MD Network Research Articles

Functional dissociation of the language network and other cognition in early childhood.

Is language distinct from other cognition during development? Does neural machinery for language emerge from general-purpose neural mechanisms, becoming tuned for language after years of experience and maturation? Answering these questions will shed light on the origins of domain-specificity in the brain. We address these questions using precision fMRI, scanning young children (35 months to 9 years of age) on an auditory language localizer, spatial working memory localizer (engaging the domain-general multiple demand [MD] network), and a resting-state scan. We create subject-specific functional regions of interest for each network and examine their selectivity, specificity, and functional connectivity. We find young children show domain-specific, left-lateralized language activation, and that the language network is not responsive to domain-general cognitive load. Additionally, the cortically adjacent MD network is selective to cognitive load, but not to language. These networks show higher within versus between-network functional connectivity. This connectivity is stable across ages (examined cross-sectionally and longitudinally), whereas language responses increase with age and across time within subject, reflecting a domain-specific developmental change. Overall, we provide evidence for a double dissociation of the language and MD network throughout development, in both their function and connectivity. These findings suggest that domain-specificity, even for uniquely human cognition like language, develops early and distinctly from mechanisms that presumably support other human cognition.

Virtual screening of bioactive anti-SARS-CoV natural products and identification of 3β,12-diacetoxyabieta-6,8,11,13-tetraene as a potential inhibitor of SARS-CoV-2 virus and its infection related pathways by MD simulation and network pharmacology

Since the first prevalence of COVID-19 in 2019, it still remains the most devastating pandemic throughout the world. The current research aimed to find potential natural products to inhibit the novel coronavirus and associated infection by MD simulation and network pharmacology approach. Molecular docking was performed for 39 natural products having potent anti-SARS-CoV activity. Five natural products showed high binding interaction with the viral main protease for the SARS-CoV-2 virus, where 3β,12-diacetoxyabieta-6,8,11,13 tetraene showed stable binding in MD simulation until 100 ns. Both 3β,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A targeted 11 common genes that are related to COVID-19 and interact with each other. Gene ontology development analysis further showed that all these 11 genes are attached to various biological processes. The KEGG pathway analysis also showed that the proteins that are targeted by 3β,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A are associated with multiple pathways related to COVID-19 infection. Furthermore, the ADMET and MDS studies reveals 3β,12-diacetoxyabieta-6,8,11,13 as the best-suited compound for oral drug delivery.Communicated by Ramaswamy H. Sarma

Causal Contributions of the Domain-General (Multiple Demand) and the Language-Selective Brain Networks to Perceptual and Semantic Challenges in Speech Comprehension.

Listening to spoken language engages domain-general multiple demand (MD; frontoparietal) regions of the human brain, in addition to domain-selective (frontotemporal) language regions, particularly when comprehension is challenging. However, there is limited evidence that the MD network makes a functional contribution to core aspects of understanding language. In a behavioural study of volunteers (n = 19) with chronic brain lesions, but without aphasia, we assessed the causal role of these networks in perceiving, comprehending, and adapting to spoken sentences made more challenging by acoustic-degradation or lexico-semantic ambiguity. We measured perception of and adaptation to acoustically degraded (noise-vocoded) sentences with a word report task before and after training. Participants with greater damage to MD but not language regions required more vocoder channels to achieve 50% word report, indicating impaired perception. Perception improved following training, reflecting adaptation to acoustic degradation, but adaptation was unrelated to lesion location or extent. Comprehension of spoken sentences with semantically ambiguous words was measured with a sentence coherence judgement task. Accuracy was high and unaffected by lesion location or extent. Adaptation to semantic ambiguity was measured in a subsequent word association task, which showed that availability of lower-frequency meanings of ambiguous words increased following their comprehension (word-meaning priming). Word-meaning priming was reduced for participants with greater damage to language but not MD regions. Language and MD networks make dissociable contributions to challenging speech comprehension: Using recent experience to update word meaning preferences depends on language-selective regions, whereas the domain-general MD network plays a causal role in reporting words from degraded speech.

Exploring K v 1.2 Channel Inactivation Through MD Simulations and Network Analysis.

The KCNA2 gene encodes the K v 1.2 channel, a mammalian Shaker-like voltage-gated K+ channel, whose defections are linked to neuronal deficiency and childhood epilepsy. Despite the important role in the kinetic behavior of the channel, the inactivation remained hereby elusive. Here, we studied the K v 1.2 inactivation via a combined simulation/network theoretical approach that revealed two distinct pathways coupling the Voltage Sensor Domain and the Pore Domain to the Selectivity Filter. Additionally, we mutated some residues implicated in these paths and we explained microscopically their function in the inactivation mechanism by computing a contact map. Interestingly, some pathological residues shown to impair the inactivation lay on the paths. In summary, the presented results suggest two pathways as the possible molecular basis of the inactivation mechanism in the K v 1.2 channel. These pathways are consistent with earlier mutational studies and known mutations involved in neuronal channelopathies.

An Integrative MD Simulation and Network Analysis Approach to Study Glycosylation of Spike in SARS-CoV-2

The ongoing SARS-COV-2 pandemic continues to have devastating consequences on human public health and global economy. A major characteristic of all coronaviruses is a spike glycoprotein on the surface of the virus, which mediates host cell entry and is the target of all current antibody design efforts. A receptor binding domain in the spike protein is responsible for binding to host cell receptor when the spike protein is the open conformation. The spike protein of coronavirus is shielded by an array of glycans, providing a sugar-coated barrier to help the virus evade the human immune response.

Failure Prediction of Hard Disk Drives Based on Adaptive Rao–Blackwellized Particle Filter Error Tracking Method

Active failure prediction of hard disk drives (HDDs) is critical to prevent data loss and spare parts replacement decisions. Existing methods for failure predictions of HDDs always used a binary classifier to distinguish the healthy or failed HDDs and cannot address the problem of variable degradation states. In this article, an adaptive error tracking method is proposed for the HDD failure prediction. This method regards the extracted degradation feature as time serials and uses a state filter to estimate the real-time HDD's health status. Then, the HDD failure online prediction is achieved according to the alarm threshold determined by the adaptive error tracking. The degradation of an HDD is described by a first-order Markov hybrid jump degradation model, and the advanced Rao-Blackwellized particle filter algorithm, together with the expectation-maximization (EM) algorithm, is derived to estimate the model parameters adaptively. Finally, to verify the effectiveness of the proposed method, an accelerated degradation test (ADT) based on the vibration was carried out. And the data from ADT and real data center show that the proposed method performs much better than the previous methods, such as Kalman filter, SVM, MD, and recurrent neural network (RNN) based methods, with respect to failure prediction and the alarm distance, which helps to backup data and optimize maintenance decision costs for users.

Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence

Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal “multiple-demand” (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions – functionally defined in individual participants – was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.

Individual-subject Functional Localization Increases Univariate Activation but Not Multivariate Pattern Discriminability in the "Multiple-demand" Frontoparietal Network.

The frontoparietal "multiple-demand" (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a ROI using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study, we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared with when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers' data, compared with the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data and serve as another step toward standardization and increased comparability across studies.

Reconstruction of ARNT PAS-B Unfolding Paths by Steered MD and Artificial Neural Network Reveals New Putative Binding Conformations

Several proteins have been identified that can switch between different folds, expanding their functional role through previously unknown binding interactions. In this context, experimental evidence suggests that the PAS-B domain of the Aryl Hydrocarbon Receptor Nuclear Translocator protein (ARNT) has a fragile β-sheet structure that can undergo a +3 register shift upon a single mutation, changing its dimerization surface. Both unfolding and interconversion mechanisms require the same uncharacterized partially-unfolded state. Single-molecule Atomic Force Microscopy measurements (AFM) confirmed that the ARNT PAS-B domain is mechanically labile and revealed two distinct unfolding pathways, one following a single-stage mechanism, and the other showing an intermediate state. An atomistic characterisation of unfolding paths and relevant intermediate states has been carried out to identify new ARNT conformations possibly involved in partner recruitment. The unfolding pathways for ARNT PAS-B were simulated by steered Molecular Dynamics. Multiple replicas were recorded to explore different paths at low pulling speed. The overall model of unfolding was then reconstructed using a self-organizing map (SOM), a type of artificial neural network with an explicit visual representation. The map was trained on inter-residue distances of native contacts. The SOM model clearly depicts two alternative pathways in a human-understandable two-dimensional map. Retracing of single replicas on the map highlights a two-stage mechanism with initial unfolding of the C-terminal strand in 1/3 of the simulations, in agreement with AFM findings. Shortest path analysis on the map correctly detects replicas with minimal steering work. The partially-folded states extracted from the map could have a role in selectivity for partner recruitment during ARNT transcriptional activity.

Progressive Recruitment of the Frontoparietal Multiple-demand System with Increased Task Complexity, Time Pressure, and Reward.

A distributed, frontoparietal "multiple-demand" (MD) network is involved in tasks of many different kinds. Integrated activity across this network may be needed to bind together the multiple features of a mental control program (Duncan, 2013). Previous data suggest that, especially with low cognitive load, there may be some differentiation between MD regions (e.g., anterior vs. posterior regions of lateral frontal cortex), but with increasing load, there is progressive recruitment of the entire network. Differentiation may reflect preferential access to different task features, whereas co-recruitment may reflect information exchange and integration. To examine these patterns, we used manipulations of complexity, time pressure, and reward while participants solved a spatial maze. Complexity was manipulated by combining two simple tasks. Time pressure was added by fading away the maze during route planning, and on some of these trials, there was the further possibility of a substantial reward. Simple tasks evoked activity only in posterior MD regions, including posterior lateral frontal cortex, pre-supplementary motor area/anterior cingulate, and intraparietal sulcus. With increasing complexity, time pressure, and reward, increases in activity were broadly distributed across the MD network, though with quantitative variations. Across the MD network, the results show a degree of functional differentiation, especially at low load, but strong co-recruitment with increased challenge or incentive.

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions.

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks.SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network", whereas domain-general mechanisms are implemented in the bilateral "multiple demand" (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking.

A Putative Multiple-Demand System in the Macaque Brain

In humans, cognitively demanding tasks of many types recruit common frontoparietal brain areas. Pervasive activation of this "multiple-demand" (MD) network suggests a core function in supporting goal-oriented behavior. A similar network might therefore be predicted in nonhuman primates that readily perform similar tasks after training. However, an MD network in nonhuman primates has not been described. Single-cell recordings from macaque frontal and parietal cortex show some similar properties to human MD fMRI responses (e.g., adaptive coding of task-relevant information). Invasive recordings, however, come from limited prespecified locations, so they do not delineate a macaque homolog of the MD system and their positioning could benefit from knowledge of where MD foci lie. Challenges of scanning behaving animals mean that few macaque fMRI studies specifically contrast levels of cognitive demand, so we sought to identify a macaque counterpart to the human MD system using fMRI connectivity in 35 rhesus macaques. Putative macaque MD regions, mapped from frontoparietal MD regions defined in humans, were found to be functionally connected under anesthesia. To further refine these regions, an iterative process was used to maximize their connectivity cross-validated across animals. Finally, whole-brain connectivity analyses identified voxels that were robustly connected to MD regions, revealing seven clusters across frontoparietal and insular cortex comparable to human MD regions and one unexpected cluster in the lateral fissure. The proposed macaque MD regions can be used to guide future electrophysiological investigation of MD neural coding and in task-based fMRI to test predictions of similar functional properties to human MD cortex. In humans, a frontoparietal "multiple-demand" (MD) brain network is recruited during a wide range of cognitively demanding tasks. Because this suggests a fundamental function, one might expect a similar network to exist in nonhuman primates, but this remains controversial. Here, we sought to identify a macaque counterpart to the human MD system using fMRI connectivity. Putative macaque MD regions were functionally connected under anesthesia and were further refined by iterative optimization. The result is a network including lateral frontal, dorsomedial frontal, and insular and inferior parietal regions closely similar to the human counterpart. The proposed macaque MD regions can be useful in guiding electrophysiological recordings or in task-based fMRI to test predictions of similar functional properties to human MD cortex.

Abstract ADV08: Bridging the gaps in service.

Abstract Susan's Rally is a Houston based 501c3 organization that started as a group supporting breast cancer research and has expanded to support services provided for those with all types of cancer. What began as a fun road rally for Bruce and Susan Roberson morphed into a fun-raising event for cancer after Susan was taken by breast cancer. For 17 years, Bruce and his friends have continued the rally in her honor and now the proceeds from the rally, golf tournament, and auctions are used to fight the disease and assist with providing patients support services. Susan's Rally has raised over a million dollars in cash and in-kind donations utilizing all volunteer labor. Except for a few office supplies, postage and printing, all of Susan's Rally dollars go to fight cancer and support patients. Some of the programs supported included MD Anderson Patients Support Network, research at the Christus Stehlin Foundation for Cancer Research, provide diagnostic imaging for uninsured women at The Rose, counseling at Y-Me, and assists with making available low-cost short-term housing at The Sojourn House-Apartment, homes for cancer patients. In 2012 Susan's Rally also began funding “Dia De la Mujer community health festivals providing free breast exams, HIV testing an pap smears for women in need. Citation Format: Mary Kay Dauria. Bridging the gaps in service. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr ADV08. doi:10.1158/1538-7445.AM2013-ADV08

A brief educational intervention to increase communication about complementary and alternative medicine (CAM) in community oncology settings.

6054 Background: The widespread use of CAM in academic oncology settings has been well documented. However, there is a lack of communication between patients and health care professionals on CAM that may have negative health consequences. No comprehensive study of CAM use in community oncology settings exists. We examined the benefits of a brief educational intervention on nurse discussions of CAM with patients. Methods: A multi-site, randomized trial of an educational intervention (brief video, resource list) designed to encourage oncology nurses to discuss CAM use with their patients was conducted within the MD Anderson CCOP network. Nurses (N=175) and patients (baseline N=699 and different set of patients after intervention N=650) completed questions about CAM use, communication, and knowledge. Results: Nurses were 97% female, 96% non-Hispanic white. Two months after the intervention, nurses in the intervention group reported that they were more likely to ask about CAM use than those in the control group (OR=4.2; p<.005) and asked more of their last 5 patients about CAM use (p=.003). No significant intervention effect was found for the proportion of patients in the clinic who indicated they were asked about CAM use after the intervention (OR=1.6, p>.10). Approximately 40% of patients reported using CAM following their cancer diagnosis yet the majority of nurses estimated less than 25% of their patients used CAM. Conclusions: CAM use in community-based oncology patients is high and there is an underestimation of use by the oncology nurses. This very brief intervention significantly improved how often nurses reported asking patients about their CAM use. However, the patients of the nurses did not reflect this change in communication. Additional types of interventions are needed to increase communication between patients and nurses.

Development of a model emergency oncology response system (EORS): The California oncology emergency master plan (COEMP)

6142 Background: Hurricane Katrina displaced patients (PT) and physicians (MD). Time & resources were needed to match displaced PT with oncology (ONC) practices (PRC) for ongoing care. Disruption of care can be life-threatening to ONC PT who need to adhere to schedules & monitoring. California (CA) is particular risk for natural disaster. In order to better support PT in a future emergency (E) MOASC & ANCO created a model EORS with pilot implementation of select elements. Methods: MOASC & ANCO prepared a multi-component COEMP. Joint calls & meetings yielded a draft EORS approved by both society boards. Initial & long-term funding is expected via granting mechanisms. Pilot implementation of specific elements began in 11/2005. Results: Goals of the COEMP are: Establish dedicated E ONC MD network; Create a voluntary ONC-specific PT medical record (MR) template & secure data repository; Create & test a functional E MD responder system for CA ONC MD; Develop liaison with relief providers (Homeland Security, FEMA, Red Cross); Strengthen existing role with Bio-Pharma (BP) for delivery of medicine (Rx) to displaced PT and PRC; Develop specific educational materials for the EORS (PT, MDs, Insurers, BP); Create & distribute an EORS “PT Wallet Card” with PT national digital record (NDR) and E responder contacts. To prioritize components of the COEMP, a needs assessment, reviewing past E experience, response, and current capacity, has been initiated. The specific active components of the EORS include: 1-E ONC MD support network: ONC MDs have been invited to volunteer via email and at regional meetings. 2-Secure PT MR repository: MDs and PT will coordinate relevant MR, insurance, and E contact data, in locations (home of PT, w/relatives, NDR). 3-E coordinator MDs & staff will respond to E calls from PT/MD via phone & email. 4-Open dialogue with BP for E access to Rx for displaced PT. 5-Educational & training for PRC and PT in printed & electronic form. Conclusions: COEMP provides an initial E model for ONC. It may be applicable for state societies or national ONC groups. At ASCO 2006, MDs will be invited to participate and expand COEMP capabilities. Although we hope COEMP/EORS need never be used, its development will enable ONC practices to be more prepared for loco-regional E. No significant financial relationships to disclose.

Structural characterization of InGaAs/InP heterostructures grown under compressive and tensile stress

A systematic study of strain relaxation mechanisms by TEM, XRD, RBS-channelling, SEM-CL and AFM in In x Ga 1− x As/InP heterostructures grown by MOCVD under tensile and compressive initial misfit is reported. It is found that the layers under compression (0.61< x<0.74) relax nearly symmetrically along the two 〈1 1 0〉 directions. The residual strain vs. the layer thickness follows the same law experimentally determined for compressive MBE-grown In x Ga 1−x As/GaAs (x<0.2) specimens showing no composition, growth technique or temperature effect. The tensile layers (0.2< x<0.36) relax asymmetrically with a critical thickness larger than for the compressive case. The relaxation is faster along the [1 1 0]-direction with the asymmetry that increases by increasing the tensile misfit. Cracks, formed after the growth, are found to present a different density along [1 1 0] and [1 −1 0] depending on the residual strain. Grooves also develop along the two 〈1 1 0〉 directions as soon as a measurable strain relaxation appears. A possible correlation between grooves, planar defects and the mechanism of strain release is discussed. Strain-balanced In x Ga 1− x As/In y Ga 1− y As multi-quantum wells (MQWs) grown on a compositionally graded In x Ga 1− x As/GaAs buffer to extend the absorption edge of photovoltaic devices to 1 eV are finally investigated. The MD network results in a marked cross-hatched morphology that affects the lateral strain distribution in the whole structure. Lateral composition fluctuations in the MQW are discussed in terms of step bunching mechanism occurring at the valleys of the cross-hatched surface.