Structure and function of complex brain networks

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

FDG-PET-based brain network analysis: a brief review of metabolic connectivity

BACKGROUND

Over the past decades, the analysis metabolic connectivity patterns has received significant attention in exploring the underlying mechanism of human behaviors, and the neural underpinnings of brain neurological disorders. Brain network can be considered a powerful tool and play an important role in the analysis and understanding of brain metabolic patterns. With the advantages and emergence of metabolic-based network analysis, this study aims to systematically review how brain properties, under various conditions, can be studied using Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images and graph theory, as well as applications of this approach. Additionally, this study provides a brief summary of graph metrics and their uses in studying and diagnosing different types of brain disorders using FDG-PET images.

MAIN BODY

In this study, we used several databases in Web of Science including Web of Science Core Collection, MEDLINE to search for related studies from 1980 up to the present, focusing on FDG-PET images and graph theory. From 68 articles that matched our keywords, we selected 28 for a full review in order to find out the most recent findings and trends. Our results reveal that graph theory and its applications in analyzing metabolic connectivity patterns have attracted the attention of researchers since 2015. While most of the studies are focusing on group-level based analysis, there is a growing trend in individual-based network analysis. Although metabolic connectivity can be applied to both neurological and psychiatric disorders, the majority of studies concentrate on neurological disorders, particularly Alzheimer's Disease and Parkinson's Disease. Most of the findings focus on changes in brain network topology, including brain segregation and integration.

CONCLUSION

This review provides an insight into how graph theory can be used to study metabolic connectivity patterns under various conditions including neurological and psychiatric disorders.

Surfacic networks

Surfacic networks are structures built upon a 2D manifold. Many systems, including transportation networks and various urban networks, fall into this category. The fluctuations of node elevations imply significant deviations from typical plane networks and require specific tools to understand their impact. Here, we present such tools, including lazy paths that minimize elevation differences, graph arduousness which measures the tiring nature of shortest paths (SPs), and the excess effort, which characterizes positive elevation variations along SPs. We illustrate these measures using toy models of surfacic networks and empirically examine pedestrian networks in selected cities. Specifically, we examine how changes in elevation affect the spatial distribution of betweenness centrality. We also demonstrate that the excess effort follows a nontrivial power law distribution, with an exponent that is not universal, which illustrates that there is a significant probability of encountering steep slopes along SPs, regardless of the elevation difference between the starting point and the destination. These findings highlight the significance of elevation fluctuations in shaping network characteristics. Surfacic networks offer a promising framework for comprehensively analyzing and modeling complex systems that are situated on or constrained to a surface environment.

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery

In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population-level features separately, inevitably neglecting the multi-level characteristics of brain disorders. To address this issue, we propose an end-to-end multi-graph neural network model called KMGCN. This model simultaneously leverages individual- and population-level features for brain network analysis. At the individual level, we construct multi-graph using both knowledge-driven and data-driven approaches. Knowledge-driven refers to constructing a knowledge graph based on prior knowledge, while data-driven involves learning a data graph from the data itself. At the population level, we construct multi-graph using both imaging and phenotypic data. Additionally, we devise a pooling method tailored for brain networks, capable of selecting brain regions that impact brain disorders. We evaluate the performance of our model on two large datasets, ADNI and ABIDE, and experimental results demonstrate that it achieves state-of-the-art performance, with 86.87% classification accuracy for ADNI and 86.40% for ABIDE, accompanied by around 10% improvements in all evaluation metrics compared to the state-of-the-art models. Additionally, the biomarkers identified by our model align well with recent neuroscience research, indicating the effectiveness of our model in brain network analysis and potential biomarker discovery. The code is available at https://github.com/GN-gjh/KMGCN.

Diverging network architecture of the C. elegans connectome and signaling network

The connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the C. elegans connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons. Understanding how effective communication pathways relate to or diverge from the underlying structure is a central question in neuroscience. Here, we analyze the modular architecture of the C. elegans signal propagation network, measured via calcium imaging and optogenetics, and compare it to the underlying anatomical wiring measured by electron microscopy. Compared to the connectome, we find that signaling modules are not aligned with the modular boundaries of the anatomical network, highlighting an instance where function deviates from structure. An exception to this is the pharynx which is delineated into a separate community in both anatomy and signaling. We analyze the cellular compositions of the signaling architecture and find that its modules are enriched for specific cell types and functions, suggesting that the network modules are neurobiologically relevant. Lastly, we identify a "rich club" of hub neurons in the signaling network. The membership of the signaling rich club differs from the rich club detected in the anatomical network, challenging the view that structural hubs occupy positions of influence in functional (signaling) networks. Our results provide new insight into the interplay between brain structure, in the form of a complete synaptic-level connectome, and brain function, in the form of a system-wide causal signal propagation atlas.

Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction

Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.

Contrasting topologies of synchronous and asynchronous functional brain networks

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity that the other type of network does not.

Normative modeling of brain MRI data identifies small subcortical volumes and associations with cognitive function in youth with fetal alcohol spectrum disorder (FASD)

AIM

To quantify regional subcortical brain volume anomalies in youth with fetal alcohol spectrum disorder (FASD), assess the relative sensitivity and specificity of abnormal volumes in FASD vs. a comparison group, and examine associations with cognitive function.

METHOD

Participants: 47 children with FASD and 39 typically-developing comparison participants, ages 8-17 years, who completed physical evaluations, cognitive and behavioral testing, and an MRI brain scan. A large normative MRI dataset that controlled for sex, age, and intracranial volume was used to quantify the developmental status of 7 bilateral subcortical regional volumes. Z-scores were calculated based on volumetric differences from the normative sample. T-tests compared subcortical volumes across groups. Percentages of atypical volumes are reported as are sensitivity and specificity in discriminating groups. Lastly, Pearson correlations examined the relationships between subcortical volumes and neurocognitive performance.

RESULTS

Participants with FASD demonstrated lower mean volumes across a majority of subcortical regions relative to the comparison group with prominent group differences in the bilateral hippocampi and bilateral caudate. More individuals with FASD (89%) had one or more abnormally small volume compared to 72% of the comparison group. The bilateral hippocampi, bilateral putamen, and right pallidum were most sensitive in discriminating those with FASD from the comparison group. Exploratory analyses revealed associations between subcortical volumes and cognitive functioning that differed across groups.

CONCLUSION

In this sample, youth with FASD had a greater number of atypically small subcortical volumes than individuals without FASD. Findings suggest MRI may have utility in identifying individuals with structural brain anomalies resulting from PAE.

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

Beyond Focal Lesions: Dynamical Network Effects of White Matter Hyperintensities

White matter (WM) tracts shape the brain's dynamical activity and their damage (e.g., white matter hyperintensities, WMH) yields relevant functional alterations, ultimately leading to cognitive symptoms. The mechanisms linking the structural damage caused by WMH to the arising alterations of brain dynamics is currently unknown. To estimate the impact of WMH on brain dynamics, we combine neural-mass whole-brain modeling with a virtual-lesioning (disconnectome) approach informed by empirical data. We account for the heterogeneous effects of WMH either on inter-regional communication (i.e., edges) or on dynamics (i.e., nodes) and create models of their local versus global, and edge versus nodal effects using a large fMRI dataset comprising 188 non-demented individuals (120 cognitively normal, 68 with mild cognitive impairment) with varying degrees of WMH. We show that, although WMH mainly determine local damage to specific WM tracts, these lesions yield relevant global dynamical effects by reducing the overall synchronization of the brain through a reduction of global coupling. Alterations of local nodal dynamics through disconnections are less relevant and present only at later stages of WMH damage. Exploratory analyses suggest that education might play a beneficial role in counteracting the reduction in global coupling associated with WMH. This study provides generative models linking the structural damage caused by WMH to alterations in brain dynamics. These models might be used to evaluate the detrimental effects of WMH on brain dynamics in a subject-specific manner. Furthermore, it validates the use of whole-brain modeling for hypothesis-testing of structure-function relationships in diseased states characterized by empirical disconnections.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

Connectivity in the Dorsal Visual Stream Is Enhanced in Action Video Game Players

Action video games foster competitive environments that demand rapid spatial navigation and decision-making. Action video gamers often exhibit faster response times and slightly improved accuracy in vision-based sensorimotor tasks. Background/Objectives: However, the underlying functional and structural changes in the two visual streams of the brain that may be contributing to these cognitive improvements have been unclear. Methods: Using functional and diffusion MRI data, this study investigated the differences in connectivity between gamers who play action video games and nongamers in the dorsal and ventral visual streams. Results: We found that action video gamers have enhanced functional and structural connectivity, especially in the dorsal visual stream. Specifically, there is heightened functional connectivity-both undirected and directed-between the left superior occipital gyrus and the left superior parietal lobule during a moving-dot discrimination decision-making task. This increased connectivity correlates with response time in gamers. The structural connectivity in the dorsal stream, as quantified by diffusion fractional anisotropy and quantitative anisotropy measures of the axonal fiber pathways, was also enhanced for gamers compared to nongamers. Conclusions: These findings provide valuable insights into how action video gaming can induce targeted improvements in structural and functional connectivity between specific brain regions in the visual processing pathways. These connectivity changes in the dorsal visual stream underpin the superior performance of action video gamers compared to nongamers in tasks requiring rapid and accurate vision-based decision-making.

Neuromodulation on the ground and in the clouds: a mini review of transcranial direct current stimulation for altering performance in interactive driving and flight simulators

Transcranial direct current stimulation (tDCS) has emerged as a promising tool for cognitive enhancement, especially within simulated virtual environments that provide realistic yet controlled methods for studying human behavior. This mini review synthesizes current research on the application of tDCS to improve performance in interactive driving and flight simulators. The existing literature indicates that tDCS can enhance acute performance for specific tasks, such as maintaining a safe distance from another car or executing a successful plane landing. However, the effects of tDCS may be context-dependent, indicating a need for a broader range of simulated scenarios. Various factors, including participant expertise, task difficulty, and the targeted brain region, can also influence tDCS outcomes. To further strengthen the rigor of this research area, it is essential to address and minimize different forms of research bias to achieve true generalizability. This comprehensive analysis aims to bridge the gap between theoretical understanding and practical application of neurotechnology to study the relationship between the brain and behavior, ultimately providing insights into the effectiveness of tDCS in transportation settings.

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior

Traditional models of human brain activity often represent it as a network of pairwise interactions between brain regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order interactions from temporal brain signals involving three or more regions. However, to this day it remains unclear whether methods based on inferred higher-order interactions outperform traditional pairwise ones for the analysis of fMRI data. To address this question, we conducted a comprehensive analysis using fMRI time series of 100 unrelated subjects from the Human Connectome Project. We show that higher-order approaches greatly enhance our ability to decode dynamically between various tasks, to improve the individual identification of unimodal and transmodal functional subsystems, and to strengthen significantly the associations between brain activity and behavior. Overall, our approach sheds new light on the higher-order organization of fMRI time series, improving the characterization of dynamic group dependencies in rest and tasks, and revealing a vast space of unexplored structures within human functional brain data, which may remain hidden when using traditional pairwise approaches.

Disrupted topologic efficiency of white matter structural connectome in migraine: a graph-based connectomics study

OBJECTIVE

To delineate the structural connectome alterations in patients with chronic migraine (CM), episodic migraine (EM), and healthy controls (HCs).

BACKGROUND

The pathogenesis of migraine chronification remains elusive, with structural brain network changes potentially playing a key role. However, there is a paucity of research employing graph theory analysis to explore changes in the whole brain structural networks in patients with CM and EM.

METHODS

The individual structural brain connectome of 60 patients with CM, 34 patients with EM, and 39 healthy control participants were constructed by using deterministic diffusion-tensor tractography. Graph metrics including global efficiency, characteristic path length, local efficiency, clustering coefficient, and small-world parameters were evaluated to describe the topologic organization of the white matter structural networks. Additionally, nodal clustering coefficient and efficiency were considered to assess the regional characteristics of the brain connectome. A graph-based statistic was used to assess brain network properties across the groups.

RESULTS

Graph theory analysis revealed significant disruptions in the structural brain networks of CM patients, characterized by reduced global efficiency, local efficiency, and increased characteristic path length compared to HCs. Additionally, CM patients exhibited significantly lower local efficiency than EM patients. Notably, the CM group demonstrated marked reductions in local clustering coefficient and nodal local efficiency in the frontal and temporal regions compared with the healthy control group and EM group. Nodal local efficiency can effectively distinguish CM from EM and HCs. Moreover, the disrupted topologic efficiency was significantly associated with attack frequency and MIDAS score in patients with migraine after Bonferroni correction.

CONCLUSION

Decreased structural connectivity in the frontal and temporal regions may serve as a neuroimaging marker for migraine chronification and disease progression, providing valuable insights into the pathophysiology of chronic migraine.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

Objective

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).

Materials and methods

Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.

Results

In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.

Conclusion

This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Co-evolutionary dynamics for two adaptively coupled Theta neurons

Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple network of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, a, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity a, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.

Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder

AIMS

The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC).

METHODS

We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms.

RESULTS

Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores.

CONCLUSION

Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.

Functional brain networks in clinical high-risk for bipolar disorder and psychosis

Abnormal connectivity in the brain has been linked to the pathophysiology of severe mental illnesses, including bipolar disorder and schizophrenia. The current study aimed to investigate large-scale functional networks and global network metrics in clinical high-risk for bipolardisorder (CHR-BD, n = 25), clinical high-risk for psychosis (CHR-P, n = 30), and healthy controls (HCs, n = 19). Help-seeking youth at CHR-BD and CHR-P were recruited from the early intervention program at Dokuz Eylul University, Izmir, Turkey. Resting-state functional magnetic resonance imaging scans were obtained from youth at CHR-BD, CHR-P, and HCs. Graph theoretical analysis and network-based statistics were employed to construct and examine the topological features of the whole-brain metrics and large-scale functional networks. Connectivity was increased (i) between the visual and default mode, (ii) between the visual and salience, (iii) between the visual and cingulo-opercular networks, and decreased (i) within the default mode and (ii) between the default mode and fronto-parietal networks in the CHR-P compared to HCs. Decreased global efficiency was found in CHR-P compared to CHR-BD. Functional networks were not different between CHR-BD and HCs. Global efficiency was negatively correlated with subthreshold positive symptoms and thought disorder in the high-risk groups. The current results suggest disrupted networks in CHR-P compared to HCs and CHR-BD. Moreover, transdiagnostic psychosis features are linked to functional brain networks in the at-risk groups. However, given the small, medicated sample, results are exploratory and hypothesis-generating.

The unique role of anosognosia in the clinical progression of Alzheimer's disease: a disorder-network perspective

Alzheimer's disease (AD) encompasses a long continuum from a preclinical phase, characterized by neuropathological alterations albeit normal cognition, to a symptomatic phase, marked by its clinical manifestations. Yet, the neural mechanisms responsible for cognitive decline in AD patients remain poorly understood. Here, we posit that anosognosia, emerging from an error-monitoring failure due to early amyloid-β deposits in the posterior cingulate cortex, plays a causal role in the clinical progression of AD by preventing patients from being aware of their deficits and implementing strategies to cope with their difficulties, thus fostering a vicious circle of cognitive decline.

Dynamic patterns of functional connectivity in the human brain underlie individual memory formation

Remembering our everyday experiences involves dynamically coordinating information distributed across different brain regions. Investigating how momentary fluctuations in connectivity in the brain are relevant for episodic memory formation, however, has been challenging. Here we leverage the high temporal precision of intracranial EEG to examine sub-second changes in functional connectivity in the human brain as 20 participants perform a paired associates verbal memory task. We first identify potential functional connections by selecting electrode pairs across the neocortex that exhibit strong correlations with a consistent time delay across random recording segments. We then find that successful memory formation during the task involves dynamic sub-second changes in functional connectivity that are specific to each word pair. These patterns of dynamic changes are reinstated when participants successfully retrieve the word pairs from memory. Therefore, our data provide direct evidence that specific patterns of dynamic changes in human brain connectivity are associated with successful memory formation.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation

The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.

An information-theoretic framework for conditional causality analysis of brain networks

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

Peak width of skeletonized mean diffusivity as a neuroimaging biomarker in first-episode schizophrenia

Background and objective

Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics.

Methods

A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics.

Results

Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = -2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = -0.409, p = 0.038; lambda: r = -0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (β = -0.426, t = -2.260, p = 0.034) and increased lambda (β = -0.490, t = -2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively (R a d j 2 = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05).

Conclusion

PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.

Symmetries and synchronization from whole-neural activity in C. elegans connectome: Integration of functional and structural networks

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the C.elegans backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals.

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

BACKGROUND

Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.2 deletion syndrome (22q11.2DS) offers an important opportunity to study the development of FSDs and their specific association with the pathophysiology of psychosis.

METHODS

Previously, we used graph signal processing to combine brain activity and structural connectivity measures in adults, quantifying FSD. Here, we combined FSD with longitudinal multivariate partial least squares correlation to evaluate FSD alterations across groups and among patients with and without mild to moderate positive psychotic symptoms. We assessed 391 longitudinally repeated resting-state functional and diffusion-weighted magnetic resonance images from 194 healthy control participants and 197 deletion carriers (ages 7-34 years, data collected over a span of 12 years).

RESULTS

Compared with control participants, patients with 22q11.2DS showed a persistent developmental offset from childhood, with regions of hyper- and hypocoupling across the brain. Additionally, a second deviating developmental pattern showed an exacerbation during adolescence, presenting hypocoupling in the frontal and cingulate cortices and hypercoupling in temporal regions for patients with 22q11.2DS. Interestingly, the observed aggravation during adolescence was strongly driven by the group with positive psychotic symptoms.

CONCLUSIONS

These results confirm a central role of altered FSD maturation in the emergence of psychotic symptoms in 22q11.2DS during adolescence. The FSD deviations precede the onset of psychotic episodes and thus offer a potential early indication for behavioral interventions in individuals at risk.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or 'g')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.

Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.

EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life

Brain networks are hypothesized to undergo significant changes over development, particularly during infancy. Thus, the aim of this study is to evaluate brain maturation in the first year of life in terms of electrophysiological (EEG) functional connectivity (FC). Whole-brain FC metrics (i.e., magnitude-squared coherence, phase lag index, and parameters derived from graph theory) were extracted, for multiple frequency bands, from baseline EEG data recorded from 146 typically developing infants at 6 (T6) and 12 (T12) months of age. Generalized linear mixed models were used to test for significant differences in the computed metrics considering time point and sex as fixed effects. Correlational analyses were performed to ascertain the potential relationship between FC and subjects' cognitive and language level, assessed with the Bayley-III scale at 24 (T24) months of age. The results obtained highlighted an increased FC, for all the analyzed frequency bands, at T12 with respect to T6. Correlational analyses yielded evidence of the relationship between FC metrics at T12 and cognition. Despite some limitations, our study represents one of the first attempts to evaluate brain network evolution during the first year of life while accounting for correspondence between functional maturation and cognitive improvement.

On the neural networks of self and other bias and their role in emergent social interactions

Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

AIM

Recent imaging studies have found significant abnormalities in the brain's functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.

METHODS

We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.

RESULTS

In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).

CONCLUSION

HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

Contrasting topologies of synchronous and asynchronous functional brain networks

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to the correlation-based synchronous functional networks (sFNs) which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the Default Mode Network (DMN) in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity which the other type of network does not.

Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort

BACKGROUND

Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear.

METHODS

The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed.

RESULTS

Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group.

LIMITATIONS

Relatively shorter follow-up duration, medications confounding, and modest sample size.

CONCLUSION

These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.

Machine Learning and Graph Signal Processing Applied to Healthcare: A Review

Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.

Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment

BACKGROUND

Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development.

METHODS

Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects).

RESULTS

Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities.

CONCLUSION

Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.

Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome

Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.

Indirect functional connectivity does not predict overall survival in glioblastoma

BACKGROUND

Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM).

METHODS

A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable.

RESULTS

The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration.

CONCLUSIONS

Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

Photobiomodulation Therapy on Brain: Pioneering an Innovative Approach to Revolutionize Cognitive Dynamics

Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson's disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM's potential as a therapeutic modality capable of effectively impeding disease progression within the organism-a task often elusive with conventional pharmacological interventions.

The relationship between negative life events and cortical structural connectivity in adolescents

Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach

BACKGROUND AND OBJECTIVE

Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization.

METHODS

To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features.

RESULTS

Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively.

CONCLUSION

This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder

BACKGROUND

Social anxiety disorder (SAD) and major depressive disorder (MDD) are characterized by behavioral abnormalities in motivational systems, namely the behavioral inhibition system (BIS) and behavioral activation system (BAS). Limited studies indicate brain volume in regions that support emotion, learning/memory, reward, and cognitive functions relate to BIS/BAS. To increase understanding of BIS/BAS, the current study used a network approach.

METHODS

Patients with SAD (n = 59), patients with MDD (n = 64), and healthy control participants (n = 36) completed a BIS/BAS questionnaire and structural magnetic resonance imaging scans; volumetric regions of interest comprised cortical and limbic structures based on previous BIS/BAS studies. A Bayesian Gaussian graphical model was used for each diagnostic group, and groups were compared. Among network metrics, bridge centrality was of primary interest. Analysis of variance evaluated BIS/BAS behaviors between groups.

RESULTS

Bridge centrality showed hippocampus positively related to BAS, but not to BIS, in the MDD group; no findings were observed in the SAD or control groups. Yet, network density (i.e., overall strength of relationships between variables) and degree centrality (i.e., overall relationship between one variable to all other variables) showed that cortical (e.g., precuneus, medial orbitofrontal) and subcortical (e.g., amygdala, hippocampus) regions differed between diagnostic groups. Analysis of variance results showed BAS was lower in the MDD/SAD groups compared with the control group, while BIS was higher in the SAD group relative to the MDD group, which in turn was higher than the control group.

CONCLUSIONS

Preliminary findings indicate that network-level aberrations may underlie motivational abnormalities in MDD and SAD. Evidence of BIS/BAS differences builds on previous work that points to shared and distinct motivational differences in internalizing psychopathologies.

Brain connectivity dynamics during listening to music and potential impact on task performance

To analyze brain connectivity dynamics during listening to music and estimate the potential impact on task performance. Fifteen participants (13 males and 2 females) participated in this study based on their interest in Indian classical music. Measurements of the influence of Indian music on task performance were obtained by assessing brain activation using EEG signals. Brain connectivity analysis was performed to visualize the connections between brain regions under various experimental conditions. Visual Go/No Go Stimuli was used to evaluate visual spatial attention during operation by evaluating misses, committed errors, and reaction times. In Task 1 (listening to music only), it was reported that there was a change in the positions of the electrodes (F3, F7) located in the left frontal lobe. The energy of the relative beta component was significantly higher only at F7 during task 1 (p = 0.005). Event-related desynchronization alpha and theta synchronization were significant (p = 0.005) at all electrode sites in the bilateral frontal lobes (F3, F4, F7 and F8) while listening to music and performing tasks (task 2). When the task without music (task 3) was performed, the energy of the relative alpha component was significantly higher at the Fp2 electrode position (p = 0.005). It is noteworthy that the energy of the theta component was significantly lower at the location of the Fp2 electrode (p = 0.005). The frontal asymmetry index score measures were significantly high at F4/F3 and F8/F7 during task 1. The connectivity map of theta synchronization showed a robust association between Fp2 and F8 which was in turn connected to P4 and O2 during Task 2. Results indicated an increased omission and commission errors during Task 3.

Duality between predictability and reconstructability in complex systems

Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.