Hierarchy is the basic character of the human brain. Neuronal oscillation is one of the fundamental features of brain function, revealing abnormal hierarchical structures in psychiatric disorders from a system-level perspective. However, to date, no research has yet quantified the normal and abnormal brain functional hierarchy based on oscillation patterns. Therefore, this study aimed to quantify brain hierarchy based on neuronal oscillation patterns using the wide-scale information across multiple frequency bands of functional magnetic resonance imaging (fMRI) data and further investigate atypical oscillation patterns in autism spectrum disorder (ASD) at the system level. We analyzed resting-state fMRI data from the Autism Brain Imaging Data Exchange II, including 132 participants with ASD and 132 healthy controls. The energy distribution patterns (EDPs) across frequency bands were calculated for different brain networks using multivariate empirical mode decomposition and Hilbert Transform to represent oscillation patterns. The gradient analysis was applied to quantify the EDP segregation among networks, and the network median distance of gradients was compared between the two groups. The k-means clustering was applied to intuitively verify the atypical EDP in ASD. Across all participants, we observed that the EDPs of different brain regions were spatially coupled to the brain hierarchy. Compared to healthy controls, the ASD exhibited reduced segregation between unimodal and transmodal regions on both energy gradient and clustering analyses, correlating with social deficits. Our results quantitatively confirm that oscillation patterns can reflect the functional segregation among networks and provide novel evidence of the system-level imbalances in neuronal oscillations in ASD.

Neural oscillations facilitate the functioning of the human brain in spatial and temporal dimensions at various frequencies. These oscillations feature a universal frequency architecture that is governed by brain anatomy, ensuring frequency specificity remains invariant across different measurement techniques. Initial magnetic resonance imaging (MRI) methodology constrained functional MRI (fMRI) investigations to a singular frequency range, thereby neglecting the frequency characteristics inherent in blood oxygen level-dependent oscillations. With advancements in MRI technology, it has become feasible to decode intricate brain activities via multi-band frequency analysis (MBFA). During the past decade, the utilization of MBFA in fMRI studies has surged, unveiling frequency-dependent characteristics of spontaneous slow oscillations (SSOs) believed to base dark energy in the brain. There remains a dearth of conclusive insights and hypotheses pertaining to the properties and functionalities of SSOs in distinct bands. We surveyed the SSO MBFA studies during the past 15 years to delineate the attributes of SSOs and enlighten their correlated functions. We further proposed a model to elucidate the hierarchical organization of multi-band SSOs by integrating their function, aimed at bridging theoretical gaps and guiding future MBFA research endeavors.

The brain is a highly complex and delicate system, and its internal neural processes are manifested as the interweaving and superposition of multi-frequency neural signals. However, traditional brain network studies are often limited to the whole frequency band or a specific frequency band, ignoring the potentially profound impact of the diversity of information within the frequency on the dynamics of brain networks. To comprehensively and deeply analyze this phenomenon, the present study is devoted to exploring the specific performance of brain networks at different frequencies. We used the maximum overlap discrete wavelet transform technique to finely divide the time series data into the following frequency bands: scale 1 (0.125-0.25 Hz), scale 2 (0.06-0.125 Hz), scale 3 (0.03-0.06 Hz) and scale 4 (0.015-0.03 Hz). Based on these frequency bands, we constructed multilayer networks from both dynamic and static perspectives, respectively. From the dynamic perspective, we quantitatively evaluated the dynamic differences among different frequency bands using metrics such as flexibility, promiscuity, integration, and recruitment, and found that scale 3 and scale 4 bands performed particularly well. In contrast, from a static perspective, we measured the cross-frequency interaction capability between different frequency bands through metrics such as multilayer clustering coefficient and entropy of multiplexing degree, and the results show that scale 2, scale 3, and scale 4 band networks have enhanced global integration capability and local capability. In addition, we explored the correlation of gender and age with the properties of brain networks in different frequency bands. In the scale 1 frequency band, the organization of brain functions showed significant gender differences, while in the scale 2 frequency band, there was a significant correlation between age and global efficiency. By integrating the dual perspectives of time and frequency domains, this study not only reveals the critical role of frequency specificity in the brain's information processing and functional organization but also provides new perspectives for understanding the complex working mechanisms of the brain as well as gender- and age-related cognitive differences.

Wearable devices have gained increasing attention for use in multifunctional applications related to health monitoring, particularly in research of the circadian rhythms of cognitive functions and metabolic processes. In this comprehensive review, we encompass how wearables can be used to study circadian rhythms in health and disease. We highlight the importance of these rhythms as markers of health and well-being and as potential predictors for health outcomes. We focus on the use of wearable technologies in sleep research, circadian medicine, and chronomedicine beyond the circadian domain and emphasize actigraphy as a validated tool for monitoring sleep, activity, and light exposure. We discuss various mathematical methods currently used to analyze actigraphic data, such as parametric and non-parametric approaches, linear, non-linear, and neural network-based methods applied to quantify circadian and non-circadian variability. We also introduce novel actigraphy-derived markers, which can be used as personalized proxies of health status, assisting in discriminating between health and disease, offering insights into neurobehavioral and metabolic status. We discuss how lifestyle factors such as physical activity and light exposure can modulate brain functions and metabolic health. We emphasize the importance of establishing reference standards for actigraphic measures to further refine data interpretation and improve clinical and research outcomes. The review calls for further research to refine existing tools and methods, deepen our understanding of circadian health, and develop personalized healthcare strategies.

Pain is a complex problem that is triaged, diagnosed, treated, and billed based on which body part is painful, almost without exception. While the "body part framework" guides the organization and treatment of individual patients' pain conditions, it remains unclear how to best conceptualize, study, and treat pain conditions at the population level. Here, we investigate (1) how the body part framework agrees with population-level, biologically derived pain profiles; (2) how do data-derived pain profiles interface with other symptom domains from a whole-body perspective; and (3) whether biologically derived pain profiles capture clinically salient differences in medical history.

To understand how pain conditions might be best organized, we applied a carefully designed a multi-variate pattern-learning approach to a subset of the UK Biobank (n = 34,337), the largest publicly available set of real-world pain experience data to define common population-level profiles. We performed a series of post hoc analyses to validate that each pain profile reflects real-world, clinically relevant differences in patient function by probing associations of each profile across 137 medication categories, 1425 clinician-assigned ICD codes, and 757 expert-curated phenotypes.

We report four unique, biologically based pain profiles that cut across medical specialties: pain interference, depression, medical pain, and anxiety, each representing different facets of functional impairment. Importantly, these profiles do not specifically align with variables believed to be important to the standard pain evaluation, namely painful body part, pain intensity, sex, or BMI. Correlations with individual-level clinical histories reveal that our pain profiles are largely associated with clinical variables and treatments of modifiable, chronic diseases, rather than with specific body parts. Across profiles, notable differences include opioids being associated only with the pain interference profile, while antidepressants linked to the three complimentary profiles. We further provide evidence that our pain profiles offer valuable, additional insights into patients' wellbeing that are not captured by the body-part framework and make recommendations for how our pain profiles might sculpt the future design of healthcare delivery systems.

Overall, we provide evidence for a shift in pain medicine delivery systems from the conventional, body-part-based approach to one anchored in the pain experience and holistic profiles of patient function. This transition facilitates a more comprehensive management of chronic diseases, wherein pain treatment is integrated into broader health strategies. By focusing on holistic patient profiles, our approach not only addresses pain symptoms but also supports the management of underlying chronic conditions, thereby enhancing patient outcomes and improving quality of life. This model advocates for a seamless integration of pain management within the continuum of care for chronic diseases, emphasizing the importance of understanding and treating the interdependencies between chronic conditions and pain.

Functional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.

Aims to examine and compare white matter functional networks in neonates and 8-year-old children.

We acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks' characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.

White matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p < 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p < 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01-0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.

White matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

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.

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 ADHD-200 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.

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.

Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear.

We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed.

The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group.

The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

The association between visual abnormalities and impairments in cerebral blood flow and brain region potentially results in neural dysfunction of amblyopia. Nevertheless, the differences in the complex mechanisms of brain neural network coupling and its relationship with neurotransmitters remain unclear. Here, the neurovascular coupling mechanism and neurotransmitter activity in children with anisometropic amblyopia (AA) and visual deprivation amblyopia (VDA) was explored. The neurovascular coupling of 17 brain regions in amblyopia children was significantly abnormal than in normal controls. The classification abilities of coupling units in brain regions differed between two types of amblyopia. Correlations between different coupling effects and neurotransmitters were different. The findings of this study demonstrate a correlation between the neurovascular coupling and neurotransmitter in children with AA and VDA, implying their impaired neurovascular coupling function and potential molecular underpinnings. The neuroimaging evidence revealed herein offers potential for the development of neural therapies for amblyopia.

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.

Table tennis players have adaptive visual and sensorimotor networks, which are the key brain regions to acquire environmental information and generate motor output. This study examined 20 table tennis players and 21 control subjects through ultrahigh field 7 Tesla magnetic resonance imaging. First, we measured percentage amplitude of fluctuation across five different frequency bands and found that table tennis players had significantly lower percentage amplitude of fluctuation values than control subjects in 18 brain regions, suggesting enhanced stability of spontaneous brain fluctuation amplitudes in visual and sensorimotor networks. Functional connectional analyses revealed increased static functional connectivity between two sensorimotor nodes and other frontal-parietal regions among table tennis players. Additionally, these players displayed enhanced dynamic functional connectivity coupled with reduced static connectivity between five nodes processing visual and sensory information input, and other large-scale cross-regional areas. These findings highlight that table tennis players undergo neural adaptability through a dual mechanism, characterized by global stability in spontaneous brain fluctuation amplitudes and heightened flexibility in visual sensory networks. Our study offers novel insights into the mechanisms of neural adaptability in athletes, providing a foundation for future efforts to enhance cognitive functions in diverse populations, such as athletes, older adults, and individuals with cognitive impairments.

Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI.

Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling.

The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model.

The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.

How does co-presence change our neural experience of the world? Can a conversation change how we synchronise with our partner during later events? Using fNIRS hyperscanning, we measured brain activity from 27 pairs of familiar adults simultaneously over frontal, temporal and parietal regions bilaterally, as they co-watched two different episodes of a short cartoon. In-between the two episodes, each pair engaged in a face-to-face conversation on topics unrelated to the cartoon episodes. Brain synchrony was calculated using wavelet transform coherence and computed separately for real pairs and shuffled pseudo) pairs. Findings reveal that real pairs showed increased brain synchrony over right Dorso-Lateral Pre-Frontal cortex (DLPFC) and right Superior Parietal Lobe (SPL), compared to pseudo pairs (who had never seen each other and watched the same movie at different times; uncorrected for multiple comparisons). In addition, co-watching after a conversation was associated with greater synchrony over right TPJ compared to co-watching before a conversation, and this effect was significantly higher in real pairs (who engaged in conversation with each other) compared to pseudo pairs (who had a conversation with someone else; uncorrected for multiple comparisons). The present study has shed the light on the role of social interaction in modulating brain synchrony across people not just during social interaction, but even for subsequent non-social activities. These results have implications in the growing domain of naturalistic neuroimaging and interactive neuroscience.

Hypertension (HTN) affects over 1.2 billion individuals worldwide and is defined as systolic blood pressure (BP) ≥ 140 mmHg and diastolic BP ≥ 90 mmHg. Hypertension is also considered a high risk factor for cerebrovascular diseases, which may lead to vascular cognitive impairment (VCI). VCI is associated with executive dysfunction and is also a transitional stage between hypertension and vascular dementia. Hence, it is essential to establish a reliable approach to diagnosing the severity of VCI. In 28 HTN (51-83 yrs; 18 males, 10 females) and 28 healthy controls (HC) (51-75 yrs; 7 males, 21 females), we investigated which regions demonstrate alterations in the resting-state functional connectome due to vascular cognitive impairment in HTN by using the amplitude of the low-frequency fluctuations (ALFF), regional homogeneity (ReHo), graph theoretical analysis (GTA), and network-based statistic (NBS) methods. In the group comparison between ALFF/ReHo, HTN showed reduced spontaneous activity in the regions corresponding to vascular or metabolic dysfunction and enhanced brain activity, mainly in the primary somatosensory cortex and prefrontal areas. We also observed cognitive dysfunction in HTN, such as executive function, processing speed, and memory. Both the GTA and NBS analyses indicated that the HTN demonstrated complex local segregation, worse global integration, and weak functional connectivity. Our findings show that resting-state functional connectivity was altered, particularly in the frontal and parietal regions, by hypertensive individuals with potential vascular cognitive impairment.

Earlier neuroimaging investigations showed that abnormal brain activity in patients with alcohol use disorder (AUD) was frequency dependent. However, there is lacking of a comprehensive method to capture the amplitude of multi-frequency bands directly. Here, we used a new method, the power spectrum slope (PSS) to explore abnormal spontaneous activity of brain in patients with AUD.

Thirty-three AUD patients and 29 healthy controls (HCs) enrolled in this study. The coefficient b and the power-law slope b' were calculated and compared between two groups. We also used the receiver operating characteristic (ROC) curve to examine the ability of the PSS analysis to distinguish between AUD and HCs. We next examined the correlation between PSS difference in the brain areas and the severity of alcohol dependence.

Thirty AUD patients and 26 HCs were retained after head motion correction. The two metrics of PSS values increased in the left precentral gyrus in AUD patients. The area under the curve values of PSS differences in the specific brain area were respectively 0.836 and 0.844, with sensitivities of 86.7% and 83.3% and specificities of 73.1% and 76.9%. The Michigan Alcoholism Screening Test (MAST) and Alcohol drinking scale (ADS) scores were not significantly correlated with the PSS values in the specific brain area.

As a novel method, the PSS can well detect abnormal local brain activity in the AUD patients and may offer new insights for future fMRI studies.

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Parkinson's disease (PD) is a common neurodegenerative disorder that is predominantly known for its motor symptoms but is also accompanied by non-motor symptoms, including anxiety.

The underlying neurobiological substrates and brain network changes associated with comorbid anxiety in PD require further exploration.

An analysis of oscillation-specific nodal properties in patients with and without anxiety was conducted using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory. We used a band-pass filtering approach to differentiate oscillatory frequency bands for subsequent functional connectivity (FC) and graph analyses.

The study included 68 non-anxiety PD (naPD) patients, 62 anxiety PD (aPD) patients, and 64 healthy controls (NC). Analyses of nodal betweenness centrality (BC), degree centrality (DC), and efficiency were conducted across multiple frequency bands. The findings indicated no significant differences in BC among naPD, aPD, and NC within the 0.01-0.08 Hz frequency range. However, we observed a specific reduction in BC at narrower frequency ranges in aPD patients, as well as differing patterns of change in DC and efficiency, which are believed to reflect the neurophysiological bases of anxiety symptoms in PD.

Differential oscillation-specific nodal characteristics have been identified in PD patients with anxiety, suggesting potential dysregulations in brain network dynamics. These findings emphasize the complexity of brain network alterations in anxiety-associated PD and identify oscillatory frequencies as potential biomarkers. The study highlights the importance of considering oscillatory frequency bands in the analysis of brain network changes.

Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.

The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1 Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.

Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.

The expanded simple view of reading (SVR) model suggests that word decoding, language comprehension and executive functions are necessary for reading comprehension. Children with reading difficulties (RDs) often have deficits in critical components of reading established in the expanded SVR model and alterations in brain function of reading-related regions. Maternal education could provide children with advantageous educational opportunities or resources that support reading acquisition. The primary goal of this study was to examine the contributions of maternal education to the behavioural and neurobiological correlates of the expanded SVR model. Seventy-two 8- to 12-year-old children with RDs and typical readers (TRs) completed reading, behavioural and an functional magnetic resonance imaging stories-listening task to determine the functional connectivity of the receptive language network to the whole brain in association with maternal education. Higher maternal education was associated with better vocabulary in children with RDs and positive functional connectivity between the receptive language network and regions related to visual processing in children with RDs versus TRs. These data suggest that maternal education supports the ability to comprehend oral language and engagement of neural networks that support imagination/visualization in children with RDs.

This study was designed to examine the feasibility of analyzing heart rate variability (HRV) data from repeat-flier astronauts at matching days on two separate missions to assess any effect of repeated missions on brain plasticity and psychological resilience, as conjectured by Demertzi. As an example, on the second mission of a healthy astronaut studied about 20 days after launch, sleep duration lengthened, sleep quality improved, and spectral power (ms²) co-varying with activity of the salience network (SN) increased at night. HF-component (0.15-0.50 Hz) increased by 61.55%, and HF-band (0.30-0.40 Hz) by 92.60%. Spectral power of HRV indices during daytime, which correlate negatively with psychological resilience, decreased, HF-component by 22.18% and HF-band by 37.26%. LF-component and LF-band, reflecting activity of the default mode network, did not change significantly. During the second mission, 24-h acrophases of HRV endpoints did not change but the 12-h acrophase of TF-HRV did (P < 0.0001), perhaps consolidating the circadian system to help adapt to space by taking advantage of brain plasticity at night and psychological resilience during daytime. While this N-of-1 study prevents drawing definitive conclusions, the methodology used herein to monitor markers of brain plasticity could pave the way for further studies that could add to the present results.

Previous studies have reported that patients with white matter lesions (WMLs) have abnormal spontaneous brain activity in the resting state. However, the spontaneous neuronal activity of specific frequency bands in WMLs patients is unknown. Here, we included 16 WMLs patients and 13 gender and age-matched healthy controls (HCs) underwent resting-state magnetic resonance imaging (rs-fMRI) scan and studied the specificity of the amplitude of low-frequency fluctuations (ALFF) in WMLs patients in the slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and typical (0.01-0.08 Hz) frequency bands. In addition, ALFF values of different frequency bands were extracted as classification features and support vector machines (SVM) were used to classify WMLs patients. In all three frequency bands, significant increases in ALFF values in WMLs patients were observed in the cerebellum. In the slow-5 band, the ALFF values of the left anterior cingulate and paracingulate gyri (ACG), and the right precentral gyrus, rolandic operculum and inferior temporal gyrus in WMLs patients were lower than those in HCs. In the slow-4 band, ALFF values were lower in WMLs patients than in HCs at the left ACG, the right median cingulate and paracingulate gyri, parahippocampal gyrus, caudate nucleus, and the bilateral lenticular nucleus, putamen. In the SVM classification model, the classification accuracy of slow-5, slow-4 and typical frequency bands is 75.86%, 86.21% and 72.41%, respectively. The results indicate that the ALFF abnormalities in WMLs patients have frequency specificity, and the ALFF abnormalities in the slow-4 frequency band may serve as imaging markers for WMLs.

At present, fMRI studies mainly focus on the entire low-frequency band (0. 01-0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency-based dynamic functional connectivity (dFC) analysis method was proposed in this study, which was then applied to a schizophrenia study. First, three frequency bands (Conventional: 0.01-0.08 Hz, Slow-5: 0.0111-0.0302 Hz, and Slow-4: 0.0302-0.0820 Hz) were obtained using Fast Fourier Transform. Next, the fractional amplitude of low-frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by the sliding time window method at four window-widths. Finally, recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of patients with schizophrenia and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with the conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.

The self-to-other model of empathy (SOME) states that a key reason for the empathic deficiency in autistic individuals is the imbalance of the self-other switch. The existing interventions of theory of mind contain training of self-other transposition ability but combined with other cognitive trainings. The self-other distinction brain areas of autistic individuals have been revealed, but the brain areas of the self-other transposition ability and its intervention have not been investigated. There are normalized amplitudes of low-frequency fluctuations (mALFFs) within 0.01-0.1 Hz and many normalized amplitudes of frequency fluctuations (mAFFs) within 0-0.01, 0.01-0.05, 0.05-0.1, 0.1-0.15, 0.15-0.2, and 0.2-0.25 Hz. Therefore, the current study established a progressive self-other transposition group intervention to specifically and systematically improve autistic children's self-other transposition abilities. The transposition test with a three mountains test, an unexpected location test, and a deception test was used to directly measure autistic children's transposition abilities. The Interpersonal Responsiveness Index Empathy Questionnaire with perspective-taking and fantasy subscales (IRI-T) was used to indirectly measure autistic children's transposition abilities. The Autism Treatment Evaluation Checklist (ATEC) was used to measure autistic children's autism symptoms. The experiment was designed with two (intervention: experimental group vs. control group) independent variables and two (test time: pretest vs. posttest or tracking test) × three (test: transposition test vs. IRI-T test vs. ATEC test) dependent variables. Furthermore, it used eyes-closed resting-state functional magnetic resonance imaging to investigate and compare the relevant maternal mALFFs and average energy rank and energy rank variability of mAFFs of autistic children's transposition abilities, autism symptoms, and intervention effects. The results showed the following: (1) There were many improvements (pretest vs. posttest or tracking test) greater than chance 0 in the experimental group, such as the three mountains, lie, transposition, PT, IRI-T, PT tracking, cognition, behavior, ATEC, language tracking, cognition tracking, behavior tracking, and ATEC tracking improvements. However, there was no improvement greater than chance 0 in the control group. (2) The maternal mALFFs and maternal average energy rank and energy rank variability of mAFFs could predict the autistic children's transposition abilities, autism symptoms, and intervention effects with some overlap and some difference in maternal self-other distinction, sensorimotor, visual, facial expression recognition, language, memory and emotion, and self-consciousness networks. These results indicated that the progressive self-other transposition group intervention successfully improved autistic children's transposition abilities and reduced their autism symptoms; the intervention effects could be applied to daily life and last up to a month. The maternal mALFFs, average energy rank, and energy rank variability of mAFFs were three effective neural indictors of autistic children's transposition abilities, autism symptoms, and intervention effects, and the average energy rank and energy rank variability of mAFFs were two new neural indictors established in the current study. The maternal neural markers of the progressive self-other transposition group intervention effects for autistic children were found in part.

Functional connectome studies have revealed widespread connectivity alterations in major depressive disorder (MDD). However, the low frequency bandpass filtering (0.01-0.08 Hz or 0.01-0.1 Hz) in most studies have impeded our understanding on whether and how these alterations are affected by frequency of interest.

Here, we performed frequency-resolved (0.01-0.06 Hz, 0.06-0.16 Hz and 0.16-0.24 Hz) connectome analyses using a large-sample resting-state functional MRI dataset of 1002 MDD patients and 924 healthy controls from seven independent centers.

We reported significant frequency-dependent connectome alterations in MDD in left inferior parietal, inferior temporal, precentral, and fusiform cortices and bilateral precuneus. These frequency-dependent connectome alterations are mainly derived by abnormalities of medium- and long-distance connections and are brain network-dependent. Moreover, the connectome alteration of left precuneus in high frequency band (0.16-0.24 Hz) is significantly associated with illness duration.

Multisite harmonization model only removed linear site effects. Neurobiological underpinning of alterations in higher frequency (0.16-0.24 Hz) should be further examined by combining fMRI data with respiration, heartbeat and blood flow recordings in future studies.

These results highlight the frequency-dependency of connectome alterations in MDD and the benefit of examining connectome alteration in MDD under a wider frequency band.

To investigate frequency-specific alterations of spontaneous brain activity in first-episode drug-naïve schizophrenia (SZ) patients and the associations with clinical symptoms.

We collected the resting-state functional MRI (rs-fMRI) data from 84 first-episode drug-naïve SZ patients and 94 healthy controls (HCs) and calculated the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) of four frequency bands, including slow-2, slow-3, slow-4, and slow-5. Two-sample t-tests were used to evaluate the intergroup differences in ALFF and ReHo, while partial correlation analyses were conducted to explore the associations between abnormal ALFF and ReHo and the severity of clinical symptoms in the SZ group.

Compared with HCs, the SZ group showed reduced ALFF in superior cerebellum and cerebellar vermis across slow-2, slow-3, and slow-4 bands, while increased ALFF was found in left superior temporal gyrus, middle temporal gyrus, and superior temporal pole at slow-4 band. Moreover, reduced ReHo was observed in the right precentral and postcentral gyri at slow-3 band in the SZ group. Additionally, the ALFF of left superior temporal gyrus, middle temporal gyrus, and superior temporal pole in slow-4 band showed a trend of positive correlation with the excited factor score of Positive and Negative Syndrome Scale (PANSS) in the SZ group.

Our results suggest that local alterations of spontaneous brain activity were frequency-specific in first-episode drug-naïve SZ patients.

The analysis of brain function in resting-state network (RSN) models, ascertained through the functional connectivity pattern of resting-state functional magnetic resonance imaging (rs-fMRI), is sufficiently powerful for studying large-scale functional integration of the brain. However, in RSN-based research, the network architecture has been regarded as the same through different frequency bands. Thus, here, we aimed to examined whether the network architecture changes with frequency. The blood oxygen level-dependent (BOLD) signal was decomposed into four frequency bands-ranging from 0.007 to 0.438 Hz-and the clustering algorithm was applied to each of them. The best clustering number was selected for each frequency band based on the overlap ratio with task activation maps. The results demonstrated that resting-state BOLD signals exhibited frequency-specific network architecture; that is, the networks finely subdivided in the lower frequency bands were integrated into fewer networks in higher frequency bands rather than reconfigured, and the default mode network and networks related to perception had sufficiently strong architecture to survive in an environment with a lower signal-to-noise ratio. These findings provide a novel framework to enable improved understanding of brain function through the multiband frequency analysis of ultra-slow rs-fMRI data.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.

This study features an functional magnetic resonance imaging (fMRI) hyperscanning experiment from 2 sites, 305 km apart. The experiment contains 2 conditions: the dyad collaborated to win and then split the reward in the cooperation condition, whereas the winner took all the reward in the competition condition, thereby resulting in dynamic strategic interactions. To calculate the cerebral coherence in such jittered event-related fMRI tasks, we first iteratively estimated the feedback-related blood oxygenation level-dependent responses of each trial, using 8 finite impulse response functions (16 s) and then concatenated the beta volume series. With the right temporal-parietal junction (rTPJ) as the seed, the interpersonal connected brain areas were separately identified: the right superior temporal gyrus (rSTG) (cooperation) and the left precuneus (lPrecuneus) (competition), both peaking at the designated frequency bin (1/16 s = 0.0625 Hz), but not in permuted pairs. In addition, the extended coherence analyses on shorter and longer concatenated volumes verified that only in the optimal trial frequency did the rTPJ-rSTG and rTPJ-lPrecuneus couplings peak. In sum, our approach both showcases a flexible analysis method that widens the applicability of interpersonal coherence in the rapid event-related fMRI hyperscanning and reveals a context-based inter-brain coupling between interacting pairs during cooperation and during competition.

Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

Apathy is among the most prevalent and incapacitating non-motor symptoms of Parkinson's disease (PD). PD patients with apathy (PD-A) have been reported to have abnormal spontaneous brain activity mainly in 0.01-0.08 Hz. However, the frequency-dependence of brain activity in PD-A remains unclear. Therefore, this study aimed to examine whether abnormalities in PD-A are associated with specific frequency bands.

Overall, 28 patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 gender-, age-matched healthy controls (HCs) were enrolled. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data, demographic information, and neuropsychological assessments, including apathy, depression, anxiety and cognitive function for every participant. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in the conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) frequency bands based on statistical parametric mapping (SPM12) and RESTplus V1.25. Two-sample t-tests were performed to compare the differences among the three groups.

PD-A reduced ALFF in the right anterior cingulate gyri in the slow-5 band and decreased fALFF in the right middle frontal gyrus in the conventional band, compared to patients with PD-NA. However, PerAF, ReHo, and DC could not distinguish PD-A from PD-NA in the three bands. PD-A had higher ALFF and fALFF in the left middle occipital gyrus and lower fALFF in the bilateral insula in the slow-5 band compared to the HCs. Furthermore, abnormal DC value in hippocampus and parahippocampus was observed separately in the conventional band and in the slow-4 band between PD-A and HCs. Moreover, PD-A and PD-NA showed lower ReHo in cerebellum in the three bands compared to the HCs.

Our study revealed that PD-A and PD-NA might have different neurophysiological mechanisms. Concurrently, the ALFF in the slow-5 band and fALFF in the conventional band were sensitive in differentiating PD-A from PD-NA. The influence of apathy on the disease can be considered in the future research on PD, with the effects of frequency band taken into account when analyzing spontaneous brain activities in PD-A.

Bipolar patients (BD) in the euthymic phase show almost no symptoms, nevertheless possibility of relapse is still present. We expected to find a psychobiological trace of their vulnerability by analyzing a specific network-the Language Network (LN)-connecting many high-level processes and brain regions measured at rest. According to Crow's hypothesis on the key role of language in the origin of psychoses, we expected an altered asymmetry of the LN in euthymic BDs. Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and 16 healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a functional magnetic resonance imaging scan at rest. The LN was extracted through independent component analysis. Then, LN time series was used to compute the fractional amplitude of the low-frequency fluctuation (fALFF) index, which was then correlated with clinical scales. Compared with HC, euthymic patients showed an altered LN with greater activation of Broca's area right homologous and anterior insula together with reduced activation of left middle temporal gyrus. The normalized fALFF analysis on BD patients' LN time series revealed that the Slow-5 fALFF band was positively correlated with residual mania symptoms but negatively associated with depression scores. In line with Crow's hypothesis postulating an altered language hemispheric asymmetry in psychoses, we revealed, in euthymic BD patients, a right shift involving both the temporal and frontal linguistic hubs. The fALFF applied to LN allowed us to highlight a number of significant correlations of this measure with residual mania and depression psychiatric symptoms.

Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.