Dorsal Attention Network Research Articles

Altered resting-state network connectivity in internet gaming disorder

BackgroundThe growing popularity of internet gaming among adolescents and young adults has driven an increase in both casual and excessive gaming behavior. Nevertheless, it remains unclear how progressive increases in internet gaming engagement led to changes within and between brain networks. This study aims to investigate these connectivity alterations across varying levels of gaming involvement.MethodsIn this cross-sectional study, 231 participants were recruited and classified into three groups according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for Internet Gaming Disorder (IGD): IGD group, highly engaged gaming(HEG) group, and lowly engaged gaming (LEG) group. Resting-state fMRI data from 217 participants (143 males, 74 females) were included in the final analysis. Independent component analysis was used to examine differences in intra- and inter-network functional connectivity (FC)across the three groups.ResultsNo significant differences were found in intra-network FC across the three groups. However, significant inter-network differences between the dorsal attention network(dAN)and the visual network (VN) among the three groups were observed. The HEG group exhibited significantly higher dAN-VN functional network connectivity (FNC) compared to the LEG group. Linear correlation analyses showed no significant correlation between the dAN-VN FNC values and IGD-20T scores.ConclusionThroughout the development of IGD, increasing levels of engagement are associated with a rise and subsequent decline in FNC of DAN-VN. This pattern may reflect top-down attentional regulation in the early stages of addiction, followed by attentional bias as addiction progresses.

Visual statistical learning alters low-dimensional cortical architecture.

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.Significance statement The current work is the first to investigate changes in whole-brain manifold architecture that underlie visual statistical learning (SL). We found that areas of the visual cortex and dorsal attention network showed significant connectivity changes during learning, reflecting their decreased, and increased covariance with other networks, respectively. Notably, when SL was later disrupted, regions within the medial temporal lobe, which had shown no evidence of initial learning, now began to increase connectivity with higher-order cortex. Together, these findings not only reveal the widespread neural interactions that underlie visual SL, but also extend prior work, suggesting separable cortical and medial temporal lobe contributions for the encoding versus violation of learned associations.

Functional Connectivity Changes Associated With Depression in Dementia With Lewy Bodies.

Depressive symptoms are frequent in the early stages of dementia with Lewy bodies (DLB), and more than half of DLB patients would have a history of depression. Our study sought to investigate the functional connectivity (FC) changes associated with depressive symptoms in prodromal to mild DLB patients compared with controls. MRI data were collected from 66 DLB patients and 18 controls. Depression was evaluated with the Mini International Neuropsychiatric Interview. Resting-state FC (rsFC) was investigated with the CONN toolbox using a seed-based approach and both regression and comparison analyses. Correlations were found between the depression scores and the rsFC between fronto-temporal and primary visual areas in DLB patients (p<0.05, FDR corrected). Depressed DLB patients also showed decreased rsFC within the salience network (SN), increased rsFC between the default mode network (DMN) and the language network (LN) and decreased rsFC between the cerebellar network (CN) and the fronto-parietal network (FPN) compared to non-depressed DLB patients (p<0.05, uncorrected). Comparison analyses between antidepressant-treated and non-treated DLB patients highlighted FC changes in treated patients involving the SN, the DMN, the FPN and the dorsal attentional network (p<0.05, uncorrected). Our findings revealed that depressive symptoms would especially be associated with rsFC changes between fronto-temporal and primary visual areas in DLB patients. Such alterations could contribute to difficulties in regulating emotions, processing biases towards negative stimuli, and self-focused ruminations. This study is part of the cohort study AlphaLewyMA (https://clinicaltrials.gov/ct2/show/NCT01876459).

Using EEG Microstates to Examine Whole-Brain Neuronal Networks During Offline Rest Consolidation after Visual Perceptual Learning.

Using EEG Microstates to Examine Whole-Brain Neuronal Networks During Offline Rest Consolidation after Visual Perceptual Learning.

Age- and Sex-Specific Patterns in Adult Brain Network Segregation.

The human brain is organized into several segregated associative and sensory functional networks, each responsible for various aspects of cognitive and sensory processing. These functional networks become less segregated over the adult lifespan, possibly contributing to cognitive decline that is observed during advanced age. To date, a comprehensive understanding of decreasing network segregation with age has been hampered by (1) small sample sizes, (2) lack of investigation at different spatial scales, (3) the limited age range of participants, and more importantly (4) an inadequate consideration of sex (biological females and males) differences. This study aimed to address these shortcomings. Resting-state functional magnetic resonance imaging data were collected from 357 cognitively intact participants (18.2-91.8 years; 49.9 ± 17.1 years; 27.70 ± 1.72 MoCA score, 203 [56.8%] females), and the segregation index (defined as one minus the ratio of between-network connectivity to within-network connectivity) was calculated at three spatial scales of brain networks: whole-brain network, intermediate sensory and associative networks, as well as core visual (VIS), sensorimotor (SMN), frontoparietal (FPN), ventral attention (VAN), dorsal attention (DAN), and default mode networks (DMN). Where applicable, secondary within-, between-, and pairwise connectivity analyses were also conducted to investigate the origin of any observed age and sex effects on network segregation. For any given functional metric, linear and quadratic age effects, sex effects, and respective age by sex interaction effects were assessed using backwards iterative linear regression modeling. Replicating previous work, brain networks were found to become less segregated across adulthood. Specifically, negative quadratic decreases in whole-brain network, intermediate associative network, VAN, and DMN segregation index were observed. Intermediate sensory networks, VIS, and SMN exhibited negative linear decreases in segregation index. Secondary analysis revealed that this process of age-related functional reorganization was preferential as functional connectivity was observed to increase either between anatomically adjacent associative networks (DMN-DAN, FPN-DAN) or between anterior associative and posterior sensory networks (VIS-DAN, VIS-DMN, VIS-FPN, SMN-DMN, and SMN-FPN). Inherent sex differences in network segregation index were also observed. Specifically, whole-brain, associative, DMN, VAN, and FPN segregation index was greater in females compared to males, irrespective of age. Secondary analysis found that females have reduced functional connectivity between associative networks (DAN-VAN, VAN-FPN) compared to males and independent of age. A notable linear age-related decrease in FPN SI was also only observed for females and not males. The observed findings support the notion that functional networks reorganize across the adult lifespan, becoming less segregated. This decline may reflect underlying neurocognitive aging mechanisms like neural dedifferentiation, inefficiency, and compensation. The aging trajectories and rates of decreasing network segregation, however, vary across associative and sensory networks. This study also provides preliminary evidence of inherent sex differences in network organization, where associative networks are more segregated in females than males. These inherent sex differences suggest that female functional networks may be more efficient and functionally specialized compared to males across adulthood. Given these findings, future studies should take a more focused approach to examining sex differences across the lifespan, incorporating multimodal methodologies.

Beyond the uniform creative brain: Inter-individual variability in functional connectivity correlates with creativity.

Beyond the uniform creative brain: Inter-individual variability in functional connectivity correlates with creativity.

Resting-state network alterations in depression: a comprehensive meta-analysis of functional connectivity.

Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression. A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks. A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN-FPN and limbic network (LN)-DMN connectivity, decreased DMN-somatomotor network and LN-FPN connectivity, and varied ventral attention network (VAN)-dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN. These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.

Ultrasound Neuromodulation With Transcranial Pulse Stimulation in Alzheimer Disease

Given the increasing prevalence of dementia and the limited treatment options available, ultrasound neuromodulation could serve as a novel add-on therapy to standard treatments for Alzheimer disease (AD). As ultrasound neuromodulation is still in its early stages, further research is essential to fully explore its potential in treating brain disorders. To evaluate clinical and functional imaging effects of transcranial pulse stimulation (TPS) in patients with AD. A randomized, double-blind, sham-controlled, crossover clinical trial was conducted at the Medical University of Vienna between January 1, 2017, and July 27, 2022. Sixty patients with clinically diagnosed AD receiving state-of-the-art treatment were randomly allocated to treatment sequence groups verum-sham (first cycle verum, second cycle sham, n = 30) and sham-verum (n = 30). Data analysis was performed from July 28, 2022, to September 5, 2024. Each participant received 6 verum and 6 sham TPS sessions (6000 pulses, 0.20 mJ/mm2, 5 Hz) to frontoparietal brain areas. Neuropsychological tests, including the primary outcome Consortium to Establish a Registry for Alzheimer's Disease (CERAD) corrected total score (CTS), were performed at baseline and 1 week, 1 month, and 3 months following the stimulations in each cycle. Primary and secondary outcomes, including functional magnetic resonance imaging and Beck Depression Inventory-II, were analyzed by intention-to-treat analysis and, for sensitivity, by per protocol analysis. For the intention-to-treat analysis, 60 patients between ages 51 and 82 years (mean [SD], 70.65 [8.16] years; 30 females; 30 males) were included. The CERAD CTS increased by a mean (SD) of 2.22 (6.87) points in the verum condition from 70.93 (14.27) points at baseline to 73.15 (14.90) 3 months after stimulation, while the mean (SD) score in the sham condition increased by 1.00 (6.82) point vs baseline from 71.68 (13.62] at baseline to 72.68 (14.48) 3 months after stimulation. Primary data analysis of the condition × session interaction was not significant (P = .68; partial η2 [ηp2] = 0.01), but its interaction with age was P = .003; ηp2 = 0.08, followed by post hoc analyses of age subsamples. Although several patients older than 70 years benefited from verum TPS, only the younger subgroup (≤70 years) showed significantly higher CTS increases for verum in all poststimulation sessions (condition × session: P = .005; ηp2 = 0.16). At 3 months after stimulation, for example, a mean (SD) 3.91 (7.86)-point increase was found for verum TPS in the younger patients, but a mean (SD) CTS decrease of 1.83 (5.80) was observed for sham. Memory-associated brain activation was significantly higher after verum TPS in the precuneus, visual, and frontal areas, while resting state functional connectivity was significantly upregulated in the dorsal attention network. In the per protocol sample, a significant reduction of the Beck Depression Inventory-II scores 3 months following verum TPS was found (verum baseline: 7.27 [5.87]; verum 3 months after stimulation: 5.27 [5.27]; sham baseline: 6.70 [5.65]; sham 3 months after stimulation: 6.22 [4.40]; P = .008; ηp2 = 0.23). During both verum and sham conditions, the most common observed adverse symptom was depression; no major neuropathologic change was detected in the patients by detailed neuroradiologic assessments. In this randomized clinical trial of TPS in patients with AD, a 2-week verum treatment improved cognitive scores in the younger subgroup, ameliorated depressive symptoms, and induced upregulation of functional brain activation and connectivity. These findings suggest TPS may be a safe and promising add-on therapy for patients with AD receiving state-of-the-art treatment. ClinicalTrials.gov Identifier: NCT03770182.

Prestimulus functional connectivity reflects attention orientation in a prospective memory task: A magnetoencephalographic (MEG) study.

Prospective Memory (PM) is the ability to encode an intention in memory and retrieve it at the right time in the future. After the intention is formed, it must be maintained in memory while simultaneously monitoring the environment until the occurrence of the stimulus associated with its retrieval. Therefore, monitoring and maintenance processes must work in conjunction to subserve PM processing (monitoring/maintenance phase). Several brain regions play a role in PM, such as the anterior prefrontal cortex, inferior parietal lobules, and precuneus. Notably, these regions belong to different brain networks and are differently involved depending on the memory and attentional requests of the PM task. In this study, we investigate the neural bases of PM from a network perspective, using functional connectivity (FC) analysis to identify the networks involved in the attentional and memory mechanisms underlying PM. To this end, we analyzed MEG data collected in two different PM conditions, enhancing either the monitoring (i.e., attention) or the maintenance (i.e., memory) loads of the PM task. To disentangle the neural correlates of these mechanisms from other processes occurring after stimulus presentation, the analysis focused on the prestimulus time window (monitoring/maintenance phase). The monitoring-load condition was characterized by increased inter-network FC of the Dorsal Attention Network (DAN) in the alpha band, a marker of increased top-down monitoring. In contrast, the maintenance-load condition was associated with increased connectivity of the Ventral Attention Network (VAN) with the FrontoParietal Control and the Default-Mode Networks (FPCN and DMN, respectively). Additionally, response times were found to correlate with prestimulus alpha connectivity of different networks in the two conditions. These differences in connectivity within and between networks support the hypothesis that different networks (DAN, or VAN and DMN) and mechanisms (top-down or bottom-up, respectively) are involved in PM processing depending on the features of the PM task.

Dynamic reconfiguration of brain functional networks in world class gymnasts: a resting-state functional MRI study.

Long-term intensive training has enabled world class gymnasts to attain exceptional skill levels, inducing notable neuroplastic changes in their brains. Previous studies have identified optimized brain modularity related to long-term intensive training based on resting-state functional MRI, which is associated with higher efficiency in motor and cognitive functions. However, most studies assumed that functional topological networks remain static during the scans, neglecting the inherent dynamic changes over time. This study applied a multilayer network model to identify the effect of long-term intensive training on dynamic functional network properties in gymnasts. The imaging data were collected from 13 gymnasts and 14 age- and gender-matched non-athlete controls. We first construct dynamic functional connectivity matrices for each subject to capture the temporal information underlying these brain signals. Then, we applied a multilayer community detection approach to analyse how brain regions form modules and how this modularity changes over time. Graph theoretical parameters, including flexibility, promiscuity, cohesion and disjointedness, were estimated to characterize the dynamic properties of functional networks across global, network, and nodal levels in the gymnasts. The gymnasts showed significantly lower flexibility, cohesion and disjointedness at the global level than the controls. Then, we observed lower flexibility and cohesion in the auditory, dorsal attention, sensorimotor, subcortical, cingulo-opercular and default mode networks in the gymnasts than in the controls. Furthermore, these gymnasts showed decreased flexibility and cohesion in several regions associated with motor function. Together, we found brain functional neuroplasticity related to long-term intensive training, primarily characterized by decreased flexibility of brain dynamics in the gymnasts, which provided new insights into brain reorganization in motor skill learning.

Reduced temporal variability of cortical excitation/inhibition ratio in schizophrenia

Altered neural excitation/inhibition (E/I) balance has long been suspected as a potential underlying cause for clinical symptoms in schizophrenia (SZ). Recent methodological advancements linking the spectral slope (β) of neurophysiological recordings – such as them electroencephalogram (EEG) – to E/I ratio provided much-needed tools to better understand this plausible relationship. Importantly, most approaches treat E/I ratio as a stationary feature in a single scaling range. On the other hand, previous research indicates that this property might change over time, as well as it can express different characteristics in low- and high-frequency regimes. In line, in this study we analyzed resting-state EEG recordings from 30 patients with SZ and 31 healthy controls (HC) and characterized E/I ratio via β separately for low- (1–4 Hz) and high- (20–45 Hz) frequency regimes in a time-resolved manner. Results from this analysis confirmed the bimodal nature of power spectra in both HC and SZ, with steeper spectral slopes in the high- compared to low-frequency ranges. We did not observe any between-group differences in stationary (i.e., time-averaged) neural signatures, however, the temporal variance of β in the 20–45 Hz regime was significantly reduced in SZ patients when compared to HC, predominantly over regions corresponding to the dorsal attention network. Furthermore, this alteration was found correlated to positive clinical symptom scores. Our study indicates that altered E/I ratio dynamics are a characteristic trait of SZ that reflect pathophysiological processes involving the parietal and occipital cortices, potentially responsible for some of the clinical features of the disorder.

Impacts of night shift on medical professionals: a pilot study of brain connectivity and gut microbiota.

Night shift is a prevalent workstyle in medical hospitals, demanding continuous health monitoring and rapid decision making of medical professionals. Night shifts may cause serious health problems to medical staff, including cognitive impairments, poor sleep, and inflammatory responses, leading to the altered gut-brain axis. However, how night shifts impact gut-brain axis and how long the impact lasts remain to be studied. Hence, we investigated the dynamic changes of brain-microbiota relations following night shifts and subsequent recovery days among medical shift workers. Young medical staffs were recruited for the 3-session assessments over the scheduled night shifts (pre-shift, post-shift, and recovery) by measuring (a) sleep metrics, (b) brain functions, (c) gut bacteriome compositions, and (d) cognitive assessments. Participants experienced partial sleep deprivation only during the 5-day night shifts but rapidly returned to baseline after the 4-day recovery, so as the elevated brain fluctuations in the superior frontal gyrus after night shifts. Meanwhile, the night shifts caused elongated connectivity changes of default-mode and dorsal attention networks without recovery. Nevertheless, we did not find prevailing night-shift effects on cognition and gut bacteriome compositions, except the Gemellaceae concentration and the multi-task performance. Collectively, night shifts may induce prolonged alterations on brain connectivity without impacts on gut bacteriome, suggesting the vulnerable brain functions and the resilient gut bacteriome to the short-term night shifts among medical shift workers.

Infraslow Dynamic Patterns in Human Cortical Networks Track a Spectrum of External to Internal Attention.

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

Decoding Visual Spatial Attention Control.

In models of visual spatial attention control, it is commonly held that top-down control signals originate in the dorsal attention network, propagating to the visual cortex to modulate baseline neural activity and bias sensory processing. However, the precise distribution of these top-down influences across different levels of the visual hierarchy is debated. In addition, it is unclear whether these changes in baseline neural activity directly translate into improved performance. We analyzed attention-related baseline activity during the anticipatory period of a trial-by-trial voluntary spatial attention task, using two independent fMRI datasets, and two different analytic approaches. First, as in prior studies, univariate analysis showed that covert attention significantly enhanced baseline neural activity in higher-order visual areas contralateral to the attended visual hemifield, while effects in lower-order visual areas (e.g., V1) were weaker and more variable. Second, in contrast, multivariate pattern analysis (MVPA) revealed significant decoding of attention conditions across all visual cortical areas, with lower-order visual areas exhibiting higher decoding accuracies than higher-order areas. Third, decoding accuracy, rather than the magnitude of univariate activation, was a better predictor of a subject's stimulus discrimination performance. Finally, the MVPA results were replicated across two experimental conditions, where the direction of spatial attention was either externally instructed by a cue or based on the participants free choice decision about where to attend. Together, these findings offer new insights into the extent of attentional biases in the visual hierarchy under top-down control, and how these biases influence both sensory processing and behavioral performance.Significance Statement Attention can be deployed in advance of stimulus processing. Understanding how top-down control of attention facilitates the processing of the attended stimuli and enhances task performance has remained a longstanding question in attention research. Here, applying multivariate pattern analysis (MVPA) to fMRI data, we showed that throughout the entire visual hierarchy including the primary visual cortex, there exist distinct neural representations for different attended information in anticipatory visual spatial attention, and the distinctiveness of these neural representations is positively associated with behavioral performance. Importantly, the MVPA findings were consistent across two experimental conditions where the direction of spatial attention was driven either by external instructions or from purely internal decisions.

Atypical attentional network dynamics in adolescent depression during emotional movie viewing.

Emotion studies have commonly reported atypical emotional processing in clinically depressed adolescents in the context of short-lasting emotional cues. However, interindividual differences in the moment-to-moment brain network dynamics that underlie this impaired emotional reactivity remain unclear, and the use of poorly matched controls and relatively small sample sizes represents major limitations in most neuroimaging depression studies to date. Here, we address these concerns by using the temporal features of a rich naturalistic paradigm (i.e. a clip from the movie 'Despicable Me') to investigate brain network dynamics in 42 clinically depressed and 42 nondepressed adolescents aged 16-21 years, matched for age, gender, and psychiatric comorbidities. Using a dynamics functional connectivity analysis technique called Leading Eigenvector Dynamics Analysis, we found that the clinical group exhibited significantly higher probability of occurrence of the dorsal attention network and lower recruitment of the fronto-parietal, default mode network, ventral attention, and somato-motor networks throughout the task. This brain/behaviour relationship was prominent during less emotional moments of the movie, consistent with previous findings. Our findings demonstrate the key role of continuous affective measures in providing information about how activity in the depressed brain evolves as emotional intensity unfolds throughout the movie. Future studies with a larger sample size are needed in order to corroborate the present findings.

Abnormal resting-state functional connectivity in adolescent depressive episodes.

Abnormal resting-state functional connectivity in adolescent depressive episodes.

Connectome-based Predictive Models of General and Specific Executive Functions.

Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common executive function factor. To investigate the relationships among the three executive function components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared executive function component can be predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. The Updating-specific component showed significant cross-prediction with the general executive function factor, suggesting a relatively stronger role than the other components. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating more distinct and specialized roles. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates a novel approach to infer common and specific components of executive function.

Age-related changes in brain signal variability in autism spectrum disorder

BackgroundBrain signal variability (BSV) is an important understudied aspect of brain function linked to cognitive flexibility and adaptive behavior. Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and restricted and repetitive behaviors (RRBs). While atypical brain function has been identified in individuals with ASD using fMRI task-activation and functional connectivity approaches, little is known about age-related relationships with resting-state BSV and repetitive behaviors in ASD.MethodsWe conducted a cross-sectional examination of resting-state BSV and its relationship with age and RRBs in a cohort of individuals with Autism Brain Imaging Data Exchange (n = 351) and typically developing (TD) individuals (n = 402) aged 5–50 years obtained from the Autism Brain Imaging Data Exchange. RRBs were assessed using the Autism Diagnostic Interview-Revised (ADI-RRB) scale. BSV was quantified using the root-mean-square successive difference (rMSSD) of the resting-state fMRI time series. We examined categorical group differences in rMSSD between ASD and TD groups, controlling for both linear and quadratic age. To identify dimensional relationships between age, group, and rMSSD, we utilized interaction regressors for group x age and group x quadratic age. Within a subset of individuals with ASD (269 subjects), we explored the relationship between rMSSD and ADI-RRB scores, both with and without age considerations. The relationship between rMSSD and ADI-RRB scores was further analyzed while accounting for linear and quadratic age. Additionally, we investigated the relationship between BSV, age, and ADI-RRB scores using interaction regressors for age x RRB and quadratic age x RRB.ResultsWhen controlling for linear age effects, we observed significant group differences between individuals with ASD and TD individuals in the default-mode network (DMN) and visual network, with decreased BSV in ASD. Similarly, controlling for quadratic age effects revealed significant group differences in the DMN and visual network. In both cases, individuals with ASD showed decreased BSV compared with TD individuals in these brain regions. The group × age interaction demonstrated significant group differences in the DMN, and visual network brain areas, indicating that rMSSD was greater in older individuals compared with younger individuals in the ASD group, while rMSSD was greater in younger individuals compared with older individuals in the TD group. The group × quadratic age interaction showed significant differences in the brain regions included in DMN, with an inverted U-shaped rMSSD-age relationship in ASD (higher rMSSD in younger individuals that slightly increased into middle age before decreasing) and a U-shaped rMSSD-age relationship in TD (higher rMSSD in younger and older individuals compared with middle-aged individuals). When controlling for linear and quadratic age effects, we found a significant positive association between rMSSD and ADI-RRB scores in brain regions within the DMN, salience, and visual network. While no significant results were observed for the linear age × RRB interaction, a significant association between quadratic age and ADI-RRB scores emerged in the DMN, dorsal attention network, and sensorimotor network. Individuals with high ADI-RRB scores exhibited an inverted U-shaped relationship between rMSSD and age, with lower rMSSD levels observed in both younger and older individuals, and higher rMSSD in middle-aged individuals. Those with mid-range ADI-RRB scores displayed a weak inverted U-shaped rMSSD-age association. In contrast, individuals with low ADI-RRB scores showed a U-shaped rMSSD-age association, with higher rMSSD levels in younger and older individuals, but a lower rMSSD in middle-aged individuals.ConclusionThese findings highlight age-related atypical BSV patterns in ASD and their association with repetitive behaviors, contributing to the growing literature on understanding alterations in functional brain maturation in ASD.

A multimodal characterization of low-dimensional thalamocortical structural connectivity patterns

The human thalamus is a heterogeneous subcortical structure coordinating whole-brain activity. Investigations of its internal organization reveal differentiable subnuclei, however, a consensus on subnuclei boundaries remains absent. Recent work suggests that thalamic organization additionally reflects continuous axes transcending nuclear boundaries. Here, we study how low-dimensional axes of thalamocortical structural connectivity relate to intrathalamic microstructural features, functional connectivity, and structural covariance. Using diffusion MRI, we compute a thalamocortical structural connectome and derive two main axes of thalamic organization. The principal axis, extending from medial to lateral, relates to intrathalamic myelin, and functional connectivity organization. The secondary axis corresponds to the core-matrix cell distribution. Lastly, exploring multimodal associations globally, we observe the principal axis consistently differentiating limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varies. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.

Disease-specific alterations of effective connectivity across anti-correlated networks in major depressive disorder and bipolar disorder.

Disease-specific alterations of effective connectivity across anti-correlated networks in major depressive disorder and bipolar disorder.