Functional Connectivity Patterns Research Articles

Disrupted functional connectivity of the emotion regulation network in major depressive disorder and its association with symptom improvement: A multisite resting-state functional MRI study.

The emotion regulation network (ERN) in the brain provides a framework for understanding the neuropathology of affective disorders. Although previous neuroimaging studies have investigated the neurobiological correlates of the ERN in major depressive disorder (MDD), whether patients with MDD exhibit abnormal functional connectivity (FC) patterns in the ERN and whether the abnormal FC in the ERN can serve as a therapeutic response signature remain unclear. A large functional magnetic resonance imaging dataset comprising 709 patients with MDD and 725 healthy controls (HCs) recruited across five sites was analyzed. Using a seed-based FC approach, we first investigated the group differences in whole-brain resting-state FC of the 14 ERN seeds between participants with and without MDD. Furthermore, an independent sample (45 MDD patients) was used to evaluate the relationship between the aforementioned abnormal FC in the ERN and symptom improvement after 8 weeks of antidepressant monotherapy. Compared to the HCs, patients with MDD exhibited aberrant FC between 7 ERN seeds and several cortical and subcortical areas, including the bilateral middle temporal gyrus, bilateral occipital gyrus, right thalamus, calcarine cortex, middle frontal gyrus, and the bilateral superior temporal gyrus. In an independent sample, these aberrant FCs in the ERN were negatively correlated with the reduction rate of the HAMD17 score among MDD patients. These results might extend our understanding of the neurobiological underpinnings underlying unadaptable or inflexible emotional processing in MDD patients and help to elucidate the mechanisms of therapeutic response.

Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity

To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. The cognitive function, clinical symptoms, FC patterns, and correlation with neurotransmitter systems of biotype profiles were compared. Three distinct CHR biotypes were identified based on 646 significant ROI-ROI connectivity features, comprising 29.8%, 19.2%, and 51.0% of the CHR sample, respectively. Despite the absence of overall FC differences between CHR and HC groups, each CHR biotype demonstrated unique FC abnormalities. Biotype 1 displayed augmented somatomotor connection, Biotype 2 shown compromised working memory with heightened subcortical and network-specific connectivity, and Biotype 3, characterized by significant negative symptoms, revealed extensive connectivity reductions along with increased limbic-subcortical connectivity. The neurotransmitter correlates differed across biotypes. Biotype 2 revealed an inverse trend to Biotype 3, as increased neurotransmitter concentrations improved functional connectivity in Biotype 2 but reduced it in Biotype 3. The identification of CHR biotypes provides compelling evidence for the early manifestation of heterogeneity within the psychosis spectrum, suggesting that distinct pathophysiological mechanisms may underlie these subgroups.

Dynamic functional connectivity patterns predict early antidepressant treatment response in drug-naïve, first-episode adolescent MDD

ObjectiveAdolescents with major depressive disorder (MDD) exhibit abnormal dynamic functional connectivity (dFC) patterns, but it remains unclear whether these aberrant dFC patterns are linked to antidepressant treatment. The aim of this study is to investigate whether dFC patterns will be changed by antidepressant treatment, as well as whether baseline dFC pattern could predict treatment response in adolescent MDD patients.MethodWe included 35 drug-naïve, first-episode MDD adolescents (age 14.40 ± 1.24; 8 males and 27 females) and 24 healthy controls (HCs, age 14.21 ± 1.41; 11 males and 13 females). All MDD adolescents received 6 weeks of antidepressant treatment. Resting state and T1 MRI data were collected in MDD adolescents before and after treatment and in HCs. Independent component analysis (ICA) was used to compare the different dFC pattern between MDD adolescents and HCs at baseline, as well as which between before and after treatment in MDD adolescents. Finally, Pearson correlation and multivariate linear regression analyses were used to explore the associations between dFC pattern and changed score of BDI in MDD adolescents.ResultsThe mean dFC value between right inferior frontal gyrus (IFG) and bilateral insular cortex (IC; right, r = −0.461, p-FDR = 0.012; left, r = −0.518, p-FDR = 0.007) at baseline were negatively correlated with BDI score reduction. The mean dFC value between left frontal pole (FP) and right superior parietal lobule (SPL) after treatment was positively correlated with BDI score reduction (r = 0.442, p-FDR = 0.014). And the mean dFC values between right IFG and bilateral IC (right, β = −1.563, p-FDR = 0.021; left, β = −1.868, p-FDR = 0.012) at baseline could predict antidepressant treatment response.ConclusionThese findings demonstrate that dFC patterns between some brain areas could be a prospective factor for predicting antidepressant treatment response.

A Statistical Characterization of Dynamic Brain Functional Connectivity.

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.

Resting-State Functional Connectivity Does Not Predict Individual Differences in Perceived Psychological Stress Among Midlife Adults: Evidence From a Preregistered Cross-Validation Study.

It is theorized that appraisals of perceived psychological stress are represented in the brain. However, a neural signature that reliably predicts perceived stress has yet to be fully characterized. Accordingly, the present preregistered study tested whether whole-brain resting-state functional connectivity patterns predict individual differences in perceived stress. Participants (N = 417; 53% female; 24.2% non-White; aged 30-54 years) completed the 10-item Perceived Stress Scale and underwent a 5-minute resting-state functional magnetic resonance imaging scan. Functional connectivity (FC) was computed between areas distributed across the brain. In total, 19,900 functional connections (edges) were retained for analyses. Cross-validated and multivariate machine learning methods were implemented. Using this approach, two penalized regression models with cross-validation-elastic net and ridge-were conducted to predict perceived stress from the edges. Across the elastic net and ridge regression models, whole-brain resting-state FC patterns failed to predict individual differences in perceived stress. However, in exploratory analyses, they successfully generalized in cross-validation to predict age for both models (elastic net: r = 0.193, p < .0001, 95% CI = 0.099-0.284, RMSE = 6.661, MAE = 5.715, R2 = 0.037; ridge: r = 0.197, p < .0001, 95% CI = 0.103-0.287), RMSE = 6.613, MAE = 5.8140, R2 = 0.039). These results suggest that resting-state FC patterns may not reliably predict individual differences in self-reported perceived stress among midlife adults.

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain functional connectivity (FC), affecting over 100 million people worldwide. This study aims to identify distinct FC patterns as potential biomarkers for differential diagnosis. Resting-state EEG data from 36 AD patients, 23 FTD patients, and 29 healthy controls were analyzed using time-frequency and bandpass filtering FC metrics. These metrics were estimated through Pearson's correlations, mutual information, and phase lag index, and served as input features in a support vector machine (SVM) with Leave-One-Out Cross-Validation for group classification. Both AD and FTD exhibited significantly decreased FC in the theta band within the frontal lobe and increased FC in the beta band in the posterior regions. Additionally, a decreased FC in central regions at theta band was observed uniquely in AD, but not in FTD. SVM classification accuracies reached 95% for AD and 86% for FTD. High classification accuracies underscore the potential of these FC alterations as reliable biomarkers for AD and FTD. This is the first study to integrate time-frequency and bandpass filtering FC metrics to reveal brain network alterations in AD and FTD, providing new insights for diagnostics and neurodegenerative pathologies.

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel.

Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual's brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual's time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.

Exploring imitation of within hand prehensile object manipulation using fMRI and graph theory analysis

This study aims to establish an imitation task of multi-finger haptics in the context of regular grasping and regrasping processes during activities of daily living. A video guided the 26 healthy, right-handed volunteers through the three phases of the task: (1) fixation of a hand holding a cuboid, (2) observation of the sensori-motor manipulation, (3) imitation of that motor action. fMRI recorded the task; graph analysis of the acquisitions revealed the associated functional cerebral connectivity patterns. Inferred from four 60 ROI weighted graphs, the functional connectivities are consistent with a motor plan for observation and manipulation in the left hemisphere and with a network in the right hemisphere involving the inferior frontal gyrus, the site of intentional control of imitation. The networks exhibit (1) rich clubs which include sensori-motor hand, dorsal attention and cingulo-opercular communities for observation and motor execution in both hemispheres and (2) diversity clubs, significant only for manipulation and observation of the left hand, which include the dorsal visual association cortex, suggesting enhanced visual perception required for guiding the movement-limited left fingers. Short pathway analyses are consistent with these findings, confirming preferential involvement of ventral premotor cortices in the mirror network.

Decoding HIV-associated neurocognitive disorders: a new perspective from multimodal connectomics

Currently, HIV-associated neurocognitive disorders (HAND) remains one of the major challenges faced by people living with HIV (PLWH). HAND involves the vulnerability of neural circuits caused by synaptic degeneration and abnormal synaptic pruning. In recent years, connectomics has been gradually applied to HAND research as a cutting-edge method for describing the structural and functional connectivity patterns of the brain, to further elucidate the specific mechanisms underlying these neural circuit vulnerabilities. Using multimodal neuroimaging techniques such as diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and resting-state functional magnetic resonance imaging (rs-fMRI), researchers can detail the connectome network changes in the brains of PLWH. These technologies offer potential biomarkers for the early diagnosis, prognosis, and treatment monitoring of HAND, while also providing new avenues for personalized prediction of cognitive status. Here, we start with the pathogenesis and risk factors of HAND, providing a comprehensive review of the basic concepts of unimodal and multimodal macro connectomics and related graph theory methods, and we review the latest progress in HAND connectomics research. We emphasize the use of connectomics to identify specific disease patterns of HIV-associated neurodegeneration and discuss the potential research directions and challenges in understanding these diseases from a connectomics perspective.

Neural cue reactivity and intrinsic functional connectivity in individuals with alcohol use disorder following treatment with topiramate or naltrexone.

Both topiramate and naltrexone have been shown to affect neural alcohol cue reactivity in alcohol use disorder (AUD). However, their comparative effects on alcohol cue reactivity are unknown. Moreover, while naltrexone has been found to normalize hyperactive localized network connectivity implicated in AUD, no studies have examined the effect of topiramate on intrinsic functional connectivity or compared functional connectivity between these two widely used medications. This study compared topiramate versus naltrexone on alcohol cue-elicited brain activation and intrinsic functional connectivity in patients with alcohol use disorder. Forty-seven participants with alcohol use disorder received daily topiramate (titrating the dose up to 200mg/day n = 21) or naltrexone (50mg/day, n = 26) for at least 6 weeks. Using functional magnetic resonance imaging (fMRI), we examined intrinsic functional connectivity during rest and alcohol cue-elicited neural activation during a visual alcohol cue reactivity task 120min following treatment administration. Functional connectivity and alcohol cue reactivity and percentage of heavy drinking days (% HDD) associations were assessed. No differences in either intrinsic functional connectivity or alcohol cue-elicited neural activity were seen between topiramate and naltrexone-treated groups. Overall, participants showed increased alcohol cue-elicited activation in three clusters spanning occipital regions involved in visual recognition of stimuli, and hypoactivation to both alcohol and control cues in three clusters involved in salience attribution and processing of emotional valence of external stimuli. No differences between topiramate versus naltrexone were observed for either functional measure or associations with post-scan % HDD. Topiramate and naltrexone enacted comparable alcohol cue reactivity and intrinsic functional connectivity patterns. Some overall responses of increased brain activation to alcohol cues in visual processing regions coupled with reduced activation to alcohol and control cues were evidenced for both treatments. These activation patterns were in regions expected to show attenuation of brain activity resulting from treatment. Topiramate and naltrexone may thus enact functional effects through similar modulation of functional neural activity in individuals with AUD. ClinicalTrials.gov, NCT03479086 https://www. gov/study/NCT03479086 .

Common and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior

BackgroundAlthough childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.MethodsThis study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.ResultsThe results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.ConclusionsWe speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

Should you hold onto the treadmill handrails or not? Cortical evidence at different walking speeds

BackgroundTreadmill-based gait training is part of rehabilitation programs focused on walking abilities. The use of handrails embedded in treadmill systems is debated, and current literature only explores the issue from a behavioral perspective.MethodsWe examined the cortical correlates of treadmill walking in healthy participants using functional near-infrared spectroscopy. We investigated whether the utilization of treadmill handrails at varying walking speeds could affect cortical activation associated with the task, and we evaluated potential differences in task-based functional connectivity across the various walking conditions.ResultsSignificant differences in cortical activation were found between the two walking speeds (3 and 5 km/h) in the unsupported condition; these differences were reduced when using the handrails. Specifically, cortical activation was significantly higher when the participants swung their arms freely while walking at a speed of 5 compared to 3 km/h in several Brodmann’s Areas (BA): left BA10, BA3 and BA39, and right BA10, BA9, BA8, BA3, and BA40. No significant differences were found when participants were holding onto the handrails. A significant difference was found in the left BA40 between the two speeds, regardless of whether the participants were holding onto the handrails. Furthermore, at the higher speed and without the use of handrails, a wider pattern of task-based functional connectivity was observed, with significantly stronger connectivity between the left BA10 and BA40.ConclusionsWe suggest that speed and handrails use play a role in walking cortical activity patterns, therefore they are key ingredients to take into account when planning a rehabilitation program.

EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis

Understanding the neural mechanisms underlying emotional processing is critical for advancing neuroscience and mental health interventions. This study examined these mechanisms by analyzing EEG connectivity patterns across different brain regions while participants evoked various emotions. After applying independent component analysis (ICA) to eliminate non-cortical activity, we assessed frequency-specific connectivity patterns using coherence, Granger causality, and graph theoretical measures to evaluate both functional and effective connectivity. Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. These results indicated modulations in key brain regions, such as the precentral, superior frontal, and temporal areas, suggesting that these regions play a crucial role in emotional processing. Coherence analysis demonstrated predominant alpha band activity across all emotions, with specific emotional states, such as fear, grief, and jealousy, exhibiting enhanced beta band activity. In terms of coherence strength, we observed that the gamma band was largely inactive, except for the emotion of sadness, which displayed significant activity in the right lobe, particularly in regions such as the supplementary motor area and the lingual gyrus. Additionally, Granger causality analysis highlighted that the beta and gamma bands were dominant across all emotional states, with minimal modulation observed in the theta band. Clustering coefficients from the graph analysis further revealed distinct patterns of connectivity in the delta and beta bands, with significant variations across different emotions, particularly in the temporal and frontal regions. These findings enhance our understanding of emotional processing and have practical applications in mental health, biomarker identification, and human-computer interaction.

Parosmia: Pathophysiology and Management.

Parosmia is a qualitative olfactory disorder in which there is a mismatch between the memory of an odor and the actual experience triggered by an odor. There has been a surge in parosmia-related publications since the COVID-19 pandemic. This review summarizes the latest clinical findings, theories on pathophysiology and potential treatment options. Potential models of parosmia include peripheral or central hypotheses, which refer to aberrancies in olfactory neuron regeneration or information processing in central olfactory centers respectively. This leads to an incomplete or disorganized pattern of olfactory information relay. Studies using gas chromatography and functional magnetic resonance imaging have identified molecular triggers and intracranial functional connectivity patterns in parosmia respectively. Parosmia tends to occur in a delayed fashion after virus-induced anosmia. It may run a protracted course, but typically improves over time. Currently there are no generally approved, objective ways to ascertain the presence and measure the extent of parosmia. Evidence-based treatment for parosmia remains elusive. In some people, this can lead to health and quality of life issues.

Static and dynamic brain functional connectivity patterns in patients with unilateral moderate-to-severe asymptomatic carotid stenosis.

Asymptomatic carotid stenosis (ACS) is an independent risk factor for ischemic stroke and vascular cognitive impairment, affecting cognitive function across multiple domains. This study aimed to explore differences in static and dynamic intrinsic functional connectivity and temporal dynamics between patients with ACS and those without carotid stenosis. We recruited 30 patients with unilateral moderate-to-severe (stenosis ≥ 50%) ACS and 30 demographically-matched healthy controls. All participants underwent neuropsychological testing and 3.0T brain MRI scans. Resting-state functional MRI (rs-fMRI) was used to calculate both static and dynamic functional connectivity. Dynamic independent component analysis (dICA) was employed to extract independent circuits/networks and to detect time-frequency modulation at the circuit level. Further imaging-behavior associations identified static and dynamic functional connectivity patterns that reflect cognitive decline. ACS patients showed altered functional connectivity in multiple brain regions and networks compared to controls. Increased connectivity was observed in the inferior parietal lobule, frontal lobe, and temporal lobe. dICA further revealed changes in the temporal frequency of connectivity in the salience network. Significant differences in the temporal variability of connectivity were found in the fronto-parietal network, dorsal attention network, sensory-motor network, language network, and visual network. The temporal parameters of these brain networks were also related to overall cognition and memory. These results suggest that ACS involves not only changes in the static large-scale brain network connectivity but also dynamic temporal variations, which parallel overall cognition and memory recall.

Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach.

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways. This network model proposes that neural dynamics are constrained by a two-layered structural network interconnecting both astrocytic and neuronal populations, allowing us to investigate astrocytes' modulatory influences on whole-brain activity and emerging functional connectivity patterns. By developing a simulation methodology, informed by bifurcation and multilayer network theories, we demonstrate that the dialogue between astrocytic and neuronal networks manifests over fast-slow fluctuation mechanisms as well as through phase-amplitude connectivity processes. The findings from our research represent a significant leap forward in the modeling of glial-neuronal collaboration, promising deeper insights into their collaborative roles across health and disease states.

Altered brain functional connectivity in patients with tension-type headache.

To evaluate whether patients with tension-type headache (TTH) exhibit abnormal brain functional connectivity compared to healthy controls. TTH is one of the most prevalent headache disorders throughout the world. The present study delves into brain functional connectivity in patients with TTH to enhance the understanding of its underlying pathophysiology. A cross-sectional study was conducted, enrolling patients with TTH diagnosed in line with the International Classification of Headache Disorders, 3rd edition beta criteria and a cohort of healthy controls (HCs). We used four metrics-global brain functional connectivity, functional connectivity, Granger causality analysis, and dynamic functional connectivity-to evaluate alterations of functional connectivity patterns in patients with TTH from both static and dynamic perspectives. Furthermore, correlational analyses were performed to explore the relationships between abnormal brain activities and clinical characteristics. A total of 33 patients with TTH (mean age = 42.3; 13 males/20 females) and 30 HCs (mean age = 37.1; 13 males/17 females) were included in the current study. Compared to HCs, patients with TTH showed altered global brain functional connectivity in the right dorsolateral superior frontal gyrus (SFGdor, t = 4.60). Abnormal functional connectivity was also detected between the right SFGdor and the right superior temporal gyrus (t = 4.56). Furthermore, the right SFGdor exhibited altered information flow with several brain regions, including the left precuneus (t = 5.16), right middle temporal gyrus (MTG, t = 4.72/-4.41), right inferior temporal gyrus (t = 4.64), right caudate nucleus (t = 4.09), and right thalamus (THA, t = -4.04). In terms of dynamic functional connectivity, disconnection was observed between the right SFGdor and the right MTG (t = -3.10), right Rolandic operculum (ROL, t = 3.60), left opercular inferior frontal gyrus (t = -3.48), and left medial superior frontal gyrus (t = -3.00). In addition, the correlation analyses revealed that activities in the MTG (r = 0.48), THA (r = -0.38), and ROL (r = 0.36) were significantly correlated with disease duration, while THA activity was associated with Visual Analogue Scale scores (r = 0.50). This study revealed alterations in both static and dynamic brain functional connectivity in patients with TTH within regions implicated in sensory perception, emotional processing, cognition, and pain regulation. These results may promote the understanding of the neural networks involved in TTH and potentially inform future therapeutic approaches for the condition.

Obsessive-compulsive disorder secondary to focal brain lesions: from lesions to networks.

Obsessive-compulsive disorder (OCD) may develop following brain lesions, but lesion distribution and connectivity patterns are unknown. OCD-associated lesions, identified from systematic literature search, were traced on common brain space and compared to control lesions (N=608). Topography was analyzed using brain atlases, and lesion location networks computed using normative functional connectivity (N=1000). Lesional network maps (LNMs) were contrasted to data from primary OCD. Among 129 case-descriptions of lesional OCD, traced lesions (n=40) were more specifically located in orbitofrontal cortex bilaterally and right temporal pole, and more connected to orbitofrontal cortex and ventral basal ganglia, bilaterally. LNMs overlapped with primary OCD functional peaks from Neurosynth, revealed abnormal functional connectivity in patients with primary OCD (n=54) compared to controls (n=61), and aligned with brain stimulation targets. Lesional OCD has specific patterns of brain lesion topography and functional connectivity, with LNMs associated to brain functional patterns in primary OCD.

Aberrant functional connectivity patterns in the pregenual anterior cingulate cortex and anterior midcingulate cortex of patients with irritable bowel syndrome accompanied by depressive symptoms.

Irritable bowel syndrome (IBS) is a common brain-gut disorder often accompanied by depressive symptoms, with atrophy and hyperactivity of the anterior cingulate gyrus (ACC) being key drivers of both IBS and its psychiatric comorbidities. This study aimed to investigate the functional connectivity (FC) patterns of pregenual ACC (pgACC) and anterior midcingulate cortex (aMCC) in IBS patients with depressive symptoms (DEP-IBS). A whole-brain FC analysis was conducted using pgACC and aMCC as regions of interest in three groups: 28 DEP-IBS patients, 21 IBS patients without depressive symptoms (nDEP-IBS), and 36 matched healthy controls (HCs). Partial correlation and mediation analyses were performed between abnormal FC and clinical symptoms. The ability of aberrant FC to identify IBS and its psychiatric comorbidity was evaluated using receiver operating characteristic (ROC) curve. DEP-IBS patients exhibited increased pgACC-related FC in the left medial prefrontal cortex (mPFC) and aMCC-related FC in the right middle frontal gyrus, angular gyrus and cerebellum, while showing decreased aMCC-related FC in the right precentral gyrus, superior parietal gyrus and precuneus. Both patient groups demonstrated increased FC between aMCC and left dorsolateral prefrontal cortex (dlPFC), effectively distinguishing them from HCs (AUC = 0.755). The FC between pgACC and left mPFC partially mediated the relationship between gastrointestinal and depressive symptoms, effectively distinguishing DEP-IBS from nDEP-IBS patients (AUC = 0.808). Aberrant FC within the emotional arousal network may serve as a neurobiological marker for IBS with comorbid depression. Furthermore, abnormal FC between the aMCC and the dlPFC may underlie the neural mechanism of IBS.

Non-Rigid Cycle Consistent Bidirectional Network with Transformer for Unsupervised Deformable Functional Magnetic Resonance Imaging Registration.

In neuroscience research about functional magnetic resonance imaging (fMRI), accurate inter-subject image registration is the basis for effective statistical analysis. Traditional fMRI registration methods are usually based on high-resolution structural MRI with clear anatomical structure features. However, this registration method based on structural information cannot achieve accurate functional consistency between subjects since the functional regions do not necessarily correspond to anatomical structures. In recent years, fMRI registration methods based on functional information have emerged, which usually ignore the importance of structural MRI information. In this study, we proposed a non-rigid cycle consistent bidirectional network with Transformer for unsupervised deformable functional MRI registration. The work achieves fMRI registration through structural MRI registration, and functional information is introduced to improve registration performance. Specifically, we employ a bidirectional registration network that implements forward and reverse registration between image pairs and apply Transformer in the registration network to establish remote spatial mapping between image voxels. Functional and structural information are integrated by introducing the local functional connectivity pattern, the local functional connectivity features of the whole brain are extracted as functional information. The proposed registration method was experimented on real fMRI datasets, and qualitative and quantitative evaluations of the quality of the registration method were implemented on the test dataset using relevant evaluation metrics. We implemented group ICA analysis in brain functional networks after registration. Functional consistency was evaluated on the resulting t-maps. Compared with non-learning-based methods (Affine, Syn) and learning-based methods (Transmorph-tiny, Cyclemorph, VoxelMorph x2), our method improves the peak t-value of t-maps on DMN, VN, CEN, and SMN to 18.7, 16.5, 16.6, and 17.3 and the mean number of suprathreshold voxels (p < 0.05, t > 5.01) on the four networks to 2596.25, and there is an average improvement in peak t-value of 23.79%, 12.74%, 12.27%, 7.32%, and 5.43%. The experimental results show that the registration method of this study improves the structural and functional consistency between fMRI with superior registration performance.