Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influence.

Mammalian functional architecture flexibly adapts, transitioning from integration where information is distributed across the cortex, to segregation where information is focal in densely connected communities of brain regions. This flexibility in cortical brain networks is hypothesized to be driven by control signals originating from subcortical pathways, with the basal ganglia shifting the cortex towards integrated processing states and the cerebellum towards segregated states. In a sample of healthy human participants (N=242), we used fMRI to measure temporal variation in global brain networks while participants performed two tasks with similar cognitive demands (Stroop and Multi-Source Inference Task (MSIT)). Using the modularity index, we determined cortical networks shifted from integration (low modularity) at rest to high modularity during easier i.e. congruent (segregation). Increased task difficulty (incongruent) resulted in lower modularity in comparison to the easier counterpart indicating more integration of the cortical network. Influence of basal ganglia and cerebellum was measured using eigenvector centrality. Results correlated with decreases and increases in cortical modularity respectively, with only the basal ganglia influence preceding cortical integration. Our results support the theory the basal ganglia shifts cortical networks to integrated states due to environmental demand. Cerebellar influence correlates with shifts to segregated cortical states, though may not play a causal role.

Computational insights into zinc silicate MOF structures: topological modeling, structural characterization and chemical predictions.

Metal-organic frameworks (MOFs) play a pivotal role in modern material science, offering unique properties such as flexibility, substantial pore space, distinctive structure, and large surface area. Recently, zinc-based MOFs have attracted significant attention, particularly in the biomedical arena, owing to their versatile applications in drug delivery, biosensing, and cancer imaging. However, there remains a crucial need to explore and understand the structural properties of zinc silicate-based MOFs to fully exploit their potential in various applications. The objective of this study is to address this need by employing topological modeling techniques to characterize zinc silicate networks. Utilizing connection number concept of chemical graph theory and novel AL molecular descriptors, we aim to investigate the structural intricacies of these MOFs. More precisely, zinc silicate-based MOF networks are topologically modeled via novel AL topological indices, and derived mathematical closed form formulae for them. By comparing experimental and calculated values and constructing linear regression models, the predictive capabilities of the proposed descriptors are evaluated. Specifically, the performance of derived topological indices against the physico-chemical properties of octane isomers is assessed, which provide valuable insights into their predictive potential. The findings of this study demonstrated the potential of novel AL indices in predicting a wide range of important physico-chemical properties, further enhancing their practicality in materials science and beyond.

Automatic molecular fragmentation by evolutionary optimisation.

Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ  mol - 1 , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ  mol - 1 for MBE2 and 24.3 kJ  mol - 1 for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ  mol - 1 were observed at the MBE2 and MBE3 levels, respectively.Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease.

Background: Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse. Objective: To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression. Methods: We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy. Results: The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups. Conclusion: These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency.

The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.

A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic.

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.

Cortical hubs of highly superior autobiographical memory.

Highly Superior Autobiographical Memory (HSAM) is a rare form of enhanced memory in which individuals demonstrate an extraordinary ability to remember details of their personal lives with high levels of accuracy and vividness. Neuroimaging studies have identified brain regions - specifically, midline areas within the default network - associated with remembering events from one's past. Extending this research on the neural underpinnings of autobiographical memory, the present study utilizes graph theory analyses to compare functional brain connectivity in a cohort of HSAM (n = 12) and control participants (n = 29). We perform seed-based analysis in resting-state fMRI data to assess how specific cortical regions within the autobiographical memory network are differentially connected in HSAM individuals. Additionally, we apply a whole-brain connectivity analysis to identify differences in brain hub-network topology associated with enhanced autobiographical memory. Seed-based results show converging patterns of increased connectivity in HSAM across midline areas. Whole-brain analysis also reveals enhanced connectivity across medial prefrontal and posterior cingulate cortex in HSAM individuals. Together, these results extend prior research, highlighting cortical hubs within the default network associated with enhanced autobiographical memory.

On tricyclic graphs with maximum atom-bond sum-connectivity index.

The sum-connectivity, Randic, and atom-bond connectivity indices have a prominent place among those topological indices that depend on the graph's vertex degrees. The ABS (atom-bond sum-connectivity) index is a variant of all the aforementioned three indices, which was recently put forward. Let T ( n ) be the class of all connected tricyclic graphs of order n. Recently, the problem of determining graphs from T ( n ) having the least possible value of the ABS index was solved in (Zuo et al., 2024 [39]) for the case when the maximum degree of the considered graphs does not exceed 4. The present paper addresses the problem of finding graphs from T ( n ) having the largest possible value of the ABS index for n ≥ 5 .

Brain Responses Difference between Sexes for Strong Desire to Void: A Functional Magnetic Resonance Imaging Study in Adults Based on Graph Theory.

Background: The alternations of brain responses to a strong desire to void were unclear, and the gender differences under the strong desire to void remain controversial. The present study aims to identify the functional brain network's topologic property changes evoked by a strong desire to void in healthy male and female adults with synchronous urodynamics using a graph theory analysis. Methods: The bladders of eleven healthy males and eleven females were filled via a catheter using a specific infusion and withdrawal pattern. A resting-state functional magnetic resonance imaging (fMRI) was performed on the enrolled subjects, scanning under both the empty bladder and strong desire to void states. An automated anatomical labeling (AAL) atlas was used to identify the ninety cortical and subcortical regions. Pearson's correlation calculations were performed to establish a brain connection matrix. A paired t-test (p < 0.05) and Bonferroni correction were applied to identify the significant statistical differences in topological properties between the two states, including small-world network property parameters [gamma (γ) and lambda (λ)], characteristic path length (Lp), clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), and regional nodal efficiency (Enodal). Results: The final data suggested that females and males had different brain response patterns to a strong desire to void, compared with an empty bladder state. Conclusions: More brain regions involving emotion, cognition, and social work were active in females, and males might obtain a better urinary continence via a compensatory mechanism.

Network Insights of Dementia Comorbidities Based on Genetic Background.

Dementia is a global public health concern. This study focuses on the genetic factors underlying dementia. We analyzed electronic medical records (EMR) from Taichung Veterans General Hospital, Taiwan, to confirm differences between dementia and non-dementia patients. This work was supported by Taipei Medical University [TMU111-AE1-B45].

Generalized flow calculation of the gas flow in a network of capillaries used in gas chromatography.

A general method for the calculation of the flow and pressure of a gas in a network of cylindrical capillaries is presented. This method is used specifically for gas chromatographic systems in this work. With this approach, it is possible to easily calculate flow and pressures in complex gas chromatographic systems, like flow-modulated or thermal-modulated multidimensional gas chromatographic systems, or systems with multiple outlets at different pressures. A mathematic abstraction using graph theory is used to represent the system of capillaries. With this graph, the flow balance equations at the connections of the capillaries can easily be set up. Using a computer algebra system, the system of flow balance equations can be solved for the pressures at the connection points. For simple systems, this approach is presented, and calculated flows, pressures, and hold-up times are compared with measured values. In addition, two complex systems (4-Way-Splitter, Deans Switch system) of capillaries are presented with calculations only. For these systems, certain conditions were formulated, that is, a certain difference in hold-up times and a defined split ratio between different paths of these systems. Using a numeric non-linear solver, configurations of these systems were found, that fulfill these conditions.

Cortical Network Disruption Is Minimal in Early Stages of Psychosis.

Background and Hypothesis: Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Study Design: Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Study Results: Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. Conclusions: The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis.

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Chemistry-informed Machine Learning Explains Calcium-binding Proteins' Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions.

Proteins' fuzziness are features for communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. Binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit, but it is unclear whether the limited experimental data available can be used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. With our designs, we provided a framework of explainable machine learning model to annotate atomic charges of calcium ions in calcium-binding proteins with domain knowledge in response to the chemical changes in an environment based on the limited size of scientific data in a genome space.

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss.

BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. OBJECTIVE: This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. MATERIALS AND METHODS: Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. RESULTS: The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. CONCLUSIONS: These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.

Corrigendum: Beyond nodes and edges: a bibliometric analysis on graph theory and neuroimaging modalities.

[This corrects the article DOI: 10.3389/fnins.2024.1373264.].

Common functional mechanisms underlying dynamic brain network changes across five general anesthetics: A rat fMRI study.

BACKGROUND: Reversible loss of consciousness is the primary therapeutic endpoint of general anesthesia; however, the drug-invariant mechanisms underlying anesthetic-induced unconsciousness are still unclear. This study aimed to investigate the static, dynamic, topological and organizational changes in functional brain network induced by five clinically-used general anesthetics in the rat brain. METHOD: Male Sprague-Dawley rats (n = 57) were randomly allocated to received propofol, isoflurane, ketamine, dexmedetomidine, or combined isoflurane plus dexmedetomidine anesthesia. Resting-state functional magnetic resonance images were acquired under general anesthesia and analyzed for changes in dynamic functional brain networks compared to the awake state. RESULTS: Different general anesthetics induced distinct patterns of functional connectivity inhibition within brain-wide networks, resulting in multi-level network reorganization primarily by impairing the functional connectivity of cortico-subcortical networks as well as by reducing information transmission capacity, intrinsic connectivity, and network architecture stability of subcortical regions. Conversely, functional connectivity and topological properties were preserved within cortico-cortical networks, albeit with fewer dynamic fluctuations under general anesthesia. CONCLUSIONS: Our findings highlighted the effects of different general anesthetics on functional brain network reorganization, which might shed light on the drug-invariant mechanism of anesthetic-induced unconsciousness.

Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis.

OBJECTIVE: The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD. METHODS: Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties. RESULTS: For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group. CONCLUSION: There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.

Graph Theory to Map Nursing Knowledge of Diagnosis and Interventions in Women's Health.

OBJECTIVE: Apply Graph Theory to analyze and map knowledge about nursing diagnoses and interventions, based on records of consultations carried out by nurses, in women's health, in primary health care. METHODS: Secondary data from a cross-sectional study were used. Records of nursing consultations carried out during the month of October 2016, in 21 health units, in a Brazilian municipality were analyzed. Network analysis was carried out using Graphs from 61 nursing consultations. RESULTS: 175 diagnoses were recorded, an average of three per consultation; and 380 interventions, an average of six per consultation. In the analysis, four diagnostic and four intervention network groupings were identified. CONCLUSIONS: The mapping allowed reflection on phenomena of interest to Nursing and fostering critical thinking in decision making. The findings are useful for teaching and training nurses, as well as strengthening the use of standardized language systems.