Ventral Attention Network Research Articles

Processing of pain by the developing brain: evidence of differences between adolescent and adult females.

Adolescence is a sensitive period for both brain development and the emergence of chronic pain particularly in females. However, the brain mechanisms supporting pain perception during adolescence remain unclear. This study compares perceptual and brain responses to pain in female adolescents and adults to characterize pain processing in the developing brain. Thirty adolescent (ages 13-17 years) and 30 adult (ages 35-55 years) females underwent a functional magnetic resonance imaging scan involving acute pain. Participants received 12 ten-second noxious pressure stimuli that were applied to the left thumbnail at 2.5 and 4 kg/cm 2 , and rated pain intensity and unpleasantness on a visual analogue scale. We found a significant group-by-stimulus intensity interaction on pain ratings. Compared with adults, adolescents reported greater pain intensity and unpleasantness in response to 2.5 kg/cm 2 but not 4 kg/cm 2 . Adolescents showed greater medial-lateral prefrontal cortex and supramarginal gyrus activation in response to 2.5 kg/cm 2 and greater medial prefrontal cortex and rostral anterior cingulate responses to 4 kg/cm 2 . Adolescents showed greater pain-evoked responses in the neurologic pain signature and greater activation in the default mode and ventral attention networks. Also, the amygdala and associated regions played a stronger role in predicting pain intensity in adolescents, and activity in default mode and ventral attention regions more strongly mediated the relationship between stimulus intensity and pain ratings. This study provides first evidence of greater low-pain sensitivity and pain-evoked brain responses in female adolescents (vs adult women) in regions important for nociceptive, affective, and cognitive processing, which may be associated with differences in peripheral nociception.

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson's Disease.

BackgroundDepression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression.MethodsWe performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.ResultsWe observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.ConclusionOur results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted.

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and 18F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

506 Evaluating Traditional and Non-Traditional Eloquent Areas in Patients with Brain Tumors: Large-scale Network Analysis Using a Machine Learning-Based Platform

INTRODUCTION: Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS: We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS: Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSION: The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.

Large-scale network dysfunction in α-Synucleinopathy: A meta-analysis of resting-state functional connectivity.

SummaryBackgroundAlthough dysfunction of large-scale brain networks has been frequently demonstrated in patients with α-Synucleinopathy (α-Syn, i.e., Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy), a consistent pattern of dysfunction remains unclear. We aim to investigate network dysfunction in patients with α-Syn through a meta-analysis.MethodsWhole-brain seed-based resting-state functional connectivity studies (published before September 1st, 2020 in English) comparing α-Syn patients with healthy controls (HC) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE). Seeds from each study were categorized into networks by their location within a priori functional networks. Seed-based effect size mapping with Permutation of Subject Images analysis of between-group effects identified the network systems in which α-Syn was associated with hyperconnectivity (increased connectivity in α-Syn vs. HC) or hypoconnectivity (decreased connectivity in α-Syn vs. HC) within and between each seed-network. This study was registered on PROSPERO (CRD42020210133).FindingsIn total, 136 seed-based voxel-wise resting-state functional connectivity datasets from 72 publications (3093 α-Syn patients and 3331 HC) were included in the meta-analysis. We found that α-Syn patients demonstrated imbalanced connectivity among subcortical network, cerebellum, and frontal parietal networks that involved in motor functioning and executive control. The patient group was associated with hypoconnectivity in default mode network and ventral attention network that involved in cognition and attention. Additionally, the patient group exhibited hyperconnectivity between neural systems involved in top-down emotion regulation and hypoconnectivity between networks involved in bottom-up emotion processing.InterpretationThese findings supported neurocognitive models in which network dysfunction is tightly linked to motor, cognitive and psychiatric symptoms observed in α-Syn patients.

Disrupted Resting State Attentional Network Connectivity in Adolescent and Young Adult Cannabis Users following Two-Weeks of Monitored Abstinence.

Background. Numerous neuropsychological studies have shown that cannabis use during adolescence and young adulthood led to deficits in sustained and selective attention. However, few studies have examined functional connectivity in attentional networks among young cannabis users, nor have characterized relationships with cannabis use patterns following abstinence. Methods. Differences in resting state functional connectivity (RSFC) within the dorsal (DAN) and ventral (VAN) attention networks were examined in 36 adolescent and young adult cannabis users and 39 non-substance using controls following two weeks of monitored abstinence. Observed connectivity differences were then correlated with past-year and lifetime cannabis use, length of abstinence, age of regular use onset, and Cannabis Use Disorder symptoms (CUD). Results. After controlling for alcohol and nicotine use, cannabis users had lower RSFC within the DAN network, specifically between right inferior parietal sulcus and right anterior insula, as well as white matter, relative to controls. This region was associated with more severe cannabis use measures, including increased lifetime cannabis use, shorter length of abstinence, and more severe CUD symptoms. Conclusions. Findings demonstrate that regular cannabis use by adolescents and young adults is associated with subtle differences in resting state connectivity within the DAN, even after two weeks of monitored abstinence. Notably, more severe cannabis use markers (greater lifetime use, CUD symptoms, and shorter abstinence) were linked with this reduced connectivity. Thus, findings support public policy aimed at reducing and delaying cannabis use and treatments to assist with sustained abstinence. Future longitudinal studies are needed to investigate causation.

Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia

Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson’s disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.

Altered Intra- and Inter-Network Connectivity in Drug-Naïve Patients With Early Parkinson's Disease.

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.

Functional brain connectomes reflect acute and chronic cannabis use

Resting state fMRI has been employed to identify alterations in functional connectivity within or between brain regions following acute and chronic exposure to Δ9-tetrahydrocannabinol (THC), the psychoactive component in cannabis. Most studies focused a priori on a limited number of local brain areas or circuits, without considering the impact of cannabis on whole-brain network organization. The present study attempted to identify changes in the whole-brain human functional connectome as assessed with ultra-high field (7T) resting state scans of cannabis users (N = 26) during placebo and following vaporization of cannabis. Two distinct data-driven methodologies, i.e. network-based statistics (NBS) and connICA, were used to identify changes in functional connectomes associated with acute cannabis intoxication and history of cannabis use. Both methodologies revealed a broad state of hyperconnectivity within the entire range of major brain networks in chronic cannabis users compared to occasional cannabis users, which might be reflective of an adaptive network reorganization following prolonged cannabis exposure. The connICA methodology also extracted a distinct spatial connectivity pattern of hypoconnectivity involving the dorsal attention, limbic, subcortical and cerebellum networks and of hyperconnectivity between the default mode and ventral attention network, that was associated with the feeling of subjective high during THC intoxication. Whole-brain network approaches identified spatial patterns in functional brain connectomes that distinguished acute from chronic cannabis use, and offer an important utility for probing the interplay between short and long-term alterations in functional brain dynamics when progressing from occasional to chronic use of cannabis.

Individual variation in brain network topology is linked to course of illness in major depressive disorder.

Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.

Linking functional connectome gradient to individual creativity.

Human brain network is organized as a hierarchical organization, exhibiting various connectome gradients. The principal gradient is anchored by the modality-specific primary areas and the transmodal regions. Previous studies have suggested that the unimodal-transmodal gradient in the functional connectome may offer an overarching framework for high-order cognitions of human brain. However, there is still a lacking of direct evidence to associate these two. Therefore, we aim to explore the association between creativity, a typical human high-order cognitive function, and unimodal-transmodal gradient, using two independent datasets of young adults. For each individual, we identified the unimodal-transmodal gradient in functional connectome and calculated its global measures. Then we correlated the individual creativity score with measures of unimodal-transmodal gradient at global-brain, subsystem, and regional level. The results suggested that better creative performance was associated with greater distance between primary areas and transmodal regions in gradient axes, and less distance between ventral attention network and default mode network. Individual creativity was also found positively correlated with regional gradients in ventral attention network, and negatively correlated with gradients of regions in visual cortex. Together, these findings directly link the unimodal-transmodal gradient to individual creativity, providing empirical evidence for the cognitive implications of functional connectome gradient.

The Role of School Environment in Brain Structure, Connectivity, and Mental Health in Children: A Multimodal Investigation

BackgroundMuch work has been dedicated to understanding the effects of adverse home environments on brain development. While the school social and learning environment plays a role in child development, little work has been done to investigate the impact of the school environment on the developing brain. The goal of the present study was to examine associations between the school environment, brain structure and connectivity, and mental health. MethodsIn this preregistered study we investigated these questions in a large sample of adolescents (9–10 years of age) from the Adolescent Brain Cognitive Development (ABCD) Study. We examined the association between school environment and gray matter (n = 10,435) and white matter (n = 10,770) structure and functional connectivity (n = 9528). We then investigated multivariate relationships between school-associated brain measures and mental health. ResultsSchool environment was associated with connectivity of the auditory and retrosplenial temporal network as well as of higher-order cognitive networks like the cingulo-opercular, default mode, ventral attention, and frontoparietal networks. Multivariate analyses revealed that connectivity of the cingulo-opercular and default mode networks was also associated with mental health. ConclusionsFindings shed light on the neural mechanisms through which favorable school environments may contribute to positive mental health outcomes in children. Our findings have implications for interventions targeted at promoting positive youth functioning through improving school environments.

Abstract WMP64: Multimodal Bayesian Modeling Of Stroke Severity

Introduction: Understanding the relations between lesions and outcomes is a particularly promising avenue to support tailored stroke care. We here employed a novel Bayesian framework integrating lesion location and functional lesion connectivity, i.e., lesion network mapping data, aiming to augment the prediction of stroke severity and the interpretability of lesion-symptom associations in a multi-center cohort of acute ischemic stroke (AIS) patients. Methods: Analyses relied on 1,077 AIS patients of the MRI-GENIE study [age: 64.2(14.7), 38%women]. NIHSS-based stroke severity was modeled via Bayesian linear regression. Structural location and functional connectivity information was represented by ten unique patterns each. We compared the out-of-sample predictive performances of structural vs. structural-functional models in Bayesian model comparisons. In ancillary analyses, we extracted functional connectivity pattern relevant for stroke severity and repeated analyses after stratifying for lesion size (50% of patients with larger lesions). Models were generally adjusted for age, sex, comorbidities and total DWI lesion volume. Results: Model comparison indicated a benefit of the combined structural-functional stroke severity prediction model compared to the structural one (model weights: 82% vs. 18%). This benefit of the combined model became even more apparent in case of patients with large lesions (97% vs. 3%). Positive lesion connectivity to the bilateral ventral attention (VA) and somatomotor networks, as well as temporal default mode network (DMN) predicted higher severity, while positive connectivity to more medial parts of the DMN and the visual network predicted lower severity. Additionally, negative lesion connectivity to the bilateral DMN, somatomotor, dorsal attention, and control networks predicted higher, and negative connectivity to the right control and bilateral VA networks predicted lower severity. Conclusions: Model comparisons indicated a benefit of integrating structural and functional information for the prediction of stroke severity, especially in case of larger lesions. Lesion connectivity data can enhance our understanding of why specific lesion locations can lead to symptoms post-stroke.

Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment.

The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.

Distinct Brain Mechanisms for Conflict Adaptation within and across Conflict Types.

Cognitive conflict, like other cognitive processes, shows the characteristic of adaptation, that is, conflict effects are attenuated when immediately following a conflicting event, a phenomenon known as the conflict adaptation effect (CAE). One important aspect of CAE is its sensitivity to the intertrial coherence of conflict type, that is, behavioral CAE occurs only if consecutive trials are of the same conflict type. Although reliably observed behaviorally, the neural mechanisms underlying such a phenomenon remains elusive. With a paradigm combining the classic Simon task and Stroop task, this fMRI study examined neural correlates of conflict adaptation both within and across conflict types. The results revealed that when the conflict type repeated (but not when it alternated), the CAE-like neural activations were observed in dorsal ACC, inferior frontal gyrus (IFG), superior parietal lobe, and so forth (i.e., regions within typical task-positive networks). In contrast, when the conflict type alternated (but not when it repeated), we found CAE-like neural deactivations in the left superior frontal gyri (i.e., a region within the typical task-negative network). Network analyses suggested that the regions of ACC, IFG, superior parietal lobe, and superior frontal gyrus can be clustered into two antagonistic networks, and the ACC-IFG connection was associated with the within-type CAE. This evidence suggests that our adaptation to cognitive conflicts within a conflict type and across different types may rely on these two distinct neural mechanisms.

Altered canonical and striatal-frontal resting state functional connectivity in children with pathogenic variants in the Ras/mitogen-activated protein kinase pathway.

Mounting evidence supports the role of the Ras/mitogen-activated protein kinase (Ras/MAPK) pathway in neurodevelopmental disorders. Here, the authors used a genetics-first approach to examine how Ras/MAPK pathogenic variants affect the functional organization of the brain and cognitive phenotypes including weaknesses in attention and inhibition. Functional MRI was used to examine resting state functional connectivity (RSFC) in association with Ras/MAPK pathogenic variants in children with Noonan syndrome (NS). Participants (age 4–12 years) included 39 children with NS (mean age 8.44, SD=2.20, 25 females) and 49 typically developing (TD) children (mean age 9.02, SD=9.02, 33 females). Twenty-eight children in the NS group and 46 in the TD group had usable MRI data and were included in final analyses. The results indicated significant hyperconnectivity for the NS group within canonical visual, ventral attention, left frontoparietal and limbic networks (p<0.05 FWE). Higher connectivity within canonical left frontoparietal and limbic networks positively correlated with cognitive function within the NS but not the TD group. Further, the NS group demonstrated significant group differences in seed-based striatal-frontal connectivity (Z>2.6, p<0.05 FWE). Hyperconnectivity within canonical brain networks may represent an intermediary phenotype between Ras/MAPK pathogenic variants and cognitive phenotypes, including weaknesses in attention and inhibition. Altered striatal-frontal connectivity corresponds with smaller striatal volume and altered white matter connectivity previously documented in children with NS. These results may indicate delayed maturation and compensatory mechanisms and they are important for understanding the pathophysiology underlying cognitive phenotypes in NS and in the broader population of children with neurodevelopmental disorders.

Combined TMS-fMRI reveals behavior-dependent network effects of right temporoparietal junction neurostimulation in an attentional belief updating task.

Updating beliefs after unexpected events is fundamental for an optimal adaptation to the environment. Previous findings suggested a causal involvement of the right temporoparietal junction (rTPJ) in belief updating in an attention task. We combined offline continuous theta-burst stimulation (cTBS) over rTPJ with functional magnetic resonance imaging (fMRI) to investigate local and remote stimulation effects within the attention and salience networks. In a sham-controlled, within-subject crossover design, 25 participants performed an attentional cueing task during fMRI with true or false information about cue predictability. By estimating learning rates from response times, we characterized participants' belief updating. Model-derived cue predictability entered the fMRI analysis as a parametric regressor to identify the neural correlates of updating. rTPJ-cTBS effects showed high interindividual variability. The expected learning rate reduction with false cue predictability information by cTBS was only observed in participants showing higher updating in false than in true blocks after sham. cTBS modulated the neural signatures of belief updating, both in rTPJ and in nodes of the attention and salience networks. The interindividual variability of the behavioral cTBS effect was related to differential activity and rTPJ connectivity of the right anterior insula. These results demonstrate a crucial interaction between ventral attention and salience networks for belief updating.

Catching wandering minds with tapping fingers: neural and behavioral insights into task-unrelated cognition.

When the human mind wanders, it engages in episodes during which attention is focused on self-generated thoughts rather than on external task demands. Although the sustained attention to response task is commonly used to examine relationships between mind wandering and executive functions, limited executive resources are required for optimal task performance. In the current study, we aimed to investigate the relationship between mind wandering and executive functions more closely by employing a recently developed finger-tapping task to monitor fluctuations in attention and executive control through task performance and periodical experience sampling during concurrent functional magnetic resonance imaging (fMRI) and pupillometry. Our results show that mind wandering was preceded by increases in finger-tapping variability, which was correlated with activity in dorsal and ventral attention networks. The entropy of random finger-tapping sequences was related to activity in frontoparietal regions associated with executive control, demonstrating the suitability of this paradigm for studying executive functioning. The neural correlates of behavioral performance, pupillary dynamics, and self-reported attentional state diverged, thus indicating a dissociation between direct and indirect markers of mind wandering. Together, the investigation of these relationships at both the behavioral and neural level provided novel insights into the identification of underlying mechanisms of mind wandering.

Investigating the overlapping associations of prior concussion, default mode connectivity, and executive function-based symptoms.

Growing evidence suggests that younger athletes with greater concussion history are more likely to endorse greater subjective cognitive (e.g., executive function) symptoms, but not perform worse on objective cognitive testing. We sought to identify biological correlates of elevated cognitive symptoms in 100 healthy, collegiate-aged athletes with varying degrees of concussion history. Associations between concussion history with subjectively-rated executive function were assessed with generalized linear models. Using resting state fMRI, we examined associations between concussion history and between-and within-network connectivity across three networks integral to executive function; default mode network (DMN), frontoparietal network (FPN), and ventral attention network (VAN). Relationships of between-and within-network connectivity with subjective executive function were assessed. Although the large majority of participants did not report clinically relevant levels of executive difficulties, there was a significant association between concussion history and higher behavioral regulation-related symptoms; B = .04[.01, .07], p = .011. A significant elevation in total within-network connectivity was observed among those with a greater concussion history, B = .02[.002, .03], p = .028, which was primarily driven by a positive association between concussion history and within DMN connectivity, B = .02[.004, .04], p = .014. Higher behavioral regulation-related symptoms were associated with greater total within-network connectivity, B = 0.57[0.18, 0.96], p = .005, and increased within-network connectivity for the DMN, B = .49[.12, .86], p = .010). The current study identified a distinct biological correlate, increased within-DMN connectivity, which was associated with both a greater history of concussion and greater behavioral regulation symptoms. Future studies are required to determine the degree to which these changes associated with concussion history may evolve toward objective cognitive decline over the lifespan.

Involvement of the dorsal and ventral attention networks invisual attention span.

Visual attention span (VAS), which refers to the window size of multielement parallel processing in a short time, plays an important role in higher‐level cognition (e.g., reading) as required by encoding large amounts of information input. However, it is still a matter of debate about the underlying neural mechanism of VAS. In the present study, a modified visual 1‐back task was designed by using nonverbal stimuli and nonverbal responses, in which possible influences of target presence and position were considered to identify more pure VAS processing. A task‐driven functional magnetic resonance imaging (fMRI) experiment was then performed, and 30 healthy adults participated in this study. Results of confirmatory and exploratory analyses consistently revealed that both dorsal attention network (DAN) and ventral attention network (VAN) were significantly activated during this visual simultaneous processing. In particular, more significant activation in the left superior parietal lobule (LSPL), as compared to that in the bilateral inferior frontal gyrus (IFGs), suggested a greater involvement of DAN in VAS‐related processing in contrast to VAN. In addition, it was also found that the activation in temporoparietal junctions (TPJs) were suppressed during multielement processing only in the target‐absent condition. The current results suggested the recruitment of LSPL in covert attentional shifts and top‐down control of VAS resources distribution during the rapid visual simultaneous processing, as well as the involvement of bilateral IFGs (especially RIFG) in both VAS processing and inhibitory control. The present findings might bring some enlightenments for diagnosis of the atypicality of attentional disorders and reading difficulties.