women. Conclusions: Changes in affect following GAHT appear related to GAHT-induced changes in network connectivity; these influences appear independent of differences between cis and transgender individuals. Presentation: 6/2/2024

Assessing consciousness in acute coma using name-evoked responses

Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients’ relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject’s own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.

Resting-state functional connectivity in anxiety disorders: a multicenter fMRI study.

Anxiety disorders (AD) are associated with altered connectivity in large-scale intrinsic brain networks. It remains uncertain how much these signatures overlap across different phenotypes due to a lack of well-powered cross-disorder comparisons. We used resting-state functional magnetic resonance imaging (rsfMRI) to investigate differences in functional connectivity (FC) in a cross-disorder sample of AD patients and healthy controls (HC). Before treatment, 439 patients from two German multicenter clinical trials at eight different sites fulfilling a primary diagnosis of panic disorder and/or agoraphobia (PD/AG, N = 154), social anxiety disorder (SAD, N = 95), or specific phobia (SP, N = 190) and 105 HC underwent an 8 min rsfMRI assessment. We performed categorical and dimensional regions of interest (ROI)-to-ROI analyses focusing on connectivity between regions of the defensive system and prefrontal regulation areas. AD patients showed increased connectivity between the insula and the thalamus compared to controls. This was mainly driven by PD/AG patients who showed increased (insula/hippocampus/amygdala-thalamus) and decreased (dorsomedial prefrontal cortex/periaqueductal gray-anterior cingulate cortex) positive connectivity between subcortical and cortical areas. In contrast, SAD patients showed decreased negative connectivity exclusively in cortical areas (insula-orbitofrontal cortex), whereas no differences were found in SP patients. State anxiety associated with the scanner environment did not explain the FC between these regions. Only PD/AG patients showed pronounced connectivity changes along a widespread subcortical-cortical network, including the midbrain. Dimensional analyses yielded no significant results. The results highlighting categorical differences between ADs at a systems neuroscience level are discussed within the context of personalized neuroscience-informed treatments. PROTECT-AD's registration at NIMH Protocol Registration System: 01EE1402A and German Register of Clinical Studies: DRKS00008743. SpiderVR's registration at ClinicalTrials.gov: NCT03208400.

Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG

Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.

Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Insights from triggers and prodromal symptoms on how migraine attacks start: The threshold hypothesis.

The prodrome or premonitory phase is the initial phase of a migraine attack, and it is considered as a symptomatic phase in which prodromal symptoms may occur. There is evidence that attacks start 24-48 hours before the headache phase. Individuals with migraine also report several potential triggers for their attacks, which may be mistaken for premonitory symptoms and hinder migraine research. This review aims to summarize published studies that describe contributions to understanding the fine difference between prodromal/premonitory symptoms and triggers, give insights for research, and propose a way forward to study these phenomena. We finally aim to formulate a theory to unify migraine triggers and prodromal symptoms. For this purpose, a comprehensive narrative review of the published literature on clinical, neurophysiological and imaging evidence on migraine prodromal symptoms and triggers was conducted using the PubMed database. Brain activity and network connectivity changes occur during the prodromal phase. These changes give rise to prodromal/premonitory symptoms in some individuals, which may be falsely interpreted as triggers at the same time as representing the early manifestation of the beginning of the attack. By contrast, certain migraine triggers, such as stress, hormone changes or sleep deprivation, acting as a catalyst in reducing the migraine threshold, might facilitate these changes and increase the chances of a migraine attack. Migraine triggers and prodromal/premonitory symptoms can be confused and have an intertwined relationship with the hypothalamus as the central hub for integrating external and internal body signals. Differentiating migraine triggers and prodromal symptoms is crucial for shedding light on migraine pathophysiology and improve migraine management.