Patient-initiated video consultations: A qualitative study on reasons for patient-booking in Danish general practice.

Objective: This qualitative study explores when and why patients book video consultations through an online booking platform within the context of Danish general practice and how the technology affects patients' use of this consultation type. Methods: We conducted thirteen semi-structured interviews with patients from the same general practice who were experienced users of video consultations scheduled through the clinic's online booking platform. Interviews were analysed using thematic analysis and drawing on actor-network theory and Bruno Latour's concept of technical mediation as an analytical framework. Results: We introduce the concept of "hybrid patients," highlighting how values tied to video consultation that motivates patients to book them emerge through technical mediation in the network between patients, general practitioners (GPs) or practice staff, and the video technology. We identified three emerging values: efficiency, control and diminished presence. Video consultation affords efficient consultations that save patients' time. It mediates patients' sense of control when they experience certainty concerning their health issues. Video consultation mediates diminished presence that increases relational distance. However, it simultaneously allows for efficiency and emotional distance between patients and their GP, and between patients and their health issues. Conclusions: When initiating the use of video consultation, the patient plays an active and conscious role in adjusting to the mediated values (efficiency, control and diminished presence) linked to this form of consultation. These emerging values are context-specific, and patients employ them based on their individual requirements. Patients trust their GPs to prevent severe or vulnerable topics from being discussed in a video consultation.

Exploring health inequities through the actor-network theory lens.

Social theory plays an important role in the nursing discipline and nursing inquiry as it helps conceptually embed nursing in the larger picture of the social world. For example, a broad category of critical theory provides a unique lens for uncovering social conditions of inequity and oppression. Among the sociological theories, actor-network theory (ANT) is an approach to research and analysis that has recently gained interest among nurse philosophers and researchers. Studies guided by ANT seek to understand phenomena of interest as constituted within the relationships between human and nonhuman actors to understand how care practices are co-created/enacted and how they can be made more humane. In this paper, we describe the benefits of ANT for examining healthcare access for incarcerated individuals with life-limiting illnesses accessing palliative care and for people using illicit drugs. We argue that attention to the materiality of care practices can contribute to efforts of advancing health equity for these groups.

iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.

Long non-coding RNAs (lncRNAs) have emerged as significant contributors to the regulation of various biological processes, and their dysregulation has been linked to a variety of human disorders. Accurate prediction of potential correlations between lncRNAs and diseases is crucial for advancing disease diagnostics and treatment procedures. The authors introduced a novel computational method, iGATTLDA, for the prediction of lncRNA-disease associations. The model utilised lncRNA and disease similarity matrices, with known associations represented in an adjacency matrix. A heterogeneous network was constructed, dissecting lncRNAs and diseases as nodes and their associations as edges. The Graph Attention Network (GAT) is employed to process initial features and corresponding adjacency information. GAT identified significant neighbouring nodes in the network, capturing intricate relationships between lncRNAs and diseases, and generating new feature representations. Subsequently, the transformer captures global dependencies and interactions across the entire sequence of features produced by the GAT. Consequently, iGATTLDA successfully captures complex relationships and interactions that conventional approaches may overlook. In evaluating iGATTLDA, it attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 and an area under the precision recall curve (AUPRC) of 0.96 with a two-layer multilayer perceptron (MLP) classifier. These results were notably higher compared to the majority of previously proposed models, further substantiating the model's efficiency in predicting potential lncRNA-disease associations by incorporating both local and global interactions. The implementation details can be obtained from https://github.com/momanyibiffon/iGATTLDA.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

A morpho-viscoelasticity theory for growth in proliferating aggregates.

Despite significant research efforts in the continuum modeling of biological growth, certain aspects have been overlooked. For instance, numerous investigations have examined the influence of morphogenetic cell behaviors, like division and intercalation, on the mechanical response of passive (non-growing) tissues. Yet, their impact on active growth dynamics remains inadequately explored. A key reason for this inadequacy stems from challenges in the continuum treatment of cell-level processes. While some coarse-grained models have been proposed to address these shortcomings, a focus on cell division and cell expansion has been missing, rendering them unusable when it comes to modeling growth. Moreover, existing studies are limited to two-dimensional tissues and are yet to be formally extended to three-dimensional multicellular systems. To address these limitations, we here present a generalized multiscale model for three-dimensional aggregates that accounts for complex morphogenetic movements that include division, expansion, and intercalation. The proposed continuum theory thus allows for a comprehensive exploration into the growth and dissipation mechanics of proliferating aggregates, such as spheroids and organoids.

Combined effects of landscape fragmentation and sampling frequency of movement data on the assessment of landscape connectivity.

BACKGROUND: Network theory is largely applied in real-world systems to assess landscape connectivity using empirical or theoretical networks. Empirical networks are usually built from discontinuous individual movement trajectories without knowing the effect of relocation frequency on the assessment of landscape connectivity while theoretical networks generally rely on simple movement rules. We investigated the combined effects of relocation sampling frequency and landscape fragmentation on the assessment of landscape connectivity using simulated trajectories and empirical high-resolution (1 Hz) trajectories of Alpine ibex (Capra ibex). We also quantified the capacity of commonly used theoretical networks to accurately predict landscape connectivity from multiple movement processes. METHODS: We simulated forager trajectories from continuous correlated biased random walks in simulated landscapes with three levels of landscape fragmentation. High-resolution ibex trajectories were reconstructed using GPS-enabled multi-sensor biologging data and the dead-reckoning technique. For both simulated and empirical trajectories, we generated spatial networks from regularly resampled trajectories and assessed changes in their topology and information loss depending on the resampling frequency and landscape fragmentation. We finally built commonly used theoretical networks in the same landscapes and compared their predictions to actual connectivity. RESULTS: We demonstrated that an accurate assessment of landscape connectivity can be severely hampered (e.g., up to 66% of undetected visited patches and 29% of spurious links) when the relocation frequency is too coarse compared to the temporal dynamics of animal movement. However, the level of landscape fragmentation and underlying movement processes can both mitigate the effect of relocation sampling frequency. We also showed that network topologies emerging from different movement behaviours and a wide range of landscape fragmentation were complex, and that commonly used theoretical networks accurately predicted only 30-50% of landscape connectivity in such environments. CONCLUSIONS: Very high-resolution trajectories were generally necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long periods should thus grow in the movement ecology sphere. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.

"Pray Observe How Time Slips By:" Collaborators, Assistants, and the Background Dynamics in the Publication of Darwin's Cirripedia Project.

This study investigates nineteenth century natural history practices through the lens of the Actor-Network Theory, which posits that scientific practice is shaped by an intricate network of interactions between human and non-human actors. At the core of this research is the analysis of correspondence between Charles Darwin and his collaborators during the Cirripedia Project, which unveils a complex landscape of negotiations with illustrators, funders, specimen owners, and translators, among other stakeholders and interested parties. The study goes beyond the final outcomes of scientific research, delving into behind-the-scenes interactions, and hidden constructions, shedding light on the complex dynamics and actors that conventional scientific narratives often overlook. In general, this approach provides a detailed and insightful view of the underlying processes of nineteenth-century scientific practice, underscoring the importance of epistolary correspondence as a central element in producing scientific knowledge at the time, and in particular it reveals to us how much Darwin was himself involved in the production of his famous work on barnacles. By emphasizing the intricacies of research, this study enriches our understanding of Darwin's work as well as natural history practices in the 19th century, highlighting the complexity and diversity of actors and agents involved in shaping scientific knowledge.

Evolving through multiple, co-existing pressures to change: a case study of self-organization in primary care during the COVID-19 pandemic in Canada.

BACKGROUND: Primary care is often described as slow to change. But conceptualized through complexity theory, primary care is continually changing in unpredictable, non-linear ways through self-organization processes. Self-organization has proven hard to study directly. We aimed to develop a methodology to study self-organization and describe how a primary care clinic self-organizes over time. METHODOLOGY: We completed a virtual case study of an urban primary care clinic from May-Nov 2021, applying methodological insights from actor-network theory to examine the complexity theory concept of self-organization. We chose to focus our attention on self-organization activities that alter organizational routines. Data included fieldnotes of observed team meetings, document collection, interviews with clinic members, and notes from brief weekly discussions to detect actions to change clinical and administrative routines. Adapting schema analysis, we described changes to different organizational routines chronologically, then explored intersecting changes. We sought feedback on results from the participating clinic. FINDINGS: Re-establishing equilibrium remained challenging well into the COVID-19 pandemic. The primary care clinic continued to self-organize in response to changing health policies, unintended consequences of earlier adaptations, staff changes, and clinical care initiatives. Physical space, technologies, external and internal policies, guidelines, and clinic members all influenced self-organization. Changing one created ripple effects, sometimes generating new, unanticipated problems. Member checking confirmed we captured most of the changes to organizational routines during the case study period. CONCLUSIONS: Through insights from actor-network theory, applied to studying actions taken that alter organizational routines, it is possible to operationalize the theoretical construct of self-organization. Our methodology illuminates the primary care clinic as a continually changing entity with co-existing and intersecting processes of self-organization in response to varied change pressures.

Dataset from the bell and dean paper on the structure of vitreous silica.

Digitized data is presented based on the 1966 and 1972 seminal papers by R. J. Bell and P. Dean, which in turn are based on a physical ball and stick model of vitreous silica. The original Bell and Dean paper supports the random network theory for glass structure proposed by Zachariasen in 1932. The data were collected and digitized by verifying the data set in the Appendix of the Bell and Dean paper. One error in location was corrected. The dataset provides a convenient method to determine specific information about bond locations and angles of silica and oxygen atoms if researchers want to test theories of glass structure or fracture in amorphous materials.

A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance.

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity Ci, and the HCCI of the whole river network is defined as the mean of the Ci for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that Ci showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of Ci in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management.

Evolution of Telencephalon Anterior-Posterior Patterning through Core Endogenous Network Bifurcation.

How did the complex structure of the telencephalon evolve? Existing explanations are based on phenomena and lack a first-principles account. The Darwinian dynamics and endogenous network theory-established decades ago-provides a mathematical and theoretical framework and a general constitutive structure for theory-experiment coupling for answering this question from a first-principles perspective. By revisiting a gene network that explains the anterior-posterior patterning of the vertebrate telencephalon, we found that upon increasing the cooperative effect within this network, fixed points gradually evolve, accompanied by the occurrence of two bifurcations. The dynamic behavior of this network is informed by the knowledge obtained from experiments on telencephalic evolution. Our work provides a quantitative explanation for how telencephalon anterior-posterior patterning evolved from the pre-vertebrate chordate to the vertebrate and provides a series of verifiable predictions from a first-principles perspective.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

Network ecology: Tie fitness in social context(s).

Social relations are embedded in material, cultural, and institutional settings that affect network dynamics and the resulting topologies. For example, romantic entanglements are subject to social and cultural norms, interfirm alliances are constrained by country-specific legislation, and adolescent friendships are conditioned by classroom settings and neighborhood effects. In short, social contexts shape social relations and the networks they give rise to. However, how and when they do so remain to be established. This paper presents network ecology as a general framework for identifying how the proximal environment shapes social networks by focusing interactions and social relations, and how these interactions and relations in turn shape the environment in which social networks form. Tie fitness is introduced as a metric that quantifies how well particular dyadic social relations would align with the setting. Using longitudinal networks collected on two cohorts each in 18 North American schools, i.e., 36 settings, we develop five generalizable observations about the time-varying fitness of adolescent friendship. Across all 252 analyzed networks, tie fitness predicted new tie formation, tie longevity, and tie survival. Dormant fit ties cluster in relational niches, thereby establishing a resource base for social identities competing for increased representation in the relational system.

Symptom Network Analysis in a Large Sample of Children and Adults with a Chronic Tic Disorder.

BACKGROUND: Chronic tic disorders (CTD) are multifaceted disorders characterized by multiple motor and/or vocal tics. They are often associated with complex tics including echophenomena, paliphenomena, and coprophenomena as well as psychiatric comorbidities such as attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). OBJECTIVES: Our goal was to uncover the inter-relational structure of CTD and comorbid symptoms in children and adults and to understand changes in symptom structure across development. METHODS: We used network and graph analyses to uncover the structure of association of symptoms in childhood/adolescence (n = 529) and adulthood (n = 503) and how this structure might change from childhood to adulthood, pinpointing core symptoms as a main target for interventions. RESULTS: The analysis yielded core symptom networks in young and adult patients with CTD including complex tics and tic-related phenomena as well as touching people and objects. Core symptoms in childhood also included ADHD symptoms, whereas core symptoms in adults included symptoms of OCD instead. Interestingly, self-injurious behavior did not play a core role in the young CTD network, but became one of the central symptoms in adults with CDT. In addition, we found strong connections between complex motor and vocal tics as well as echolalia and echopraxia. CONCLUSIONS: Next to other complex tics, echophenomena, paliphenomena, and coprophenomena can be regarded core symptoms of CTD. ADHD symptoms are closely related to CTD in childhood, whereas symptoms of OCD and self-injurious behavior are closely associated with CTD in adults. Our results suggest that a differentiation between motor and vocal tics is somewhat arbitrary.

"The influence of auditory stimulation on whole body variability in healthy older adults during gait".

Acoustic stimulation appears to be a promising strategy in reducing the risk of falling in older adults, demonstrating effectiveness in improving stability. However, its impact on movement variability, another crucial indicator of fall risk, seems to be limited. This study aims to assess movement variability during walking in a cohort of healthy older adults exposed to three different frequencies of acoustic stimulation (90%, 100% and 110% of each subject's average cadence). Using a systemic approach based on network theory, which considers the intricate relationships between all body segments, we constructed connectivity matrices composed of nodes, represented by bony landmarks, and edges, consisting of the standardised covariance of accelerations between each pair of nodes. By introducing a new metric called Similarity Score (S-score), we quantified the ability of each individual to repeat the same motor pattern at each gait cycle under different experimental conditions. The study revealed that rhythmic auditory stimulation (RAS) at 100% and 90% of the mean cadence significantly increased the S-scores compared to the baseline. These results highlight the effects of RAS in increasing gait repeatability in healthy older adults, with a focus on global kinematics.

An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Graph-theoretical chirality measure and chirality-property relations for chemical structures with multiscale mirror asymmetries.

Chirality is an essential geometric property unifying small molecules, biological macromolecules, inorganic nanomaterials, biological microparticles, and many other chemical structures. Numerous chirality measures have attempted to quantify this geometric property of mirror asymmetry and to correlate these measures with physical and chemical properties. However, their utility has been widely limited because these correlations have been largely notional. Furthermore, chirality measures also require prohibitively demanding computations, especially for chiral structures comprised of thousands of atoms. Acknowledging the fundamental problems with quantification of mirror asymmetry, including the ambiguity of sign-variable pseudoscalar chirality measures, we revisit this subject because of the significance of quantifying chirality for quantitative biomimetics and describing the chirality of nanoscale materials that display chirality continuum and scale-dependent mirror asymmetry. We apply the concept of torsion within the framework of differential geometry to the graph theoretical representation of chiral molecules and nanostructures to address some of the fundamental problems and practical limitations of other chirality measures. Chiral gold clusters and other chiral structures are used as models to elaborate a graph-theoretical chirality (GTC) measure, demonstrating its applicability to chiral materials with different degrees of chirality at different scales. For specific cases, we show that GTC provides an adequate description of both the sign and magnitude of mirror asymmetry. The direct correlations with macroscopic properties, such as chiroptical spectra, are enhanced by using the hybrid chirality measures combining parameters from discrete mathematics and physics. Taking molecular helices as an example, we established a direct relation between GTC and optical activity, indicating that this chirality measure can be applied to chiral metamaterials and complex chiral constructs.

Reassembling nursing in the digital age: An actor-network theory perspective.

This article explores the application of actor-network theory (ANT) to the nursing profession, proposing a novel perspective in understanding nursing in the context of modern digital healthcare. Traditional grand nursing theories, while foundational, often fail to encapsulate the dynamic and complex nature of nursing, particularly in an era of rapid technological advancements and shifting societal dynamics. ANT, with its emphasis on the relationships between human and nonhuman actors, offers a framework to understand nursing beyond traditional paradigms. This article makes two key arguments: first, that nursing can be viewed as a highly organised social assemblage, where both human (nurses, patients and policymakers) and nonhuman actors (technologies, medical equipment, institutional policies) play a crucial role, and second, that ANT can be used to enhance existing nursing theory to better understand the role of technology in nursing practice. The article considers how ANT can provide a more holistic and adaptable model for describing the nursing profession, particularly in an era where technology plays an integral role in healthcare delivery. It discusses the implications of viewing nursing through ANT, highlighting the need for nursing education and practice to adapt to the interconnected and technologically advanced nature of modern healthcare. The article also acknowledges the limitations of ANT, particularly its potential oversimplification of the complex ethical dimensions inherent in nursing and its focus on observable phenomena.

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes.

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

A comparative anatomical network analysis of the human and chimpanzee brains.

Spatial interactions among anatomical elements help to identify topological factors behind morphological variation and can be investigated through network analysis. Here, a whole-brain network model of the chimpanzee (Pan troglodytes, Blumenbach 1776) is presented, based on macroanatomical divisions, and compared with a previous equivalent model of the human brain. The goal was to contrast which regions are essential in the geometric balance of the brains of the two species, to compare underlying phenotypic patterns of spatial variation, and to understand how these patterns might have influenced the evolution of human brain morphology. The human and chimpanzee brains share morphologically complex inferior-medial regions and a topological organization that matches the spatial constraints exerted by the surrounding braincase. These shared topological features are interesting because they can be traced back to the Chimpanzee-Human Last Common Ancestor, 7-10 million years ago. Nevertheless, some key differences are found in the human and chimpanzee brains. In humans, the temporal lobe, particularly its deep and medial limbic aspect (the parahippocampal gyrus), is a crucial node for topological complexity. Meanwhile, in chimpanzees, the cerebellum is, in this sense, more embedded in an intricate spatial position. This information helps to interpret brain macroanatomical change in fossil hominids.