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Background and Purpose Methods Results Conclusions Neuropathic pain (NP) is a debilitating condition following spinal cord injury (SCI). The role of periaqueductal gray (PAG) in NP development following SCI remains underexplored. Using resting‐state functional MRI (rsfMRI), our study aimed to demonstrate the alterations in functional connectivity (FC) of PAG in NP following SCI.Ten SCI patients (SCI + NP, n = 7, and SCI − NP, n = 3), alongside 10 healthy controls (HCs), were enrolled. rsfMRI was conducted followed by seed‐to‐voxel analysis using PAG as the seed region and then group‐based analysis comprising three groups (SCI + NP, SCI − NP, and HC). Age and gender were considered as confounding variables.Compared to HCs, SCI + NP demonstrated decreased FC between PAG and right insula, right frontal orbital cortex, right pallidum, dorsal raphe nucleus (DRN), red nuclei (RN), substantia nigra (SN), and ventral posterolateral (VPL) thalamic nuclei. Compared to SCI − NP, SCI + NP demonstrated increased FC between PAG and posterior cingulate cortex (PCC), hippocampus, cerebellar vermis lobules IV and V, and thalamic structures (posterior and lateral pulvinar, the mediodorsal nuclei, and the ventral lateral nuclei). Additionally, decreased FC between the PAG and VPL, geniculate bodies, intralaminar nuclei of thalamus, DRN, RN, SN, and prefrontal cortex was observed in this comparison.Altered FC between PAG and right anterior insula, VPL, DRN, RN, SN, cerebellar vermis lobules IV and V, frontal cortex, and PCC was associated with NP sequelae of SCI. Additionally, SCI was independently associated with decreased FC between PAG and right posterior insula, cerebellar lobules IV and V, and cerebellar vermis lobules III, IV, and V. [ABSTRACT FROM AUTHOR]

Background: Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Methods: Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Results: Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Conclusion: Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis. [ABSTRACT FROM AUTHOR]

Background: Individualized cortical functional networks parcellation has been reported as highly reproducible at 3.0 T. However, in view of the complexity of cortical networks and the greatly increased sensitivity provided by ultra-high field 5.0 T MRI, the parcellation consistency between different magnetic fields is unclear. Purpose: To explore the consistency and stability of individualized cortical functional networks parcellation at 3.0 T and 5.0 T MRI based on spatial and functional connectivity analysis. Materials and methods: Thirty healthy young participants were enrolled. Each subject underwent resting-state fMRI at both 3.0 T and 5.0 T in a random order in less than 48 h. The individualized cortical functional networks was parcellated for each subject using a previously proposed iteration algorithm. Dice coefficient was used to evaluate the spatial consistency of parcellated networks between 3.0 T and 5.0 T. Functional connectivity (FC) consistency was evaluated using the Euclidian distance and Graph-theory metrics. Results: A functional cortical atlas consisting of 18 networks was individually parcellated at 3.0 T and 5.0 T. The spatial consistency of these networks at 3.0 T and 5.0 T for the same subject was significantly higher than that of inter-individuals. The FC between the 18 networks acquired at 3.0 T and 5.0 T were highly consistent for the same subject. Positive cross-subject correlations in Graph-theory metrics were found between 3.0 T and 5.0 T. Conclusion: Individualized cortical functional networks at 3.0 T and 5.0 T showed consistent and stable parcellation results both spatially and functionally. The 5.0 T MR provides finer functional sub-network characteristics than that of 3.0 T. [ABSTRACT FROM AUTHOR]

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting‐state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49–76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross‐validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender‐common and gender‐specific aging‐related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age‐related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to comprehensively investigate the functional connectivity of ten sub-regions within the premotor and supplementary motor areas (Right and Left Premotor 6d1, 6d2, 6d3, and Right and Left pre-Supplementary Motor (presma) and SMA). Using advanced magnetic resonance imaging (MRI), the objective was to understand the neurophysiological integrative characteristics of these regions by examining their connectivity with eight distinct functional brain networks. While previous studies have largely treated these areas as homogeneous entities, there is a significant gap in our understanding of the specific roles and connectivity profiles of their distinct sub-regions. The goal was to uncover the roles of these regions beyond conventional motor functions, contributing to a more holistic understanding of brain functioning. Methods: The study involved 198 healthy volunteers, with the primary methodology being functional connectivity analysis using advanced MRI techniques. Ten sub-regions within the premotor and supplementary motor areas served as seed regions, and their connectivity with eight distinct brain regional functional networks, including the Sensorimotor, Dorsal Attention, Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks, was investigated. This approach allowed for the exploration of synchronized activity between these critical brain areas, shedding light on their integrated functioning and relationships with other brain networks. Results: The study revealed a nuanced landscape of functional connectivity for the premotor and supplementary motor areas with the main functional brain networks. Despite their high functional connectedness within the motor network, these regions displayed diverse functional integrations with other networks. There was moderate connectivity with the Sensorimotor and Dorsal Attention networks, highlighting their roles in motor execution and attentional processes. However, connectivity with the Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks was generally low, indicating a primary focus on motor-related tasks. Conclusions: This study emphasized the multifaceted roles of the sub-regions of the premotor and supplementary motor areas. Beyond their crucial involvement in motor functions, these regions exhibited varied functional integrations with different brain networks. The observed disparities, especially in the Sensorimotor and Dorsal Attention networks, indicated a nuanced and specialized involvement of these regions in diverse cognitive functions. By delineating the specific connectivity profiles of these sub-regions, this study addresses the existing knowledge gap and suggests unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. The results suggested unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. This study underscores the importance of considering the broader neurophysiological landscape to comprehend the intricate roles of these brain areas, contributing to ongoing efforts in unravelling the complexities of brain function. [ABSTRACT FROM AUTHOR]

The putamen has been proposed to play a critical role in the development of obsessive-compulsive disorder (OCD). The primary objective of this study was to examine the resting-state regional activity and functional connectivity patterns of the putamen in individuals diagnosed with OCD. To achieve this, we employed resting-state functional magnetic resonance imaging (rs-fMRI) to collect data from a sample of 45 OCD patients and 53 healthy control participants. We aimed to use the regional amplitude of low-frequency fluctuation (ALFF) analysis to generate the ROI masks of the putamen and then conduct the whole brain functional connectivity of the putamen in individuals with OCD. Compared to controls, the OCD group demonstrated decreased ALFF in bilateral putamen. The right putamen also displayed decreased FC with the left putamen extending to the inferior frontal gyrus (IFG), bilateral precuneus extending to calcarine, the right middle occipital cortex extending to the right middle temporal cortex, and the left middle occipital gyrus. The decreased connectivity between the right putamen and the left IFG was negatively correlated with Yale-Brown Obsessive Compulsive scale (Y-BOCS) Obsession Scores. This study aimed to reveal the putamen changes in resting-state activity and connectivity in OCD patients, highlighting the significance of aberrant ALFF/FC of the putamen is a key characteristic of OCD. • Explored network-level neural function of the putamen in OCD patients. • Patients showed decreased ALFF in bilateral putamen. • Abnormal FC with the left putamen/IFG, precuneus, visual and auditory networks. • Dysconnectivitybetween the right patumen and the left putamen/IFG negatively correlated with Y-BOCS Obsession Scores. [ABSTRACT FROM AUTHOR]

Suicidal ideation (SI) is a common symptom of major depressive disorder (MDD), often accompanied by cognitive alterations and emotional dysregulation. However, it is unclear whether cognitive dysfunction in patients with MDD is related to the presence or absence of SI and impaired connectivity within or between large-scale neurocognitive networks. Previous studies have shown that the frontoparietal network (FPN) and default mode network (DMN) are critical for cognitive control and emotional regulation. Participants were 51 MDD patients with suicidal ideation (MDDSI), 52 MDD patients without suicidal ideation (MDDNSI), and 55 healthy controls (HC). Using areas located within FPN and DMN networks as regions of interest (ROIs), we compared the cognitive performance of the three groups and the strength of the resting state functional connections (RSFC) within and between the FPN and DMN networks. Additionally, we examined the correlation between the strength of FC within the FPN and cognitive function in the SI group. Furthermore, network-based statistics (NBS) were used to correct for the strength of FPN and DMN functional connections. The study identified significant cognitive deficits in MDD patients. Reduced strength of FC was observed within the FPN and DMN networks in the SI group compared to the NSI group. In the SI group, the strength of FC within the FPN network was positively correlated with attention/vigilance. These insights underscore the critical roles of the FPN and DMN in the suicidal ideation, shedding light on the cognitively relevant neurobiological characteristics of MDDSI, providing new insights into the neural mechanisms of MDDSI. URL: https://www.chictr.org.cn/bin/project/edit?pid=131537. Registration number: ChiCTR2100049646. • This study examines how suicidal ideation, cognitive function, and brain connectivity are related in depressed patients. • Distinct connectivity patterns linked to suicidal ideation were found in frontoparietal (FPN) and default mode networks (DMN). • These findings offer new insights into MDD-related suicidal ideation and may influence treatment strategies. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood. Numerous resting-state functional magnetic resonance imaging (rs-fMRI) studies in ADHD have been performed using traditional low-frequency bands (0.01–0.08 Hz). However, the neural activity patterns of frequency subbands in ADHD still require further investigation. The purpose of this study is to explore the frequency-dependent characteristics and neural activity patterns of ADHD subtypes. We selected the ADHD combined type (ADHD-C, N = 25), ADHD inattentive type (ADHD-I, N = 26) and typically developing (TD, N = 28) children from the ADHD200 Consortium. Based on the slow-5 band (0.01–0.027 Hz) and slow-4 band (0.027–0.073 Hz), we generated static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) maps for each participant. A flexible-factorial analysis of variance model was performed on static and temporal dynamic rs-fMRI measurements within two subbands. Results revealed that the orbital-frontal gyrus, precuneus, superior temporal gyrus and angular gyrus were found to have obvious frequency band and group interaction effects. The intrinsic neural activity differences among three groups were more prominent in the slow-5 frequency band compared to the slow-4 band. In addition, the indices of significant interaction regions showed correlations with the progression of the disease and the features in slow-5 showed an advantageous diagnostic performance compared with those in slow-4. The results suggested the intrinsic neural activities of ADHD subtypes were frequency-dependent. The frequency-specific analysis of static and dynamic brain activity may provide a deeper understanding of neurophysiological dysfunction patterns in ADHD subtypes and provide supplementary information for assessing ADHD subtypes. [ABSTRACT FROM AUTHOR]

Background and Purpose Methods Results Conclusion This study sought to explore dynamic degree centrality (DC) variability in particular regions of the brain in patients with poststroke Broca aphasia (BA) using a resting‐state functional magnetic resonance imaging (rs‐fMRI) approach, comparing differences between Uyghur and Chinese BA patients.This study investigated two factors, language and BA status, and divided patients into four groups: Uyghur aphasia patients (UA), Uyghur normal control subjects (UN), Chinese aphasia patients (CA), and Chinese normal subjects (CN) who underwent rs‐fMRI analysis. Two‐way analysis of variance (ANOVA) was used to calculate the comprehensive differences in dynamic DC among these four groups. Correlations between DC and language behavior were assessed with partial correlation analyses.Two‐way ANOVA revealed comparable results for the results of pairwise comparisons of dynamic DC variability among the four groups in the right middle frontal gyrus/orbital part (ORBmid.R), right superior frontal gyrus/dorsolateral, and right precuneus (PCUN.R), with results as follows: UA < UN, CA > CN, UA < CA, and UN > CN (p < .05, with the exception of the p‐values for UA and UN in superior frontal gyrus/dorsolateral). In contrast, the opposite results were observed for the right calcarine fissure and surrounding cortex (CAL.R, p < .05).The observed enhancement of dynamic DC variability in ORBmid.R and PCUN.R among Chinese BA patients and in CAL.R in Uyghur BA patients may be attributable to language network restructuring. Overall, these results suggest that BA patients who use different language families may exhibit differences in the network mechanisms that characterize observed impairments of language function. [ABSTRACT FROM AUTHOR]

Background: The development of neuroimaging biomarkers in patients with schizophrenia (SCZ) requires a refined clinical characterization. A limitation of the neuroimaging literature is the partial uptake of progress in characterizing disease-related features, particularly negative symptoms (NS) and cognitive impairment (CI). In the present study, we assessed NS and CI using up-to-date instruments and investigated the associations of abnormalities in brain restingstate (rs)-activity with disease-related features. Methods: Sixty-two community-dwelling SCZ subjects participated in the study. Multiple regression analyses were performed with the rs-activity of nine regions of interest as dependent variables and disease-related features as explanatory variables. Results: Attention/vigilance deficits were negatively associated with dorsal anterior cingulate rs-activity and, together with depression, were positively associated with right dorsolateral prefrontal cortex rs-activity. These deficits and impairment of Reasoning/problem-solving, together with conceptual disorganization, were associated with right inferior parietal lobule and temporal parietal junction rs-activity. Independent of other features, the NS Expressive Deficit domain was associated with the left ventral caudate, while the Motivational Deficit was associated with the dorsal caudate rs-activity. Conclusion: Neurocognitive deficits and the two negative symptom domains are associated with different neural markers. Replications of these findings could foster the identification of clinically actionable biomarkers of poor functional outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressivemodel) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results: Conventional whiteningmethods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (<1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power. [ABSTRACT FROM AUTHOR]

Even before the advent of fMRI, the amygdala occupied a central space in the affective neurosciences. Yet this amygdala-centred view on emotion processing gained even wider acceptance after the inception of fMRI in the early 1990s, a landmark that triggered a goldrush of fMRI studies targeting the amygdala in vivo. Initially, this amygdala fMRI research was mostly confined to task-activation studies measuring the magnitude of the amygdala's response to emotional stimuli. Later, interest began to shift more towards the study of the amygdala's resting-state functional connectivity and task-based psychophysiological interactions. Later still, the test-retest reliability of amygdala fMRI came under closer scrutiny, while at the same time, amygdala-based real-time fMRI neurofeedback gained widespread popularity. Each of these major subdomains of amygdala fMRI research has left its marks on the field of affective neuroscience at large. The purpose of this review is to provide a critical assessment of this literature. By integrating the insights garnered by these research branches, we aim to answer the question: What part (if any) can amygdala fMRI still play within the current landscape of affective neuroscience? Our findings show that serious questions can be raised with regard to both the reliability and validity of amygdala fMRI. These conclusions force us to cast doubt on the continued viability of amygdala fMRI as a core pilar of the affective neurosciences. [ABSTRACT FROM AUTHOR]

Risk-taking in the 'work' domain constitutes a fundamental building block for a wide range of important decisions (e.g., investment) and behaviors (e.g., creativity) of individuals, groups, and organizations. Yet, what remains unknown is the neurofunctional basis of work-domain risk-taking (WRT) and how these brain substrates act as mediators in relating individual personality traits such as regulatory focus and the Big Five to WRT. This study, with a sample of 201 healthy full-time employees, investigated the above questions using resting-state fMRI. The results indicated that individuals who engage more in WRT showed increased brain activity (indicated by fALFF) in the right medial frontal gyrus (MFG) and right insula brain areas involved in goal-directed and self-regulated functions, therefore providing unique neuroimaging evidence for the notion that risk-taking is highly domain specific. More importantly, we found that fALFF in the right MFG and right insula areas has a significant mediating role in relating promotion focus and neuroticism to WRT, respectively, suggesting that these traits might have more important roles in associating with WRT, and the brain activity of the two regions (i.e., right MFG and right insula) may act as the underlying mediating mechanisms. Managerial implications are discussed. [ABSTRACT FROM AUTHOR]

Mood variability, the day‐to‐day fluctuation in mood, differs between individuals and develops during adolescence. Because adolescents show higher mood variability and average mood than children and adults, puberty might be a potential biological mechanism underlying this increase. The goal of this preregistered developmental study was to examine the neural and hormonal underpinnings of adolescent‐specific within‐person changes in mood variability, with a specific focus on testosterone, cortisol, pubertal status, and resting‐state functional brain connectivity. Data from two longitudinal cohorts were used: the L‐CID twin study (aged 7–13, N at the first timepoint = 258) and the accelerated Leiden Self‐Concept study (SC; aged 11–21, N at the first timepoint = 138). In both studies resting‐state functional magnetic resonance imaging (rs‐fMRI) data was collected, as well as daily mood. Additionally, in the SC study self‐reported puberty testosterone and cortisol were collected. Random intercept cross‐lagged panel models (RI‐CLPM) were used to study the within‐person relations between these biological measures and mood variability and average mood. Mood variability and average mood peaked in adolescence and testosterone levels and self‐reported puberty also showed an increase. Connectivity between prefrontal cortex (dlPFC and vmPFC) and subcortical regions (caudate, amygdala) decreased across development. Moreover, higher testosterone predicted average negative mood at the next time point, but not vice versa. Further, stronger vmPFC–amygdala functional connectivity predicted decreases in mood variability. Here, we show that brain connectivity during development is an important within‐person biological mechanism of the development of mood in adolescents. Practitioner Points: Mood variability peaks in adolescence.Within‐person changes in testosterone predict within‐person changes in mood.Within‐person changes in vmPFC–amygdala connectivity predict within‐person changes in mood variability. [ABSTRACT FROM AUTHOR]

Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub. [ABSTRACT FROM AUTHOR]

Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine‐related changes in large‐scale brain network dynamics and its implications in clinical features. We pooled data from two disease‐control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting‐state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single‐photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age‐ and sex‐matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age‐ and sex‐matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F‐DOPA‐positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F‐DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre‐processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k‐means clustering were conducted separately for each cohort to derive dFC states (reemerging intra‐ and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L‐dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large‐scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. Practitioner Points: Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD‐specific changes in dynamic functional connectivity.Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state.Results only in the PPMI cohort suggest striatal dopamine availability influences large‐scale network dynamics that are relevant in motor control. [ABSTRACT FROM AUTHOR]

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post‐operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre‐ and intra‐operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non‐invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre‐existing deficits. Connectome fingerprinting (CF) is a machine‐learning approach that learns associations between resting‐state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting‐state data. Here we utilized CF to train models on high‐quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting‐state fMRI (rs‐fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%–100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task‐related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out‐of‐sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. Practitioner Points: Precision motor network prediction using connectome fingerprinting.Carefully trained models' performance limited by stability of task‐fMRI data.Successful cross‐scanner predictions and motor network mapping in patients with tumor. [ABSTRACT FROM AUTHOR]

Resting‐state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence‐based and correlation‐based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging. [ABSTRACT FROM AUTHOR]

Objective: Acute unilateral vestibulopathy (AUVP) is the second leading cause of peripheral vestibular vertigo. Full recovery of AUVP is related to sufficient central vestibular compensation. It has been confirmed that the vestibular nucleus and vestibular cortex are involved in the process of vestibular compensatory in AUVP patients. However, few studies have focused on the functional compensation of thalamus in patients with AUVP. This study aimed to explore the alterations of resting-state functional connectivity (FC) focused on thalamus using functional magnetic resonance imaging (fMRI) in AUVP patients. Methods: Data of 3D-T1 and resting-state fMRI were collected from 40 AUVP patients and 35 healthy controls (HC). Seeds-based (bilateral thalamus) FC was analyzed to investigate the changes in FC between the two groups. Furthermore, we evaluated the associations between altered thalamus FC and clinical features in AUVP patients using Pearson's partial correlation. Results: Compared with HC, AUVP patients showed decreased FC between bilateral thalamus and left insula. We also observed decreased FC between right thalamus and left supramarginal gyrus. Additionally, we found increased FC between left thalamus and right postcentral gyrus (PCG), as well as increased FC between right thalamus and regions of bilateral PCG, right middle frontal gyrus and right middle occipital gyrus in AUVP patients. Furthermore, the FC between left thalamus and left insula was negatively correlated with values of canal paresis in patients with AUVP (p = 0.010, r = -0.434). Conclusion: Our results provided first evidence for the decreased thalamovestibular cortex pathway, as well as increased thalamo-somatosensory and thalamo-visual cortex pathway in AUVP patients. These findings help us better understand the underlying mechanisms of central dynamic compensatory following an acute unilateral peripheral vestibular damage. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurological condition characterized by impaired functional connectivity (FC) networks in the brain. There are several brain networks associated with ASD that have been studied for ASD diagnosis, but the results are inconsistent. A functional magnetic resonance imaging (fMRI) study was performed to address this gap by comparing brain networks among autistic individuals and individuals with typical development (TD) using data from the ABIDE-I and ABIDE-II databases. Blood oxygen level-dependent (BOLD) time series were extracted from 236 regions of interest (ROI) in fMRI data using three atlases: Gordon's, Harvard Oxford, and Diedrichsen. Consequently, 27,730 nonlinear features are extracted from FC matrices, including fractals, non-fractals, and Pearson correlation coefficients (PCC). A parametric and nonparametric classifier was used to analyze the top 0.1%, 0.3%, 0.5%, 0.7%, 1%, 2%, and 3% of features based on the XGBoost feature ranking algorithm. In the study, we found that non-fractal brain FC measures can accurately identify ASD and TD more effectively than fractal and PCC measures. Classifiers performed well, with FC features at the top 0.3%. The classification model at OHSU was more accurate than the model at other sites. There was 100% accuracy at a single site and 96.17% accuracy at all sites using a multilayer perceptron classifier with non-fractal features. The classifier model shows that Cingulo-Parietal Task Control (13.6%), Retro-Splenial Temporal (RST) (13.3%), and Salience (11.6%) are significant contributors. The optimal performance was observed in features derived from networks such as RST and default (4 connections), auditory and Fronto-Parietal Task Control (3 connections), Cingulo-Opercular Task Control (COTC), and ventral attention (3 connections) within COTC (3 connections), visual and COTC (3 connections), and cerebellum and COTC (3 connections). Based on the results, non-fractal-based FC has value in distinguishing ASD from TD using resting-state fMRI. [ABSTRACT FROM AUTHOR]

Self-ambivalence, a prevalent phenomenon in daily life, has been increasingly substantiated by research. It refers to conflicting self-views and evaluations, primarily concerning self-worth and morality. Previous behavioral research has distinguished self-worth and moral ambivalence, but it remains unclear whether they have separable neural bases. The present study addressed this question by examining resting-state brain activity (i.e., the fractional amplitude of low-frequency fluctuations, fALFF) and connectivity (i.e., resting-state functional connectivity, RSFC) in 112 college students. The results found that self-worth ambivalence was positively related to the fALFF in the orbitofrontal cortex (OFC) and left superior parietal lobule (SPL). The RSFC strength between the SPL and precuneus/posterior cingulate cortex (PCC) was positively related to self-worth ambivalence. Moral ambivalence was positively associated with the fALFF in the left SPL (extending into the temporoparietal junction) and right SPL. The RSFC strengths between the left SPL/TPJ and OFC, as well as the RSFC strengths between the right SPL as a seed and the bilateral middle and inferior temporal gyrus, were associated with moral ambivalence. Overall, the neural bases of self-worth and moral ambivalence are associated with the SPL and OFC, involved in attentional alertness and value representation, respectively. Additionally, the neural basis of moral ambivalence is associated with the TPJ, responsible for mentalizing. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. The brain state involving phase-synchronization between default mode network and sensory networks is particularly related to sustained pain, likely indicating a shift toward internally directed self-referential processes. Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain. [ABSTRACT FROM AUTHOR]

Background: Coupling between neuronal activity and blood perfusion is termed neurovascular coupling (NVC), and it provides a potentially new mechanistic perspective into understanding numerous brain diseases. Although abnormal brain activity and blood supply have been separately reported in mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke‐like episodes (MELAS), whether anomalous NVC would be present is unclear. Purpose: To investigate NVC changes and potential neural basis in MELAS by combining resting‐state functional MRI (rs‐fMRI) and arterial spin labeling (ASL). Study Type: Prospective. Subjects: Twenty‐four patients with MELAS (age: 29.8 ± 7.3 years) in the acute stage and 24 healthy controls (HCs, age: 26.4 ± 8.1 years). Additionally, 12 patients in the chronic stage were followed up. Field Strength/Sequence: 3.0 T, resting‐state gradient‐recalled echo‐planar imaging and pseudo‐continuous 3D ASL sequences. Assessment: Amplitude of low‐frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity strength (FCS) were calculated from rs‐fMRI, and cerebral blood flow (CBF) was computed from ASL. Global NVC was assessed by correlation coefficients of CBF‐ALFF, CBF‐fALFF, CBF‐ReHo, and CBF‐FCS. Regional NVC was also evaluated by voxel‐wise and lesion‐wise ratios of CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS. Statistical Tests: Two‐sample t‐test, paired‐sample t‐test, Gaussian random fields correction. A P value <0.05 was considered statistically significant. Results: Compared with HC, MELAS patients in acute stage showed significantly reduced global CBF‐ALFF, CBF‐fALFF, CBF‐ReHo, and CBF‐FCS coupling (P < 0.001). Altered CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS ratios were found mainly distributed in the middle cerebral artery territory in MELAS patients. In addition, significantly increased NVC ratios were found in the acute stroke‐like lesions in acute stage (P < 0.001), with a recovery trend in chronic stage. Data Conclusions: This study showed dynamic alterations in NVC in MELAS patients from acute to chronic stage, which may provide a novel insight for understanding the pathogenesis of MELAS. Evidence Level: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background Heterogeneity in the etiology, pathophysiology, and clinical features of schizophrenia challenges clinicians and researchers. A helpful approach could be stratifying patients according to the presence or absence of clinical features of the deficit syndrome (DS). DS is characterized by enduring and primary negative symptoms, a clinically less heterogeneous subtype of the illness, and patients with features of DS are thought to present abnormal brain network characteristics, however, this idea has received limited attention. We investigated functional brain network topology in patients displaying deficit features and those who do not. Design We applied graph theory analytics to resting-state functional magnetic resonance imaging data of 61 antipsychotic medication-naïve first episode psychosis patients, 18 DS and 43 non-deficit schizophrenia (NDS), and 72 healthy controls (HC). We quantified small-worldness, global and nodal efficiency measures, shortest path length, nodal local efficiency, and synchronization and contrasted them among the 3 groups. Results DS presented decreased network integration and segregation compared to HC and NDS. DS showed lower global efficiency, longer global path lengths, and lower global local efficiency. Nodal efficiency was lower and the shortest path length was longer in DS in default mode, ventral attention, dorsal attention, frontoparietal, limbic, somatomotor, and visual networks compared to HC. Compared to NDS, DS showed lower efficiency and longer shortest path length in default mode, limbic, somatomotor, and visual networks. Conclusions Our data supports increasing evidence, based on topological perturbations of the functional connectome, that deficit syndrome may be a distinct form of the illness. [ABSTRACT FROM AUTHOR]

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting-state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain-body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time-frequency analysis across resting-state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain-body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal.

BACKGROUND: Caudate functional abnormalities have been identified as one critical neural substrate underlying sensory gating impairments that lead to auditory phantom hallucinations in both patients with schizophrenia (SZ) and tinnitus, characterized by the perception of internally generated sounds in the absence of external environmental auditory stimuli. In this study, we tested the hypothesis as to whether functional connectivity abnormalities in distinct caudate subdivisions implicated in sensory gating and auditory phantom percepts in tinnitus, which are currently being localized for neuromodulation targeting using deep brain stimulation techniques, would be associated with auditory phantom hallucination severity in SZ. METHODS: Twenty five SZ and twenty eight demographically-matched healthy control (HC) participants, completed this fMRI resting-state study and clinical assessments. RESULTS: Between-group seed-to-voxel analyses revealed only one region, the caudate anterior head, which showed reduced functional connectivity with the thalamus that survived whole-brain multiple comparison corrections. Importantly, connectivity between the caudate anterior head with thalamus negatively correlated with hallucination severity. CONCLUSIONS: In the present study, we deliver the first evidence of caudate subdivision specificity for the neural pathophysiology underlying hallucinations in schizophrenia within a sensory gating framework that has been developed for auditory phantoms in patients with tinnitus. Our findings provide transdiagnostic convergent evidence for the role of the caudate in the gating of auditory phantom hallucinations, observed across patients with SZ and tinnitus by specifying the anterior caudate division is key to mediation of hallucinations, and creating a path towards personalized treatment approaches to arrest auditory phantom hallucinations from reaching perceptual awareness.

Anhedonia stands as a life-threatening transdiagnostic feature of many mental illnesses, most notably major depression and involves neural circuits for processing reward information. The paraventricular nucleus of the thalamus (PVT) is associated with reward-seeking behavior, however, links between the PVT circuit and anhedonia have not been investigated in humans. In a sample of adults with and without psychiatric symptoms (n = 75, 18–41 years, 55 female), we generated an anhedonia factor score for each participant using a latent factor analysis, utilizing data from depression and anxiety assessments. Functional connectivity between the PVT and the nucleus accumbens (NAc) was calculated from high-resolution (1.5 mm) resting state fMRI. Anhedonia factor scores showed a positive relationship with functional connectivity between the PVT and the NAc, principally in males and in those with psychiatric symptoms. In males, connectivity between other midline thalamic nuclei and the NAc did not show these relationships, suggesting that this link may be specific to PVT. This cohort was originally recruited to study depression and not anhedonia per se. The distribution of male and female participants in our cohort was not equal. Partial acquisition in high-resolution fMRI scans restricted regions of interest outside of the thalamus and reward networks. We report evidence that anhedonia is associated with enhanced functional connectivity between the PVT and the NAc, regions that are relevant to reward processing. These results offer clues as to the potential prevention and prevention and treatment of anhedonia. • Anhedonia has been associated with alterations in reward processing neural circuits. • The paraventricular nucleus of the thalamus (PVT) projects to reward and threat regions. • Altering PVT is associated with anhedonia in animal models. • In humans, anhedonia symptoms are associated with increased functional connectivity between PVT and nucleus accumbens, particularly in males. • This provides evidence that the human PVT circuit is involved in anhedonia. [ABSTRACT FROM AUTHOR]

Parenting styles encompass negative and positive approaches, potentially affecting adolescents' brain reward and emotion regulation systems. However, the association between parenting style and brain networks remains unknown. This study investigates the link between parenting style and functional connectivity (FC) within the reward and emotion regulation brain networks, using resting-state functional magnetic resonance imaging (rs-fMRI). A total of forty-two middle-aged adolescents (26 males; 16 females) with no neurological or psychiatric symptoms participated in this study. We assessed parenting behaviors and extracted reward/emotion regulation FC from rs-fMRI. We examined the association between FC and parenting style, identified through principal component analysis. Correlation analysis investigated these links while controlling for sex. We delineated both positive (love-autonomy) and negative (hostility-control) parenting styles, accounting for 79 % of the explained variance in parenting behaviors. The negative parenting style displayed connections with FC within the reward system, particularly in the left nucleus accumbens (NAc), showcasing links to multiple frontal regions. Furthermore, it correlated with the social reward network, specifically the insula-NAc FC in bilateral hemispheres. Conversely, the positive parenting style exhibited an association with FC between the hippocampus and right lateral prefrontal cortex. Our findings support negative parenting's association with an immature reward system and suggest positive parenting's potential to enhance emotion regulation in brain function. These observations highlight two distinct parenting styles, including single-parenting behaviors. Thus, we advance understanding of each style's unique contributions to adolescent reward- and emotion regulation-related brain network development. • We identified positive (love-autonomy) and negative (hostility-control) parenting style components. • Negative style component was related to functional connectivity in the reward system during adolescence. • Positive style component was associated with brain network of emotion regulation. • Advanced insights into the differential roles of parenting style in brain functions during adolescence. [ABSTRACT FROM AUTHOR]

[Display omitted] • Functional neuroimaging alterations in AOID patients with anxiety. • Correlations analysis support neural basis of anxiety symptoms in AOID. • Candidate imaging biomarkers to distinguish AOID patients with/without anxiety. Psychiatric disturbances are commonly associated with adult-onset isolated dystonia (AOID); however, the mechanisms underlying psychiatric abnormalities in AOID remain unknown. We aimed to investigate the structural and functional brain changes in AOID patients with anxiety, and identify imaging biomarkers for diagnosing anxiety. Structural and functional magnetic resonance was performed on 69 AOID patients and 35 healthy controls (HCs). The Hamilton Anxiety Scale (HAMA) was used to assess anxiety symptoms in AOID patients and assign patients to AOID with and without anxiety groups. Group differences in grey matter volume, amplitude of low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity (ReHo) were evaluated. Area under the receiver operating characteristic curve (ROC AUC) was used as a metric to identify imaging biomarkers for diagnosing anxiety. AOID patients with anxiety exhibited an increased ALFF and ReHo in the left angular gyrus (ANG.L) compared with those without and HCs (voxel P <0.001 and cluster P <0.05, corrected using GRF). A significant positive correlation was observed between ALFF (r = 0.627, P <0.001) and ReHo (r = 0.515, P <0.001) in the ANG.L and HAMA scores in AOID patients. ALFF and ReHo in the ANG.L exhibited an ROC AUC of 0.904 and 0.851, respectively, in distinguishing AOID patients with anxiety from those without and an ROC AUC of 0.887 and 0.853, respectively, in distinguishing AOID patients with anxiety from HCs. These findings provide new insights into the pathophysiology of psychiatric disturbances and highlight potential candidate biomarkers for identifying anxiety in AOID patients. [ABSTRACT FROM AUTHOR]

Abstract The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.

Abstract Background Analyzing distance-dependent functional connectivity density (FCD) yields valuable insights into patterns of brain activity. Nevertheless, whether alterations of FCD in non-acute stroke patients are associated with the anatomical distance between brain regions remains unclear. This study aimed to explore the distance-related functional reorganization in non-acute stroke patients following left and right hemisphere subcortical lesions, and its relationship with clinical assessments. Methods In this study, we used resting-state fMRI to calculate distance-dependent (i.e., short- and long-range) FCD in 25 left subcortical stroke (LSS) patients, 22 right subcortical stroke (RSS) patients, and 39 well-matched healthy controls (HCs). Then, we compared FCD differences among the three groups and assessed the correlation between FCD alterations and paralyzed motor function using linear regression analysis. Results Our findings demonstrated that the left inferior frontal gyrus displayed distance-independent FCD changes, while the bilateral supplementary motor area, cerebellum, and left middle occipital gyrus exhibited distance-dependent FCD alterations in two patient subgroups compared with HCs. Furthermore, we observed a positive correlation between increased FCD in the bilateral supplementary motor area and the motor function of lower limbs, and a negative correlation between increased FCD in the left inferior frontal gyrus and the motor function of both upper and lower limbs across all stroke patients. These associations were validated by using a longitudinal dataset. Conclusions The FCD in the cerebral and cerebellar cortices shows distance-related changes in non-acute stroke patients with motor dysfunction, which may serve as potential biomarkers for predicting motor outcomes after stroke. These findings enhance our comprehension of the neurobiological mechanisms driving non-acute stroke. Trial registration All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (NCT05648552, registered 05 December 2022, starting from 01 January 2022).

Abstract Reading learning disability (RLD) is characterized by a specific difficulty in learning to read that is not better explained by an intellectual disability, lack of instruction, psychosocial adversity, or a neurological disorder. According to the domain-general hypothesis, a working memory deficit is the primary problem. Working memory in this population has recently been linked to altered resting-state functional connectivity within the default mode network (DMN), salience network (SN), and frontoparietal network (FPN) compared to that in typically developing individuals. The main purpose of the present study was to compare the within-network functional connectivity of the DMN, SN, FPN, and reading network in two groups of children with RLD: a group with lower-than-average working memory (LWM) and a group with average working memory (AWM). All subjects underwent resting-state functional magnetic resonance imaging (fMRI), and data were analyzed from a network perspective using the network brain statistics framework. The results showed that the LWM group had significantly weaker connectivity in a network that involved brain regions in the DMN, SN, and FPN than the AWM group. Although there was no significant difference between groups in reading network in the present study, other studies have shown relationship of the connectivity of the angular gyrus, supramarginal gyrus, and inferior parietal lobe with the phonological process of reading. The results suggest that although there are significant differences in functional connectivity in the associated networks between children with LWM and AWM, the distinctive cognitive profile has no specific effect on the reading network.

The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Analyzing distance-dependent functional connectivity density (FCD) yields valuable insights into patterns of brain activity. Nevertheless, whether alterations of FCD in non-acute stroke patients are associated with the anatomical distance between brain regions remains unclear. This study aimed to explore the distance-related functional reorganization in non-acute stroke patients following left and right hemisphere subcortical lesions, and its relationship with clinical assessments. Methods: In this study, we used resting-state fMRI to calculate distance-dependent (i.e., short- and long-range) FCD in 25 left subcortical stroke (LSS) patients, 22 right subcortical stroke (RSS) patients, and 39 well-matched healthy controls (HCs). Then, we compared FCD differences among the three groups and assessed the correlation between FCD alterations and paralyzed motor function using linear regression analysis. Results: Our findings demonstrated that the left inferior frontal gyrus displayed distance-independent FCD changes, while the bilateral supplementary motor area, cerebellum, and left middle occipital gyrus exhibited distance-dependent FCD alterations in two patient subgroups compared with HCs. Furthermore, we observed a positive correlation between increased FCD in the bilateral supplementary motor area and the motor function of lower limbs, and a negative correlation between increased FCD in the left inferior frontal gyrus and the motor function of both upper and lower limbs across all stroke patients. These associations were validated by using a longitudinal dataset. Conclusions: The FCD in the cerebral and cerebellar cortices shows distance-related changes in non-acute stroke patients with motor dysfunction, which may serve as potential biomarkers for predicting motor outcomes after stroke. These findings enhance our comprehension of the neurobiological mechanisms driving non-acute stroke. Trial registration: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (NCT05648552, registered 05 December 2022, starting from 01 January 2022). [ABSTRACT FROM AUTHOR]

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level‐dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs‐fMRI is spontaneous event‐related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low‐valued inflection points. Here, we extract the event‐related temporal onsets from the rs‐fMRI time courses using a zero‐frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal‐to‐noise ratio (SNR) segments are also obtained in the rs‐fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR‐based CR distinguishes the coactivation and non‐coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR‐driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an immune-mediated and multi-systemic disease which may affect the nervous system, causing neuropsychiatric SLE (NPSLE). Recent neuroimaging studies have examined brain functional alterations in SLE. However, discrepant findings were reported. This meta-analysis aims to identify consistent resting-state functional abnormalities in SLE. PubMed and Web of Science were searched to identify candidate resting-state functional MRI studies assessing SLE. A voxel-based meta-analysis was performed using the anisotropic effect-size version of the seed-based d mapping (AES-SDM). The abnormal intrinsic functional patterns extracted from SDM were mapped onto the brain functional network atlas to determine brain abnormalities at a network level. Twelve studies evaluating fifteen datasets were included in this meta-analysis, comprising 572 SLE patients and 436 healthy controls (HCs). Compared with HCs, SLE patients showed increased brain activity in the bilateral hippocampus and right superior temporal gyrus, and decreased brain activity in the left superior frontal gyrus, left middle temporal gyrus, bilateral thalamus, left inferior frontal gyrus and right cerebellum. Mapping the abnormal patterns to the network atlas revealed the default mode network and the limbic system as core neural systems commonly affected in SLE. The number of included studies is relatively small, with heterogeneous analytic methods and a risk of publication bias. Brain functional alterations in SLE are predominantly found in the default mode network and the limbic system. These findings uncovered a consistent pattern of resting-state functional network abnormalities in SLE which may serve as a potential objective neuroimaging biomarker. • This study is the first meta-analysis to investigate the resting-state functional alterations in SLE. • SLE-related brain abnormalities are predominantly found in the default mode network and the limbic system. • Resting-state fMRI may be an objective neuroimaging biomarker for identifying brain abnormalities in SLE. [ABSTRACT FROM AUTHOR]

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain–behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research. [ABSTRACT FROM AUTHOR]

Background: Fatigue is a central feature of myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), but many ME/CFS patients also report comorbid pain symptoms. It remains unclear whether these symptoms are related to similar or dissociable brain networks. This study used resting-state fMRI to disentangle networks associated with fatigue and pain symptoms in ME/CFS patients, and to link changes in those networks to clinical improvements following cognitive behavioral therapy (CBT). Methods: Relationships between pain and fatigue symptoms and cortico-cortical connectivity were assessed within ME/CFS patients at baseline (N = 72) and after CBT (N = 33) and waiting list (WL, N = 18) and compared to healthy controls (HC, N = 29). The analyses focused on four networks previously associated with pain and/or fatigue, i.e. the fronto-parietal network (FPN), premotor network (PMN), somatomotor network (SMN), and default mode network (DMN). Results: At baseline, variation in pain and fatigue symptoms related to partially dissociable brain networks. Fatigue was associated with higher SMN-PMN connectivity and lower SMN-DMN connectivity. Pain was associated with lower PMN-DMN connectivity. CBT improved SMN-DMN connectivity, compared to WL. Larger clinical improvements were associated with larger increases in frontal SMN-DMN connectivity. No CBT effects were observed for PMN-DMN or SMN-PMN connectivity. Conclusions: These results provide insight into the dissociable neural mechanisms underlying fatigue and pain symptoms in ME/CFS and how they are affected by CBT in successfully treated patients. Further investigation of how and in whom behavioral and biomedical treatments affect these networks is warranted to improve and individualize existing or new treatments for ME/CFS. [ABSTRACT FROM AUTHOR]

Backgound: Although cognitive control is essential for efficient gait, the associations between cognitive and motor networks regarding gait in individuals with Parkinson's disease (PD) remain to be determined. Herein, we enrolled 28 PD and 28 controls to compare internetwork coupling among cognitive and motor networks and examine its relationship with single- and dual-task gait performance in PD. Methods: The dorsal attention network (DAN), left and right frontoparietal control networks (FPNs), sensorimotor network, and lateral motor network were identified using resting-state functional magnetic resonance imaging data. The time taken to complete a 10-m walk test during cognitive or physical dual-tasks in PD was calculated representing gait performance. Results: We observed that the internetwork couplings between the DAN and motor networks and between the motor networks decreased whereas those between the left FPN and DAN and motor networks increased in PD compared to controls using a permutation test. There was no significant correlation between the internetwork couplings and single- and dual-task gait performance in PD. Nevertheless, improved cognitive dual-task performance showed a positive correlation with the DAN and left FPN coupling and a negative correlation with the DAN and lateral motor network coupling in a good performance group. The opposite relationship was observed in the poor cognitive dual-task performance group. Conclusion: Our findings suggest a neural mechanism of cognitive control on gait to compensate for reduced goal-directed attention in PD who maintain cognitive dual-task performance. [ABSTRACT FROM AUTHOR]

Objectives Generativity, the desire and action to improve the well-being of younger generations, is associated with purpose in life among older adults. However, the neurobehavioral factors supporting the relationship between generativity and purpose in life remain unknown. This study aims to identify the functional neuroanatomy of generativity and mechanisms linking generativity with purpose in life in at-risk older adults. Methods Fifty-eight older adults (mean age = 70.8, SD =  5.03, 45 females) with a family history of Alzheimer's disease (AD) were recruited from the PREVENT-AD cohort. Participants underwent brain imaging and completed questionnaires assessing generativity, social support, and purpose in life. Mediation models examined whether social support mediated the association between generativity and purpose in life. Seed-to-voxel analyses investigated the association between generativity and resting-state functional connectivity (rsFC) to the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS), and whether this rsFC moderated the relationship between generativity and purpose in life. Results Affectionate social support mediated the association between generative desire and purpose in life. Generative desire was associated with rsFC between VS and precuneus, and, vmPFC and right dorsolateral prefrontal cortex (rdlPFC). The vmPFC–rdlPFC rsFC moderated the association between generative desire and purpose in life. Discussion These findings provide insight into how the brain supports complex social behavior and, separately, purpose in life in at-risk aging. Affectionate social support may be a putative target process to enhance purpose in life in older adults. This knowledge contributes to future developments of personalized interventions that promote healthy aging. [ABSTRACT FROM AUTHOR]

Purpose: Vestibular migraine (VM) is a disorder with prominent vestibular symptoms that are causally correlated with migraine and is the most prevalent neurological cause of episodic vertigo. Nevertheless, the functional underpinnings of VM remain largely unclear. This study aimed to reveal concordant alteration patterns of functional connectivity (FC) in VM patients. Methods: We searched literature measuring resting‐state FC abnormalities of VM patients in PubMed, Embase, Cochrane, and Scopus databases before May 2023. Furthermore, we applied the anisotropic effect size‐signed differential mapping (AES‐SDM) to conduct a whole‐brain voxel‐wise meta‐analysis to identify the convergence of FC alterations in VM patients. Results: Nine studies containing 251 VM patients and 257 healthy controls (HCs) were included. Relative to HCs, VM patients showed reduced activity in the left superior temporal gyrus and left midcingulate/paracingulate gyri, and increased activity in the precuneus, right superior parietal gyrus, and right middle frontal gyrus. Jackknife's analysis and subgroup analysis further supported the generalization and robustness of the main results. Furthermore, meta‐regression analyses indicated that the Dizziness Handicap Inventory (DHI) ratings were positively correlated with the activity in the precuneus, while higher Headache Impact Test‐6 and DHI scores were associated with lower activity within the left midcingulate/paracingulate gyri. Conclusions: The study indicates that VM is associated with specific functional deficits of VM patients in crucial regions involved in the vestibular and pain networks and provides further information on the pathophysiological mechanisms of VM. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an autoimmune degenerative disease targeting white matter in the central nervous system. The most common animal model that mimics MS is experimental autoimmune encephalomyelitis (EAE) and it plays a crucial role in pharmacological research, from the identification of a therapeutic target to the in vivo validation of efficacy. Magnetic resonance imaging (MRI) is largely used to detect MS lesions, and resting‐state functional MRI (rsfMRI) to investigate alterations in the brain functional connectivity (FC). MRI was mainly used in EAE studies to detect lesions in the spinal cord and brain. The current longitudinal MRI study aims to validate rsfMRI as a biomarker of the disease progression in the myelin oligodendrocyte glycoprotein 35–55 induced EAE animal model of MS. MR images were acquired 14, 25, and 50 days postimmunization. Seed‐based analysis was used to investigate the whole‐brain FC with some predefined areas, such as the thalamic regions, cerebellum, motor and somatosensory cortex. When compared with the control group, the EAE group exhibited a slightly altered FC and a decreasing trend in the total number of activated voxels along the disease progression. The most interesting result regards the whole‐brain FC with the cerebellum. A hyperconnectivity behavior was found at an early phase and a significant reduced connectivity at a late phase. Moreover, we found a negative correlation between the total number of activated voxels during the late phase and the cumulative disease index. The results obtained provide a clinically relevant experimental platform that may be pivotal for the elucidation of the key mechanisms of accumulation of irreversible disability, as well as the development of innovative therapies for MS. Moreover, the negative correlation between the disease severity and the size of the activated area suggests a possible research pathway to follow for the resolution of the clinico‐radiological paradox. [ABSTRACT FROM AUTHOR]

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD. [ABSTRACT FROM AUTHOR]

Previous studies have explored the neural bases of forgiveness, however, the neural associations of decisional and emotional forgiveness remain unclear. Regional homogeneity (ReHo) and functional connectivity (FC) measured by resting-state functional magnetic resonance imaging (fMRI) were used to investigate the neural associations of individual differences in decisional and emotional forgiveness among healthy volunteers (256 participants, 85 males). The results of the ReHo analysis showed that decisional forgiveness was positively correlated with the left inferior parietal lobule (IPL). Furthermore, emotional forgiveness was positively correlated with the dorsal anterior cingulate cortex (dACC) and left supramarginal gyrus (SMG). The results of the FC analysis showed that decisional forgiveness was positively associated with the FC strength between the left IPL and left middle frontal gyrus (MFG) and negatively correlated with the FC strength among the left IPL, right superior temporal gyrus (STG), and left SMG. Furthermore, there was a significant positive correlation between emotional forgiveness and FC strength between the left SMG and right IPL. These findings suggest an association between decisional and emotional forgiveness and spontaneous brain activity in brain regions related to empathy, emotion regulation, and cognitive control. [ABSTRACT FROM AUTHOR]

The hippocampus plays an important role in the pathophysiological mechanism of Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis. Nevertheless, the connection between the resting-state activity of the hippocampal subregions and neuropsychiatric disorders in patients remains unclear. This study aimed to explore the changes in functional connectivity (FC) in the hippocampal subregions of patients with anti-NMDAR encephalitis and its association with clinical symptoms and cognitive performance. Twenty-three patients with anti-NMDAR encephalitis and 23 healthy controls (HC) were recruited. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans and completed clinical cognitive scales. Based on the Brainnetome Atlas, the rostral (anterior) and caudal (posterior) hippocampi of both the left and right hemispheres were selected as regions of interest (ROIs) for FC analysis. First, a one-sample t-test was used to observe the whole-brain connectivity distribution of hippocampal subregions within the patient and HC groups at a threshold of p < 0.05. The two-sample t-test was used to compare the differences in hippocampal ROIs connectivity between groups, followed by a partial correlation analysis between the FC values of brain regions with statistical differences and clinical variables. This study observed that the distribution of whole-brain functional connectivity in the rostral and caudal hippocampi aligned with the connectivity differences between the anterior and posterior hippocampi. Compared to the HC group, the patients showed significantly decreased FC between the bilateral rostral hippocampus and the left inferior orbitofrontal gyrus and between the right rostral hippocampus and the right cerebellum. However, a significant increase in FC was observed between the right rostral hippocampus and left superior temporal gyrus, the left caudal hippocampus and right superior frontal gyrus, and the right caudal hippocampus and left gyrus rectus. Partial correlation analysis showed that FC between the left inferior orbitofrontal gyrus and the right rostral hippocampus was significantly negatively correlated with the California Verbal Learning Test (CVLT) and Brief Visuospatial Memory Test (BVMT) scores. The FC between the right rostral hippocampus and the left superior temporal gyrus was negatively correlated with BVMT scores. FC abnormalities in the hippocampal subregions of patients with anti-NMDAR encephalitis were associated with cognitive impairment, emotional changes, and seizures. These results may help explain the pathophysiological mechanisms and clinical manifestations of anti-NMDAR encephalitis and NMDAR dysfunction-related diseases such as schizophrenia. [ABSTRACT FROM AUTHOR]

Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing. [ABSTRACT FROM AUTHOR]

Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP−), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP− patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP− patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms. [ABSTRACT FROM AUTHOR]