Browsing by Subject "NETWORKS"

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  • Andrienko, Gennady; Andrienko, Natalia; Boldrini, Chiara; Caldarelli, Guido; Cintia, Paolo; Cresci, Stefano; Facchini, Angelo; Giannotti, Fosca; Gionis, Aristides; Guidotti, Riccardo; Mathioudakis, Michael; Muntean, Cristina Ioana; Pappalardo, Luca; Pedreschi, Dino; Pournaras, Evangelos; Pratesi, Francesca; Tesconi, Maurizio; Trasarti, Roberto (2021)
    The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and geo-referenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.
  • Pensar, Johan; Talvitie, Topi; Hyttinen, Antti; Koivisto, Mikko (The Association for the Advancement of Artificial Intelligence (AAAI), 2020)
    AAAI Conference on Artificial Intelligence
    We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time O(3(d) d) for d variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity. significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.
  • Chen, Qiuzhen; Knickel, Karlheinz; Tesfai, Mehreteab; Sumelius, John; Turinawe, Alice; Isoto, Rosemary; Medyna, Galyna (2021)
    An important goal across Sub-Saharan Africa (SSA), and globally, is to foster a healthy nutrition. A strengthening of the diversity, sustainability, resilience and connectivity of food systems is increasingly seen as a key leverage point. Governance arrangements play a central role in connecting sustainable, resilient farming with healthy nutrition. In this article, we elaborate a framework for assessing, monitoring and improving the governance of food systems. Our focus is on food chains in six peri-urban and urban regions in SSA. A literature review on food chain governance and a mapping of current agri-food chains in the six regions provide the basis for the elaboration of an indicator-based assessment framework. The framework is adapted to the specific conditions of SSA and related goals. The assessment framework is then used to identify the challenges and opportunities in food chain governance in the six regions. The first testing of the framework indicates that the approach can help to identify disconnects, conflicting goals and tensions in food systems, and to formulate strategies for empowering agri-food chain actors in transitioning toward more efficient, equitable and sustainable agri-food systems. The article is concluded with a brief reflection on the strengths and weaknesses of the framework and suggests further testing and refinement.
  • Sacchi, Giovanna; Cei, Leonardo; Stefani, Gianluca; Lombardi, Ginevra Virginia; Rocchi, Benedetto; Belletti, Giovanni; Padel, Susanne; Sellars, Anna; Gagliardi, Edneia; Nocella, Giuseppe; Cardey, Sarah; Mikkola, Minna Maria; Ala-Karvia, Urszula Anna; Macken-Walsh, Àine; McIntyre, Bridin; Hyland, John; Henchion, Maeve; Bocci, Riccardo; Bussi, Bettina; De Santis, Giuseppe; Rodriguez y Hurtado, Ismael; de Kochko, Patrick; Riviere, Pierre; Carrascosa-García, María; Martínez, Ignacio; Pearce, Bruce; Lampkin, Nic; Vindras, Camille; Rey, Frederic; Chable, Véronique; Cormery, Antoine; Vasvari, Gyula (2018)
    Organic and low-input food systems are emerging worldwide in answer to the sustainability crisis of the conventional agri-food sector. “Alternative” systems are based on local, decentralized approaches to production and processing, regarding quality and health, and short supply-chains for products with strong local identities. Diversity is deeply embedded in these food systems, from the agrobiodiversity grown in farmers’ fields, which improves resilience and adaptation, to diverse approaches, contexts and actors in food manufacturing and marketing. Diversity thus becomes a cross-sectoral issue which acknowledges consumers’ demand for healthy products. In the framework of the European project “CERERE, CEreal REnaissance in Rural Europe: embedding diversity in organic and low-input food systems”, the paper aims at reviewing recent research on alternative and sustainable food systems by adopting an innovative and participatory multi-actor approach; this has involved ten practitioners and twenty-two researchers from across Europe and a variety of technical backgrounds in the paper and analysis stages. The participatory approach is the main innovation and distinctive feature of this literature review. Partners selected indeed what they perceived as most relevant in order to facilitate a transition towards more sustainable and diversity based cereal systems and food chains. This includes issues related to alternative food networks, formal and informal institutional settings, grass root initiatives, consumer involvement and, finally, knowledge exchange and sustainability. The review provides an overview of recent research that is relevant to CERERE partners as well as to anyone interested in alternative and sustainable food systems. The main objective of this paper was indeed to present a narrative of studies, which can form the foundation for future applied research to promote alternative methods of cereal production in Europe.
  • Haq, Ehsan ul; Braud, Tristan; Kwon, Young D.; Hui, Pan (2020)
    Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.
  • Kallio, Galina; Houtbeckers, Eeva (2020)
    We have seen an emergence of transformative food studies as part of sustainability transitions. While some scholars have successfully opened up their experiences of pursuing transformation through scholar-activism, assumptions underlying researchers' choices and how scholars orient to and go about their work often remain implicit. In this article, we bring forth a practice theoretical understanding of knowledge production and advocate that researchers turn to examining their own research practice. We ask how to make our own academic knowledge production/research practice more explicit, and why it is important to do so in the context of transformative food studies. To help scholars to reflect on their own research practice, we mobilize the framework of practical activity (FPA). We draw on our own experiences in academia and use our ethnographic studies on self-reliant food production and procurement to illustrate academic knowledge production. Thus, this article provides conceptual and methodological tools for reflection on academic research practice and knowledge production. We argue that it is important for researchers to turn to and improve their own academic practice because it advances academic knowledge production in the domain of transformative food studies and beyond. While we position ourselves within the qualitative research tradition, we believe that the insights of this article can be applied more broadly in different research fields and across various methodological approaches.
  • Lek, Monkol; Karczewski, Konrad J.; Minikel, Eric V.; Samocha, Kaitlin E.; Banks, Eric; Fennell, Timothy; O'Donnell-Luria, Anne H.; Ware, James S.; Hill, Andrew J.; Cummings, Beryl B.; Tukiainen, Taru; Birnbaum, Daniel P.; Kosmicki, Jack A.; Duncan, Laramie E.; Estrada, Karol; Zhao, Fengmei; Zou, James; Pierce-Hollman, Emma; Berghout, Joanne; Cooper, David N.; Deflaux, Nicole; DePristo, Mark; Do, Ron; Flannick, Jason; Fromer, Menachem; Gauthier, Laura; Goldstein, Jackie; Gupta, Namrata; Howrigan, Daniel; Kiezun, Adam; Kurki, Mitja I.; Moonshine, Ami Levy; Natarajan, Pradeep; Orozeo, Lorena; Peloso, Gina M.; Poplin, Ryan; Rivas, Manuel A.; Ruano-Rubio, Valentin; Rose, Samuel A.; Ruderfer, Douglas M.; Shakir, Khalid; Stenson, Peter D.; Stevens, Christine; Thomas, Brett P.; Tiao, Grace; Tusie-Luna, Maria T.; Weisburd, Ben; Palotie, Aarno; Tuomilehto, Jaakko; Daly, Mark J.; Exome Aggregation Consortium (2016)
    Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
  • Rahko, Jukka S.; Vuontela, Virve A.; Carlson, Synnove; Nikkinen, Juha; Hurtig, Tuula M.; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jussila, Katja K.; Remes, Jukka J.; Jansson-Verkasalo, Eira M.; Aronen, Eeva T.; Pauls, David L.; Ebeling, Hanna E.; Tervonen, Osmo; Moilanen, Irma K.; Kiviniemi, Vesa J. (2016)
    The present study examined attention and memory load-dependent differences in the brain activation and deactivation patterns between adolescents with autism spectrum disorders (ASDs) and typically developing (TD) controls using functional magnetic resonance imaging. Attentional (0-back) and working memory (WM; 2-back) processing and load differences (0 vs. 2-back) were analysed. WM-related areas activated and default mode network deactivated normally in ASDs as a function of task load. ASDs performed the attentional 0-back task similarly to TD controls but showed increased deactivation in cerebellum and right temporal cortical areas and weaker activation in other cerebellar areas. Increasing task load resulted in multiple responses in ASDs compared to TD and in inadequate modulation of brain activity in right insula, primary somatosensory, motor and auditory cortices. The changes during attentional task may reflect compensatory mechanisms enabling normal behavioral performance. The inadequate memory load-dependent modulation of activity suggests diminished compensatory potential in ASD.
  • Turkki, Riku; Byckhov, Dmitrii; Lundin, Mikael; Isola, Jorma; Nordling, Stig; Kovanen, Panu E.; Verrill, Clare; von Smitten, Karl; Joensuu, Heikki; Lundin, Johan; Linder, Nina (2019)
    PurposeRecent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.MethodsUtilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N=1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.ResultsIn univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p=0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p=0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.ConclusionsOur findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
  • Khazaei, Mohammad; Raeisi, Khadijeh; Croce, Pierpaolo; Tamburro, Gabriella; Tokariev, Anton; Vanhatalo, Sampsa; Zappasodi, Filippo; Comani, Silvia (2021)
    Neonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies-the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states-AS and QS-in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that similar to 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.
  • Hirayama, Jun-ichiro; Hyvarinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito (2016)
    Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated "eigenconnectivity" patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra-and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.
  • Forsberg, David; Horn, Zachi; Tserga, Evangelia; Smedler, Erik; Silberberg, Gilad; Shvarev, Yuri; Kaila, Kai; Uhlen, Per; Herlenius, Eric (2016)
    Inflammation-induced release of prostaglandin E-2 (PGE(2)) changes breathing patterns and the response to CO2 levels. This may have fatal consequences in newborn babies and result in sudden infant death. To elucidate the underlying mechanisms, we present a novel breathing brainstem organotypic culture that generates rhythmic neural network and motor activity for 3 weeks. We show that increased CO2 elicits a gap junction-dependent release of PGE(2). This alters neural network activity in the preBotzinger rhythm-generating complex and in the chemosensitive brainstem respiratory regions, thereby increasing sigh frequency and the depth of inspiration. We used mice lacking eicosanoid prostanoid 3 receptors (EP3R), breathing brainstem organotypic slices and optogenetic inhibition of EP3R(+/+) cells to demonstrate that the EP3R is important for the ventilatory response to hypercapnia. Our study identifies a novel pathway linking the inflammatory and respiratory systems, with implications for inspiration and sighs throughout life, and the ability to autoresuscitate when breathing fails.
  • Wirta, Helena K.; Hebert, Paul D. N.; Kaartinen, Riikka; Prosser, Sean W.; Varkonyi, Gergely; Roslin, Tomas (2014)
  • Räsänen, Aleksi; Lein, Haakon; Bird, Deanne; Setten, Gunhild (2020)
    Community resilience is often assessed in disaster risk management (DRM) research and it has been argued that it should be strengthened for more robust DRM. However, the term community is seldom precisely defined and it can be understood in many ways. We argue that it is crucial to explore the concept of community within the context of DRM in more detail. We identify three dominating views of conceptualizing community (place-based community, interaction-based community, community of practice and interest), and discuss the relevance of these conceptualizations. We base this discussion on quantitative and qualitative empirical and policy document data regarding flood and storm risk management in Finland, wildfire risk management in Norway and volcanic risk management Iceland. According to our results, all three conceptualizations of community are visible but in differing situations. Our results emphasize the strong role of public sector in DRM in the studied countries. In disaster preparedness and response, a professionalized community of practice and interest appear to be the most prominent within all three countries. The interaction-based community of informal social networks is of less relevance, although its role is more visible in disaster response and recovery. The place-based (local) community is visible in some of the policy documents, but otherwise its role is rather limited. Finally, we argue that the measured resilience of a community depends on how the community is conceptualized and operationalized, and that the measures to strengthen resilience of a particular community should be different depending on what the focal community is.
  • Räsänen, Aleksi; Kauppinen, Vera; Juhola, Sirkku; Setten, Gunhild; Lein, Haakon (2020)
    Despite a notable increase in the literature on community resilience, the notion of 'community' remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.
  • Kujala, Heini; Moilanen, Atte; Araujo, Miguel B.; Cabeza, Mar (2013)
  • Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo (2019)
    Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues (2015). Our results indicate that the framework can efficiently perform simulations using Python, C++, and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C++ devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.
  • Hokkanen, Henri; Andalibi, Vafa; Vanni, Simo (2019)
    Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear. We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and GABAB and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (GABAB) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time. Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.
  • Holster, S.; Repsilber, D.; Geng, D.; Hyotylainen, T.; Salonen, A.; Lindqvist, C. M.; Rajan, S. K.; de Vos, W. M.; Brummer, R. J.; König, J. (2021)
    Faecal microbiota transfer (FMT) consists of the infusion of donor faecal material into the intestine of a patient with the aim to restore a disturbed gut microbiota. In this study, it was investigated whether FMT has an effect on faecal microbial composition, its functional capacity, faecal metabolite profiles and their interactions in 16 irritable bowel syndrome (IBS) patients. Faecal samples from eight different time points before and until six months after allogenic FMT (faecal material from a healthy donor) as well as autologous FMT (own faecal material) were analysed by 16S RNA gene amplicon sequencing and gas chromatography coupled to mass spectrometry (GS-MS). The results showed that the allogenic FMT resulted in alterations in the microbial composition that were detectable up to six months, whereas after autologous FMT this was not the case. Similar results were found for the functional profiles, which were predicted from the phylogenetic sequencing data. While both allogenic FMT as well as autologous FMT did not have an effect on the faecal metabolites measured in this study, correlations between the microbial composition and the metabolites showed that the microbe-metabolite interactions seemed to be disrupted after allogenic FMT compared to autologous FMT. This shows that FMT can lead to altered interactions between the gut microbiota and its metabolites in IBS patients. Further research should investigate if and how this affects efficacy of FMT treatments.
  • Tokariev, Anton; Oberlander, Victoria C.; Videman, Mari; Vanhatalo, Sampsa (2022)
    Up to five percent of human infants are exposed to maternal antidepressant medication by serotonin reuptake inhibitors (SRI) during pregnancy, yet the SRI effects on infants' early neurodevelopment are not fully understood. Here, we studied how maternal SRI medication affects cortical frequency-specific and cross-frequency interactions estimated, respectively, by phase-phase correlations (PPC) and phase-amplitude coupling (PAC) in electroencephalographic (EEG) recordings. We examined the cortical activity in infants after fetal exposure to SRIs relative to a control group of infants without medical history of any kind. Our findings show that the sleep-related dynamics of PPC networks are selectively affected by in utero SRI exposure, however, those alterations do not correlate to later neurocognitive development as tested by neuropsychological evaluation at two years of age. In turn, phase-amplitude coupling was found to be suppressed in SRI infants across multiple distributed cortical regions and these effects were linked to their neurocognitive outcomes. Our results are compatible with the overall notion that in utero drug exposures may cause subtle, yet measurable changes in the brain structure and function. Our present findings are based on the measures of local and inter-areal neuronal interactions in the cortex which can be readily used across species, as well as between different scales of inspection: from the whole animals to in vitro preparations. Therefore, this work opens a framework to explore the cellular and molecular mechanisms underlying neurodevelopmental SRI effects at all translational levels.