More widespread functionality of posterior language area in patients with brain tumors.

Damage to the posterior language area (PLA), or Wernicke's area causes cortical reorganization in the corresponding regions of the contralateral hemisphere. However, the details of reorganization within the ipsilateral hemisphere are not fully understood. In this context, direct electrical stimulation during awake surgery can provide valuable opportunities to investigate neuromodulation of the human brain in vivo, which is difficult through the non-invasive approaches. Thus, in this study, we aimed to investigate the characteristics of the cortical reorganization of the PLA within the ipsilateral hemisphere. Sixty-two patients with left hemispheric gliomas were divided into groups depending on whether the lesion extended to the PLA. All patients underwent direct cortical stimulation with a picture-naming task. We further performed functional connectivity analyses using resting-state functional magnetic resonance imaging (MRI) in a subset of patients and calculated betweenness centrality, an index of the network importance of brain areas. During direct cortical stimulation, the regions showing positive (impaired) responses in the non-PLA group were localized mainly in the posterior superior temporal gyrus (pSTG), whereas those in the PLA group were widely distributed from the pSTG to the posterior supramarginal gyrus (pSMG). Notably, the percentage of positive responses in the pSMG was significantly higher in the PLA group (47%) than in the non-PLA group (8%). In network analyses of functional connectivity, the pSMG was identified as a hub region with high betweenness centrality in both the groups. These findings suggest that the language area can spread beyond the PLA to the pSMG, a hub region, in patients with lesion progression to the pSTG. The change in the pattern of the language area may be a compensatory mechanism to maintain efficient brain networks.

Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function.

Objective: White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective. Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system. Results: The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch." Conclusion: Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education.

Aberrant functional connectivity in anterior cingulate gyrus subregions in migraine without aura patients.

Background: The anterior cingulate gyrus (ACG) is an important regulatory region for pain-related information. However, the ACG is composed of subregions with different functions. The mechanisms underlying the brain networks of different subregions of the ACG in patients with migraine without aura (MwoA) are currently unclear. Methods: In the current study, resting-state functional magnetic resonance imaging (rsfMRI) and functional connectivity (FC) were used to investigate the functional characteristics of ACG subregions in MwoA patients. The study included 17 healthy volunteers and 28 MwoA patients. The FC calculation was based on rsfMRI data from a 3 T MRI scanner. The brain networks of the ACG subregions were compared using a general linear model to see if there were any differences between the two groups. Spearman correlation analysis was used to examine the correlation between FC values in abnormal brain regions and clinical variables. Results: Compared with healthy subjects, MwoA patients showed decreased FC between left subgenual ACG and left middle cingulate gyrus and right middle temporal gyrus. Meanwhile, MwoA patients also showed increased FC between pregenual ACG and right angular gyrus and increased FC between right pregenual ACG and right superior occipital gyrus. The FC values between pregenual ACG and right superior occipital gyrus were significantly positively correlated with the visual analogue scale. Conclusion: Disturbances of FC between ACG subregions and default model network and visual cortex may play a key role in neuropathological features, perception and affection of MwoA. The current study provides further insights into the complex scenario of MwoA mechanisms.

Brain modulation after exergaming training in advanced forms of Parkinson's disease: a randomized controlled study.

BACKGROUND: Physical activity combined with virtual reality and exergaming has emerged as a new technique to improve engagement and provide clinical benefit for gait and balance disorders in people with Parkinson's disease (PD). OBJECTIVE: To investigate the effects of a training protocol using a home-based exergaming system on brain volume and resting-state functional connectivity (rs-FC) in persons with PD. METHODS: A single blind randomized controlled trial was conducted in people with PD with gait and/or balance disorders. The experimental (active) group performed 18 training sessions at home by playing a custom-designed exergame with full body movements, standing in front of a RGB-D Kinect® motion sensor, while the control group played using the computer keyboard. Both groups received the same training program. Clinical scales, gait recordings, and brain MRI were performed before and after training. We assessed the effects of both training on both the grey matter volumes (GVM) and rs-FC, within and between groups. RESULTS: Twenty-three patients were enrolled and randomly assigned to either the active (n = 11) or control (n = 12) training groups. Comparing pre- to post-training, the active group showed significant improvements in gait and balance disorders, with decreased rs-FC between the sensorimotor, attentional and basal ganglia networks, but with an increase between the cerebellar and basal ganglia networks. In contrast, the control group showed no significant changes, and rs-FC significantly decreased in the mesolimbic and visuospatial cerebellar and basal ganglia networks. Post-training, the rs-FC was greater in the active relative to the control group between the basal ganglia, motor cortical and cerebellar areas, and bilaterally between the insula and the inferior temporal lobe. Conversely, rs FC was lower in the active relative to the control group between the pedunculopontine nucleus and cerebellar areas, between the temporal inferior lobes and the right thalamus, between the left putamen and dorsolateral prefrontal cortex, and within the default mode network. CONCLUSIONS: Full-body movement training using a customized exergame induced brain rs-FC changes within the sensorimotor, attentional and cerebellar networks in people with PD. Further research is needed to comprehensively understand the neurophysiological effects of such training approaches. Trial registration ClinicalTrials.gov NCT03560089.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.

The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as "resting-state brain networks" (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.

The effects of N-acetyl cysteine on intrinsic functional connectivity and neural alcohol cue reactivity in treatment-seeking individuals with alcohol use disorder: a preliminary study.

N-acetyl cysteine (NAC) is a potential pharmacotherapy for alcohol use disorder (AUD), but it is not known whether it modulates neural activation to alcohol cues or intrinsic functional connectivity. We investigated whether NAC attenuates (i) alcohol cue-elicited activation, and (ii) intrinsic functional connectivity compared to placebo in patients with AUD. In this preliminary study, twenty-three individuals (7 females) with moderate-severe AUD received daily NAC (2400 mg/day, n = 9), or a placebo (n = 14) for at least 2 weeks. Participants completed a pre-treatment functional magnetic resonance imaging session (T0) and a post-treatment session (T1) comprising resting-state and visual alcohol cue reactivity task acquisitions. Activation differences between sessions, treatment, and session-by-treatment interaction were assessed. Resting-state functional connectivity examined using 377 node ROI-to-ROIs evaluated whether NAC reduced intrinsic functional connectivity after treatment. There were no differences in alcohol cue reactivity for brain activation or subjective craving between NAC and placebo during treatment or across sessions, or significant interaction. A significant treatment-by-time interaction, with reduced intrinsic connectivity was observed after treatment (T1) for NAC-treated compared to placebo-treated patients in the posterior cingulate node (9, left hemisphere) of the dorsal attentional network and connections to salience, ventral-attentional, somatosensory, and visual-peripheral networks implicated in AUD. NAC reduced intrinsic functional connectivity in patients with moderate-severe AUD after treatment compared to placebo, but did not attenuate alcohol cue-elicited activation. However, the absence of cue reactivity findings may result from low power, rather than the absence of cue reactivity findings associated with NAC. These results provide preliminary evidence that NAC treatment may modulate intrinsic functional connectivity brain activation in patients with alcohol use disorder, but replication in larger studies are required to determine the strength of this effect and any associations with clinical outcomes. Clinical Trials Registration: ClinicalTrials.gov Identifier: NCT03879759.

MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes.

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

Sleep deprivation effects on brain state dynamics are associated with dopamine D2 receptor availability via network control theory.

BACKGROUND: Sleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on 'static' brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored. METHODS: We used functional MRI to examine resting brain state dynamics after one night of SD compared to rested wakefulness (RW) and assessed their association with striatal brain dopamine D2 receptor availability (D2R) measured by PET-[11C]raclopride using network control theory. RESULTS: SD reduced dwell time and persistence probabilities with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontal parietal network and low somatomotor network activity. Using network control theory, we showed that after SD there was an overall increase in the control energy required for brain state transitions with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and RW and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2R distribution. CONCLUSIONS: These findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2R.

Leveraging distributed brain signal at rest to predict internalizing symptoms in youth.

BACKGROUND: The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. METHODS: Youth from the Adolescent Brain and Cognitive DevelopmentSM Study (ages 9-10 years at baseline) with a resting-state fMRI scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor - Youth Form were combined across two years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (n = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity (RSFC) with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. RESULTS: The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute ß-weight) were retained. The RSFC networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1*10-6) as accounted for by permutation testing (n = 7000). CONCLUSIONS: These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between RSFC and behavior.

Social Cognition and Functional Connectivity in Early and Chronic Schizophrenia.

BACKGROUND: Individuals with schizophrenia (SZ) experience impairments in social cognition that contribute to poor functional outcomes. However, mechanisms of social cognitive dysfunction in SZ remain poorly understood, which impedes the design of novel interventions to improve outcomes. This pre-registered project (https://doi.org/10.17605/OSF.IO/JH5FC) examines the representation of social cognition in the brain's functional architecture across early and chronic SZ. METHODS: The study contains two parts: a confirmatory and an exploratory portion. In the confirmatory portion, we identified resting-state connectivity disruptions evident in early and chronic SZ. We performed a connectivity analysis using regions associated with social cognitive dysfunction in early and chronic SZ to test whether aberrant connectivity observed in chronic SZ (N=47; HC=52) was also present in early SZ (N=71, HC=47). In the exploratory portion, we assessed the out-of-sample generalizability and precision of predictive models of social cognition. We used machine learning to predict social cognition and established generalizability with out-of-sample testing and confound control. RESULTS: Results reveal decreases between left inferior frontal gyrus and intraparietal sulcus in early and chronic SZ, which are significantly associated with social and general cognition and global functioning in chronic SZ and with general cognition and global functioning in early SZ. Predictive modeling reveals the importance of out-of-sample evaluation and confound control. CONCLUSION: This work provides insights into the functional architecture in early and chronic SZ and suggests that IFG-IPS connectivity could be a prognostic biomarker of social impairments and a target for future interventions (e.g. neuromodulation) focused on improved social functioning.

EEG microstates are associated with music training experience.

Background: Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. Aims: To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. Method: In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. Results: The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Conclusion: Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

AIMS: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. CONCLUSION: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.

Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early studies on neonates suggest that individualized FC signatures are absent. We posit that individual uniqueness is present in neonatal FC data and that conventional linear models fail to capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as a variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state functional MRI (rs-fMRI) scans from 100 adults and the other from 464 neonates. VAE models trained on rs-fMRI from both adults and newborns produced superior age prediction performance (with r between predicted- and actual age ∼ 0.7) and individual identification accuracy (∼45 %) compared to models trained solely on adult or neonatal data. The VAE model also showed significantly higher individual identification accuracy than linear models (=10∼30 %). Importantly, the VAE differentiated connections reflecting age-related changes from those indicative of individual uniqueness, a distinction not possible with linear models. Moreover, we derived 20 latent variables, each corresponding to distinct patterns of cortical functional network (CFNs). These CFNs varied in their representation of brain maturation and individual signatures; notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. CFNs associated with neonatal neurodevelopment predominantly encompassed unimodal regions such as visual and sensorimotor areas, whereas those linked to individual uniqueness spanned multimodal and transmodal brain regions. The VAE's capacity to extract features from rs-fMRI data beyond the capabilities of linear models positions it as a valuable tool for delineating cognitive traits inherent in rs-fMRI and exploring individualized imaging phenotypes.

From convergence insufficiency to functional reorganization: A longitudinal randomized controlled trial of treatment-induced connectivity plasticity.

INTRODUCTION: Convergence Insufficiency (CI) is the most prevalent oculomotor dysfunction of binocular vision that negatively impacts quality of life when performing visual near tasks. Decreased resting-state functional connectivity (RSFC) is reported in the CI participants compared to binocularly normal control participants. Studies report that therapeutic interventions such as office-based vergence and accommodative therapy (OBVAT) can improve CI participants' clinical signs, visual symptoms, and task-related functional activity. However, longitudinal studies investigating the RSFC changes after such treatments in participants with CI have not been conducted. This study aimed to investigate the neural basis of OBVAT using RSFC in CI participants compared to the placebo treatment to understand how OBVAT improves visual function and symptoms. METHODS: A total of 51 CI participants between 18 and 35 years of age were included in the study and randomly allocated to receive either 12 one-hour sessions of OBVAT or placebo treatment for 6 to 8 weeks (1 to 2 sessions per week). Resting-state functional magnetic resonance imaging and clinical assessments were evaluated at baseline and outcome for each treatment group. Region of interest (ROI) analysis was conducted in nine ROIs of the oculomotor vergence network, including the following: cerebellar vermis (CV), frontal eye fields (FEF), supplementary eye fields (SEF), parietal eye fields (PEF), and primary visual cortices (V1). Paired t-tests assessed RSFC changes in each group. A linear regression analysis was conducted for significant ROI pairs in the group-level analysis for correlations with clinical measures. RESULTS: Paired t-test results showed increased RSFC in 10 ROI pairs after the OBVAT but not placebo treatment (p < 0.05, false discovery rate corrected). These ROI pairs included the following: Left (L)-SEF-Right (R)-V1, L-SEF-CV, R-SEF-R-PEF, R-SEF-L-V1, R-SEF-R-V1, R-SEF-CV, R-PEF-CV, L-V1-CV, R-V1-CV, and L-V1-R-V1. Significant correlations were observed between the RSFC strength of the R-SEF-R-PEF ROI pair and the following clinical visual function parameters: positive fusional vergence and near point of convergence (p < 0.05). CONCLUSION: OBVAT, but not placebo treatment, increased the RSFC in the ROIs of the oculomotor vergence network, which was correlated with the improvements in the clinical measures of the CI participants.

Triple network resting-state functional connectivity patterns of alcohol heavy drinking.

AIMS: Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse. METHODS: Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures. RESULTS: Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: ß = .337, p-FDR = .016; left LPFC: ß = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03). CONCLUSIONS: These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI.

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 Autoregressive model) 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 whitening methods, 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.

Structural parameters are superior to eigenvector centrality in detecting progressive supranuclear palsy with machine learning & multimodal MRI.

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized initially by falls and eye movement impairment. This multimodal imaging study aimed at eliciting structural and functional disease-specific brain alterations. T1-weighted and resting-state functional MRI were applied in multi-centric cohorts of PSP and matched healthy controls. Midbrain, cerebellum, and cerebellar peduncles showed severely low gray/white matter volume, whereas thinner cortical gray matter was observed in cingulate cortex, medial and temporal gyri, and insula. Eigenvector centrality analyses revealed regionally specific alterations. Multivariate pattern recognition classified patients correctly based on gray and white matter segmentations with up to 98 % accuracy. Highest accuracies were obtained when restricting feature selection to the midbrain. Eigenvector centrality indices yielded an accuracy around 70 % in this comparison; however, this result did not reach significance. In sum, the study reveals multimodal, widespread brain changes in addition to the well-known midbrain atrophy in PSP. Alterations in brain structure seem to be superior to eigenvector centrality parameters, in particular for prediction with machine learning approaches.

Altered local intrinsic neural activity and molecular architecture in internet use disorders.

BACKGROUND: Internet gaming disorder (IGD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in inhibitory control, whose neurobiological basis remains unclear. Then, we will investigate local intrinsic neural activity (INA) alterations in IGD, ascertain whether these potential alterations are related to clinical characteristics, and further explore the underlying molecular architecture. METHOD: In this study, we performed the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) derived from resting-state functional magnetic resonance imaging (rs-fMRI) to explore the impact of IGD on local INA. Correlation analysis revealed the relationship between ReHo and fALFF in terms of group differences and clinical characteristics. Moreover, correlations between fALFF, ReHo, and PET- and SPECT-driven maps were investigated to elucidate the specific molecular architecture alternations in IGD. Finally, receiver operating characteristic curve (ROC) analysis was used to show the potential abilities of fALFF and ReHo in distinguishing individuals with IGD (IGDs) from healthy controls (HCs). RESULT: Compared with HCs, IGDs revealed increased ReHo and fALFF in the prefrontal cortex. Significantly decreased ReHo was observed in the temporal lobe, occipital lobe, and cerebellum. In addition, the ReHo values in the cerebellum_7b_R were positively correlated with internet addiction severity. ROC curve analysis showed that ReHo and fALFF-altered brain regions could effectively distinguish IGDs from HCs. More importantly, cross-modal correlations revealed local INA changes in brain regions associated with the monoamine neurotransmitter system and the less studied cholinergic/GABAergic system. CONCLUSION: These results suggest that local functional impairments are shown in the audiovisual and inhibitory control circuits in IGDs. This may be associated with underlying neurotransmitter system alterations. Therefore, this study provides the possibility of GABAergic receptor agonists and cholinergic receptor inhibitors for the treatment of IGD.

Differential role of negative and positive parenting styles on resting-state brain networks in middle-aged adolescents.

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.

Dynamic degree centrality in stroke-induced Broca's aphasia varies based on first language: A functional MRI study.

BACKGROUND AND PURPOSE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.