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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103 Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

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.

Hyperactivity in children with attention-deficit/hyperactivity disorder (ADHD) leads to restlessness and impulse-control impairments. Nevertheless, the relation between ADHD symptoms and brain regions interactions remains unclear. We focused on dynamic causal modeling to study the effective connectivity in a fully connected network comprised of four regions of the default mode network (DMN) (linked to response control behaviors) and four other regions with previously-reported structural alterations due to ADHD. Then, via the parametric empirical Bayes analysis, the most significant connections, with the highest correlation to the covariates ADHD/control, age, and sex were extracted. Our results demonstrated a positive correlation between ADHD and effective connectivity between the right cerebellum and three DMN nodes (intrinsically inhibitory connections). Therefore, an increase in the effective connectivity leads to more inhibition imposition from the right cerebellum to DMN that reduces this network activation. The lower DMN activity makes leaving the resting-state easier, which may be involved in the restlessness symptom. Furthermore, our results indicated a negative correlation between age and these connections. We showed that the difference between the average of effective connectivities of ADHD and control groups in the age-range of 7-11 years disappeared after 14 years-old. Therefore, aging tends to alleviate ADHD-specific symptoms.

BACKGROUND: Internal capsule strokes often result in multidomain cognitive impairments across memory, attention, and executive function, typically due to disruptions in brain network connectivity. Our study examines these impairments by analyzing interactions within the triple-network model, focusing on both static and dynamic aspects.
METHODS: We collected resting-state fMRI data from 62 left (CI_L) and 56 right (CI_R) internal capsule stroke patients, along with 57 healthy controls (HC). Using independent component analysis to extract the default mode (DMN), executive control (ECN), and salience networks (SAN), we conducted static and dynamic functional network connectivity analyses (DFNC) to identify differences between stroke patients and controls. For DFNC, we used k-means clustering to focus on temporal properties and multilayer network analysis to examine integration and modularity Q, where integration represents dynamic interactions between networks, and modularity Q measures how well the network is divided into distinct modules. We then calculated the correlations between SFNC/DFNC properties with significant inter-group differences and cognitive scales.
RESULTS: Compared to HC, both CI_L and CI_R patients showed increased static FCs between SAN and DMN and decreased dynamic interactions between ECN and other networks. CI_R patients also had heightened static FCs between SAN and ECN and maintained a state with strongly positive FNCs across all networks in the triple-network model. Additionally, CI_R patients displayed decreased modularity Q.
CONCLUSIONS: These findings highlight that stroke can result in the disruption of static and dynamic interactions in the triple network model, aiding our understanding of the neuropathological basis for multidomain cognitive deficits after internal capsule stroke.

Cushing\'s disease (CD) represents a state of cortisol excess, serving as a model to investigate the effects of prolonged hypercortisolism on functional brain. Potential alterations in the functional connectome of the brain may explain frequently reported cognitive deficits and affective disorders in CD patients. This study aims to elucidate the effects of chronic hypercortisolism on the principal functional gradient, which represents a hierarchical architecture with gradual transitions across cognitive processes, by integrating connectomics and transcriptomics approaches. Utilizing resting-state functional magnetic resonance imaging data from 140 participants (86 CD patients, 54 healthy controls) recruited at a single center, we explored the alterations in the principal gradient in CD patients. Further, we thoroughly explored the underlying associative mechanisms of the observed characteristic alterations with cognitive function domains, biological attributes, and neuropsychiatric representations, as well as gene expression profiles. Compared to healthy controls, CD patients demonstrated changes in connectome patterns in both primary and higher-order networks, exhibiting an overall converged trend along the principal gradient axis. The gradient values in CD patients\' right prefrontal cortex and bilateral sensorimotor cortices exhibited a significant correlation with cortisol levels. Moreover, the cortical regions showing gradient alterations were principally associated with sensory information processing and higher-cognitive functions, as well as correlated with the gene expression patterns which involved synaptic components and function. The findings suggest that converged alterations in the principal gradient in CD patients may mediate the relationship between hypercortisolism and cognitive impairments, potentially involving genes regulating synaptic components and function.

The functional organization of the frontal lobe is a source of debate, focusing on broad functional subdivisions, large-scale networks, or local refined specificities. Multiple neurocognitive models have tried to explain how functional interactions between cingulate and lateral frontal regions contribute to decision making and cognitive control, but their neuroanatomical bases remain unclear. We provide a detailed description of the functional connectivity between cingulate and lateral frontal regions using resting-state functional MRI in rhesus macaques. The analysis focuses on the functional connectivity of the rostral part of the cingulate sulcus with the lateral frontal cortex. Data-driven and seed-based analysis revealed three clusters within the cingulate sulcus organized along the rostro-caudal axis: the anterior, mid, and posterior clusters display increased functional connectivity with, respectively, the anterior lateral prefrontal regions, face-eye lateral frontal motor cortical areas, and hand lateral frontal motor cortex. The location of these clusters can be predicted in individual subjects based on morphological landmarks. These results suggest that the anterior cluster corresponds to the anterior cingulate cortex, whereas the posterior clusters correspond to the face-eye and hand cingulate motor areas within the anterior midcingulate cortex. These data provide a comprehensive framework to identify cingulate subregions based on functional connectivity and local organization.

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.
CONCLUSIONS: 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.

Nearly half of the amyotrophic lateral sclerosis (ALS) patients showed hyperintensity of the corticospinal tract (CST+), yet whether brain functional pattern differs between CST+and CST- patients remains obscure. In the current study, 19 ALS CST+, 41 ALS CST- patients and 37 healthy controls (HC) underwent resting state fMRI scans. We estimated local activity and connectivity patterns via the Amplitude of Low Frequency Fluctuations (ALFF) and the Network-Based Statistic (NBS) approaches respectively. The ALS CST+patients did not differ from the CST- patients in amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) score and disease duration. ALFF of the superior frontal gyrus (SFG) and the inferior frontal gyrus pars opercularis (OIFG) were highest in the HC and lowest in the ALS CST- patients, resulting in significant group differences (PFWE<0.05). NBS analysis revealed a frontal network consisting of connections between SFG, OIFG, orbital frontal gyrus, middle cingulate cortex and the basal ganglia, which exhibited HC>ALS CST+ > ALS CST- group differences (PFWE=0.037) as well. The ALFF of the OIFG was significantly correlated with ALSFRS-R (R=0.34, P=0.028) and mean connectivity of the frontal network was trend-wise significantly correlated with disease duration (R=-0.31, P=0.052) in the ALS CST- patients. However, these correlations were insignificant in ALS CST+patients (P values > 0.8). In conclusion, The ALS CST+patients exhibited different patterns of baseline functional activity and connectivity in the frontal cortex which may indicate a functional compensatory effect.